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The signals that initiate cell invasion are not well understood , but there is increasing evidence that extracellular physical signals play an important role . Here we show that epithelial cell invasion in the intestine of zebrafish meltdown ( mlt ) mutants arises in response to unregulated contractile tone in the surrounding smooth muscle cell layer . Physical signaling in mlt drives formation of membrane protrusions within the epithelium that resemble invadopodia , matrix-degrading protrusions present in invasive cancer cells . Knockdown of Tks5 , a Src substrate that is required for invadopodia formation in mammalian cells blocked formation of the protrusions and rescued invasion in mlt . Activation of Src-signaling induced invadopodia-like protrusions in wild type epithelial cells , however the cells did not migrate into the tissue stroma , thus indicating that the protrusions were required but not sufficient for invasion in this in vivo model . Transcriptional profiling experiments showed that genes responsive to reactive oxygen species ( ROS ) were upregulated in mlt larvae . ROS generators induced invadopodia-like protrusions and invasion in heterozygous mlt larvae but had no effect in wild type larvae . Co-activation of oncogenic Ras and Wnt signaling enhanced the responsiveness of mlt heterozygotes to the ROS generators . These findings present the first direct evidence that invadopodia play a role in tissue cell invasion in vivo . In addition , they identify an inducible physical signaling pathway sensitive to redox and oncogenic signaling that can drive this process .
Physical signaling mechanisms are increasingly recognized as playing an important role in regulating the growth , differentiation , and morphology of vertebrate tissues [1] , [2] . In vitro studies have shown that the polarization , shape , and three-dimensional arrangement of cells in culture can be altered by changing the mechanical properties of their underlying substrate [3] , [4] . Tissue remodeling in vivo can also be initiated by physical signals . In the vasculature , forces arising from changes in intraluminal pressure can activate membrane-bound ion channels or signaling molecules within endothelial cells [5] , [6] . This leads to changes in the architecture of endothelial cells themselves , as well as the surrounding smooth muscle and adventitial cells in the vessel wall . Physical signaling has also been shown to be important in tumor progression . Matrix stiffening promotes tumor cell invasion in breast cancer models and neoplastic transformation of benign papillomas to skin cancers [7] , [8] . Mechanical strain induces an oncogene expression profile in intestinal explants derived from tumor prone mice [9] . Cell proliferation within a tumor can alter vascular permeability . This increases interstitial pressure within the tumor itself [10] , [11] , which promotes tumor progression in animal models [12] . Invasion of cancer cells through their basement membrane is an early event during tumor progression and is a histological feature that distinguishes cancers from benign tumors . In vitro models suggest cancer cell invasion requires the formation of invadopodia , actin-rich membrane protrusions that provide a localized source of matrix degrading proteases [13]–[14] . Invadopodia and structurally related podosomes were first discovered in cells transformed with the Rous Sarcoma Virus oncogene v-SRC ( reviewed in [14] ) . High levels of endogenous SRC are thought to promote invadopodia that form spontaneously in invasive cancer cells or following activation of growth factor signaling pathways [14] . Changes in substrate rigidity were recently shown to alter the number and activity of invadopodia that form spontaneously in invasive breast cancer cells [15] , [16] , thus providing a potential mechanistic link between invasion and physical signaling . Although invadopodia have been studied extensively in cell culture models , their precise role in cell invasion in vivo has not yet been determined [14] . In previous work , we showed that cell invasion can be modeled in a zebrafish mutant , meltdown ( mlt ) , in which intestinal architecture is disrupted by a mutation in myosin heavy chain 11 ( myh11 ) , the gene encoding the principle myosin present in smooth muscle [17] . Biochemical and in vitro analyses showed that the mutant myosin had non-regulated ATPase activity but lacked motor activity . Metalloproteases linked to cancer invasion ( Mmp14 ( MT1-Mmp ) ; Mmp2 ) and regulators of epithelial mesenchymal transition were upregulated in mlt , and their inhibition rescued the mutant phenotype , thus linking mlt to established models of cell invasion . Here we present in vivo evidence that the mlt mutation transforms Myh11 into a constitutively active contractile protein , and that unregulated actomyosin interactions increase the basal level of smooth muscle contractile tone in the larval intestine . The physical signal arising from increased contractile tone activates a feed forward redox signaling loop that induces invadopodia-like protrusions in the epithelium , and we show that this requires Tks5 , a Src substrate that is a component of the ROS generating Nox complex and is required for formation of invadopodia and podosomes in mammalian cells [18]–[20] . Heterozygous mlt mutants normally have no detectable phenotype , however treatment with ROS generators induced both the protrusions and cell invasion . Together , these findings identify a novel inducible in vivo signaling mechanism that non-cell autonomously drives formation of invadopodia-like protrusions and cell invasion .
The mlt invasive phenotype is first visible at 74 h post-fertilization ( hpf ) , shortly after the onset of myh11 expression in the developing intestinal circular smooth muscle layer surrounding the epithelium [17] . At this stage , the wild type intestinal epithelium is comprised of a single layer of cells that have formed an apical brush border ( Figure 1A and 1D ) and are joined to one another by apical tight junctions , adherens junctions , and desmosomes . The cells have a progenitor phenotype as they are still in a phase of proliferative growth and do not express lineage-specific markers [21] . In mlt , the intestinal tube has an irregular contour in comparison to the wild type intestine ( Figure 1B and 1A ) . Histological analyses at this stage showed focal regions of basement membrane degradation with invasion of epithelial cells into the surrounding tissue or epithelial stratification ( collectively referred to as invasive remodeling ) ( Figure 1E–1G; n = 15 mlt and 15 wild type larvae; see also [17] ) . This initial phase of tissue remodeling is followed by expansive growth of the epithelium and the formation of fluid filled cysts ( Figure 1C and 1H ) . In tissue culture models , degradation of the basement membrane by invasive cells is driven by metalloproteinases associated with invadopodia , plasma membrane protrusions that are enriched in actin , and the actin binding protein Cortactin [14] , [22] . In previous work , we showed that invasion in mlt required Mmp-14 ( also known as MT1-Mmp ) , a metalloproteinase associated with invadopodia , as well as Mmp2 , a metalloproteinase that is activated by Mmp-14 [17] . To determine whether invadopodia-like protrusions were present in the mlt intestinal epithelium , we derived a zebrafish transgenic line , Tg ( miR194:Lifeact-GFP ) , in which a GFP-labeled peptide that binds F-actin ( Lifeact-GFP , [23] ) is expressed in the intestine . In vivo imaging showed that mlt epithelial cells form actin rich protrusions at their basal membrane at the stage when matrix degradation and invasion are first detected ( Figure 2A and 2B; n = 12 mlt and 10 wild type larvae; Movies S1 and S2 ) . Time-lapse imaging showed that the protrusions formed before the onset of epithelial cell invasion ( Figure 2C; Movies S3 , S4 , S5 , S6 ) and that they persisted for several hours , thus distinguishing them from other actin-rich protrusions , such as lamellipodia and filopodia [14] . Cross-sections of larvae immunostained with antibodies against laminin and GFP showed that the protrusions localize to sites of extracellular matrix degradation and that epithelial cells invade the surrounding stroma through segments of degraded basement membrane ( Figure 2D–F; n = 20 mlt and 20 wild type larvae analyzed ) . Immunostainings confirmed that Cortactin , a protein present within invadopodia and that is required for their formation , was present in the actin-rich mlt protrusions ( Figure 3A–3C; n = 18 of 19 cells with protrusions ) . These findings argue that the protrusions present in the mlt intestinal epithelial cells are invadopodia homologs . MMP-14 localizes to invadopodia , where it drives degradation of the extracellular matrix . To test whether Mmp-14 was present in the mlt protrusions we compared the cellular localization of an ectopically expressed Mmp14a-mCherry fusion protein in mlt and wild type intestinal epithelial cells . This approach was necessary because none of the antibodies we tested in immunostainings detected endogenous Mmp-14 protein in the intestine . Confocal analyses showed that the Mmp14a-mCherry fusion protein preferentially localized to the basal portion of mlt epithelial cells with protrusions ( Figure 3D–F ) , whereas in wild type larvae , Mmp14a-mCherry was evenly distributed within the epithelial cells . Fluorescence quantification showed a 1 . 5-fold increase in basal Mmp14a-mCherry in mlt ( Figure 3G ) . In many mlt cells the Mmp14-mCherry fusion protein was nearly exclusively present within or adjacent to the actin-rich protrusions in the basal cell membrane , whereas this pattern was not detected in wild type cells . To further characterize the mlt membrane protrusions , we next asked whether they could form in mutant larvae that were deficient in the Src substrate Tks5 , a scaffolding protein that is required for invadopodia and podosome formation in mammalian cells [18]–[20] , [24] . Knockdown of zebrafish Tks5 disrupts migration of developing neural crest cells , however it is not known whether these cells form matrix-degrading protrusions similar to mlt [25] . Injection of the Tks5 morpholino into newly fertilized wild type and mlt embryos caused severe developmental delay in most embryos , as previously reported [25] . However , in the mlt embryos that developed normally Tks5 knockdown blocked formation of the protrusions and rescued the invasive phenotype ( Figures 3H , 3I , and S1; n = 5 independent experiments; confirmed by genotyping in six rescued mlt homozygotes ) . Together , these findings argue that the membrane protrusions present in mlt epithelial cells are closely related , if not identical to invadopodia and or podosomes , and furthermore , that the protrusions are required for invasion . Because podosomes have not been described in epithelial cells and are associated with cell migration rather than invasion , hereafter we refer to the mlt protrusions as invadopodia-like protrusions . Invadopodia have first been described in cells transformed with the Rous Sarcoma Virus oncogene , v-src [26] , [27] . SRC is a major organizing protein of invadopodia and is present within invadopodia [28] . To localize Src in mlt intestinal epithelial cells , we expressed a fusion construct encoding Src with C-terminal mCherry in the epithelium . Confocal microscopy showed that Src-mCherry was present at the apical and lateral membrane of WT epithelial cells ( Figure 4A; n = 40 cells examined in eight larvae ) , but was concentrated at the basal cell membrane of invasive mlt epithelial cells that formed invadopodia-like protrusions ( Figure 4B; n = 25 single cells in five larvae ) . To test whether Src was sufficient to induce formation of the invadopodia-like protrusions in the absence of the mutant Myh11 , we expressed an activated form of zebrafish Src in which the conserved inhibitory Tyrosine phosphorylation site was replaced with a Phenylalanine ( Src-Y528F; hereafter caSrc ) . This mutant form of Src induces invadopodia formation in non-transformed cells [14] . Similar to wild type Src , a caSrc-mCherry fusion protein localized to basal protrusions in the intestinal epithelium of mlt mutant larvae ( Figure 4C; n = 40 cells in eight larvae ) . caSrc-mCherry ( or caSrc-GFP ) induced formation of actin-rich protrusions at the basal surface of the wild type epithelial cells by 78 hpf ( Figure 4D ) . Anti-laminin and anti-GFP immunostaining showed that the Src-induced protrusions were present at sites of basement membrane degradation ( Figure 4E–4E″; n = 53 cells examined: 40 with basal protrusions , of which 27 breached the basement membrane ) . However , none of the cells with protrusions were invasive in either 74 hpf larvae , or larvae followed to 5 d post-fertilization . The 5 d post-fertilization ( dpf ) transgenic larvae also had far fewer protrusions than the 74 hpf larvae ( Figure S2 ) , indicating a higher capability of protrusion induction at earlier time points , when the epithelial cells are highly proliferative and not fully differentiated [20] . We next tested whether Src contributed to invasion in mlt . Mutant larvae and larvae expressing the caSrc transgene were treated with three established mammalian Src inhibitors: SU6656 , PP2 , and Src-I1 ( Figure 5 ) [29] . SU6656 blocks cell migration during gastrulation and neural crest development in zebrafish embryos [25] , [30] , however it had no effect in mlt , nor did it block formation of the invadopodia-like protrusions that form in response to caSrc ( n = 21 larvae ) . PP2 blocked formation of the invadopodia-like protrusions induced by caSrc ( n = 0 protrusions detected in 34 larvae ) , but it had no effect in mlt ( n = 20 larvae ) . In contrast , the Src-I1 inhibitor had a pronounced effect on both the formation of the invadopodia-like protrusions in response to caSrc ( 0 protrusions detected in 33 larvae ) and on invasion in mlt ( n = 21 mlt larvae ) . The effect of Src-I1 was comparable to the Tks-5 knockdown ( compare Figures 5 and S1 ) . Although Src-I1 treatment had a profound effect on invasion in mlt , it did not block formation of the invadopodia-like protrusions ( Figure 5F , 5G ) . The response of mlt to Src-I1 is similar to the effect of Src knockdown in invasive breast cancer cells [24] . Actin-rich invadopodia precursors form in the Src-deficient cancer cells , but invasion and to a lesser extent matrix degradation are both inhibited . Mechanical signaling modulates the formation and activity of invadopodia [15] , [16] . This suggests the mlt protrusions formed in response to mechanical cues from the unregulated mutant Myh11 . To test this idea , we derived transgenic mlt larvae expressing fluorescent reporters in smooth muscle [31] and the intestinal epithelium ( Tg ( miR194:mCherry; sm22a:GFP ) ) and recorded intestinal peristalsis using time-lapse confocal microscopy . Contractile force in smooth muscle arises from the interaction of Smooth muscle actin ( Sma ) and myosin ( Myh11 ) filaments that are anchored to the cortical cytoskeleton [32] . In zebrafish larvae , intestinal smooth muscle contraction begins around 76 hpf [33] , several hours after Sma and Myh11 are first detected . We detected rhythmic peristaltic contractions of the circular smooth muscle layer surrounding the epithelium in wild type larvae at this stage ( Figure 6A–6C and Movie S7; n = 6 larvae examined ) . By contrast , slow tonic smooth muscle contraction that distorted tissue architecture was evident in 76 hpf mlt mutants ( Figure 6D–6F and Movie S8; n = 6 larvae examined ) . These findings indicated that the mutant myosin had non-regulated contractile activity that was not detected in our original in vitro motility assays [17] , presumably because of the slow rate of contraction . Invasive cells are already present in mlt larvae at 72 hpf ( Figure 1E and 1F ) , however at this stage we could not detect tonic or peristaltic smooth muscle contraction in either mutant or wild type intestines ( Movies S9 and S10; Figure 6G and 6H; n = 6 mlt and 6 wild type larvae ) , as previously reported [33] . This led us to hypothesize that the mutant myosin increased the resting or basal level of smooth muscle contractile tone in the intestine , and that increased tone triggered formation of the invadopodia-like protrusions . To further characterize smooth muscle contractility in mlt we examined levels of smooth muscle regulatory proteins in the mutant and wild type intestine using Western blot analyses . The enteric nervous system regulates intestinal peristalsis by controlling levels of the phosphorylated form of the smooth muscle regulatory Myosin light chain ( p-Mlc ) that binds Myh11 [34] , [35] . Mlc phosphorylation activates the Myh11 ATPase , which in turn drives crossbridge cycling of the actomyosin complex ( and hence contraction ) . We have previously identified an antibody that detected a p-Mlc isoform whose expression is restricted to the smooth muscle layer of the larval intestine [34] . Western blot analyses using this antibody showed that p-Mlc levels were low in wild type larvae before the onset of peristalsis ( 72 hpf ) and were significantly higher 6 h later ( 78 hpf ) , when circular smooth muscle contractions are evident ( Figure 6I ) . p-Mlc levels in mlt were similar to wild type at both time points ( Figure 6I ) . These findings are consistent with previous work showing that the ATPase activity of the mutant Myh11 is independent of Mlc phosphorylation ( i . e . , unregulated ) [17] . We next examined levels of the high molecular weight isoform of Caldesmon ( h-CaD ) , a smooth muscle–specific actin binding protein that regulates contractile tone independently of p-Mlc [36]–[39] . h-CaD is expressed exclusively in the smooth muscle of the zebrafish larval intestine , where it binds Myh11and Sma , but not Actb ( beta-Actin ) , the predominant actin in the cytoskeleton [34] , [39] . In its non-phosphorylated state , h-CaD inhibits Sma-Myh11 interactions , most likely by acting as a brake on actomyosin crossbridges [38] . When h-CaD is phosphorylated , its interactions with Sma are weakened . This enhances contractile force . Western blots showed that h-CaD was prematurely phosphorylated in the mlt intestine before the onset of active peristaltic contraction ( 72 hpf ) , when invasion is first detected ( Figure 6I; n = 6 independent experiments ) . Premature h-CaD phosphorylation in mlt is consistent with the idea that resting smooth muscle tone is increased in the intestine of the mutant larvae . To directly examine whether smooth muscle contractile tone initiated epithelial invasion in mlt we inhibited translation of the mRNA encoding zebrafish Sma [34] , [40] . The morpholino used to target Sma does not target Actb , the cytoskeletal actin isoform that also is present in smooth muscle but does not interact with Myh11 [32] , [34] . Knockdown of Sma rescued the early mlt invasive phenotype at 74 hpf ( Figure 7A–7F; no invasion in 120 larvae from a mlt/+ intercross; confirmed histologically in 5 mlt homozygotes ) as well as epithelial expansion and cysts that occur at later stages . Western blots confirmed the efficacy of the Sma knockdown and that it did not alter levels of Myh11 ( Figure S3 ) . Together , these findings show that smooth muscle contraction is required for the mutant phenotype and that the constitutive ATPase activity of the mutant Myh11 , which is independent of the presence of Sma [17] , is not sufficient to trigger invasion . We next asked whether enhancing contractile tone was sufficient to induce invasion in heterozygous mlt mutants . To test this idea , we inhibited translation of the h-CaD mRNA using a splice blocking morpholino that specifically targets this smooth muscle–specific CaD isoform [38] . We chose this approach because levels of phospho-h-CaD in heterozygous larvae are low at the stage when invasion is first detected in mlt homozygotes ( 72 hpf; Figure 6I ) . h-CaD knockdown was therefore predicted to increase smooth muscle contractile tone ( as reported in vascular smooth muscle [38] , [41] ) , similar to the effect of premature h-CaD phosphorylation in homozygous mutants . Indeed , h-CaD knockdown caused epithelial invasion and stratification in mlt heterozygotes , but had no effect in wild type larvae besides increasing the rate of intestinal transit ( Figures 7G , 7H , and S4; invasion detected in 69% larvae from a mlt/+ intercross ( n = 240 ) of which 66% were predicted to be mlt/+; 12 of 12 genotyped larvae were mlt/+ ) [39] . A similar epithelial response was seen in the heterozygotes treated with L-NAME , a nitric oxide synthase ( Nos ) inhibitor that increases intestinal smooth muscle contraction in zebrafish larvae ( Figure 7I; invasion was histologically confirmed in 11 genotyped mlt/+ larvae ) [42] . Unlike homozygous mlt mutants , neither h-CaD-deficient nor L-NAME-treated heterozygotes developed epithelial cysts or other features of the advanced homozygous mlt phenotype , thus indicating that these phenotypic features require sustained smooth muscle contraction . Matrix stiffening promotes cell invasion in breast cancer models , in part through activation of Focal Adhesion Kinase ( FAK ) [3] , [7] . To determine whether a comparable mechanotransductive signaling mechanism was activated in mlt we measured tissue elasticity in intestines isolated from wild type and mutant larvae using a force displacement assay [43] . Before invasive remodeling is detected ( 70 hpf ) , compliance was nearly identical in mlt larvae and their wild type siblings throughout the range of indentations tested ( Figure 7J; n = 6 mlt and 21 wild type larvae ) . When remodeling is first detected ( 74 hpf ) , compliance was slightly increased at the surface of the intestine but comparable at greater depths ( Figure 7J , K; n = 7 mlt and 12 wild type larvae examined ) . Values compatible with intestinal stiffening ( reduced compliance ) were never recorded . We next examined Fak phosphorylation in mlt using immunostainings [44] and Western blot analyses . Neither detected elevated p-Fak in mlt ( Figure S5 , unpublished data ) . Western blots also showed that levels of Collagen-1 and Fibronectin were not significantly elevated in the mlt intestine ( Figure S5 ) . All together , these data argue that mechanical force triggers epithelial invasion in mlt independently of changes in matrix composition or increases in tissue rigidity . To identify signaling pathways activated by increased smooth muscle tension in mlt we performed microarray transcriptional profiling of early ( 74 hpf ) mutants . Among the group of upregulated genes were three that neutralize reactive oxygen species ( ROS ) ( glutathione peroxidase ( gpx ) , thioredoxin , and glutathione/thioredoxin reductase ) and several members of the AP-1 family of transcription factors ( Table S1 ) , which are also ROS-responsive genes . Genes encoding MAP kinase regulators were also activated in mlt . The microarray findings were confirmed by quantitative RT-PCR experiments ( Figures 8A and S6; n = 15 intestines per genotyped pool; three pools examined ) , RNA in situ hybridization ( Figures 8C , 8D , and S6 ) , and Western blot analyses ( Figure S6 ) . Fluorescent RNA in situ hybridization experiments showed that gpx and the AP-1 transcription factor junB were nearly exclusively expressed within the mlt intestinal epithelium ( Figures 8C , 8D , and S6; n = 10 mlt and 10 wild type larvae ) , thus arguing that their expression is non-cell-autonomously activated in response to smooth muscle tension . These findings are noteworthy because there is extensive evidence linking ROS and AP-1 factors to cancer cell invasion through their regulation of expression and activation of metalloproteinases [45]–[47] , EMT regulators [48] , and the induction of invadopodia [49]–[51] . To test if redox signaling was required to induce invasive epithelial remodeling in mlt we treated 72 hpf larvae with the ROS quenchers , N-acetylcysteine , and Tiron . This treatment did not rescue the mlt invasive phenotype , but it also did not affect epithelial gpx expression ( unpublished data ) , suggesting that the neutralizing compounds could not access the ROS or that the inhibition was not strong enough to inhibit redox signaling . To determine whether oxidative stress was sufficient to induce the formation of the invasive phenotype , we treated 72 hpf larvae with intracellular ROS generators menadione and LY83583 [52] , [53] . Neither compound generated a mlt phenocopy in homozygous wild type larvae and both were lethal during prolonged exposure . However , within 3 h of exposure both compounds induced epithelial invasion in 72 hpf mlt heterozygotes that was comparable to early homozygous mutants ( Figure 9A–9F ) . Invasive morphology present in 90% of genotyped mlt/+ larvae ( n = 22 ) but absent from all +/+ wild types ( n = 17 ) . Invasion was histologically confirmed in all mlt ( n = 12 ) larvae examined but was not present in wild types ( n = 9 ) . The treated heterozygotes did not develop the cysts and epithelial expansion typical of the late mlt phenotype ( unpublished data ) . Together , these findings suggest that redox signaling is involved in the initial remodeling of the mutant intestine , but on its own it is not sufficient to cause the more pronounced architectural changes seen in more advanced homozygous mutants . Having established a role for redox signaling in mlt we next asked whether menadione induced formation of invadopodia-like protrusions in the heterozygous epithelium . Live confocal imaging of menadione-treated heterozygous larvae showed protrusions that were identical to those present in homozygous larvae ( Figure 9H and 9I; n = 10 larvae ) . By contrast , the protrusions were not seen in menadione-treated wild type larvae ( Figure 9G; n = 10 larvae ) or heterozygous larvae that did not receive menadione ( n = 10 larvae ) . Interestingly , caSrc not only induced invadopodia-like protrusions in wild type epithelial cells , but also activated expression of the redox responsive gene gpx , as occurs in mlt ( Figure S7; n = 12 larvae ) . These findings establish further links between Src , redox signaling , and formation of the invadopodia-like protrusions in mlt . One explanation that could account for the invasive response of mlt heterozygotes to menadione was that activation of redox signaling increased smooth muscle contractile tone , similar to the effect of h-CaD knockdown or Nos inhibition ( Figure 7D–7F ) . Supporting this hypothesis , Western blots showed that menadione induced premature phosphorylation of h-CaD in mlt heterozygotes , but not in sibling larvae that were homozygous for the wild type myh11 allele ( Figure 8G ) . To determine whether h-CaD phosphorylation occurred downstream of smooth muscle redox signaling , we used fluorescent RNA in situ hybridization to localize gpx and junB expression in the menadione-treated larvae . Expression of both genes was restricted to the epithelium ( Figure 8E and 8F; n = 10 larvae ) , as in mlt homozygotes ( Figures 8C , D , S6 ) . Together , these findings led us to hypothesize that h-CaD phosphorylation in the menadione-treated heterozygotes was triggered by an epithelial signal rather than a direct effect of menadione on the smooth muscle . To test this hypothesis , we asked whether menadione induced h-CaD phosphorylation in dissociated heterozygous smooth muscle cells . Intestines were dissected from heterozygous mlt and wild type larvae and divided into two groups . The first group was treated with menadione and then processed for Western blot analyses . The second group was dissociated into a suspension of epithelial and smooth muscle cells prior to treatment with menadione . We reasoned that the dissociated smooth muscle cells could still directly respond to mendaione even though their interactions with the epithelium and extracellular matrix had been disrupted . Western blots ( Figure 8H ) showed that h-CaD was phosphorylated in the non-dissociated heterozygous intestines treated with menadione , and that phospho-h-CaD already present in homozygous mlt smooth muscle cells prior to cell dissociation was not degraded during the incubation period of the assay . In contrast , phospho-h-CaD was not detected in the dissociated heterozygous smooth muscle cells exposed to menadione ( n = 3 independent experiments ) . Collectively , these data support a model in which h-CaD phosphorylation in mlt smooth muscle cells arises from an ROS-activated epithelial signal ( Figure 10 ) . Phosphorylation of h-CaD enhances smooth muscle tone , thereby generating additional oxidative stress within the epithelium . This establishes a feed-forward signaling loop with the adjacent smooth muscle that further enhances contractile tone , amplifies epithelial ROS production , and culminates in epithelial invasion . To test this model we measured epithelial ROS production in menadione-treated wild type and heterozygous larvae that express a ratiometric ROS sensor [54] . Oxidation of the sensor by ROS causes a shift in its fluorescence absorption-emission ratio , thus allowing ROS quantification . Menadione treatment led to a 33% increase in epithelial ROS levels in wild type transgenic larvae ( Tg ( beta-Actin:grx-roGFP2 ) ( Figure 8B ) . This response was increased more than 2-fold in heterozygous larvae , thus confirming amplification of ROS production in the invasive cells . Quantitative RT-PCR experiments showed that menadione caused a nearly identical increase in intestinal gpx and jun-b expression in heterozygotes compared with menadione-treated wild type larvae ( Figures 8A′ and S6 ) . Indeed , gpx and jun-b expression in the menadione-treated heterozygotes was comparable to untreated homozygous mutants ( Figures 8A and S6 ) . These findings not only support the amplification signaling model , but also confirm that gpx and jun-b are responsive to ROS and that their expression can be used to measure ROS production . Heterozygous mlt mutants had a pronounced response to menadione when treatment began at 74 hpf , however invasion was not detected when larvae were treated at later stages ( 5 dpf ) . We hypothesized that this was caused by a change in the responsiveness of the epithelium to redox signaling . To test whether the responsiveness of the 5 dpf epithelium to menadione was modified in a tumor model , we generated transgenic larvae that express an activated human KRAS allele in the intestinal epithelium ( Tg ( miR194:eGFP-KRASG12V ) ) . The KRAS transgenics were also homozygous for a loss of function allele of the Wnt regulator axin1 [55] , which like the KRAS allele causes intestinal epithelial cell hyperplasia in zebrafish larvae [56] , [57] . Previous work in zebrafish has shown that activated KRAS enhances Wnt signaling in Apc-deficient zebrafish larvae and human colorectal tumors [58] . Since zebrafish apc mutants have severe developmental delay , the axin1 mutants were used to study the combined effect of activated KRAS and enhanced Wnt signaling in older mlt heterozygotes . Prior to treatment with menadione , 5 dpf axin1 mutants that express the mutant KRAS allele ( KRAS-axin larvae ) and were heterozygous for the mlt mutation had intestinal epithelial hyperplasia without evidence of invasion . Remarkably , within 5 h of treatment , menadione generated a dramatic invasive response in the KRAS-axin larvae that were also heterozygous for mlt , whereas it had no effect in larvae that were homozygous for the wild type myh11 allele ( Figure 11; invasion confirmed in 8 of 8 menadione-treated KRAS-axin larvae and 0 of 8 untreated KRAS-axin larvae ) .
In previous work , we reported that invasive remodeling of the developing intestinal epithelium of zebrafish mlt mutants is caused by a mutation of the smooth muscle myosin heavy chain gene . Initially , we attributed the non-cell-autonomous mutant phenotype to the constitutive actin-independent ATPase activity of the mutant Myh11 protein , rather than its non-regulated contractile function , because contraction was not detected in an in vitro motility assay [17] . Here , we show that the mutant myosin does indeed have contractile function , and that the non-regulated motor activity of the mutant Myh11 is necessary and sufficient for the mutant phenotype . Specifically , we show that inhibiting smooth muscle contraction , via Sma knockdown , rescues invasion in homozygous mutants , and that enhancing smooth muscle contractility , via h-CaD knockdown and Nos inhibition , induces invasion in heterozygous mlt mutants . Together , these findings are consistent with the idea that invasive transformation of the mlt intestinal epithelium is triggered by a physical signal from the adjacent smooth muscle layer , and that on its own , the unregulated ATPase activity of the mutant myosin ( which is independent of actin ) is not sufficient to induce invasion . Physical signaling mechanisms are increasingly recognized as important for organ development and disease [59] , [60] . Recent studies suggest a role for changes in matrix rigidity ( stiffness ) and other types of mechanical forces in cancer progression [3] , [7]–[12] , [61] . Our findings distinguish mlt from these cancer progression models in several ways . First , invasive remodeling of the mlt intestinal epithelium occurs in the absence of any oncogenic stimuli . Second , invasion in mlt is triggered by increased smooth muscle tone rather than qualitative or quantitative changes in matrix proteins . Third , we did not detect tissue stiffening or increased levels of activated Fak in mlt , thus arguing that force arising from smooth muscle tension activates a distinct mechanotransductive signaling mechanism than matrix stiffening [62] . Interestingly , metalloproteinases and EMT regulators are upregulated in both the mlt intestine [17] and in cancer cells in contact with stiff matrices . Thus , the downstream effects of the different signaling pathways appear to converge in the responding cells . Although invasive cells in mlt are not transformed , they share some features with cancer cells in that they are highly proliferative and not fully differentiated [17] . In contrast , differentiated epithelial cells in homozygous mlt larvae rescued by transient Myh11 knockdown have only a modest invasive response to tension [17] . Differentiated epithelial cells in 5 dpf mlt heterozygotes also did not respond to menadione , and they formed fewer invadopodia in response to Src than undifferentiated cells . Together , these findings argue that epithelial progenitor cells , and by analogy cancer cells , have an intrinsic capacity for invasive transformation in response to mechanical signals . Supporting this , the invasive response of mature epithelial cells in heterozygous 5 dpf larvae was restored by activation of oncogenic signaling pathways known to play a role in colorectal cancer in humans . In vitro studies link cancer cell invasion to the matrix-degrading function of invadopodia , however it is not known whether invadopodia play a role in cell invasion in vivo [14] . The evidence supporting such a role is principally derived from cell transplantation studies in which tumor cell invasion correlated with Cortactin expression and invadopodia activity [63] , [64] . Here we present direct evidence that invadopodia can drive matrix degradation and cell invasion within an intact tissue in vivo . Time lapse movies show that the invadopodia-like protrusions in mlt form before the onset of cell invasion , and immunostainings show that the protrusions are located adjacent to degraded segments of the basement membrane , through which the invasive cells migrate into the tissue stroma . The mlt protrusions are enriched in the invadopodia proteins Actin , Cortactin , and Src , and their formation requires Tks5 , a Src substrate required for invadopodia formation in cancer cells . Trafficking of Mmp14 , a metalloproteinase that associates with invadopodia in vitro , was also preferentially distributed to the basal region of mlt epithelial cells with protrusions . The latter finding is consistent with our previous work showing upregulated epithelial expression of Mmp14 and Mmp2 in mlt , and that metalloproteinase inhibition rescues invasion [17] . All together , these data are , to our knowledge , the most conclusive evidence supporting a role for invadopodia in cell invasion in vivo . The mechanical properties of cell culture substrates can modify the number and activity of invadopodia that form spontaneously in cancer cells [15] , [16] . The findings presented here are novel in that they also show that formation of the invadopodia-like protrusions can be initiated in vivo by activation of a physical signaling mechanism . Src signaling is sufficient to induce invadopodia formation in non-transformed mammalian cells [14] . Similarly , activated Src induced formation of invadopodia-like protrusions in wild type zebrafish intestinal epithelial cells . The Src-induced protrusions were located at sites of basement membrane degradation , however cell invasion was never detected in this in vivo model . These findings argue that on their own invadopodia are not sufficient for invasion of mammalian cells from an intact tissue in vivo . Although activated Src did not induce invasion in wild type epithelial cells , our findings indicate that Src is required for invasion in mlt , as the well-characterized Src inhibitor , Src-I1 , blocked matrix degradation and invasion in mutant larvae . Interestingly , Src inhibition did not disrupt formation of the invadopodia-like protrusions . The response of mlt larvae to Src inhibition therefore resembles the effect of Src knockdown in invasive breast cancer cells [24] . The fact that other Src inhibitors had no effect in mlt could be explained by either their inability to access the intestine at larval stages ( SU6656 ) or their different mechanisms of action . Src-l1 competitively inhibits ATP and substrate binding to Src , whereas PP2 only competes for substrate binding [65] , [66] . An alternate explanation is that Src-I1 targets other kinases responsible for invasion in mlt , however both Src-I1 and PP2 are specific inhibitors of mammalian Src [29] , and thus are likely to function comparably to zebrafish Src , which is highly conserved . Although our data link invasion in mlt to Src , we could not detect a higher level of activated Src ( phospho-Tyrosine 417 ) in the mlt intestine by Western blot ( unpublished data ) . One explanation for this is that Src and related kinases are already highly activated in larvae due to their function in controlling proliferation and cell polarization [67] . Formation of the protrusions and invasion in mlt could therefore involve the relocalization of activated Src to the basal plasma membrane . Alternatively , invasion may require the de novo activation of a relatively small localized pool of cytoplasmic Src . Supporting this idea , force application to the plasma membrane of cultured cells can rapidly activate cytoplasmic Src [68] , [69] . We identified a role for redox signaling in mlt based on the enhanced expression of ROS sensitive genes , the requirement for Tks5 , a component of the ROS producing NAD ( P ) H oxidase ( Nox ) complex , and the invasive response of heterozygotes to exogenous oxidative stress induced by ROS generators . Redox signaling has previously been linked to cancer cell invasion and metastasis through formation of invadopodia and activation of EMT regulators [48]–[50] , [70] . Our findings are novel in that the mlt redox signaling pathway is triggered by a non-cell-autonomous physical signaling mechanism that requires the interaction of cells within adjacent tissue cell layers . A related redox signaling pathway has been implicated in vascular remodeling in mammals [69] . As in mlt , contraction of smooth muscle in this model is triggered by mechanical stress applied to the adjacent cell layer ( the endothelium ) [71] , [72] . All together , these findings indicate the presence of an evolutionarily conserved redox-regulated mechanotransductive signaling mechanism that can drive architectural remodeling in diverse vertebrate tissues . Recently , heterozygous somatic activating mutations in MYH11 , similar to mlt , were reported in human colorectal cancers [73] , [74] . Surprisingly , in the subset of the colorectal cancers examined , the MYH11 mutations were found more frequently in the epithelium , which does not normally express MYH11 , than in smooth muscle [68] . The precise role of the MYH11 mutations in colorectal cancer therefore could not be resolved by these association studies . The findings presented here argue for a primary effect of these mutations in smooth muscle rather than the epithelium . As the invasive remodeling in mlt larvae occurs without prior tumor formation , we do not consider mlt to be a model for how cancers develop , but rather a model for how cancer cells react to external physical force . Thus , mlt can be used to study how epithelial tumors progress from a localized lesion to invasive cancer . Indeed , in one study the presence of the MYH11 mutations was associated with the invasive transformation of benign adenomatous polyps [74] . The response of heterozygous mlt larvae to oxidative stress is an important finding from this study because it provides an example of how tissue architecture can be altered in the appropriate genetic context . Based on our model , we argue that activating mutations of MYH11 or other human smooth muscle genes could cause invasion of existing cancer in the setting of oxidative stress that is intrinsic to cancers or occurs in the setting of inflammatory conditions that promote cancer formation [75]–[77] . These mutations could alter the function of different types of contractile cells within the tumor stroma besides smooth muscle , such as pericytes and myoepithelial cells , as well stromal fibroblasts that express smooth muscle actin and other contractile proteins . Sustained contraction of these cells could alter extracellular tension in tumors , thus causing them to invade . Alternatively , tonic contraction could also originate from wild type cells present in the tumor stroma such as cancer-associated fibroblasts or related stromal cells .
All animals were handled in strict accordance with good animal practice as defined by the relevant national and/or local animal welfare bodies , and all animal work was approved by the animal welfare committee at the University of Pennsylvania School of Medicine . Larvae were raised at 28°C in E3 medium [78] and were staged by age and morphological criteria ( size of yolk extension and pigment pattern around yolk extension ) . Expression of mCherry , mCherry-CAAX , Lifeact-GFP , caSrc , and GFP-KRASG12V ( a generous gift from Steven Leach ) in the intestinal epithelium was driven by a 2 kb promoter fragment from the zebrafish miR194 gene . Expression of GFP in smooth muscle was driven by a promoter fragment from the zebrafish sm22-alpha gene [31] . Expression of the ROS sensor Grx1-roGFP2 ( a generous gift of Dr . Tobias Dick [54] ) in the intestinal epithelium was driven by a promoter fragment from the zebrafish beta-actin gene [79] . All fragments were cloned using the multisite gateway system [80] , [81] . GFP and mCherry were cloned C-terminal to Src . Zebrafish axin1 mutants [55] were obtained from the Zebrafish International Resource Center . 3 dpf old larvae were anesthetized with 0 . 1 mg/ml Tricaine , fixed in 4% PFA/PBS , washed in PBST ( PBS+0 . 1% Tween ) , dehydrated in methanol , and stored at −20°C . For whole mount staining with anti-laminin and anti-cytokeratin antibodies , larvae were washed in PBST and permeabilized by a 15-min Proteinase K digestion ( 100 ug/ml in PBST ) . They were then rinsed in PBST and postfixed in 4% PFA/PBST . The skin above the trunk and intestine was removed using fine forceps . Larvae were stained with antibody in 10% goat serum/PBST . The laminin antibody ( Sigma #L-9393 ) was used at 1∶50 or 1∶200 dilution; the cytokeratin antibody ( Thermo Scientific clone AE1/AE3 , MS-343-PO ) was used at 1∶100 dilution . The Cortactin antibody ( Milipore clone 4F11; #05-180 ) was used at 1∶100 . For FAK staining , larvae were dehydrated in −20°C acetone for 30 min followed by staining in 2% BSA/PBST . Anti-phospho-Fak ( Y397 ) ( Millipore , #05-1140 ) was used at 1∶200 . Secondary antibodies were labeled with Alexa 568 or 488 ( Molecular Probes/Invitrogen ) . Histological analyses of the mounted specimens were performed as described [17] . Confocal scans of live larvae and sections of immunostained larvae were performed using a Zeiss LSM710 laser scanning microscope . For time lapse microscopy , 3 dpf larvae were anesthetized with 0 . 1 mg/ml Tricaine [82] and oriented and embedded in 0 . 8% low melting agarose ( Sigma A-9539 ) in E3/Tricaine heated to 40°C . Analysis were performed at 28°C . Intestines were imaged in two orientations . In tissue cross-sections , the intestine appears as concentric rings of epithelial and surrounding smooth muscle cells . The intestinal lumen and apical surface of the epithelial cells are at the center of the two cellular rings . In sagittal confocal scans of live larvae , the intestine appears as two layers of apposing epithelial cells . A thin layer of smooth muscle cells surrounds the two epithelial cell layers . To determine the distribution of Mmp14a-mCherry in mlt and wild type intestinal epithelial cells , single confocal scans were imported into the ImageJ software . Crops of single cells were copied to a new file and mean mCherry fluorescence was measured in the apical 2/3 and basal 1/3 of each cell . To measure ROS levels in epithelial cells transgenic fish expressing Grx1-roGFP2 were crossed to heterozygous mlt fish and their offspring treated with menadione as described below . Larvae mounted for confocal microscopy were excited with 408 and 488 nm lasers , and the emission at 500–530 nm was calculated ( as described in [54] ) . The mean fluorescence emission at each excitation wavelength was calculated for groups of 15–25 cells per larva using the ImageJ software . From this , the 408/488 nm ratio was determined . Statistical analyses were performed using the one-tailed Student's t test for data sets with unequal variance . Western blots were performed as previously described [34] . Briefly , equivalent amounts of protein were separated in a 10% SDS–polyacrylamide gel and then electrophoretically transferred to a nitrocellulose membrane using a Bio-Rad protein mini gel apparatus ( Bio-Rad Laboratories , Hercules , CA-USA ) . All protein extracts were prepared from isolated intestines . The membranes were blocked in TBS containing 0 . 1% Tween 20 and 5% skim milk powder . Blots were probed with anti-Phospho-Caldesmon ( Ser789 ) ( Upstate , Lake Placid-NY #07-156 ) , anti-Caldesmon ( Calbiochem , Gibbstown , NJ , #ST1109 ) , anti-smMyosin ( Biomedical Technologies , Stoughton , Ma #BT-562 ) , anti-smooth muscle Actin ( Neomarkers , Fremont , CA , #MS-1296-P0 ) , anti-beta-Actin ( Sigma , St . Louis , MO ) , anti-Phospho-MLC ( Ser19 ) ( Cell Signaling , Danvers , MA , #3675 ) , anti-Phospho-ERK1/2 ( p44/42 ) ( Thr202/Tyr204 ) ( Cell Signaling , Danvers , MA , #4376 ) , anti-Phospho-p38 ( Thr180/Tyr182 ) ( Cell Signaling , Danvers , MA , #4631 ) , anti-Phospho-SAPK/JNK ( Thr183/Tyr185 ) ( Cell Signaling , Danvers , MA ) , and anti-Collagen 1 ( NovaTech , France ) . All blots are representative of at least triplicate experiments . Larvae were bathed in 1 . 5 µM Menadione ( MP biomedicals ) in E3 media for 3 h ( 3 dpf larvae ) or 5 h ( 5 dpf larvae; 1% DMSO added to E3 media ) . Larvae were bathed for 3 h in 10 mM l-NAME ( Sigma N-5751 ) dissolved in E3/10 mM Tris pH 7 . 2 . Whole mount in situ hybridization was performed as described [83] , [84] . Fragments were cloned into pGem-t-easy ( Promega ) . Double fluorescent in situ hybridization was performed as described [85] using TSA reagents ( Perkin-Elmer ) . Processed specimens were embedded for histological sectioning as previously described [17] . Morpholinos were injected as described [83] . The acta2-morpholino was from Open Biosystems ( Morph1609 , GCTTTCTTCGTCGTCACACATTTTC , [34] ) ; the sequence of the control morpholino is TGCGCGCCAGACAGGGTGATGAC . Although named “actin , alpha 2 , smooth muscle aorta , ” acta2 is expressed in the intestinal smooth muscle [38] , [40] and is required for peristaltic contraction [34] . Thus it is the ortholog of mammalian intestinal smooth muscle actin isoform ( gamma enteric actin; Actg2 ) and referred to as sma . The h-CaD morpholino ( TTATTCCCCTACAAACAGAACTGCA , 1 mM ) was designed against the smooth-muscle-specific exon of Caldesmon ( based on acc . # BC158175 ) , which was identified from intestinal smooth muscle cDNA . Injection of ∼20 pg caused an in-frame deletion of this exon but had no effect on transcript of the low molecular weight isoform [39] . The morpholino against the translation start site of tks5 was a generous gift from Danielle Murphy and Sara Courtneidge and was injected as described [25] . The elastic moduli ( Young's modulus ) of intestines isolated from wild type and mlt larvae were measured using a microprobe indenter device [43] . This assay measures the upward force generated by the intestine in response to indentation of the probe applied to the outer ( serosal ) surface . Tissue compliance ( Young's modulus ) is the slope of the force versus probe indentation curve . Briefly , a tensiometer probe ( Kibron , Inc . , Helsinki ) with a 100 µm radius flat-bottom needle was mounted on a 3-D micromanipulator with 160 nm step size ( Eppendorf , Inc . ) attached to an inverted microscope . The tissue was adhered to the bottom of a plastic dish filled with DMEM and imaged by bright field illumination . The bottom of the probe was brought through the air-water interface until it rested at the surface of the cylindrical tissue with a diameter of approximately 80 microns . The probe was calibrated using the known surface tension of a pure water/air interface , and the stress applied by the tissue to the probe as it was lowered was measured as a function of indentation depth . In principle , this deformation geometry is that of an infinite plane compressing a cylindrical object , and the absolute values of elastic modulus can be calculated from appropriate models that require assumptions about the adherence of the tissue to the probe and the glass slide , whether the sample is modeled as a uniform cylinder or an elastic shell , and other structural factors that confound calculation of the absolute value of elastic modulus from the force-indentation data . In this study the primary interest is in the relative stiffness of wild type and mutant tissue , and therefore we present only the primary data , which consists of the elastic resistance of the tissues as a function of indentation depth . Indentations ( ≥13 per intestine ) spanned the range from 160 nm , which would measure small strain reflecting linear elasticity to indentations , up to 20 microns , which would reveal differences in large strain deformation or rupture . After the largest indentations , measurements were repeated at small strains to confirm that the deformations were recoverable . For statistical analyses , a one-tailed Student's t test for data sets with unequal variance was performed to determine the significance of differences between Young's moduli of wild type and mlt intestines samples . RNA was isolated from trunk sections of 74 hpf larvae that encompassed the mid- and posterior intestine . Six pairs of mutants and wild type siblings were analyzed ( 25–35 trunks per sample ) . RNA was recovered using Trizol ( Invitrogen ) and Qiagen RNeasy columns . Probes were hybridized to the Affymetrix zebrafish genome array ( Affymetrix #900487 ) . The array data were evaluated by importing affymetrix * . cel files into genespring v . 7 . 3 . 1 , and expression intensities were calculated using gcrma for each probe set . Gene expression data were validated by quantitative RT-PCR using sybr green and by RNA in-situ hybridization . RNA was recovered from intestines manually dissected from larvae using Trizol and Qiagen RNeasy columns . For each PCR amplification RNA from 15 or more larvae was pooled . An amplified fragment from the tata-box binding protein cDNA ( tbp ) was used as internal standard . PCR amplification and data analysis were performed as described [86] using sybr green ( Applied Biosystems , UK ) . Statistical analyses were performed using the one-tailed Student's t test for data sets with unequal variance . glutathione peroxidase: 5′ gctgttcagcctggactttt; 3′ QPCR cgttgctgagtttggactttt; 3′ in situ ctcagatgaacgagctgcac jun B: 5′ tctgttgggttacggtcaca; 3′ QPCR cgtctggatgatgagcctct; 3′ in-situ ggaccttctgcttgagttgc src ( including attB sites ) : 5′ ggggacaagtttgtacaaaaaagcaggctgccaccatgggtggagtcaagagtaa; 3′ ggggaccactttgtacaagaaagctgggtcgaggttttctccgggttggta; 3′ Y528F ggggaccactttgtacaagaaagctgggtcgaggttttctccgggttggaattgtggttc . 6 hpf ( shield stage ) zebrafish embryos were sterilized by two 10-min washes with 0 . 1% sodium hypochlorite and raised in sterile embryo medium . Intestines were dissected from sterilized embryos at 74 hpf and dissociated in 0 . 25% trypsin/EDTA . Cells were collected by centrifugation for 5 min at 800 g and treated with 1 . 5 µM menadione for 1 h in DMEM supplemented with 10% FBS , 100 ug/ul pen/strep , and 0 . 2% amphotericin B . Treated cells were centrifuged and immediately fixed in sample buffer and stored at −80°C for Western blots . | The epithelial cells lining the digestive tract are separated from the connective tissue stroma by a thin layer of extracellular matrix called the basement membrane . During cell invasion , as occurs during cancer metastasis , epithelial cells breach the basement membrane and invade the tissue stroma . The proteases used by invasive cells to degrade basement membrane in vitro are localized in specialized plasma membrane protrusions known as invadopodia . It is not known , however , whether invadopodia are required for cell invasion in vivo or what triggers their formation . Here , we show that epithelial cells in the intestine of the zebrafish mutant meltdown form invadopodia-like protrusions and invade the tissue stroma in response to unregulated contractile tone in the surrounding smooth muscle layer . The invadopodia-like protrusions that form in response to this physical signal are required for epithelial cell invasion in this in vivo model , and they can be induced when unregulated smooth muscle contraction is induced by oxidative stress . These findings provide the first direct evidence that invadopodia play a role in tissue cell invasion in vivo and identify a novel inducible physical signaling mechanism that can drive this process . | [
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"deve... | 2012 | Smooth Muscle Tension Induces Invasive Remodeling of the Zebrafish Intestine |
Extrastriate cortical areas are frequently composed of subpopulations of neurons encoding specific features or stimuli , such as color , disparity , or faces , and patches of neurons encoding similar stimulus properties are typically embedded in interconnected networks , such as the attention or face-processing network . The goal of the current study was to examine the effective connectivity of subsectors of neurons in the same cortical area with highly similar neuronal response properties . We first recorded single- and multi-unit activity to identify two neuronal patches in the anterior part of the macaque intraparietal sulcus ( IPS ) showing the same depth structure selectivity and then employed electrical microstimulation during functional magnetic resonance imaging in these patches to determine the effective connectivity of these patches . The two IPS subsectors we identified—with the same neuronal response properties and in some cases separated by only 3 mm—were effectively connected to remarkably distinct cortical networks in both dorsal and ventral stream in three macaques . Conversely , the differences in effective connectivity could account for the known visual-to-motor gradient within the anterior IPS . These results clarify the role of the anterior IPS as a pivotal brain region where dorsal and ventral visual stream interact during object analysis . Thus , in addition to the anatomical connectivity of cortical areas and the properties of individual neurons in these areas , the effective connectivity provides novel key insights into the widespread functional networks that support behavior .
Extracellular recording studies have provided detailed information on the properties of individual neurons and neuronal populations during task performance , which can be correlated with [1 , 2] , and even causally related to , behavior [3 , 4] . However in order to fully understand the function of neurons in any given brain area and how these neurons subserve behavior , one also needs information about their anatomical connectivity , i . e . , from which areas these neurons receive information ( input ) and to which areas they project ( output ) . Anatomical tracer studies provide a general roadmap of connectivity but cannot identify how specific types of visual information are transmitted between different levels in the cortical hierarchy , since most far extrastriate cortical areas are highly heterogeneous and frequently contain specialized modules for different types of visual information or cognitive processes [5–7] . Functional magnetic resonance imaging ( fMRI ) provides a static bird’s-eye view of cortical activations elicited by specific stimuli or tasks [8] , yet this indirect measure of brain activity cannot in itself determine how the different nodes of the network are connected and how information flows between these different nodes . Electrical microstimulation in monkeys during fMRI ( EM-fMRI ) allows the study in vivo of how neural systems are connected ( i . e . , effective connectivity [9–14] ) at a scale of patches or clusters of neurons . However , no study has used this approach to investigate the areas in the macaque intraparietal sulcus ( IPS ) , which have been implicated in a large number of cognitive processes such as motor planning , spatial attention , decision , reward , timing , 3-D vision , and even categorization [15–21] . In this study , we wanted to relate function to connectivity by implementing single-cell recordings and EM-fMRI in the anterior lateral bank of the IPS . We first identified patches of neurons encoding the depth structure of objects in the anterior intraparietal area ( AIP ) and subsequently performed EM-fMRI experiments in these functionally defined patches of neurons . Although neurons in anterior and posterior AIP showed highly similar neuronal selectivity , we observed markedly distinct networks of cortical areas in occipital , parietal , frontal , and temporal cortex when stimulating each of these subsectors; anterior AIP was embedded in a somatomotor network , while posterior AIP was connected to areas involved in object processing . Our results demonstrate that the posterior subsector of area AIP may be a critical site of convergence of dorsal and ventral stream object information .
We recorded fMRI-guided single-unit activity ( SUA ) along the lateral bank of the IPS prior to the EM-fMRI sessions ( see overview of EM/recording-positions in S1 Table , S1 Fig . for electrode locations ) . In two monkeys ( M and K ) , we identified two grid positions in AIP with a high proportion of neurons selective for disparity-defined depth structure ( e . g . , convex versus concave ) ( 424 recording sites in total , in 22 grid positions ) , one in anterior AIP ( aAIP ) and one in posterior AIP ( pAIP , [22] , Fig . 1A and B ) . In both subsectors of AIP , a high proportion of the neurons ( 45% to 65% ) preserved their preferences for the depth structure of surfaces ( 3-D shape ) across positions in depth [21] , as illustrated by the example neurons in Fig . 1A and B . The aAIP and the pAIP patches contained neurons with highly similar selectivities for disparity-defined curved surfaces . Furthermore , consistent with previous research , we confirmed the presence of object-selective responses ( single and multi-unit activity ( MUA ) , monkeys M , K , and C , [23] ) and grasping activity in pAIP ( monkey C , [24] ) . As a control , we also recorded spatially selective saccadic activity ( SUA and MUA; one-way ANOVA with factor target position; p < 0 . 01 ) in neighboring lateral intraparietal area ( LIP ) using a visually guided saccade task , in which the saccade target was positioned at seven to ten different locations in the contralateral visual hemifield ( monkeys M , K , and T ) ( example neuron in Fig . 1E; average spike rate during visually guided saccades towards seven different target locations on the screen ) . Thus , electrophysiological recordings identified three functionally distinct stimulation sites covering two-thirds of the anterior-posterior extent of the lateral bank of the IPS . We stimulated aAIP in three different animals ( M , K , and C ) , in ten scan sessions ( 87 runs × 245 functional volumes , S1 Table for overview , S1 Fig . for electrode locations , data in [25] ) . Fig . 2A ( left column ) shows the t-score maps overlaid on coronal sections for monkey M during sedation ( contrast EM versus NoEM; p < 0 . 001 , uncorrected; n = 17 runs ) . Focal increases in fMRI-activity were observed in area AIP , consisting of both aAIP and pAIP ( Fig . 2A , left column , first and second row ) , in the anterior lateral bank of the IPS . Furthermore , aAIP-EM elicited significant fMRI activations in the medial bank of the IPS ( area MIP ) , in area PFG in the rostral portion of the inferior parietal lobule , in the most anterior sector of somatosensory area S2 and in ventral premotor cortex ( PMv , or area F5; Fig . 2A , left column , rows 2–5 ) . The fMRI activation evoked by aAIP-EM in PMv was located in the posterior bank of the inferior ramus of the arcuate sulcus , comprising both F5p and F5a [26] . Remarkably similar results were obtained when aAIP was electrically stimulated while the animal was awake and performing a task: the same somatomotor network consisting of pAIP , MIP , PFG , S2 , and F5 was activated ( Fig . 2A , second column , n = 41 runs ) . To quantify the similarity between awake and sedated fMRI-EM in monkey M , we considered 32 pre-defined regions of interest ( ROIs ) throughout the cortex ( see Materials and Methods ) and calculated the correlation between the percentage of significant voxels ( p < 0 . 001 , uncorrected ) per ROI in both states ( awake/sedated; data averaged over runs ) . Awake fMRI-EM correlated strongly ( Pearson correlation: 0 . 78; permutation test , p = 0 . 0002 ) with fMRI-EM during sedation in the same animal , which allowed us to combine the data from the awake and sedated states in the group analysis . Similarly , the average t-value per ROI in the awake state correlated strongly with the average t-values in the sedated state ( Pearson r = 0 . 85 , permutation test: p < 0 . 0001 ) . The effects of aAIP-EM were not only similar in the awake and sedated states , but also in different individual animals ( compare results obtained from monkey M in first and second column with results from monkeys K-awake and C-sedated in the two rightmost columns of Fig . 2A ) : although the centers of the activations varied slightly among animals , aAIP-EM consistently elicited fMRI activations in areas AIP ( both aAIP and pAIP ) , MIP , PFG , S2 , and F5 in all three animals . The percentage of voxels significantly activated ( in the 32 pre-defined ROIs , see Materials and Methods ) by aAIP-EM in monkey M was highly correlated with those in monkeys K ( Pearson r = 0 . 60 , p = 0 . 01 ) and C ( Pearson r = 0 . 72 , p = 0 . 0042 ) . Moreover , the average t-value per ROI in monkey M correlated closely with those obtained in monkeys K ( r = 0 . 36 , p = 0 . 04 ) and C ( r = 0 . 78 , p = 0 . 01 ) . The activation pattern evoked by aAIP-EM was also evident in the group average ( fixed effects analysis on all 87 runs; Fig . 2B , average of three monkeys and awake/sedated , p < 0 . 001 uncorrected; see also S2A Fig . for coronal sections ) . Note that qualitatively similar results were obtained when including the same number of runs per animal ( S3 Fig . ) . An ROI-based analysis showed significant increases in percent signal change ( PSC ) in areas AIP , MIP , PFG , S2 , and F5p during aAIP-EM compared to no-EM ( Fig . 2C , t-test; p < 0 . 05 corrected for multiple comparisons [32 ROIs]; No-EM is set as the baseline with a zero-value ) , but not in any of the temporal , occipital or prefrontal ROIs . Note that we did not obtain a significant effect in the ROI of F5a ( t-test; p = 0 . 37 ) in the group data , most likely because aAIP-EM activated only a fraction of F5a ( Fig . 2B , bottom row ) . The group data in Fig . 2B also illustrate that the strongest activation in PMv during aAIP-EM was located in area F5p . aAIP-EM did not activate subcortical structures except the putamen ( Fig . 2B inset , white arrow ) , even when the statistical threshold was lowered . Furthermore , the group analysis did not show an effect of aAIP-EM on the contralateral hemisphere ( see examples in Fig . 2A , group results in S2A Fig . ; PSC in S4A Fig . ) . To assess the specificity of our aAIP-EM results , we performed a similar analysis of PSC on ROIs which are not connected to AIP ( motor areas F1 , F2 , F3 , F4 , F6 , and F7; note that early visual areas V1 , V2 , and V3 in Fig . 2C are also not connected to AIP [27] ) . We observed no significant increase in PSC in the latter areas during aAIP-EM ( S5A Fig . ) , and the percentage of activated voxels in these areas was very low ( S5B Fig . ) . To verify the consistency of our results across animals we performed a conjunction analysis on the aAIP-EM data of all individual animals and states ( at p < 0 . 05 uncorrected for each animal ) . The network connected to the anterior subsector of AIP consisted of MIP , PFG , and S2 ( S6A Fig . ) . Although we observed significant AIP-EM–induced activations in area F5 in each animal , the conjunction analysis did not contain F5 due to interindividual differences in the location of the F5 activations ( see also Fig . 2A , bottom row ) and the very localized character of activations within F5p and F5a . To test the reciprocity of the anatomical connectivity of aAIP , we also stimulated in two target areas of aAIP , namely , PFG ( monkey M , 2 sessions , 17 runs ) and MIP ( monkey K , 14 runs ) . As expected , PFG-EM activated AIP , S2 , and F5p , and MIP-EM activated AIP , PMd , and S2 ( S7 Fig . ) , consistent with the existence of reciprocal connections at this level in the hierarchy of cortical areas [28] . Thus , the most anterior subsector of area AIP—where neurons encoding the depth structure of objects were recorded—is connected to a network of somatosensory and motor areas implicated in reaching and grasping [29–31] . Although neuronal characteristics in pAIP were highly similar to those in aAIP , EM of pAIP activated a network of cortical areas that was markedly distinct from the network activated by aAIP-EM ( eight scan sessions , 61 runs × 245 functional volumes; see S1 Table for overview per animal; see S1 Fig . for electrode locations; data in [25] ) . Application of EM to pAIP induced fMRI activations throughout the lateral bank of the IPS; not only in pAIP itself , but also in areas aAIP , LIP , the more posterior subsector of MIP , and in the caudal intraparietal area ( CIP ) ( Fig . 3A , first three rows ) . In addition , we consistently obtained stimulation-induced activations in the temporal lobe , which was not observed during aAIP-EM ( Fig . 3A , third and fourth row ) : in the lower bank of the superior temporal sulcus ( STS ) , corresponding to the dorsal and ventral part of the posterior inferotemporal cortex ( PITd and PITv ) ; the occipitotemporal area ( OT ) ; the anterior part of the inferotemporal cortex ( TE ) ; the fundus of the STS ( FST ) ; and the contralateral STS ( Fig . 3A , third row ) . The pattern of EM-induced fMRI activations was also distinct in the frontal lobe: in contrast to aAIP-EM , stimulating pAIP elicited significant activations in area 45b ( in the anterior bank of the inferior ramus of the arcuate sulcus ) and in area 46v ( Fig . 3A , last two rows ) . Finally , pAIP-EM also caused scattered activations in and around the lunate and inferior occipital sulcus , corresponding to areas V3 and V4 , and even in parts of primary visual cortex V1 . As was observed for aAIP-EM , microstimulation of pAIP elicited similar results during awake and sedated experiments in the same animal ( Monkey M: Fig . 3A , first and second columns , correlation between the percentage of significantly-activated voxels induced by pAIP-EM during awake versus sedated fMRI: 0 . 62 , p = 0 . 0014 ) . Similar results were obtained in monkeys C ( sedated ) and K ( sedated; Fig . 3A , third and fourth columns; Pearson r = 0 . 66; permutation test: p < 0 . 0001; and r > 0 . 30; p < 0 . 05 between C and M , and K , and M ) . Likewise , the average t-values per ROI in monkey M showed a high degree of correlation between the awake and sedated state ( r = 0 . 57 , p = 0 . 0018 ) , and between monkeys C and M ( r = 0 . 73; p < 0 . 0001 ) and K and M ( r = 0 . 52; p = 0 . 003 ) respectively . The consistency of the pattern of activations elicited by pAIP-EM across animals was confirmed by a conjunction analysis across all three animals , which showed activations in CIP , LIP , pAIP , aAIP , FST , middle temporal area ( MT ) , the ventral part of medial superior temporal area ( MSTv ) , PIT , OT , TE , and 45B ( S6B Fig . ) . Group data ( fixed-effect analysis including all 61 runs; qualitatively similar results were obtained when including the same number of runs per animal , S3B Fig . ) of the effect of pAIP-EM are shown in Fig . 3B ( see S2B Fig . for coronal sections ) . In general , pAIP-EM did not evoke significant activations in subcortical structures such as the basal ganglia and the cerebellum . However , in contrast to aAIP , a restricted part of the pulvinar—possibly corresponding to the dorsal pulvinar—was significantly activated by pAIP-EM ( Fig . 3B , inset , white arrow ) . Furthermore , we observed significant activations in posterior parietal cortex ( AIP , LIP , CIP in the lateral bank , and MIP and posterior intraparietal area [PIP] in the medial bank of the IPS ) ; in prefrontal areas 45B and part of 46v; and extensive activations in temporal areas FST , PITd/v , OTd , and TE ( see S2B Fig . for coronal sections ) . The PSC was significantly greater than zero in all aforementioned ROIs during pAIP-stimulation compared to no-stimulation ( t-test; p < 0 . 05 , corrected for multiple comparisons [32 ROIs]; Fig . 3C ) , except for 46v ( p = 0 . 37 , most likely due to the relatively small part of the area activated by pAIP-EM ) . In contrast to aAIP , pAIP-EM also elicited contralateral activations in temporal cortex ( Fig . 3A , top three rows; S2B Fig . ; S4B Fig . for PSC ) , including areas FST , PITd/v , and OTd . As for aAIP-EM , a similar analysis of PSC was performed on ROIs which were previously found not to be connected to AIP ( motor areas F1 , F2 , F3 , F4 , F6 , and F7 ) . No significant increases in PSC in the latter areas were measured during pAIP-EM ( S5A Fig . ) , and the percentage of activated voxels was very low in the latter ROIs ( S5B Fig . ) . Conversely , the only cortical area found to be connected to AIP as described by tracer studies [27] that was not activated in the current study was the dorsal part of parieto-occipital area V6A . To quantify the difference in effective connectivity between aAIP and pAIP , we performed an ANOVA on the PSC evoked by EM versus No-EM in all voxels of the 32 predefined ROIs ( Materials and Methods ) . The interaction between the factors Stimulation [EM/NoEM] and area [aAIP/pAIP] was significant ( p < 0 . 05 ) for early visual areas ( V1 , V2 , V3 , V3a , V4 , V4t , V4A , and V6 ) , temporal areas ( OTd , PITd , PITv , TE , FST , MSTv , MT , and STP ) , prefrontal areas 45b and 45a and parietal areas ( S2 , MIP , LIP , PIP , and CIP ) , but not for AIP ( see S2 Table ) . Hence the ROI of AIP was equally activated by aAIP- and pAIP-EM . These results remained essentially unchanged after correcting for the different number of runs ( 61 for pAIP-EM and 87 for aAIP-EM ) . Similarly , a conjunction analysis ( p < 0 . 01 uncorrected , S2C Fig . ) showed that areas AIP , part of area MIP , and a small region in the arcuate sulcus corresponding to F5a were the only areas activated by both aAIP-EM and pAIP-EM . Taken together , electrical microstimulation in two functionally defined subsectors of area AIP—with very similar neuronal properties and in some cases separated by no more than 3 mm—activated markedly different networks of cortical areas in parietal , temporal , and frontal cortex . As a control , we measured the effective connectivity of neighboring area LIP . Data were collected in three monkeys ( K , M , and T , see S1 Fig . for electrode locations ) , in six sessions ( 60 runs × 245 functional volumes , see S1 Table for overview , data in [25] ) . LIP-EM enhanced fMRI-activations in parietal areas LIP , MIP , CIP , and also in area FST in the fundus of the STS ( Fig . 4A , group data in Fig . 4B , S2D Fig . ) . Furthermore , LIP-EM evoked focal increases in Frontal Eye Fields ( FEF ) -activity in individual stimulation sessions ( three out of six sessions; illustrated in Fig . 4B , upper row ) . As with the aAIP and pAIP experiments , we obtained comparable results in the sedated and awake states , and similar results in all animals individually ( Fig . 4A , correlations between percentage of significant voxels per ROI per monkey/state in 32 predefined ROIs: Pearson r > 0 . 58 , permutation test: p < 0 . 0066; correlations between average t-values per ROI per monkey/state in 32 predefined ROIs: r > 0 . 62 , p < 0 . 002 ) . The consistency of the activation patterns elicited by LIP-EM across animals was confirmed by a conjunction analysis ( S6C Fig . ) across all three animals , which showed activations in CIP , MIP , FST , and V3A . The group analysis of the LIP-EM experiments revealed a small but significant stimulation-induced increase in activity in FEF ( Fig . 4C ) , in parietal areas LIP , MIP , CIP , and PIP and in temporal area FST . Note that qualitatively similar results were obtained when including the same number of runs per animal ( S3C Fig . ) . Similarly , LIP-EM significantly increased the PSC in parietal areas LIP , MIP , CIP , and PIP , and in temporal area FST ( Fig . 4D , t-test , p < 0 . 05 , corrected for multiple comparisons ) . In contrast to pAIP-EM , LIP-microstimulation did not modulate the PSC in visual areas V4 and V4A , in temporal areas TE and PITd/v , nor in parietal area AIP or prefrontal area 45B ( two-way ANOVA with factors microstimulation [EM versus no-EM] and area [pAIP versus LIP]; interaction: p < 0 . 05; S2 Table ) . Thus the IPS sector characterized by the presence of spatially selective saccadic activity ( LIP ) is effectively connected to a network of cortical areas that only partially overlaps with that of neighboring pAIP .
To our knowledge , our study provides the first causal evidence relating the properties of individual neurons to their effective connectivity in posterior parietal cortex . fMRI-EM in three functionally defined patches of neurons in the lateral bank of the IPS revealed distinct networks of cortical areas in parietal , temporal , and frontal cortex . Our EM-fMRI results obtained in AIP are highly comparable to earlier work using traditional tracer injections in AIP [27] , although the latter authors did not distinguish between anterior and posterior AIP . Our current results are in line with a previous EM-fMRI study , in which FEF-EM showed increased fMRI activations in areas previously found to be anatomically connected to area FEF [11 , 32 , 33] ( see [34] for review ) . It is important to emphasize that almost all areas activated during fMRI-EM in aAIP and pAIP are monosynaptically and reciprocally connected with AIP [26 , 27 , 35–39] . The only possible exception may be the contralateral PITd/v and OTd activations elicited during pAIP-EM . Conversely , areas that are not directly connected to AIP ( e . g . , F1–F4 , F6–F7 , and V1–V3 ) were also not activated during EM-fMRI in AIP . Only very few brain regions for which AIP connections have been reported were never activated during EM-fMRI: examples include area V6Ad , which is weakly connected to AIP [27] , and the cerebellum , which is connected to the possible homologue of AIP in the cebus monkey [40] . Note that with the current resolution of monkey fMRI , it is not possible to make claims about the laminar distribution ( supra- versus infragranular ) of the connections; hence , it is not possible to make conclusive inferences about feedforward versus feedback connections . Unlike most tracer studies , we combined extensive single-cell recordings and effective connectivity measurements . However , despite the striking similarity between tracer studies and our results , the crucial advantage of in vivo EM-fMRI ( or EM-optical imaging [41] ) over tracer studies is that it allows the identification of the connections of specific clusters of neurons ( in our case 3-D–shape selective neurons within AIP ) with subsectors of other areas ( e . g . , in frontal cortex ) , which can then become the target of detailed investigations using ( a combination of ) single-cell recordings , EM-fMRI , and reversible inactivation during fMRI ( see [34] ) . Thus , in vivo effective connectivity studies furnish the possibility to investigate neural populations for which the inputs and outputs have been accurately identified in the animal under study ( possibly even without existing anatomical data ) , so that the signals can be traced throughout the hierarchy of extrastriate areas from occipital to frontal cortex ( see also [11] ) . Moreover , the same in vivo procedure can be repeated for virtually unlimited numbers of target areas within the same subject . Furthermore , EM-fMRI may also provide important information for the interpretation of the behavioral effects of microstimulation , since a general overview of the effective connectivity of a cortical stimulation site can reveal which downstream areas influence behavior . Previous tracer studies have shown connections between area LIP and many other ( sub ) cortical areas such as FEF , 46 , parieto-occipital cortex ( PO ) , dorsal prelunate area ( DP ) , 7a , V3 , V4 , MT , MST , posterior area in inferotemporal cortex ( TEO ) , and superior colliculus [36 , 37 , 42 , 43] , in which the ventral stream areas are primarily connected with dorsal LIP while MT/V5 and FEF are primarily connected with ventral LIP [36] . Our current study , however , shows mainly increased activation in temporal area FST , and to a lesser extent in FEF . The difference between our current EM study and previous anatomical tracer studies was not surprising , given the known heterogeneity of area LIP ( e . g . [7 , 44 , 45] ) and the fact that we only stimulated a single site per animal in LIP as a control for pAIP . Moreover , it is conceivable that previous studies ( e . g . , [46] ) have identified our pAIP site , located 9 to 15 mm from the anterior tip of the IPS and with foveal RFs , as anterior LIP ( most likely also explaining the connections between area 45B and LIP [38] ) , whereas we observed 45B activations during pAIP but not LIP stimulation . In addition , our pAIP stimulation site was also located more dorsally in the lateral bank of the IPS , and previous studies have reported that the anterior part of dorsal LIP is strongly connected to ventral stream areas and to area 45B [36] . Other studies [47 , 48] have investigated effective connectivity in the motor system using a combination of EM and single-cell recordings and have demonstrated that EM can elicit both excitation and inhibition in the target neurons , particularly at higher current strengths . Although marked behavioral effects can be observed with relatively low intensity EM ( 25–35 μA ) in extrastriate cortex [3 , 49] , our pilot experiments showed no reliable fMRI activations when we stimulated with currents below 200 μA ( awake ) or 1 , 000 μA ( sedated ) . Since the fMRI signal represents a sum of excitatory and inhibitory activity [50] , we probably evoked both effects in our EM-fMRI experiments . It needs to be noted that previous FEF-EM experiments revealed extensive effective connectivity networks with currents below 50 μA in awake animals [11] , which could also be obtained by optogenetic stimulation of the same areas [51] . The patterns of activations we observed were highly similar in awake and sedated sessions , similar to lateral geniculate nucleus ( LGN ) -EM [50] . This observation has important practical implications because it shows that it is possible to chart the connectivity of functional patches of extrastriate neurons in monkeys engaged in single-cell experiments but not accustomed to the scanner environment . However , the strong correspondence between awake and sedated sessions does not necessarily imply that EM-induced activations cannot be stimulus- or task-dependent ( e . g . , in the Frontal Eye Fields , [11 , 52] ) . We did not obtain sufficient data in the current experiment in the awake state to draw definitive conclusions , but future studies should investigate the task-dependency of the fMRI activations evoked by AIP-EM . Importantly , the possibility remains that similarities between the awake and sedated EM-fMRI results are region-specific . Our effective connectivity results help to understand several anatomical and physiological observations . First , neurons in the posterior part of AIP tend to be more visual , whereas neurons in anterior AIP tend to be more motor-dominant [53] , and this visual-to-motor gradient in AIP can now be linked to the connectivity of pAIP ( object processing network including the ventral stream ) and aAIP ( somatomotor network ) . Our results clearly demonstrate the status of pAIP as a pivotal brain area where dorsal and ventral visual stream interact during object analysis . Before contact with the object , the anterior IPS regions may access information about object identity—which may assist in selecting the appropriate grasp—through these connections with the ventral stream [27 , 54] . This distinction between pAIP and aAIP , made possible using both effective connectivity and physiological assessments , could not be readily detected in anatomical studies [27] . Future studies should determine to what extent and under which conditions ( e . g . , immediate versus delayed actions [55 , 56] ) these dorsal–ventral stream interactions become behaviorally relevant . Secondly , both the aAIP and the pAIP patch contained a high proportion of 3-D–shape selective neurons . The patterns of effective connectivity we observed strongly suggest that 3-D–shape information is transmitted from pAIP to aAIP and subsequently to the motor system . This stream of 3-D–shape information runs along the lateral bank of the IPS and interacts with the ventral stream at the level of pAIP . Note that 3-D–shape selective clusters in AIP are also active during object grasping [57] , and that reversible inactivation of these AIP clusters induces a grasping deficit ( Verhoef and Janssen , unpublished observations ) . Therefore our results , in concert with previous findings in AIP , also contribute to our understanding of the organization of the 3-D–shape network . Finally , the anterior-posterior extents of areas AIP and LIP have been a long-standing controversy , in which anatomical studies have conflicted with physiology studies [42 , 45 , 46] . Specifically , the region in the lateral bank of the IPS where spatially selective saccadic activity—interspersed with memory delay-period activity during saccades [7]—can be recorded ( i . e . , area LIP ) is mostly confined to the posterior third of the lateral IPS [42] . In contrast , anatomical studies have claimed , based on the pattern of myelination of LIP [43 , 45] , that LIP occupies a large part of the lateral bank of the IPS . Our data finally suggest a resolution for this issue . The functional properties of individual neurons at the pAIP stimulation site , located 9 to 14 mm posterior to the tip of the IPS , resembled those of aAIP neurons given the presence of strong grasping activity , 3-D–shape selectivity , and the absence of saccadic activity [24 , 58] , and the most posterior tracer injections in AIP in [27] were also located 15 mm posterior to the anterior tip of the IPS . However , the connectivity of pAIP with frontal and temporal areas was strikingly distinct from that of both aAIP and LIP . Therefore , a functional parcellation of the lateral IPS would identify the anterior two-thirds of the lateral IPS , where grasping activity can be recorded , as AIP [53 , 59 , 60] , and the posterior one-third of the lateral IPS , where spatially selective saccadic activity can be recorded , as LIP . Monkey fMRI studies have also demonstrated a representation of the fovea in the anterior lateral bank of the IPS [61 , 62] , and in humans , two regions in the anterior IPS ( DIPSA and DIPSM ) are activated more strongly by curved surfaces compared to flat surfaces at different positions in depth [63] , which may be homologous to monkey aAIP and pAIP . Our results are also consistent with human fMRI studies on grasping and object processing . Although considerable differences exist between the human IPS and the macaque IPS ( related to , e . g . , 3-D structure-from-motion and tool use , [64–67] ) , the more anterior IPS sectors appear more action-related , whereas the more posterior IPS sectors are more visual [68] . Similarly , resting-state connectivity analysis in humans has indicated that a region in the Lateral Occipital Complex ( LOC ) responsive to images of hands and tools is selectively connected to the IPS regions involved in action-related processing of hands and tools . It is also noteworthy that distinct patterns of resting-state connectivity can be observed for adjacent seed regions in occipitotemporal cortex [69] , similar to the distinct networks we observed when stimulating in different cortical sites that were merely 3 mm apart . Future studies will have to determine the correspondence between functional and effective connectivity as determined with EM-fMRI in the IPS areas , as already achieved in macaque somatosensory cortex [13] . It is remarkable that we observed such distinct networks of cortical areas when stimulating sites that were in some cases separated by no more than 3 mm . Electrical microstimulation at the currents we used undoubtedly activated large numbers of neurons , and most likely not exclusively 3-D–shape , object , or saccade selective neurons . Since the 3-D–shape patches we identified in AIP measured merely 1–2 mm ( i . e . , one or two grid positions ) but were very homogeneous ( containing up to 80% 3-D–shape selective neurons [21] ) , the effect of EM in these patches must have been dominated by the connectivity of 3-D–shape selective neurons . Not surprisingly then , most of the cortical areas connected to the aAIP and pAIP stimulation sites are sensitive to the depth structure of objects [3 , 5 , 21 , 70–75] . Area FST was effectively connected to both pAIP and LIP , consistent with anatomical studies [27 , 37] . Since FST neurons encode 3-D–shape defined by structure-from-motion ( SFM ) [76] , and in view of the fact that both FST and AIP are selectively activated by SFM-defined 3-D surfaces compared to control stimuli ( Mysore , Vogels , Vanduffel , and Orban , unpublished observations ) , the pAIP-FST connection may be important for the integration of two of the most powerful depth cues , binocular disparity and SFM . The patterns of connectivity we observed appeared to result mostly from feedforward ( e . g . , aAIP to F5 ) and lateral ( e . g . , aAIP to PFG [27 , 30] ) projections . However , in each stimulation site , EM activated its most likely input areas ( feedback ) : aAIP-EM activated its input area pAIP , pAIP-EM activated LIP and CIP , and LIP-EM activated CIP and V3A , a pattern that is entirely consistent with an earlier anatomical tracer study demonstrating that the main connectivity pattern in the lateral IPS runs from posterior to anterior , from V3A to CIP–LIP–AIP [35] . Visual object information is then send to the motor system ( F5p and F5a ) and to the somatosensory system ( area S2 ) , an area connected to both AIP and F5 where many neurons respond during active hand manipulation of objects but not during passive hand stimulation [77] . Thus , charting the effective connectivity of functionally defined subsectors of areas or patches of neurons in the IPS provides crucial insight into the organization of cortical networks that support behavior .
All experiments were performed in four male rhesus monkeys ( C: 8 kg; K: 6 kg; M: 5 kg; T: 6 kg ) . All animals had a custom-made , magnetic resonance imaging ( MRI ) -compatible headpost and cylinder implanted on the skull using ceramic screws and dental acrylic . All surgeries were performed under isoflurane anaesthesia and sterile conditions . The cylinders were implanted in an oblique orientation ( orthogonal to the IPS in monkey C , parallel to the IPS in monkeys M , K , and T ) over the IPS at Horsley-Clark coordinates ranging from 10 to 0 P and from 10 to 20 L . In monkey M , the recording cylinder was repositioned before the fMRI-EM experiment in aAIP from an orientation orthogonal to the IPS ( S1 Fig . , upper row , red arrow ) to an oblique orientation parallel to the IPS to allow electrode penetrations parallel to the IPS , targeting the aAIP patch as defined by its neuronal characteristics . Three monkeys ( K , M , T ) were trained in passive fixation and saccade tasks in a mock fMRI-setup . They were seated in a sphinx position [78] in a plastic monkey chair directly facing an LCD screen ( viewing distance: 57 cm ) . Eye position was monitored at 120 Hz through the pupil position ( Iscan , MA , United States ) . The fourth monkey ( C ) was scanned only under sedation . All stimuli were displayed on a CRT monitor ( Vision Research Graphics , equipped with P46 phosphor ) operating at 120Hz . Stereo test . The stimulus set of the stereo experiment consisted of random-dot stereograms in which depth was defined by horizontal disparity ( dot size 0 . 08 deg , dot density 50% , vertical size 5 . 5 deg ) presented on a grey background [70] . All stimuli were generated using Matlab ( MathWorks ) and were gamma-corrected . The stimuli in the search test consisted of three types of smoothly curved depth profiles ( 1 , one-half , or one-fourth vertical sinusoidal cycle ) together with their antiphase counterparts obtained by interchanging the monocular images between the eyes ( disparity amplitude within the surface: 0 . 5 deg ) , control stimuli ( the monocular images presented to both eyes simultaneously ) , and flat surfaces at different disparities . Each of the six depth profiles was combined with one of four different circumferential shapes and appeared at two different positions in depth ( mean disparity + or—0 . 5 deg ) , creating a set of 48 curved surfaces . Ferroelectric liquid crystal shutters ( Displaytech ) each operating at 60 Hz were used to generate dichoptic presentation . The shutters were synchronized with the vertical retrace of the display monitor . There was no measurable cross-talk between the two eyes [21] . After 200 ms of fixation , the stimulus was presented at the fixation point for 1 s . In the search test , all stimuli ( stereo and control , curved and flat ) were presented randomly interleaved at the center of the display and at the fixation plane during passive fixation . Single or multi-unit activity was recorded , and if a site was visually responsive , we isolated single neurons online and tested these neurons in more detail for higher-order disparity selectivity ( i . e . , selectivity for gradients of disparity ) in the position-in-depth test [5] . In this test the stimulus ( a combination of a depth profile and a circumferential shape ) evoking the highest response in the search test was selected together with its antiphase counterpart , and presented at five different positions in depth ranging from-0 . 5 degree ( near ) to +0 . 5 degree ( far ) disparity in equal steps . Object test . Previous studies [23 , 24 , 58] have characterized pAIP based on the presence of selective visual responses to images of objects presented foveally during passive fixation . The same stimuli as in [23] were used to confirm the presence of object-selective responses in pAIP in three animals ( M , K , and C ) . The stimulus set for the object test consisted of 21 two‐dimensional ( 2-D ) area‐equalized static images of natural and artificial objects , including faces , hands , fruits , branches , and several artificial graspable objects . The presence of object-selective SUA or MUA responses was assessed using a one-way ANOVA ( p < 0 . 05 ) . Grasping test . In the visually guided grasping test , a bar attached to a plate was positioned in the monkey’s view . The animal had to rest his right hand on a sensing device in complete darkness for a variable time ( inter‐trial interval ITI 3 , 000–5 , 000 ms ) , after which a light inside the object was illuminated , whereupon the monkey had to fixate the object ( keeping its gaze inside a ±2 . 5‐degree fixation window ) . After a 500 ms fixation period , an audible go‐signal was given for initiating the grasping movement , which consisted of reaching , grasping , and pulling the object on the plate ( holding time: 500–900 ms ) [24] . Saccade test . In the visually guided saccade task , monkeys had to maintain fixation within a window of 2 × 2 visual degrees around a small green spot in the center of the display for a fixed duration of 450 ms , after which a single green saccade target appeared at one of ten possible positions on the screen , spaced 15 ( horizontal ) or 11 ( vertical ) degrees apart . After a variable time , the green fixation spot dimmed , indicating to the animal to saccade towards the target location . The presence of spatially selective saccadic SUA or MUA responses was confirmed using a one-way ANOVA with factor target position ( p < 0 . 001 for all target-selective cells ) . Functional images were acquired with a 3 . 0 T full-body scanner ( TIM Trio; Siemens ) , using a gradient-echo T2*-weighted echo-planar imaging ( EPI ) sequence ( 40 horizontal slices; TR: 2s; TE: 16 ms; 1 . 25 mm3 isotropic voxels ) with a custom-built eight-channel phased-array receive coil , and a saddle-shaped , radial transmit-only surface coil [79] . Before each scanning session , a contrast agent , monocrystalline iron oxide nanoparticle ( MION ) ( Feraheme: AMAG pharmaceuticals; Rienso: Takeda ) was injected into the femoral/saphenous vein ( 7–11 mg/kg ) [78] . To verify the stimulation positions , structural MR images ( 0 . 6 mm resolution ) were acquired in every sedated scan session ( prior to the start of the fMRI experiment ) while the electrode was located at the exact stimulation site inside a standard recording grid ( Crist Instruments , Hagerstown , MD , US ) . In the few sessions in which the latter could not be achieved , we inserted glass capillaries filled with a 2% copper sulphate solution into the grid at several positions , acquired structural MR images ( 0 . 6 mm resolution ) and reconstructed the electrode penetrations using SPM 5 ( Statistical Parametric Mapping ) . In every scanning session , a Platinum/Iridium electrode ( impedance 50–200 kΩ in situ , FHC , Bowdoinham , ME ) was inserted in the grid through glass capillaries serving as guide tubes ( Plastics One Inc , Kent , United Kingdom; FHC , Bowdoinham , ME , US ) . A platinum wire served as ground . The electrical microstimulation ( EM ) signal was produced using an eight-channel digital stimulator ( DS8000 , World Precision Instruments ) in combination with a current isolator ( DLS100 , World Precision Instruments ) . During stimulation blocks , a single EM train was applied in every trial . In awake scanning sessions , the animals were either fixating a spot on a screen ( Fix ) or performing memory-guided saccades ( Sacc ) towards ten different positions contralateral to the stimulated hemisphere . Briefly , during the memory-guided saccade task a saccade target was flashed for 200 ms on the screen , and the animals had to maintain fixation ( 300–1 , 500 ms ) until the dimming of the fixation point instructed an eye movement to the remembered target location . During the baseline fixation task ( Fix0 ) , only a central fixation point was displayed on the screen , while during the control fixation task ( Fix1 ) , one distractor ( identical to the saccade target in the Sacc task ) was shown on the screen with the same position and timing parameters as the saccade target in the memory saccade task . The color of the fixation point indicated to the animals to either maintain fixation or to make saccades . In this study , the data collected during all three tasks were combined . The three tasks were presented to the animals in blocks , and EM was administered during all three tasks , thus creating six types of blocks which were alternated in one run in pseudo-random order . We alternated between stimulation and no-stimulation blocks ( each lasting 40 s ) , with each run lasting 245 pulses ( 490 s ) . Stimulation trains in awake scan sessions lasted 500 ms and were composed of biphasic square-wave pulses ( repetition rate 200 Hz; amplitude 200 μA ) . Note that pilot experiments showed that a current amplitude of less than 200 μA did not evoke increased fMRI-activations . Each pulse consisted of 190 μs of positive and 190 μs of negative voltage , with 0 . 1 ms between the two pulses ( total pulse duration: 0 . 48 ms ) . During sedated scanning sessions , a trial-by-trial stimulation protocol was used similar to the awake sessions ( one EM train every 3 s , approximately ) . EM trains in sedated sessions lasted 250 ms with an amplitude of 1 mA , while other EM-parameters remained similar ( 200 Hz , 0 . 48 ms pulse duration ) . The timing of the EM pulses during the fMRI experiment was computer controlled . Note that pilot experiments showed that a current amplitude of 200 μA ( = current strength during awake sessions ) during sedated sessions only caused increased fMRI-activations around the tip of the electrode . During sedated scan sessions , a 0 . 5/0 . 5 cc mixture of ketamine ( Ketalar; Pfizer ) and medetomidine ( Domitor; Orion ) was administered every 45 min . The animals were video-controlled during sedation , and body temperature was maintained using a heating pad . Off-line image reconstruction was conducted to overcome problems inherent to monkey body motion at 3T . Details about the image reconstruction protocol have been given elsewhere [79] . Briefly , the raw EPI images were corrected for lowest-order off-resonance effects and aligned with respect to the gradient-recalled-echo reference images before performing a SENSE ( sensitivity encoding ) image reconstruction [80] . Corrections for higher-order distortions were performed using a non-rigid slice-by-slice distortion correction . Data were analyzed using statistical parametric mapping ( SPM5 ) and BrainMatch software , using a fixed-effect GLM . Realignment parameters were included as covariates of no interest to remove brain motion artifacts . Spatial preprocessing consisted of realignment and rigid coregistration with a template anatomy ( M12 ) [11] . To compensate for echo-planar distortions in the images as well as inter-individual anatomical differences , the functional images were warped to the template anatomy using non-rigid matching BrainMatch software [81] . The algorithm computes a dense deformation field by the composition of small displacements minimizing a local correlation criterion . Regularization of the deformation field is obtained by low-pass filtering . The functional volumes were then resliced to 1 mm3 isotropic and smoothed with an isotropic Gaussian kernel ( full width at half maximum: 1 . 5 mm ) . Single subject and group analyses were performed , and the level of significance was set at p < 0 . 001 , uncorrected for multiple comparisons . For display purposes , SPM T-maps were presented on coronal or flattened representations of the M12 anatomical template , using xjView toolbox ( http://www . alivelearn . net/xjview ) and Caret software ( version 5 . 64; http://brainvis . wustl . edu/wiki/index . php/Caret:About ) , respectively . The exact locations and extents of the fMRI-activations were verified on the animal’s own EPI-images . Percent signal change was calculated in regions of interest ( ROIs ) , and statistical significance was tested using MarsBaR ( version 0 . 41 . 1 ) . We considered a set of 32 ROIs for early visual areas and the ROIs of all brain areas connected to AIP [27] , which included premotor , prefrontal , parietal , temporal , and visual ROIs ( F5a , F5p , F5c , 45A , 45B , 46v , FEF , AIP , LIP , MIP , CIP , PIP , PFG , STP , OT , PITv , PITd , TE , TEr , FST , MSTv , MT , S2 , V1 , V2 , V3A , V3 , V4 , V4A , V4T , V6A , V6 ) . Moreover , we also included an additional set of ROIs of frontal areas that are not connected with AIP: F1 , F2 , F3 , F4 , F6 , and F7 . Note that the no-stimulation condition served as the baseline . The significance threshold for one-tailed t-tests was set at p = 0 . 05 , corrected for multiple comparisons ( 32 t-tests calculated; p = 0 . 05/32 = 0 . 0016 ) . Standard fMRI analysis methods were used , as described in previous studies [30 , 52] . All regions of interest were described previously [11 , 30 , 62] . To quantify the similarity between the awake and sedated states and between animals , a Pearson correlation was calculated between the percentage of significant voxels ( t-value > 3 . 1: p < 0 . 001 uncorrected ) per ROI in each state ( awake-sedated ) or in each animal , across the set of 32 ROIs of all early visual areas and all areas connected to AIP . The significance of the correlations between animals was calculated using a permutation test , in which the 32 calculated percentages of significantly ( p < 0 . 001 uncorrected ) activated voxels were randomly assigned ( 5 , 000 times ) to the 32 ROIs , after which the correlations between corresponding ROIs were calculated . P-values were calculated as the proportion of correlations exceeding the actual correlation between corresponding ROIs . Moreover , to confirm the consistency of the activations across animals and states , a conjunction analysis was performed on the data of all animals ( at p < 0 . 05 uncorrected for each animal ) . | The cortex of primates consists of many areas that are highly interconnected , forming widespread functional networks engaged in specific tasks . Cortical areas frequently consist of submodules , columns , or patches of neurons that share functional properties . The neuronal characteristics of such clusters of neurons are determined by their inputs ( i . e . , from which neurons they receive information ) and outputs ( i . e . , to which neurons in other brain areas they project ) , but detailed information about the connectivity of small clusters of neurons is frequently lacking . We applied electrical microstimulation during functional magnetic resonance imaging to chart the connectivity of small patches of neurons in the Intraparietal Sulcus , a brain region that has been implicated in many cognitive operations , such as motor planning , spatial attention , 3-D vision , and grasping . We observed that the three patches of neurons we studied were embedded in very distinct functional networks , covering almost the entire cortex . The network of brain areas connected to each patch could , in turn , explain the properties of the neurons in that patch . Thus , the connectivity of clusters of neurons provides crucial information to understand how functional brain networks support behavior . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Effective Connectivity of Depth-Structure–Selective Patches in the Lateral Bank of the Macaque Intraparietal Sulcus |
Patterning and growth are linked during early development and have to be tightly controlled to result in a functional tissue or organ . During the development of the Drosophila eye , this linkage is particularly clear: the growth of the eye primordium mainly results from proliferating cells ahead of the morphogenetic furrow ( MF ) , a moving signaling wave that sweeps across the tissue from the posterior to the anterior side , that induces proliferating cells anterior to it to differentiate and become cell cycle quiescent in its wake . Therefore , final eye disc size depends on the proliferation rate of undifferentiated cells and on the speed with which the MF sweeps across the eye disc . We developed a spatio-temporal model of the growing eye disc based on the regulatory interactions controlled by the signals Decapentaplegic ( Dpp ) , Hedgehog ( Hh ) and the transcription factor Homothorax ( Hth ) and explored how the signaling patterns affect the movement of the MF and impact on eye disc growth . We used published and new quantitative data to parameterize the model . In particular , two crucial parameter values , the degradation rate of Hth and the diffusion coefficient of Hh , were measured . The model is able to reproduce the linear movement of the MF and the termination of growth of the primordium . We further show that the model can explain several mutant phenotypes , but fails to reproduce the previously observed scaling of the Dpp gradient in the anterior compartment .
During early development most tissues undergo fast changes involving cell growth , proliferation , differentiation and patterning . In order to develop into a functional tissue or organ , both patterning and growth have to be tightly controlled and coordinated . How this regulation is achieved is an extraordinarily complex problem . As is the case with many fundamental mechanisms , also the interplay between growth and patterning has been most widely investigated in Drosophila [1–4] . One tissue of particular interest in Drosophila is the eye imaginal primordium ( commonly called eye “imaginal disc” ) , that develops into the highly organized compound eye of adult flies . During the two first larval stages ( or “instars” ) the eye disc comprises undifferentiated , proliferative eye progenitors . Pattern formation in the eye disc starts during early third instar with the appearance of the morphogenetic furrow ( MF ) , a straight epithelial indentation that runs along the dorsoventral ( DV ) axis of the Drosophila eye disc and that emerges at the posterior margin of the disc [5] . The MF is a moving signaling centre that separates the proliferating ( anterior to the MF ) and the differentiating ( posterior to the MF ) zones in the disc ( Fig 1A ) . As the MF sweeps across the disc from its posterior to its anterior side , undifferentiated proliferating cells cease to proliferate at the MF and start differentiating into retina cells behind it [5] . The velocity of MF movement thus determines the time for which cells on the anterior side of the disc can proliferate . At a molecular level , many signals that are involved in the initiation and progression of the morphogenetic furrow have been described [6] . Initiation of MF movement requires the production of the diffusible morphogen Hedgehog ( Hh ) at the posterior eye disc margin . Hh is known to induce the expression of decapentaplegic ( dpp ) in the MF , another morphogen of the BMP2/4 type , and to promote differentiation of the proliferating cells into retinal cells . Furthermore , it is known that both Hh and Dpp signaling lead to the downregulation of the transcription factor homothorax ( hth ) [7] . hth is expressed on the anterior side of the eye disc and was shown to promote cell proliferation and to block the expression of later-acting transcription factors [7 , 8] . Behind the MF , the newly differentiated photoreceptor cells express hh and the delayed mutual activating loop between Hh and Dpp is able to set the MF in motion . Recent measurements describe this movement of the MF as linear in time [9] . However , it remains unclear how the signaling network determines the speed of the MF . By driving the progression of the MF , the signaling network is indirectly regulating proliferation . In addition to driving MF movement and cell differentiation , Dpp signaling affects growth [10–12] . Recently , it was reported that the gradients of the Dpp signaling targets pMad and hairy scale with the anterior length of the eye disc , and the authors suggested that the relative temporal changes in the concentration of the moving Dpp gradient control the proliferation rate and are therefore responsible for the control of growth and its termination [9] . However , the authors also note that the growth rate is not altered when the only Dpp signalling mediator mad and its downstream target brk are removed from cells . In order to explore how the spatio-temporal signaling patterns affect the movement of the MF and impact on eye disc growth we translated the signaling network into a spatio-temporal model ( Fig 1A ) . Patterning and growth are intricately linked during eye disc development , and we therefore solved the model on a growing domain . As in previous models of imaginal disc growth [13] , we modeled the epithelium as an incompressible Newtonian fluid with a source that reflects cell proliferation . In order to parameterize the model we measured two key parameters of the model , the degradation rate of Hth as well as the diffusion coefficient of Hh , by Fluorescent Recovery After Photobleaching ( FRAP ) . We show that our model can reproduce the linear movement and speed of the MF . Our model shows the observed growth termination and can reproduce several mutant phenotypes that influence the speed of the MF . We furthermore analyze the impact of parameter perturbations on the linearity and speed of the MF as well as on the final size of the eye disc . Importantly , the model fails to reproduce the scaling of the Dpp gradient with the anterior length of the tissue , suggesting that there must be additional mechanisms in place to ensure scaling . While many open questions remain , our model serves as an important step towards an integrated model for patterning and growth control during development .
We aimed at developing a parsimonious model for eye disc growth and early patterning and thus sought to keep the regulatory interactions as simple as possible while reproducing the measurements . As components of the model we will consider Hh , Dpp , pMad ( the active form of Mad which transduces the Dpp signal to the nucleus [14] ) , Eya , a gene expressed and required for retinal specification and differentiation [15] and Hth , a protein that prevents premature differentiation anterior to the MF ( Fig 1A ) . Representative confocal sections of similar late third instar eye-antennal discs stained for several proteins that are incorporated in the model are shown from different views in Fig 1B–1D . We will focus on the differentiation process beginning with the initiation of the morphogenetic furrow ( MF ) in larvae during early third instar . In front of the MF , progenitor cells proliferate ( Fig 1A; arrow ( A ) 1 ) , while behind the MF cells differentiate and eventually form the ommatidia . Experiments show that hh is expressed at the posterior margin before MF initiation and in differentiated cells , labeled Φ , posterior to the MF during disc development [16] ( Fig 1A; A2 ) . The production in the margin is incorporated via the boundary condition for Hh ( Eq 19 , see Methods ) . Given the lack of evidence for any relevant regulation of the production of hh by other members in the model , we assume a constant production rate , restricted to the differentiated cells , thus writing pHh ⋅ Φ . Hh induces the expression of dpp in MF cells , labeled Θ [17] ( Fig 1A; A3 ) . In order to keep the number of parameters small , we use the simplest function that allows us to fit the data , a linear relationship . Accordingly we write pDpp ⋅ cHh ⋅ Θ as a production term , with cHh being Hh concentration . We note that also a Hill function would have allowed us to reproduce the observed data . However , this would have introduced two additional parameters . Dpp signaling is mediated by phosphorylation of Mad to pMad [14] ( Fig 1A; A4 ) . The rate of Mad phosphorylation thus depends on the Dpp concentration , and we have ppMad ⋅ σDpp as a production term for pMad ( see definition for σ in Eq 3 ) . Expression of eya is enhanced by pMad-mediated Dpp signaling [18] ( Fig 1A; A5 ) . As the expression of eya is also induced by Hh [19] we incorporated a Hh-dependent term ( Fig 1A; A6 ) and a pMad-dependent production term , such that the presence of either pMad or Hh is sufficient to induce eya expression , i . e . pEya ⋅ ( σpMad + σHh ) . We notice that we could also have reproduced the mutant behavior if we substituted the Hh-dependent term by a positive feedback of eya on itself or by a direct link of the photoreceptor cells . hth can be expressed in all cells , but is repressed by pMad-mediated Dpp signaling ( Fig 1A; A4 , A7 ) , and by Hh signaling ( Fig 1A; A8 ) [20 , 21] . We therefore describe Hth production by pHth⋅σ¯pMad⋅σ¯Hh , such that the presence of either pMad or Hh is sufficient to repress hth expression ( compare Eq 3 ) . Hth is required to maintain the progenitor population in a proliferative and undifferentiated state ( Fig 1A; A9 ) [7] , while Hh is required for the proper differentiation of cells behind the MF into photoreceptor cells ( Fig 1A; A10 ) . Furthermore , forced maintenance of Hth is known to cause severe delays in MF movement and blocks retinal differentiation [22 , 23] . Downregulation of hth expression therefore allows MF movement [20 , 22 , 23] . The simplest way to incorporate these known concentration-dependent cell type transitions is to introduce an Hth concentration threshold , ΘHth , below which proliferating cells are becoming MF cells and an Hh concentration threshold , ΘHth , above which MF cells are differentiating into differentiated cells ( see also Eq 4 ) . All extracellular molecules can diffuse within the domain , albeit at different speeds . We therefore formulate the model as advection-reaction-dispersion equations for a component i with concentration ci , diffusion coefficient Di and reaction terms Ri . The external velocity field is denoted by u: ∂ci∂t+∇ ( uci ) =Di∇2ci+Ri ( 1 ) The reaction terms Ri of the components describe the regulatory interactions based on information from the literature and our own experiments as discussed above , and are given by: RHh=pHh⋅Φ−δHh⋅cHh ( 2 ) RDpp=pDpp⋅cHh⋅Θ−δDpp⋅cDpp RpMad=ppMad⋅σDpp−δpMad⋅cpMad REya=pEya⋅ ( σpMad+σHh ) −δEya⋅cEya RHth=pHth⋅σ¯pMad⋅σ¯Hh−δHth⋅cHth In the absence of contrary data we use the simplest model for decay , linear decay at rate δi ⋅ ci for all signaling factors i . We use Hill functions to describe regulatory influences . To describe activating influences of a component i we write σi=cinicini+Kini ( 3 ) and we use σ¯i=1−σi to describe inhibitory impacts of ci . Ki is the Hill constant which specifies the concentration of ci where half-maximal activity is observed , and the Hill coefficient ni defines the steepness of the response . The different cell types , i . e . differentiated cells Φ , cells in the MF Θ and proliferating cells Π , are defined as Π=H ( cHth−θHth ) ( 4 ) Θ= ( 1−H ( cHth−θHth ) ) ⋅ ( 1−H ( cHh−θHh ) ) Φ= ( 1−H ( cHth−θHth ) ) ⋅H ( cHh−θHh ) where H ( ci − θi ) for component i and threshold θi is the Heaviside function , which is defined according to: H ( ci−θi ) ={0ifci≤θi1ifci>θi ( 5 ) Finally , we need to define the boundary and initial conditions . Hh is expressed in the margin from where it diffuses into the eye disc . Accordingly , we use as boundary condition for Hh DHh∇cHh=η⋅Λ ( x ) ⋅τ ( t ) ( 6 ) where η is a constant , Λ ( x ) defines the spatial location of the Hh producing margin and τ ( t ) a time-dependent function ( see Methods for details ) . We use zero flux boundary conditions for all other signaling molecules , transcription factors and cell types , i . e . We use zero initial conditions for Hh , Dpp , pMad , and Eya in the entire eye disc domain; MF initiation happens in response to the influx of Hh at the posterior margin ( see Methods ) . Before the initiation of the MF , hth is expressed in all cells of the eye primordium [23] . We therefore use the steady state concentration as initial concentration , i . e . cHth ( 0 ) =pδHth . The presence of Hth prevents premature cell differentiation . In summary , the initial conditions are: cHth ( 0 ) =pδHth ( 8 ) cHh ( 0 ) =cDpp ( 0 ) =cpMad ( 0 ) =cEya ( 0 ) =0 As previously established , the eye disc can be approximated by a 2D ellipse ( Fig 1B and 1C ) [24] . On long time scales , embryonic tissue can often be described by a viscous fluid [25 , 26] . Accordingly , we model the mechanical behaviour of the eye disc as an incompressible Newtonian fluid with density ρ , dynamic viscosity μ and local source S . This approach has previously been used in simulations of early vertebrate limb development [27] and , in an extended anisotropic formulation , to Drosophila imaginal disc development [13] . The Navier-Stokes equation is given as: ρ ( ∂u∂t+ ( ∇⋅u ) u ) =−∇ρ+μ ( ∇2u+13∇ ( ∇⋅u ) ) ( 9 ) ∇⋅u=S=Π⋅k0⋅exp ( −δPL⋅PL ) where u denotes the external velocity field used in Eq 1 , S , from definition above , denotes the local growth rate and PL denotes the "posterior length" , i . e . the length from the posterior margin to the MF ( corresponding to the differentiating region ) which is a good surrogate of developmental time given the linear progression of the MF with time [9 , 24] . k0 is the initial area growth rate and has been previously estimated from experimental data [24] . We assume that growth is caused by proliferation of undifferentiated cells only ( Fig 1A; A1 ) and we have previously found that the measured growth rate can be described well by a function that declines exponentially with PL , with δPL = 0 . 0107 μm-1 [24] . Different mechanisms could , in principle , give rise to this measured decline [24] . However , in the absence of a confirmed growth-controlling mechanism , we decided to use the functional relation that most accurately describes the growth dynamics in the Drosophila eye disc [24] without making any statement about how growth may be regulated mechanistically . We also assume the same growth rule in both x and y directions . This is in agreement with experimental observations: the growth anisotropy parameter was previously determined as ϵ=∂yu∂xu≈1 [9] . In most mathematical models the parametrization is crucial for its capabilities to correctly reflect the modeled phenomena . In our model we have three classes of parameters: Production rates and coefficients of the Hill terms , diffusion coefficients and degradation rates . As the absolute protein concentrations are unknown , the production rates can be set to arbitrary values and the Hill coefficients must then be adjusted to reproduce the experimentally observed protein gradients and gene expression boundaries . We used quantitative confocal microscopy of stained eye discs to detect the spatio-temporal dynamics of the core proteins pMad , Eya , and Hth . Fig 1B–1D shows representative confocal sections of a late third instar eye-antennal disc . For profile quantification , z-stacks of disc strips were acquired . A single ( x , y ) confocal section of one of these strips is shown ( Fig 1B and 1C ) . Fig 1D shows a magnified section of a similar eye disc ( top ) and a z-section through that section ( bottom ) . pMad or merged channels ( Hth: green , Eya: red; pMad: white ) are shown . As the Dpp-producing MF moves , the concentration profiles of pMad , Eya , and Hth also move towards the anterior side . For easier comparison of the concentration profiles we plot these relative to the MF ( S1A–S1C Fig ) . The regulatory concentration thresholds could be adjusted such that the model reproduced the shapes of the concentration profiles of pMad , Eya , and Hth ( S1A–S1C Fig , red lines ) . In this way , all production rates and Hill coefficients could be determined ( S1 Table ) . With respect to the diffusion coefficients , we note that Hth , Eya , and pMad are intracellular proteins and therefore their diffusion across the tissue is negligible . In the simulations we could reflect this by setting D = 0 for all three species . However , we opted for a very low effective diffusion coefficient for numerical stability ( D = 0 . 00025 μm2 s-1 ) , which may also reflect intracellular diffusion . Regarding Dpp kinetic parameters , there are no experimental measures performed in the eye disc . However , in the wing disc , different groups have measured distinct properties regarding Dpp transport , reporting values for Dpp free extracellular diffusion [28] , effective diffusion coefficient [28 , 29] and the length of the Dpp gradient [29] . As our model considers effective parameters we choose for the Dpp diffusion constant DDpp = 0 . 1 μm2 s-1 and , based on the Dpp gradient length of the wing disc , deduced the Dpp degradation rate as δDpp = 2 . 5 × 10−4 s-1 [29] . This Dpp decay rate would correspond to a half-life of 45 min . We note that subsequent measurements showed that the Dpp gradient lengthens over time in the wing disc [4] . Based on this observation , it has been proposed that the Dpp degradation rate would decline over time [4] . However , we have since shown that the data can be fully explained with a constant degradation rate if the dynamic pre-steady state nature of the patterning process is taken into account [30] . While we estimated the Dpp half-life to be longer than 10 hours in the wing imaginal disc [30] , we are here focused on the Dpp removal rate from the extracellular space , and the rate of Dpp internalization is fast . Therefore , we will use as degradation constant δDpp = 0 . 00025 s-1 . Three crucial parameters have not been previously measured: the diffusion coefficient and the degradation rate of Hh and the degradation rate of Hth . Since the characteristic length of the Hh gradient has been determined , we focused on measuring the effective Hh diffusion coefficient and the Hth degradation rate . Both experiments were performed using FRAP and were obtained in the wing disc , as this disc is larger and flatter than the eye disc , thus facilitating the experiments and assuming the same dynamics in both disc types . Fig 2 shows the FRAP experiment for determining the Hth degradation rate . The degradation rate can be calculated by linearly fitting the time series for the bleach-chase analysis of Hth ( Fig 2B ) [31] . The slope of the fitted line yields the degradation rate , δHth = ( 6 . 97 ± 5 . 00 ) 10−5 s-1 , which corresponds to a Hth protein half-life of τ1/2 = 2 . 77 h ( see details in Materials ) . In order to determine the Hh effective diffusion coefficient , wing discs were dissected from larvae in which UAS-GFP:Hh [32] was driven in the hh-expression domain by a hh-GAL4 driver . In the FRAP experiment , the region of interest ( ROI ) ( solid circle in Fig 3A ) was photobleached and the recovery was observed ( Fig 3B–3E ) . The bleaching does not only happen in the ROI , but also in the adjacent area ( Fig 3F ) . From the FRAP profile we calculated the mean half recovery time τ1/2 = 7 . 12 min ( Fig 3G ) . This corresponds to a mean diffusion coefficient of Hh of DHh = 0 . 033 ± 0 . 006 μm2 s-1 ( see details in Materials ) . This value is similar to previous measurements for Wg in the wing disc [29] , and Wg and Hh have previously been noticed to bear important similarities in their extracellular transport in the wing disc [33] . The characteristic length for Hh has been determined in the wing disc as 7 μm [34] . From the diffusion constant DHh = 0 . 033 μm2 s-1 that we determined , the Hh decay rate then follows as approximately δHh = 6 . 7 × 10−4 s-1 . We note that the Hh gradient has been shown to be dynamic during wing disc and ocellar complex development [35 , 36] , something that we will ignore in this model as the effect can be expected to be minor for our model predictions , and it would require a major complication of the model as the Hh’s receptor Patched ( Ptc ) would need to be included explicitly . In agreement with previous measurements [9] , the MF progresses linearly with time from the posterior towards the anterior side of the domain ( Fig 4A ) . Moreover , the speed of MF progression agrees quantitatively . Here , we note that the experimental measurements report the MF position as the average posterior length at a given time point across the disc , while we monitor the MF position as the maximal posterior length at the dorso-ventral boundary to be able to use the previously determined eye disc growth rate in Eq 9 [9] . We have previously shown that the experimentally determined speed of 3 . 1 μm h-1 [9] then corresponds to 3 . 4 μm h-1 [24] , as observed in our numerical simulations of the eye disc model . The model also reproduces the observed growth dynamics ( Fig 4B–4D ) . Thus , our simulations show an initial linear increase of the total area followed by a plateau phase ( Fig 4B ) . During the plateau phase the anterior area is barely increasing and finally declines in parallel and at a similar rate as the posterior area is increasing due to the differentiation caused by the progression of the MF ( Fig 4B–4D ) . As a result of this , the MF reaches at some point the anterior end of the eye disc which leads to growth termination due to the exhaustion of anterior progenitors . In our numerical simulations , the nonlinearity and speed of the MF movement ( measured by the root-mean square error and the slope of a linear fit of the posterior length over time ) as well as the final eye disc size heavily depend on the choice of parameters . In order to quantify the effect of parameter changes in the model we increased ( Fig 5 , red boxes ) or decreased ( Fig 5 , blue boxes ) single parameters by 1% . An increased impact of Hh , either achieved by a higher production rate pHh , a lower degradation rate δHh or a lower concentration threshold for differentiation θHh , clearly has the strongest effect in speeding up the MF ( Fig 5A and 5B ) . At the same time , an increased impact of Hh also increased the linearity of the MF movement ( Fig 5C and 5D ) . As a result of the increased MF speed , the anterior tissue has less time to proliferate and therefore the final eye disc area is smaller ( Fig 5E and 5F ) . In contrast to this , an increased impact of Hth ( high production rate pHth , low degradation rate δHth or lower concentration threshold for differentiation θHth ) decreases the MF speed and increases its linearity ( Fig 5A–5D ) . As a result of the slower MF movement the anterior area has more time to proliferate and therefore the final total area increases ( Fig 5E and 5F ) . An increased impact of both Dpp and pMad has generally a smaller effect but nevertheless leads to an increase in the MF speed and in the nonlinearity of the MF movement and to a decrease in the final area ( Fig 5 ) . Experiments show that the speed of the MF and/or the size of the adult eye are severely affected in Hh , Dpp and Hth mutants . We were therefore interested to see to what extent our eye disc model is able to explain these observed effects . Mutations that reduce Hh activity in the eye disc result in a severe slowdown or stop of MF progression and eye size reduction [17 , 37–39] . In our simulations , reduction of the Hh production rate indeed results in a decreasing speed and an eventual stop of MF movement ( Fig 6A ) . Furthermore we observe that the predicted total area of the eye disc overgrows ( Fig 6B ) , because the differentiation rate of the proliferating area caused by the furrow is much smaller . This excess of undifferentiated progenitors do not make into the adult head , as hh mutants show smaller eyes but no abnormal overgrowths . This suggests that there must be some additional control of the anterior area that is not included in the eye disc model , e . g . downregulation of the growth rate or increased cell death in the absence of Hh signaling . The latter is supported by the observation that there is abundant cell death in hh-mutant discs anterior to the MF [16 , 17] . Furthermore , it was shown that large hh signaling-mutant clones in the eye disc showed a disrupted organization of photoreceptors towards the center of the clone [17] . Interestingly , we can see that also in simulations of these clones the Hh concentration in the center is not sufficient to differentiate these cells ( Fig 6C ) . It is well known that Hh is required for the initiation of the MF , and removal of hh expression in the margin thus prevents MF initiation and eye formation [40 , 41] . Complete removal of Hh signaling in the simulations precludes production of Dpp and since hth can no longer be downregulated , no MF starts ( Fig 6D , zero influx ) . On the other hand , a small reduction in the Hh influx compared to wild type increases eye size because cells have more time to proliferate before the MF is initiated ( Fig 6D ) . Similar to the phenotype of discs with hypomorphic hh alleles , eye discs harboring a hypomorphic dpp allele have very small adult eyes ( dpp blk: 100–200 ommatidia develop instead of 700–800 in the wild type ) [37] . In contrast to this , our simulations predict an overgrowth of the total area ( Fig 6E , red line ) . Again this is because in our simulations the MF moves more slowly ( Fig 6F ) , thus leaving more time to anterior cells to proliferate , which finally results in larger eyes . The predicted effect of dpp on furrow movement is supported by experiments that show that the MF slows down in clones mutant for the dpp signal transduction pathway [42–44] . The difference in the final size of the eye disc between model and experimental data can be explained by the fact that we did not include any effect of dpp on cell survival in the model and therefore it is not able to capture the experimentally observed excessive cell death in the ventral regions of the mutant eye discs [37] . Experiments show that hth-overexpressing clones slow down MF movement , something that we can also observe in our numerical simulations ( Fig 6G ) . It has also been shown that downregulation of Hth levels mediated through the expression of an RNAi construct in the eye primordium ( using an eyeless driver ) leads to a reduction in the adult eye size [20] . In our simulations , a decrease of the Hth production rate indeed leads to smaller eyes ( Fig 6H , red line ) . However , we observe overgrowth in the total eye area if we increase the hth production rate , because proliferating cells are delayed in differentiating ( Fig 6H , blue line ) . Eye overgrowth in response to hth overexpression has not been experimentally observed and therefore this result might suggest that a fundamental link between patterning and growth , such as increased cell death in the progenitor population in mutant eye discs , is not included in the model . Furthermore , it is known that excessive Hth , which is not bound to the protein Extradenticle ( Exd ) , is degraded [45] . Therefore , contrary to the assumption of the model , the overexpression of Hth might not have an impact on the total Hth concentration if the steady state Hth concentration is close to its maximum . Recently , it was reported that the gradients of the Dpp targets pMad and hairy scale with the length of the anterior domain [9] . As the anterior length is initially increasing and then decreasing , the Dpp gradient would have to initially expand and later retract . In order to compare this observation with the results of our model we normalized the simulated Dpp and pMad gradients from three time points with respect to the anterior domain length . Indeed the relative profiles at 20 hours and 40 hours overlapped almost perfectly , while the relative profile at 60 hours was expanded ( Fig 7A–7C ) . However , in our model , the overlap is not a result of an adaptation of the gradient to the anterior length , but the result of a constant decay length of the gradient and very similar anterior domain length at 20 and 40 hours , such that a constant gradient also overlaps after normalization ( Fig 7C ) . At 60 hours the anterior length is much shorter and the gradient therefore appears more expanded ( Fig 7C ) . This cannot be explained by a change in the amplitude , since for both Dpp as well as pMad the maximal concentration is relatively stable over time in the simulations ( Fig 7A and 7B ) . We conclude that our model cannot explain the observed scaling of the pMad and hairy gradients .
How growth and differentiation are integrated to ensure the proper development of tissues is still an open question . Here we developed a model for the Drosophila eye imaginal disc on a 2D growing domain that is based on a simple reaction network . We measured the two key unknown parameters , the Hh diffusion coefficient and the Hth degradation rate . We then investigated the behavior of our model and found that it can reproduce the initiation and linear progression of the MF . The linearity and speed of the MF movement are an important property for growth control because they determine the time for which progenitor cells on the anterior domain of the eye disc can proliferate . Interestingly , our model can qualitatively explain mutant phenotypes that are linked to changes in MF speed , which suggests that a very simple reaction network is sufficient to control and orchestrate the movement of the morphogenetic furrow . The reason for this is that the system is irreversibly bistable and that Hh and Dpp can diffuse . Both properties taken together generate a travelling wave , which in this case can be observed as the MF . However , we can also see that our model cannot explain some of the mutant phenotypes linked to the final eye size , which suggests that the differentiation wave caused by the movement of the MF is insufficient to explain growth control in the eye disc and that there must be additional layers of regulation between patterning and growth . One such layer of control might be the compensatory increase in cell death in response to a lack of differentiation or proper MF progression . It was shown that dppd-blk discs , in which dpp production at the MF is lacking and the MF halts , show an increment of cell death in the ventral half of the eye disc [37] . The same effect is not limited to dpp , but can also be observed in hth-overexpressing discs and hh mutant discs . However , this effect seems to be limited to situations where the whole disc is mutant and is not observed in mutant clones , suggesting that Hh and Dpp might act as survival factors and are crucial in determining the probability of cell death in the progenitor cells . In fact , it has been described that Dpp is used as a survival signal in the wing disc [46] . The Dpp gradient has been suggested as an additional level of control . In particular , it has been proposed that the relative temporal change in the concentration levels of Dpp has a direct influence on growth and ensures non-uniform growth and growth termination in the eye disc [9] , and uniform growth and growth termination in the wing disc [9 , 37 , 47] . However , the relevance of Dpp on Drosophila wing disc growth is currently being debated . Dpp has been reported to have no major impact on growth in the lateral regions of the Drosophila wing disc [48 , 49] , and recent results even suggest that Dpp has a minor impact on wing disc growth during third instar [50] . Finally , we investigated the scaling of the Dpp gradient in the anterior side . In contrast to experimental reports [9] , we do not observe scaling in our model . Scaling in the eye disc is particularly interesting because at later stages the anterior length ( i . e . the width of remaining progenitor area ) is decreasing and the gradient would therefore have to shrink in order to scale . Such shrinkage cannot be explained by previous scaling models from the wing imaginal disc [4 , 30 , 51] . A novel scaling mechanism would thus need to be in place that ensures scaling of the Dpp gradient in the eye disc . To define such scaling mechanism , it will be important to also obtain quantitative measurements of the Dpp gradient itself , in particular in late stages of eye disc development when the Dpp gradient is supposed to shorten . Our model for the Drosophila eye disc is a first step on the way to mechanistically understand the interplay between patterning and growth during development . Our model explains many key observations but also highlights important gaps in our understanding . In particular , our results show that our model is currently lacking important regulatory cues on both cell survival and cell proliferation . Additionally , the model mainly focuses on the major players along the anterior-posterior axis and neglects some factors acting along the DV axis , such as the prominent antagonist of eye differentiation , the Drosophila WNT-1 homologue Wingless ( Wg ) . Wg is produced from the anterior dorsal and ventral margins of the disc to restrict MF initiation at the posterior margin [38] . It is known that ectopic hth can repress cell differentiation independently of Wg [23] , suggesting that Hth might work downstream of Wg . Including Wg explicitly in future versions of the model might lead , for example , to a better understanding of the hypomorphic dpp mutant that shows abnormal cell death on the ventral side . At the same time , more experimental work has to go into dissecting the complex network that seems to have an influence in controlling growth .
Drosophila cultures were raised at 25°C . The strain w1118 ( Flybase identifier: FBal0018186 ) was used to determine the quantitative profiles of Hth , Eya and pMad . YFP:hth ( CPTI-000378 ) is a protein trap from the Cambridge protein trap project , FlyProt [52] . UAS-GFP:Hh was driven by a hh-GAL4 driver as in [53] . Eye imaginal discs were dissected and fixed according to standard protocols , except that primary antibodies were incubated in a hypotonic buffer solution ( 0 . 75xPBS with 0 . 1% TritonX-100 ) to allow a better separation of nuclei and their segmentation . Primary antibodies used were rabbit anti-pMad , 1/5000 ( [54]; kindly provided by G . Morata , CBMSO ) ; guinea pig anti-Hth ( GP52 ) , 1/50 ( [55] ) ; mouse anti-Eya ( eya10H6 ) , 1/100 ( Developmental Studies Hybridoma Bank ( Iowa University ) . Fluorescently labeled secondary antibodies were from Molecular Probes . Discs were counterstained with DAPI to mark nuclei . Images were obtained on an SPE Leica confocal setup and nuclei were subsequently segmented; as disc epithelia present folds , eye discs images were computationally stretched to obtain a correct measure of distances . Stacks of regularly spaced images were acquired by laser confocal microscopy ( LCM ) such that , on average , each nucleus was contained in two or three contiguous confocal planes . To this end , a narrow pinhole and a z-step of 1 . 521 μm were used for DAPI signal acquisition . The other channels were acquired subsequently with a pinhole that spanned exactly 1 . 521 μm in order to avoid over- and under-sampling upon quantification ( objective ACS-APO 40x , numeric aperture 1 . 15 ) . We developed a new software tool , iFLIC , to perform the segmentation and stretching of folded discs . A detailed description of the software will be published elsewhere . In brief , the software first segmented the nuclei in 3D by identifying all pixels that belonged to a given nucleus within the densely clustered images . Segmentation was based on bandpass filtering that made use of otsu thresholds . Subsequently , the point closest to the center of each nucleus was determined based on the intersection of solid spheres that were fitted into the segmented nuclei . Once the centers of the nuclei had been estimated , ellipsoids were fitted by altering both the lengths of the semi-axes and their orientation until the ellipsoid that covered the segmented pixels with the maximum volume had been found . Boundaries were expanded by flooding with equal chances for all ellipsoids , up to the point that no white voxels were left ( complete assignation ) . Signals were quantified inside the nuclei as the mean pixel intensity ( in 8-bit scale ) for each channel . Eye imaginal discs are frequently folded , which affects the reconstruction of reliable gene expression profiles . To correct for the deviations that are introduced by the pleating we developed an algorithm to computationally stretch out the tissue , using the centroid coordinates obtained from the previous segmentation . The epithelium is pseudostratified , and the nuclei are therefore positioned at random along the apical-basal axis . To obtain a distance measurement , we mapped the positions of the nuclei to the apical surface . In a first step the z-coordinate from an orthogonal user-selected system was estimated by a gaussian kernel whose bandwidth had been previously determined by generalized cross validation ( GCV ) [56] . The gaussian kernel mapped the z-coordinate at regular intervals in the current z-projection in order to check the z-coordinate , thus providing an continuous and differentiable surface that passes through nuclei estimated centroids . Once the surface had been determined within the 3D dataset , we determined every nucleus footpoint that maps onto the surface . With this information at hand , we could then calculate the real surface distances of the cells , to which each nucleus belongs . Finally , the immunofluorescence intensity profiles were obtained from narrow stripes of stretched sets of nuclei oriented along the anterior-posterior axis . Two different experiments were performed using Fluorescence Recovery After Photobleaching ( FRAP ) . The first one is a bleach-chase experiment [31 , 57] performed to calculate the degradation rate of Hth . The remaining was performed to determine the diffusion coefficient of the morphogen Hh . The experimental preparation was equally done for both experiments . Imaginal discs were dissected in SF-900 medium at room temperature , and transferred to a medium-containing well with a glass coverslip bottom . The samples were maintained at room temperature , which is fixed at 21°C . The data analysis was done using different software applications . For the imaging analysis ImageJ v . 1 . 47f was used; the statistics was done using the Microcal Origin v . 8 . 1 software . For these experiments , late L3 eye-antennal discs of a YFP:Hth protein trap strain ( CPTI-000378 , Cambridge protein trap project , FlyProt [52] ) were used . In the FRAP experiment for determining the Hth degradation rate , the regions of interest ( ROIs ) ( Fig 2A and 2B in yellow rectangles as examples ) were photobleached using a 30s pulse of 488nm Argon laser , with 100% laser power and 100% transmission during . The recovery was observed by exciting YFP in the sample with a 488nm Argon laser with laser power 20% and transmission 14–25% . This laser is installed on a confocal microscope Leica SP5 with 63x ( 1 . 40 ) HCX PL APO CS objective using Leica OIL 11-513-859 , zoom 1 . 4–1 . 7X , gain ~1200 . The movies had a duration of 120 min , with one frame captured every 5 minutes . Each frame was the average of three scans . In order to control cell movement along the z axis , a z-stack 6 . 3 μm wide ( approximately a cell diameter ) was captured divided into 3 steps separated by 2 . 1 μm . In this way , it was possible to capture small cell movements . The number of samples taken to perform this analysis was N = 6 . As the images are noisy it is quite difficult to perform a clear nuclei tracking . Therefore , the measures were done by acquiring mean intensities from small areas of 4–5 nuclei extension . Before performing the bleach-chase analysis , we averaged the z-projection . The projection step smoothens out noise and maps small nuclear movements into one frame . The bleach-chase protocol , as previously described [31 , 57] , is based on a comparison of the intensity in the bleached region ( yellow rectangle in Fig 2A ) and the unbleached region ( red rectangle in Fig 2A ) over time . In the following we give a brief summary of the procedure . As a result of bleaching , part of the protein concentration becomes invisible . The total protein concentration , PT , is the sum of the visible protein Pv and the invisible protein P PT ( t ) =Pv ( t ) +P ( t ) ( 10 ) The invisible protein is only produced during the bleaching process; both proteins degrade according to the following equation: dP ( t ) dt=−δHth⋅P ( t ) ( 11 ) As P is invisible it cannot be measured directly but the degradation rate coefficient of the visible and invisible protein is the same so , it can be determined by comparing the invisible and the visible intensity evolution . The solution to this equation is: P ( t ) −Pv ( t ) = ( P ( 0 ) −Pv ( 0 ) ) ⋅exp ( −δHth⋅t ) ( 13 ) with P ( 0 ) and Pv ( 0 ) being the intensity values for visible and invisible proteins at time t = 0 . Taking the logarithm we further obtain ln ( P ( t ) −Pv ( t ) ) =ln ( P ( 0 ) −Pv ( 0 ) ) −δHth⋅t ( 14 ) The degradation rate coefficient can thus be obtained by plotting the logarithm of the difference in intensity of unbleached and bleached regions versus time , and linearly . The slope of the fitted line gives −δHth . The protein half-life can then be obtained as τ1/2=ln2δHth ( 15 ) The time series for the bleach-chase analysis of Hth is shown in Fig 2B . The slope of the fitted line yields the degradation rate , δHth = ( 6 . 97 ± 5 . 00 ) 10−5 s-1 , which corresponds to a Hth protein half-life of τ1/2 = 2 . 77 h . In this experiments , wing discs were dissected from larvae in which UAS-GFP:Hh [32] was driven in the hh-expression domain by a hh-GAL4 driver . Wing discs were used due to the larger domain of Hh expression and the straight border between expressing and non-expressing cells . In the FRAP experiment for determining the Hh diffusion coefficient , the ROI ( solid circle with a radius of 5 μm in Fig 3A ) was photobleached for 40s using an Argon laser 488nm with laser power 100% and transmission 100% . The recovery was observed by exciting GFP in the sample with an Argon laser 488nm with laser power 20% and transmission 14–25% , pinhole 0 . 99 . The laser was installed on a confocal microscope Leica SP5 with 63x ( 1 . 40 ) HCX PL APO CS objective using Leica OIL 11-513-859 , zoom 3 . 0–4 . 2x , gain ~1200 . The movies recorded 60 minutes of intensity recovery , taking one frame every 2 minutes . Each frame was the average of three scans . In order to control for cell movement along the z axis , a z-stack 6 . 3 μm wide ( approximately one cell diameter ) was captured , divided into 3 steps separated 2 . 1 μm . In this way , it was possible to capture small cell movements . The number of samples taken to perform this analysis was N = 6 . The image analysis was performed following Kang et al . simplified equation to obtain diffusion coefficients from confocal FRAP data [58] . Here the diffusion coefficient is defined as: D=re2+rn28⋅τ1/2 ( 16 ) where rn is the nominal radius ( ROI radius ) , re is the effective radius ( spreading radius of postbleached profile ) and τ1/2 is the half time of the recovery . In order to calculate re , the bleaching profile ( Fig 3F ) can be approximate by a Gaussian profile fitting it to the following expression: f ( x ) =1−K⋅exp ( −x2re2 ) ( 17 ) K and re can be obtained using a nonlinear least-squares fitting routing ( nlinfit . m ) available in MATLAB . These parameters can also be obtained by applying a direct protocol . First , K can be determined from the bleaching depth in the normalized postbleach profile as referred to in Fig 3B . Then , the half width of cross-sections between the horizontal line at the height of 0 . 86K from the bottom of the postbleach profile ( Fig 3B ) and the postbleach profiles yields re without involving any fitting ( Fig 3B ) . To measure τ1/2 from the FRAP data a linear interpolation method was used . The FRAP data can be defined as a time dependent function F: {F ( 0 ) , F ( t1 ) , F ( t2 ) , … , F ( tn ) } such that F ( 0 ) = F0 and F ( tn ) = F∞ . The fluorescence intensity at half of recovery is defined as F1/2 = ( F0 + F∞ ) /2 . If F ( tk ) = F1/2 for some tk then the half-recovery time follows as τ1/2 = tk . If F ( tk ) < F1/2 < F ( tk+1 ) it is defined as: τ1/2=tk+F1/2−F ( tk ) F ( tk+1 ) −F ( tk ) ( tk+1−tk ) ( 18 ) The result of this calculation is shown for one of the samples that were used in the experiment ( Fig 3G ) . The calculated mean half recovery time is τ1/2 = 7 . 12 min . After applying equation Eq . 16 to every sample , the mean diffusion coefficient of Hh is obtained as DHh = 0 . 033 ± 0 . 006 μm2 s-1 . As previously stated in Eq 6 the boundary condition for Hh depends on a spatial function Λ Λ ( x ) and a temporal function τ ( t ) . We know that initially Hh influx only occurs at the posterior margin . During eye disc growth also the part of the boundary with a non-zero Hh influx is growing , and therefore Λ ( x ) is defined on a relative domain . If we set the x-axis to zero at the posterior end of the eye disc , pointing towards the anterior region , then the anterior-most point equals the total AP length LAP ( t ) . We then define our spatial function as 1 for the part of the boundary where the x-position is within the first 20% of the distance between 0 and the total AP length: Λ ( x ) ={1ifx≤0 . 2⋅LAP ( t ) 0ifx>0 . 2⋅LAP ( t ) ( 19 ) This is also illustrated in Fig 1A ( orange highlighted margin ) . The flux of Hh into the domain will decrease over time as the eye disc becomes filled with Hh ( also as a result of Hh production by the differentiated cells ) such that the Hh gradient between the margin and the eye disc vanishes . We could have incorporated this in the simulation by introducing a finite size margin . However , for numerical efficiency we choose a time-dependent function that qualitatively reproduces the decreasing influx of Hh: τ ( t ) ={1ift<10h1−t−10h1hif10h≤t≤10 . 5h0ift>10 . 5h ( 20 ) The equations were solved with finite element methods ( FEM ) as implemented in COMSOL Multiphysics 4 . 3b . In COMSOL both the Creeping Flow as well as the Moving Mesh module , which is an implementation of the ALE method , were activated . For all simulations the PARDISO solver with multithreaded nested dissection as the preordering algorithm and an automatic scheduling method was used . Pivoting perturbation was set to 1E-8 . In the time stepping menu , the BDF method with a maximum step size of 250 seconds and an event tolerance of 0 . 01 was chosen . The initial mesh was generated with a maximum element size of 5E-6 m and a minimum element size of 2E-8 m . The maximum element growth rate was set to 1 . 1 and the curvature factor to 0 . 2 . Remeshing was automatically enforced when the minimum mesh element quality was below the threshold of 0 . 3 . Other options were not changed from standard settings . | Patterning and growth of a tissue are linked during early development and have to be tightly controlled . During the development of the Drosophila eye , this linkage is particularly clear: A moving signaling wave sweeps across the tissue that will eventually develop into the eye of the fly . This wave is responsible for the transition from cells undergoing cell divisions in front of the wave into differentiated , specialized cells that are not dividing anymore and that eventually develop into the many individual eye units of the compound eye . Therefore , the final size of this tissue depends on how fast cells in front of the wave are growing and dividing and on the speed with which the signaling wave sweeps across the tissue . We developed a computational model based on regulatory interactions that have been experimentally determined in order to explore how the signaling patterns affect the movement of the signaling wave and impact on tissue growth . The model captures the movement of the signaling wave at a constant speed and the growth termination of the developing tissue . We further show that the model can explain the abnormal size of the eye that can be observed in several genetically modified fly strains . | [
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"... | 2016 | A Model of the Spatio-temporal Dynamics of Drosophila Eye Disc Development |
Chagas disease is caused by the parasitic protozoan Trypanosoma cruzi . It has high mortality as well as morbidity rates and usually affects the poorer sections of the population . The development of new , less harmful and more effective drugs is a promising research target , since current standard treatments are highly toxic and administered for long periods . Fractioning of methanol ( MeOH ) extract of the stem bark of Calophyllum brasiliense ( Clusiaceae ) resulted in the isolation of the coumarin soulamarin , which was characterized by one- and two-dimensional 1H- and 13C NMR spectroscopy as well as ESI mass spectrometry . All data obtained were consistent with a structure of 6-hydroxy-4-propyl-5- ( 3-hydroxy-2-methyl-1-oxobutyl ) -6″ , 6″-dimethylpyrane-[2″ , 3″:8 , 7]-benzopyran-2-one for soulamarin . Colorimetric MTT assays showed that soulamarin induces trypanocidal effects , and is also active against trypomastigotes . Hemolytic activity tests showed that soulamarin is unable to induce any observable damage to erythrocytes ( cmax . = 1 , 300 µM ) . The lethal action of soulamarin against T . cruzi was investigated by using amino ( 4- ( 6- ( amino ( iminio ) methyl ) -1H-indol-2-yl ) phenyl ) methaniminium chloride ( SYTOX Green and 1H , 5H , 11H , 15H-Xantheno[2 , 3 , 4-ij:5 , 6 , 7-i′j′]diquinolizin-18-ium , 9-[4- ( chloromethyl ) phenyl]-2 , 3 , 6 , 7 , 12 , 13 , 16 , 17-octahydro-chloride ( MitoTracker Red ) as fluorimetric probes . With the former , soulamarin showed dose-dependent permeability of the plasma membrane , relative to fully permeable Triton X-100-treated parasites . Spectrofluorimetric and fluorescence microscopy with the latter revealed that soulamarin also induced a strong depolarization ( ca . 97% ) of the mitochondrial membrane potential . These data demonstrate that the lethal action of soulamarin towards T . cruzi involves damages to the plasma membrane of the parasite and mitochondrial dysfunction without the additional generation of reactive oxygen species , which may have also contributed to the death of the parasites . Considering the unique mitochondrion of T . cruzi , secondary metabolites of plants affecting the bioenergetic system as soulamarin may contribute as scaffolds for the design of novel and selective drug candidates for neglected diseases , mainly Chagas disease .
The tree Calophyllum brasiliense is known in Brazil as “Guanandi” or “Jacareúba” . It can reach up to 40 meters in high , 1–3 meters in diameter and is usually found in Brazil in the rain forest regions of the Amazon . Its stem bark is used in folk medicine to treat rheumatism , varicose veins , haemorrhoids and ulcers , whereas the leaves have anti-inflammatory properties [1] . Previous chemical studies on C . brasiliense resulted in the isolation of several interesting natural products , e . g . xantones , flavonoids , triterpenoids , and coumarins [2] . Some coumarins isolated from C . brasiliense displayed trypanocidal activity , but unfortunately , no information about the underlying mechanism was available [3] . The parasite Trypanosoma cruzi causes American trypanosomiasis or “Chagas disease” , which has high mortality and morbidity rates [4] . Chagas disease is common to the Americas , including Mexico and the South of the USA and has become a global public health problem [5] . Due to high levels of migration , the disease has already reached non-endemic countries . An estimated 10 million people are currently infected and 14 , 000 deaths per year are documented . In Brazil alone , over 6 million people are infected and approximately 6 , 000 deaths per year are registered . The migration of millions of Latin Americans to more developed countries such as e . g . the USA , accounts for approximately 300 , 000 chronically infected patients there [6] . More than a dozen infections acquired from blood transfusions or transplantations have been reported in several European countries , the USA , and Canada [7] . Nifurtimox ( 7–10 mg/kg/day ) and benznidazole ( 5–7 mg/kg/day ) are the two prevalent drugs , currently used in the treatment of Chagas disease . Unfortunately they suffer drawbacks from high levels of toxicity and long treatment periods ( ca . 60 days ) [8] . Nifurtimox , a nitrofuran , inhibits the ability of T . cruzi to deplete free radicals through the generation of a nitro-anion in the presence of oxygen . Benznidazole , a nitroimidazole , binds to the DNA , lipids and proteins of T . cruzi [9] . The average rate for successful cures among acute and recent cases is 80% , while it is less than 20% for chronic cases [10] . Several studies have identified numerous potential candidates for more effective and less toxic drugs . Amidines [11] , [12] , azoles [13] , amiodarones [14] , natural naphthoquinone derivatives and megazols [15] as well as calcium channel blockers [16] have been proposed , but clinically effective compounds still remain elusive . Therapeutic drug combinations have also been proposed as treatment strategies , e . g . benznidazole/nifurtimox , orbenznidazole/nifurtimoxin combination with antifungals which inhibit ergosterol in double or triple associations [17] . Natural products isolated from plants are commonly used as drug prototypes or precursors to treat parasitic diseases . Natural coumarins are an important class of plant products with antitrypanosomal activity [3] , [18] . The coumarins mammea A/BA , A/BB , A/AA , A/BD and B/BA , isolated from C . brasiliense and Mamea americana , showed activity towards epimastigotes and trypomastigotes of T . cruzi for concentrations between 15 and 90 µg/mL [3] . Other coumarins isolated from the stem bark of Kielmeyera albopunctata showed in vitro activity against the trypomastigotes of T . cruzi , killing 80% of the parasites after 24 hours at 125 µg/mL [19] . Continuing the investigation of bioactive compounds from Brazilian flora , the present study was undertaken in order to determine the antitrypanosomal effects of soulamarin , which is the main compound isolated from the stem bark of C . brasiliense , against T . cruzi . This study moreover investigated the lethal action of soulamarin towards the parasite .
The compounds 3-[4 , 5-dimethylthiazol-2-yl]-2 , 5-diphenyltetrazolium bromide ( MTT; Thiazol blue ) , mesoxalonitrile 4-trifluoromethoxyphenylhydrazone ( FCCP ) , 4′ , 6-diamidino-2-phenylindole dihydrochloride ( DAPI ) , sodium dodecyl sulfate ( SDS ) , M-199 and RPMI-PR-1640 medium ( without phenol red ) as well as the NMR solvents CDCl3 and CD3OD were purchased from Sigma-Aldrich ( USA ) . Dimethylsulfoxide ( DMSO ) was purchased from Merck ( Brazil ) . 1H , 5H , 11H , 15H-Xantheno[2 , 3 , 4-ij:5 , 6 , 7-i′j′]diquinolizin-18-ium , 9-[4- ( chloromethyl ) phenyl]-2 , 3 , 6 , 7 , 12 , 13 , 16 , 17-octahydro- chloride ( MitoTracker Red CM-H2XROS ) , amino ( 4- ( 6- ( amino ( iminio ) methyl ) -1H-indol-2-yl ) phenyl ) methaniminium chloride ( SYTOX Green ) and 2′ , 7′-dichlorodihydrofluorescein diacetate ( H2DCf-DA ) were purchased from Molecular Probes ( Invitrogen , Carlsbad , CA , USA ) . Silica gel ( 230–400 mesh ) and Sephadex LH-20 , used for column chromatography and analytical TLC ( 60 PF254 ) , were either purchased from Merck ( USA ) or Sigma-Aldrich ( USA ) . Benznidazole ( 2-nitroimidazole ) was obtained from the Laboratorio Farmaceutico do Estado de Pernambuco – LAFEPE ( Recife , Brazil ) . NMR spectra were recorded on a Bruker DRX-500 ( 1H: 500 MHz , 13C:125 MHz ) spectrometer at ambient temperatures . Chemical shifts ( δ ) are reported in ppm and coupling constants ( J ) in Hz . All resonances were referenced to residual NMR solvent resonance . Low-resolution electrospray ionization mass spectra ( LR-ESI-MS ) were measured in positive mode on a Platform II-Micromass ( quadrupole ) mass spectrometer . Samples of the stem bark of C . brasiliense were collected in the Amazonian rain forest of Brazil during September 2011 . The authenticity of the plant material was verified by Dr . Eliana Rodrigues from ICAQF-UNIFESP . Sample specimens were deposited at the herbarium of the Instituto de Botânica - SEMA of São Paulo ( SP , Brazil ) . Dried and powdered stem bark samples of C . brasiliense ( 72 g ) were washed exhaustively with hexane ( 10×500 mL ) at room temperature in order to remove any residual fats . Subsequently , the plant material was extracted with MeOH ( 10×1 L ) at room temperature . The combined organic fractions afforded , after removal of all solvents under reduced pressure , 4 . 7 g of crude residue . This crude extract was dissolved in MeOH:H2O ( 1∶2 ) and extracted with EtOAc . The removal of the solvent under reduced pressure resulted in the deposition of a residue ( 3 . 0 g ) , which was subsequently subjected to column chromatography ( Sephadex LH-20 ) with MeOH as the eluent . Nine fractions ( I–IX ) were separated like this . Fraction III ( 1 . 31 g ) was further purified by column chromatography over silica gel with a crude solvent gradient of hexane/EtOAc ( starting with pure hexane and finishing with pure EtOAc ) . This second purification step afforded 544 mg of soulamarin ( see Figure 1 ) . 1H NMR ( CDCl3/CD3OD ) δH ( ppm ) : 6 . 35 ( d , J = 10 . 0 Hz , H-9 ) , 5 . 26 ( d , J = 10 . 0 Hz , H-10 ) , 3 . 93 ( m , H-3′ ) , 3 . 48 ( m , H-4 ) , 2 . 4–2 . 5 ( m , H-3a/H-3b ) , 2 . 30 ( m , H-2′ ) , 1 . 32 ( m , H-15 ) , 1 . 28 ( d , J = 6 . 4 Hz , H-4′ ) , 1 . 20 ( s , H-12/H-13 ) , 1 . 30 ( m , H-14 ) , 0 . 97 ( d , J = 6 . 4 Hz , H-5′ ) , 0 . 64 ( t , J = 7 . 5 Hz , H-16 ) . 13C NMR ( CDCl3/CD3OD ) δC ( ppm ) : 199 . 1 ( C-1′ ) , 174 . 8 ( C-2 ) , 159 . 6 ( C-8 ) , 159 . 5 ( C-8a ) , 156 . 4 ( C-6 ) , 125 . 5 ( C-10 ) , 115 . 2 ( C-9 ) , 109 . 4 ( C-4a ) , 102 . 1 ( C-7 ) , 101 . 5 ( C-5 ) , 78 . 4 ( C-3′ ) , 77 . 4 ( C-11 ) , 45 . 3 ( C-2′ ) , 38 . 5 ( C-3 ) , 35 . 1 ( C-15 ) , 30 . 2 ( C-4 ) , 27 . 8 ( C-12 ) , 28 . 0 ( C-13 ) , 19 . 2 ( C-4′ ) , 20 . 4 ( C-14 ) , 13 . 7 ( C-16 ) , 10 . 0 ( C-5′ ) . LR-ESI-MS:m/z 389 [M+H]+ ( calculated for C22H28O6: 388 ) . Mice ( swiss and BALB/c ) were supplied by the animal breeding facility of the Adolfo Lutz institute ( São Paulo , Brazil ) . Animals were kept in sterilized cages in a controlled environment , with water and food ad libitum . All experimental procedures were approved by the local ethics committee for animal use ( CEUA-IAL/Pasteur 002/2011 ) . In all in vitro assays , Y strains of T . cruzi trypomastigotes were used , which were kept at 37°C in LLC-MK2 ( ATCC CCL 7 ) cells using RPMI-1640 medium with calf serum ( 2% ) [20] . To keep the Y strains infective , trypomastigotes were also kept in swiss mice and regularly harvested from the bloodstream by heart puncture of infected animals at the peak of the parasitemia [21] . LLC-MK2 cells were maintained at 37°C in RPMI-1640 medium with fetal calf serum ( 10% ) in an incubator ( 5% CO2 atmosphere ) . Trypomastigotes were counted in a hemocytometer ( Neubauer ) and deposited on a microplate ( 96 wells; 1×106 cells/well ) . Subsequently , soulamarin was added to the cells in concentrations up to 386 µM and the cells were allowed to incubate for 24 hours at 37°C ( 5% CO2 atmosphere ) . Benznidazole was used as standard . Trypomastigote activity was based on the conversion of the soluble tetrazolium salt 3-[4 , 5-dimethylthiazol-2-yl]-2 , 5-diphenyltetrazolium bromide ( MTT ) into the insoluble formazan by mitochondrial enzymes . The extraction of formazan was carried out for 18 hours at 24°C with sodium dodecylsulfate ( 10%v/v; 100 µL/well ) [22] . In order to determine the IC50 value for soulamarin against intracellular amastigotes , the method described by De Souza and co-workers [23] was used with minor modifications . Peritoneal macrophages were collected from the peritoneal cavity of BALB/c mice and deposited on a 16-well chamber slide ( 1×105 cells/well ) before being incubated for 24 hours at 37°C ( 5% CO2 atmosphere ) . Trypomastigotes from LLC-MK2-infected cultures were washed twice in RPMI-1640 medium , counted in a hemocytometer and added to the macrophages ( parasite:macrophage ratio = 10∶1 ) . After an incubation period of 18 hours at 37°C ( 5% CO2 ) , residual free parasites were removed with two washings with medium . Soulamarin was subsequently incubated with infected macrophages ( 60 h , 37°C , 5% CO2 ) in a non-toxic concentration range between 3 . 01 and 386 µM . Benznidazole was used as a standard . At the end of the assay , slides were fixed with methanol and stained with Giemsa prior to counting under a light microscope . IC50 concentrations were obtained by counting 300 macrophages per well ( in duplicate ) and determining the number of amastigotes per infected macrophage . Peritoneal macrophages were collected from female BALB/C mice , seeded at 1×105 cells/well in 96-well microplates and incubated with soulamarin for 72 h at 37°C in an incubator with 5% CO2 . The viability of the cells was determined using MTT [16] . The data represent the mean of two independent assays ( triplicates ) . The hemolytic activity of soulamarin in concentrations up to 1 , 300 µM was evaluated from the erythrocytes of BALB/c mice [24] . A suspension ( 5% ) of erythrocytesin PBS ( phosphate buffered saline ) was incubated with soulamarin at 25°C for 1 hour in a U-shaped microplate ( 96 wells ) . The absorption of the supernatant at 550 nm was recorded ( FilterMax F5 Multi-Mode Microplate Reader-Molecular Devices ) . Trypomastigotes were washed with PBS ( phosphate buffered saline ) , deposited on a microplate ( 1×106 cells/well ) and incubated with SYTOX Green ( 1 µM ) for 15 minutes at 24°C [25] . Soulamarin was added in three concentrations ( IC100 = 386 µM , IC50 = 219 µM and IC25 = 103 µM ) and the fluorescence was measured after 20 , 40 and 60 minutes . The maximum permeability possible was observed with 0 . 1% Triton X-100 ( positive control ) . The fluorescence intensity was determined using a fluorimetric microplate reader ( FilterMax F5 Multi-Mode Microplate Reader-Molecular Devices ) with excitation and emission wavelengths of 485 and 520 nm , respectively . Untreated trypomastigotes and 0 . 5% ( v/v ) DMSO-treated parasites were used as negative controls . Trypomastigotes were washed with PBS , deposited on a microplate ( 2×106 cells/well ) and incubated with soulamarin ( IC50 = 219 µM ) for 60 minutes at 37°C . Then MitoTracker Red CM-H2XROS ( 500 nM ) was added and the incubation was continued for 40 minutes in the dark . Cells were washed twice with BSS ( Hank's buffered salt solution ) and the fluorescence was measured using a fluorimetric microplate reader ( FilterMax F5 Multi-Mode Microplate Reader-Molecular Devices ) with excitation and emission wavelengths of 540 and 595 nm , respectively [26] . Untreated trypomastigotes and DMSO-treated parasites were used as negative controls . Mesoxalonitrile 4-trifluoromethoxyphenylhydrazone ( FCCP; 10 µM ) was used as a positive control [27] . For the fluorescence microscopy analysis , trypomastigotes were co-stained with 4′ , 6-diamidino-2-phenylindoledihydrochloride ( DAPI; 10 µM ) and examined at 1000× magnification . Merged images of blue ( DAPI ) and red ( MitoTracker Red ) images were obtained using the Nikon NIS - Elements AR software . A Nikon D-FL Epimicroscope equipped with a DS-U3 digital camera was used for the experiment . Trypomastigotes ( 2×106 cells/well ) were washed in HBSS ( Hanks Balanced Salt Solution ) medium and incubated with soulamarin ( IC50 = 219 µM ) for 60 minutes at 37°C . To these cells 2′ , 7′-dichlorodihydrofluorescein diacetate ( H2DCf-DA ) was added ( 5 µM ) and incubation was prolonged for 15 minutes . Then the fluorescence was measured using a fluorimetric microplate reader ( FilterMax F5 Multi-Mode Microplate Reader-Molecular Devices ) with excitation and emission wavelengths of 485 and 520 nm , respectively . Oligomycin ( 20 µM ) was used as positive control [28] . Untreated trypomastigotes and parasites treated with DMSO were included as negative controls . Results are displayed as mean values ± standard deviations , which were obtained from at least two independent assays ( n≥2 ) . IC50 values were calculated from sigmoidal dose-response curves using the Graph Pad Prism 5 . 0 software . Confidence intervals of 95% are included in parentheses . The Student's t-test was used for significance testing ( p<0 . 05 ) for all assays .
The structure of soulamarin is shown in Figure 1 . The assigned structure is consistent with the results obtained from NMR data and LR-ESI mass spectrum . The individual assignment of proton and carbon atoms was accomplished by 1D ( 1H , 13C ) and 2D ( HMQC , HMBC and NOESY ) NMR measurements . The 1H NMR spectrum of soulamarin in CDCl3/CD3OD displayed two doublets at δ 6 . 35 and 5 . 26 , with coupling constants of 10 . 0 Hz , which were assigned to H-9 and H-10 . Together with the presence of a singlet at δ 1 . 20 ( H-12 and H-13 ) , these peaks suggested the presence of a chromene moiety [29] . The presence of a dihydrocoumarin segment was based on the multiplets at δ 2 . 4–2 . 5 ( 2H ) and δ 3 . 48 ( 1H ) , which were assigned to H-3a/H-3b and H-4 , respectively . The multiplets at δ 1 . 30 ( H-14 ) , 1 . 32 ( H-15 ) and the triplet at δ0 . 64 ( J = 7 . 5 Hz , H-16 ) were assigned to an n-propyl chain linked to C-4 [30] . The doublets at δ 0 . 97 and 1 . 28 ( J = 6 . 4 Hz ) were attributed to the methyl groups H-5′ and H-4′ , while the multiplets at δ 2 . 30 and 3 . 93 were assigned to H-2′ and H-3′ of the isoprene moiety at C-5 . The13C NMR spectra showed carbonyl carbons at δ 199 . 1 ( C-1′ ) and 174 . 8 ( C-2 ) , as well as the sp2 carbon atoms of the chromene unit at δ 125 . 5 ( C-10 ) and 115 . 2 ( C-9 ) . Resonances for aromatic carbon atoms were observed between δ 160 and 101 , while the carbinol carbon atoms C-3′ and C-11 were observed at δ 78 . 4 and 77 . 4 , respectively . Additional peaks , corresponding to an n-propyl group were observed at δ 35 . 1 ( C-15 ) , 20 . 4 ( C-14 ) and 13 . 7 ( C-16 ) . Resonances corresponding to the isoprene unit were observed at δ 45 . 3 ( C-2′ ) , 19 . 2 ( C-4′ ) and 10 . 0 ( C-5′ ) . The relative configurations of C-2′ and C-3′ were assigned by comparison of the NMR data with those reported for ( 2R* , 3R* ) - and ( 2R* , 3S* ) -3-hydroxy-2-methylpentanoic acid [31] . The configuration of C-4 was assigned as S* , due to the cross peaks between H-4ax and H-3eq as well as between H-4ax and H-14 , observed in the NOESY spectrum . All these results are consistent with a structure of 6-hydroxy-4-propyl-5- ( 3-hydroxy-2-methyl-1-oxobutyl ) -6″ , 6″-dimethylpyrane-[2″ , 3″:8 , 7]-benzopyran-2-one ( see Figure 1 ) . The assigned structure was furthermore supported by comparison of our spectroscopic data with those reported in the literature [32] . Soulamarin was incubated with trypomastigotes and the activity of cells was determined after 24 hours via MTT assay . Soulamarin thereby demonstrated activity against parasites , killing all the cells at the highest tested concentration . An IC50 value of 219 . 8 µM ( 95% confidence interval for 186 . 9–258 . 5 µM ) was established ( see Table 1 ) . Benznidazole was used as standard against and resulted in an IC50 value of 440 . 7 µM ( 95% confidence interval for 272 . 4–478 . 4 µM ) . Soulamarin was also effective against intracellular amastigotes ( IC50 = 210 . 1 µM; 95% confidence interval for 174 . 5–252 . 6 µM ) , while benznidazole showed an IC50 of 319 . 7 µM ( 95% confidence interval for 283 . 8–360 . 1 µM ) . The cytotoxicity of soulamarin was determined with peritoneal macrophages by the MTT assay . Soulamarin showed an IC99 value of 988 . 95 µM and IC50 value of 278 . 3 µM ( 95% confidence interval for 229 . 4–342 . 8 µM ) . The hemolytic activity was also examined , but soulamarin did not induce any observable hemolysis up to concentrations of 1 , 300 µM ( Table 1 ) . Three different concentrations of soulamarin were incubated for up to 60 minutes with trypomastigotes and the permeability of the plasma membrane was examined by SYTOX Green assay . Soulamarin induced significant increased ( p<0 . 05 ) fluorescence for all tested concentrations . Highest fluorescence intensities were observed after 60 minutes of incubation ( Figure 2 ) . Relative to fully permeabilized parasites ( Triton X-100 , 60 min ) , soulamarin induced the following percentages of permeability: i ) 81% for IC100 = 386 µM ( standard error of the mean SEM 6 . 2 ) ( p<0 . 05 ) ; ii ) 60% for IC50 = 219 µM ( SEM 8 . 5 ) ; iii ) 28% for IC25 = 103 µM ( SEM 1 . 02 ) . DMSO was used as internal control and resulted in lack of alteration . Soulamarin was incubated with trypomastigotes ( 60 min ) and the mitochondrial membrane potential was examined using MitoTracker Red . Spectrofluorimetric measurements indicated that soulamarin induced a significant ( 97% , p<0 . 05 ) decrease in fluorescence levels compared to untreated trypomastigotes ( Figure 3A ) . The control group showed a typical mitochondrial membrane potential . FCCP was used as positive control , which reduced the fluorescence levels by 70% ( p<0 . 05 ) relative to untreated parasites . Additional fluorescence microscopy experiments corroborated the spectrofluorimetric analysis , demonstrating a substantial reduction of fluorescence levels in soulamarin-treated parasites ( Figure 3B ) , as wells as in FCCP ( Figure 3D ) . Untreated parasites showed intense fluorescence levels of mitochondria after labeling with MitoTracker Red , which is consistent with a normal mitochondrial membrane potential ( Figure 3C ) . Panels I represent images with blue fluorescence channel labeled with the fluorescent probe DAPI; panels II represent images with red fluorescence channel labeled with the fluorescent probe MitoTracker Red and panels III , represent the merged images . Soulamarin was incubated with trypomastigotes and the up/down-regulation of ROS was examined using 2′ , 7′-dichlorodihydrofluorescein diacetate ( H2DCf-DA ) . No changes in the production of ROS could be observed after 60 minutes . Oligomycin was used as positive control ( 100% ROS up-regulation ) . Untreated parasites were used as a negative control , showed a normal level of ROS production and were used for normalization ( data not shown here ) .
Soulamarin was isolated for the first time from the stem bark of C . brasiliense and showed desirable anti-trypanosomal activity . Our results furthermore indicated that soulamarin-induced death in T . cruzi is associated with mitochondrial dysfunction and a modified permeability of the plasma membrane . Therefore , the natural product soulamarin could serve as a scaffold for the development of selective new drugs against neglected diseases , in particular Chagas disease . | Chagas disease is a parasitic protozoan that affects the poorest population in the world , causing a high mortality and morbidity . As a result of highly toxic and long-term treatments , the discovery of novel , safe and more efficacious drugs is essential . Natural products isolated from plants are commonly used as drug prototypes or precursors to treat parasitic diseases . As part of our investigation of bioactive compounds from Brazilian flora , the present study was undertaken in order to determine the antitrypanosomal effects of the soulamarin , a coumarin isolated from the stem bark of Callophyllum brasiliense ( Clusiaceae ) , against Trypanossoma cruzi . This study moreover investigated the lethal action of soulamarin towards the parasite . Considering the obtained results , secondary metabolites of plants affecting the bioenergetic system as soulamarin may contribute as scaffolds for the design of novel and selective drug candidates for neglected diseases , mainly Chagas disease . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Soulamarin Isolated from Calophyllum brasiliense (Clusiaceae) Induces Plasma Membrane Permeabilization of Trypanosoma cruzi and Mytochondrial Dysfunction |
Cellular reprogramming has been recently intensively studied experimentally . We developed a global potential landscape and kinetic path framework to explore a human stem cell developmental network composed of 52 genes . We uncovered the underlying landscape for the stem cell network with two basins of attractions representing stem and differentiated cell states , quantified and exhibited the high dimensional biological paths for the differentiation and reprogramming process , connecting the stem cell state and differentiated cell state . Both the landscape and non-equilibrium curl flux determine the dynamics of cell differentiation jointly . Flux leads the kinetic paths to be deviated from the steepest descent gradient path , and the corresponding differentiation and reprogramming paths are irreversible . Quantification of paths allows us to find out how the differentiation and reprogramming occur and which important states they go through . We show the developmental process proceeds as moving from the stem cell basin of attraction to the differentiation basin of attraction . The landscape topography characterized by the barrier heights and transition rates quantitatively determine the global stability and kinetic speed of cell fate decision process for development . Through the global sensitivity analysis , we provided some specific predictions for the effects of key genes and regulation connections on the cellular differentiation or reprogramming process . Key links from sensitivity analysis and biological paths can be used to guide the differentiation designs or reprogramming tactics .
Human pluripotent stem cells have the potential to produce any tissues in the body , which provides the motivation for many researchers to investigate the cellular reprogramming . Recently some research on cellular reprogramming show that the transformation from somatic cells to induced pluripotent stem cells ( iPSC ) or between different differentiation cell types can be implemented by manipulating a few key genes [1]–[6] . These results provide hints for the stem cell models to be applied to the regenerative medicine . However , it is still challenging to generate and manipulate human pluripotent stem cells before practical applications to human healths . The efficiency of current cellular reprogramming techniques is often low and the molecular mechanism of cellular differentiation and reprogramming is still not very clear so far . This might be one of the main hurdles for iPSC to be applied for therapy . Therefore , understanding mechanisms of cellular differentiation and reprogramming as well as finding the optimal reprogramming pathway become very important for the application of iPSC . This requires a systematic and global approach to explore underlying gene regulatory networks with marker genes and mutual regulations between them . The epigenetic landscape concept has been proposed to explain the development and differentiation of the cells as a metaphor [7] , and provided a quantitative way of understanding the dynamics of gene regulatory system that drive cell development . This picture has been quantitatively realized through exploration of the global nature of the network in terms of probabilistic landscape framework [8]–[17] . The state space of gene regulatory networks contains states with different gene expression patterns ( such as embryonic stem cell marker gene NANOG and OCT4 ) in the cell , which further determines different cellular phenotypes . Using landscape framework , cell types are represented by basins of attractions on the landscape , which reflect the probability of appearance of different cell types . States with lower potential or higher probability represent attractor states or biological functional states , surrounded by the basin of attraction . So , the biological process such as cellular differentiation or lineage commitment can be understood as the transition from an attractor state to another one in the gene regulatory network state space . By quantifying the topography of the potential landscape in terms of barrier heights and transition rates , we can explore the global stability , kinetic paths and kinetic speeds of cell fate decision making process . We will explore the underlying landscape of a human stem cell developmental and differentiation network with 52 gene nodes by constructing the corresponding chemical reaction rate equations to explore its global properties , uncover the functional mechanism of transition between stem cell states and differentiation states . The barrier heights separating basins of attractions and the transition rates serve as the quantitative measure for global stability and kinetics of cell fate decision making process from one cell fate attractor to another representing different cell types . We further quantify the kinetic paths for the differentiation and reverse differentiation process ( reprogramming ) . We show that both potential landscape and probabilistic flux determine the dynamics of the developmental system . The force from the curl flux leads the kinetic paths of the system deviating from the steepest descent gradient one of potential . As a consequence , the differentiation path and the reprogramming path are irreversible . By identifying the differentiation and reprogramming paths , we can quantitatively trace the important states along the paths . Based on this , we can find out the detailed kinetic process realizing the differentiation and reprogramming . By the global sensitivity analysis of parameters or connections between genes , we will quantitatively predict which connection links or nodes ( genes ) are critical to cellular differentiation or reprogramming , which can be directly tested from the experiments . Through the analysis on the underlying landscape , we can also understand more clearly the mechanism of differentiation and reprogramming as well as the sensitivity of the parameters or links on the stability of the stem cell system . The biological paths we acquired can be used to guide the design of new differentiation or reprogramming tactics . This also provides a way to explore the biological paths for high dimensional systems or large networks .
We obtained the steady state probability distribution and potential landscape of the 52 gene stem cell regulatory network system ( Figure 1 ) [18] by self consistent mean field approximation , according to [8] , [10]–[15] . Here , represents the probability distribution of the steady state , and is the dimensionless potential energy . Therefore , directly reflects the steady state probability . Figure S1 gives a flowchart for the methods that we employed in this work . The time evolution the dynamical systems are governed by the diffusion equations . Given the system state , where is the concentration or populations of molecules or species , we expected to have N-coupled differential equations , which are difficult to solve . Following a self consistent mean field approach [8] , [12] , [19] , we split the probability into the products of individual ones: and solve the probability self-consistently . This effectively reduces the dimensionality from to , and thus makes the problem computationally tractable . For the multi-dimensional system , it is still hard to solve diffusion equations directly . We start from moment equations and assume specific probability distribution based on physical argument , which means that we give some specific connections between moments . In principle , once we know all moments , we can construct the probability distribution . Here we use gaussian distribution as approximation ( two moments are needed , mean and variance ) . Therefore , the evolution of probabilistic distribution for each variable can be acquired after solving the moment equations ( the mean and variance ) based on gaussian approximation approach ( See Methods for detailed self consistent approximation method for obtaining landscape ) . In this work , we acquired 52 dimensional probability distribution . For a 52-dimensional system , it's hard to visualize the landscape . So we integrated out the other 50 variables and left two key variables NANOG and GATA6 , then projected the landscape to a 2-dimensional state space ( NANOG and GATA6 ) . The reason that we chose the variable NANOG and GATA6 is because that NANOG is a major stem cell marker gene , GATA6 is a major differentiation marker gene , and the regulation dynamics of the 52 nodes network is mainly determined by the mutual repression of NANOG and GATA6 and the mutual repression between OCT4 and CDX2 . Choosing other major genes for presentation ( such as any 2 genes from NANOG , GATA6 , OCT4 and CDX2 ) will give the similar bistable landscape picture . Figure 2 shows two-dimensional and three-dimensional landscape in transcription factor expression level NANOG/GATA6 state space and the kinetic paths for the system . In Figure 2 ( A ) , we can see clearly that there are two stable states or basins of attractions on the landscape ( bistability ) . One of them represents the pluripotent stem cell state , which has higher expression of stem cell marker—such as OCT4 , NANOG and lower expression of differentiation marker genes—such as GATA6; and the other stable state represents the differentiation state , corresponding to lower expression of stem cell marker genes , and higher expression of differentiation marker genes . Based on our path integral method [13] , [20] , we also acquired the quantitative developmental path from the stem cell state to the differentiation state ( and the reprogramming path from the differentiation state to the stem cell state ) . Here , parameters are chosen in order to obtain relatively balanced two states ( stem cell state and differentiation state ) . Specifically , we set degradation constant , activation constant , and repression constant ( See Methods Section ) . In order to exhibit the landscape of the complete 52 dimensional network , we also used Langevin dynamics method to obtain landscape ( Figure S2 in supporting information ) . For a 52 dimensional system , for visualization , we harnessed ( root mean squared distance ) as the coordinate to reduce the dimensionality to 2 dimension ( , is the number of variables , and is the reference state , here we chose two potential minima as the reference states ) . represents the distance between a state point and reference point in state space . In this way , from 52-dimensional trajectory , we can generate two new coordinates and , separately representing the distance from a state point to the reference state 1 ( the potential minimum of stem cell attractor ) and the reference state 2 ( the potential minimum of differentiation state attractor ) . We can find that the landscapes using RMSD method based on Langevin dynamics ( Figure S2 in supporting information ) possess the similar dynamics compared with using NANOG and GATA6 as the coordinates ( Figure 2 ) based on the self consistent approximation . This shows that the two dimensional projection of landscape in NANOG and GATA6 state space can reflect the major dynamics of the full 52-dimensional gene network . We also showed the probabilistic flux of the stem cell system on the landscape ( Figure 2 ( B ) ) . The white and red arrows respectively represent the direction of probabilistic flux and the negative gradient of the potential energy . The dynamics of the developmental system is determined by both the force from the gradient of potential and the force from the curl flux [11] . The force from the curl flux leads the paths of the system deviating from the steepest descent path calculated from gradient of potential , therefore , as we can see the two kinetic paths of differentiation and reprogramming are irreversible ( yellow line and red line are not identical ) , which was indicated in both adiabatic and non-adiabatic mechanisms for the stem cell developmental motifs with only two genes [13] , [14] . Here , we can see the basic picture holds true for the gene network at the realistic level with 52 genes . The landscape in Figure 2 only is a 2-dimensional projection of the whole 52 dimensional state space . In order to demonstrate the cell states and the transitions between different cell types in the complete state space , we projected the expression level of the 22 marker genes to binary states ( cell states ) . Here , to analyze the dynamics of the system , we chose the key 22 marker genes to explore the underlying landscape and transition jumps between two nodes based on Langevin dynamics . The first reason choosing the 22 maker genes is that the 52 dimensional state space is huge , and it will have states even in the discrete form , which cannot be easily handled computationally . Another reason is that we believe using key 22 maker genes can capture major regulatory dynamics or paths without losing the essential information , since our purpose is to explore the dynamical mechanism of the stem cell differentiation system . For example , the stem cell state is represented by the binary number ( representing expression level from gene 1 to gene 22 , 1 for high expression , 0 for low expression ) , and for the differentiation state , it is represented by . Figure 3 ( see Methods Section for detailed methods ) shows the differentiation and reprogramming process represented by 313 cell states ( nodes , characterized by expression patterns of the 22 marker genes ) and 329 transition jumps ( edges ) between the different cell states ( produced by Cytoscape [21] ) . The sizes of nodes and edges are respectively proportional to the occurrence probability of the corresponding states and paths . Red nodes represent states which are closer to stem cell states , and blue nodes represent states which are closer to differentiation states . In particular , we displayed the 22 dimensional kinetic paths ( biological paths ) from path integral methods ( see methods for the details of path integral ) , which are shown as green and magenta paths separately for differentiation and reprogramming process ( see Table S4 and Table S5 for detailed paths ) . We can see that the paths for differentiation and reprogramming are irreversible . The irreversibility of the paths implies the time asymmetry which may point out the direction of the development . From Table S4 , monitoring the differentiation process according to certain vital marker genes NANOG ( column 3 ) , GATA6 ( column 16 ) and CDX2 ( column 22 ) , we can see that the differentiation process experiences a transition from the stem cell state ( high NANOG/low GATA6/low CDX2 ) to a intermediate state ( IM1 , low NANOG/low GATA6/low CDX2 ) , and then to another intermediate state ( low NANOG/low GATA6/high CDX2 ) , and eventually to the differentiation state ( low NANOG/high GATA6/high CDX2 ) . This indicates the importance of NANOG to the maintenance of pluripotency . For differentiation proceeding , the cell needs to firstly impair the expression of NANOG , further downregulate other stem cell marker genes which are promoted by NANOG , and finally reach the differentiation state ( GATA6 dominant ) . For the reprogramming path in Table S5 , we can see that the cell experiences a transition from the differentiation state ( low NANOG/high GATA6/high CDX2 ) , to an intermediate state ( IM2 , high NANOG/high GATA6/high CDX2 ) , to another intermediate state ( high NANOG/low GATA6/high CDX2 ) , and finally to the stem cell state ( high NANOG/low GATA6/low CDX2 ) . This might imply that in the reprogramming process the cell first opens the key stem cell marker genes NANOG by the change of regulation strength between key maker genes , then other stem cell marker genes gradually acquire high expression level due to the activation regulation of NANOG to them . Finally the cell reach the stem cell state , because the stem cell marker genes which have been activated repress strongly the differentiation marker genes ( such as GATA6 and CDX2 ) . The biological paths can be validated by related experiments [18] , and we expect that it can be used to guide the design of new strategies for cellular differentiation and reprogramming . To quantify the global stability of the stem cell network in terms of landscape topography , we define global barrier height , representing potential difference between two attractor minimums and the saddle point on landscape . We define as the potential energy difference between the pluripotent stem cell state and the saddle point , , and as the potential energy difference between differentiation state and the saddle point , . Here , and denotes respectively potential at the minimum for the stem cell attractor and the differentiation attractor , and denotes the potential at the saddle point between these two basins of attractions . The results of barrier heights are based on RMSD method from Langevin dynamics ( Figure S2 ) . We projected the whole network landscape to two dimensions ( RMSD1 and RMSD2 ) . For this 2-dimensional landscape , we can acquire saddle points , local minimums and then barrier heights . In this way , measures the global stability of the stem cell state or the stability of the differentiation process and measures the stability of the differentiation state or the stability of the reprogramming process . When the system has larger ( stem cell state barrier or barrier for differentiation process ) and smaller ( differentiation state barrier or barrier for reprogramming process ) , the stem cell state is more stable and the system is inclined to stay in the stem cell state . The differentiation process ( transition from stem cell state to differentiation state ) is hard to realize , because the system must go across a large barrier in order to escape from the stem cell state to differentiation state . The reprogramming process ( the reverse process of differentiation , transition from differentiation state to stem cell state ) is relatively easy to realize in this case ( small ) . In contrast , if ( ) is small and ( ) is larger for the stem cell system , it will be advantageous for differentiation process and difficult for reprogramming process , because the system only needs to overcome a small barrier to go from the stem cell state to the differentiation state ( small ) , but a large barrier from differentiation state to pluripotent stem cell state ( large ) . Figure 4 ( A ) shows respectively the barrier heights for differentiation process ( or ) and the reprogramming process ( or ) when activation strength changes . We can see that with the activation constant increased , becomes larger and declines . It indicates that the enhancement of activation regulation in the network leads to more stable stem cell state , making it easier for the transition from the differentiation state to the stem cell state , and correspondingly the barrier height of differentiation process is raised . Meanwhile , when activation links are strengthened , the differentiation state becomes less stable , the system is not inclined to stay at differentiation state with a smaller barrier height of reprogramming process ( ) . This implies that changing the strength of the activation links in the gene regulatory network provide a way to regulate the differentiation process or the reprogramming process and make the system inclined to differentiation or inclined to reprogramming determined by the relative stability of the two basin of attraction - stem cell attractor and differentiation attractor . The relative stability of these two attractors can be quantified by the landscape topography - that is , the barrier height . Indeed , some previous work have showed that changing self activation regulatory strength provide a possible mechanism for cell differentiation and reprogramming motifs involving two genes [11] , [13] , [14] , [22] . We also investigated the kinetics or speed of differentiation and reprogramming according to the mean first passage time ( MFPT ) , in order to further quantify the dynamics of differentiation and reprogramming process . We calculated the mean first passage time ( MFPT ) from the trajectory based on Langevin dynamics . In Figure S2B , left attractor represent ES state , the right attractor represents differentiation state , and this landscape is obtained by collecting the statistics through histogram or distribution of the temporal trajectories for 52 variables . Starting from a random initial state at ES attractor , following the temporal evolution trajectory of system , we can find the time where the system first enters into the differentiation attractor . The time difference between initial time and the final time is defined as the first passage time ( FPT ) for differentiation process . Repeating this process and obtaining the average of the FPT is defined as the MFPT for differentiation process . In the same way , we can obtain the MFPT for reprogramming . MFPT reflects the average transition time of the system from one state to another state in the state space of gene regulatory networks , and therefore can be used to quantify the ability of a system switching from one state to another state . The results of MFPT are shown in Figure 4 ( B ) . It can be seen that for the differentiation process , the MFPT of differentiation ( , blue line ) is longer when the activation strength increases . This implies that when activation is larger the system need more time to jump from the stem cell state to the differentiation state . Cells have more chances to stay in the stem cell state , and therefore the increase of the activation strength is disadvantageous to the progress of differentiation . On the contrary , the decrease of activation makes MFPT of differentiation smaller , and thus cells are inclined to jump from stem cell state to differentiation state . The decrease of the activation strength represents the direction of the differentiation process . For the reprogramming process , the MFPT of the reprogramming decreases as the activation strength increases , which means that when activation strength increases the system needs less time to jump from differentiation state to stem cell state , therefore making reprogramming easier . We can find that the global barrier heights and the MFPT have the same trend for differentiation and reprogramming , and both of them can serve as the quantitative measure to global stability of the two attractors and kinetic speeds . We also explored the influence of changing the repression strength and the noise level on the landscape topography . Figure 4 ( C , D ) show the landscape results when the repression strength is changed . It shows that when is increased , the barrier for differentiation process ( ) , the barrier for reprogramming process ( ) , the MFPT for differentiation process from stem cell state to differentiation state ( ) , and the MFPT for reprogramming process from differentiation state to stem cell state ( ) all increase . This might be because that the increase of mutual repression ( a lot of the repression links are mutual repression between stem marker genes and differentiation marker genes ) makes transitions between stem cell states and differentiation states harder , and thus the barriers and the MFPT both increase . Figure 4 ( E , F ) show the landscape results when the noise level ( diffusion coefficient in Langevin dynamics ) is changed . It shows that when is increased , the barrier for differentiation process ( ) , the barrier for reprogramming process ( ) , the MFPT for differentiation process from stem cell state to differentiation state ( ) , and the MFPT for reprogramming process from differentiation state to stem cell state ( ) all go down . This can be explained that the increase of fluctuations makes both the stem cell state and the differentiation state less stable , and the transitions between the stem cell state and the differentiation state become easier , reflected by the decrease of the barriers and the MFPT . Meanwhile , in Figure 4 ( E , F ) we can find that with decreased the barrier for differentiation process ( ) and the MFPT for differentiation ( ) decline slower than the barrier for reprogramming process ( ) and the MFPT for reprogramming ( ) . This shows that as the noise goes up the differentiation state becomes more stable relatively , which might provide a possibility for noise-induced differentiation or reprogramming [23] , [24] . We need to stress that our non-equilibrium potential barrier is dimensionless and directly related to the steady state probability while the equilibrium potential barrier conventionally has a dimension as such that . plays the role of the noise . This is why our non-equilibrium dimensionless usually changes with noise while usually does not . We also used Langevin dynamics method to investigate the dynamics of the system ( See methods for details ) , because it can provides the dynamical trajectory of the developmental system under fluctuating environments . Figure S2 show the landscape comparisons at different activation strength , we can find that when is large ( Figure S2 ( A ) , ) the stem cell state attractor is dominant , showing only one stable basin of attraction on the landscape graph , and when decreases to , the differentiation state attractor is dominant ( Figure S2 ( D ) , ) . Specifically , when gradually decreases from 0 . 5 to 0 . 3 , the stem cell state becomes less and less stable ( differentiation barrier decreases and MFPT for differentiation decreases ) and the differentiation state becomes more and more stable ( reprogramming barrier increases and MFPT for reprogramming increases ) until being dominant , demonstrating that the system of stem cell experiences a transition from stem cell state to differentiation state with activation strength decreased . This implies that controlling the strengthes of the activation links between different marker genes might provide a mechanism for cell fate determination , differentiation or reprogramming . The regulation changes during the developmental processes are hinted in experiments by the effective regulations of the transcription factors mediated by Klf4 [25] . Therefore , we can see that the decrease of the activation strength represents the direction of development and differentiation . Along this direction , the Waddington landscape is downhill . This might provide a possible explanation for the direction ( time arrow ) for the developmental process hinted from the downhill trend of the Waddington landscape caused by the gene regulation changes . We suggest the direction or time arrow of the development by changing regulations leading to the underlying divergent funneled ( with more states at the bottom in contrast to convergent funnel in protein folding ) Waddington landscape is from natural selection . The regulation changes leading to the downhill Waddington landscapes for development are selected due to the emergence of the associated biological function ( differentiation ) . The regulations not leading to the downhill Waddington landscape and failed for generating successful development and differentiation will not be selected and therefore become extinct in evolution . To study the dynamics of developmental process when changes from to , we specify activation constant changing from large to small with time , . Here represents the rate of decreasing of ( parameters in this equation are selected in order to acquire a suitable dynamical transition trajectory from to ) . We assume that the activation strength decreases in the developmental and differentiation process due to the regulations of the other genes in the network . Then we can obtain the trajectory of the stem cell system with the activation strength changed ( RMSD as coordinates from Langevine dynamics ) . Figure 5 shows the dynamical transition path of the differentiation process ( green line ) and the reprogramming process ( magenta line ) on the underlying landscape . axis represents activation strength . Three 2-dimensional landscape pictures represent the landscape of the stem cell network respectively for , , and . It can be seen clearly that as the differentiation progresses ( represented by decrease of ) , the landscape of the stem cell network changes gradually from a dominant stable stem cell attractor , to a balanced bistability , and finally to a dominant stable differentiation attractor . In the mean time , we can see that the two paths of differentiation and reprogramming are irreversible ( green line and magenta line are not identical ) , which is consistent with the dominant path results from path integral . From transition path trajectories , we can see that for the development and differentiation process the trajectory first haunts around the stem cell state attractor , and then jump to the differentiation state attractor after decreases to a critical value ( we define it here ) . By contrast , for the reprogramming process , the trajectory firstly haunts around the differentiation state attractor , and jump to the stem cell attractor after increases to a critical value . We notice that and is not in the same place , which just effects the fact of irreversible transition paths . Indeed , is smaller than , providing a hysteresis loop for the bistable switch . This result reflects one of the common characteristics for biological bistability: the existence of hysteresis for bistable switch , which comes from the feedback loops and provides an explanation of the irreversibility for the bistable switch . Figure 5 provides a quantified yet realistic Waddington landscape picture of differentiation and reprogramming . As we did for the dominant path , we also monitored the differentiation and reprogramming kinetic paths with the activation strength changed ( separately shown in Table S6 and Table S7 ) in terms of certain key marker genes NANOG , GATA6 , and CDX2 . Similar to the analysis about dominant paths from path integrals , we can find that for the differentiation process the cell experiences an intermediate state ( low NANOG/low GATA6/low CDX2 or low stem cell marker/low differentiation marker ) along the path from the stem cell state to the differentiation state . For the reprogramming path , we can see that the cell also experiences an intermediate state ( high NANOG/high GATA6/high CDX2 , or high stem cell marker/high differentiation marker ) along the path from the differentiation state to the stem cell state . These results have the consistent predictions with the dominant path analysis , which is that the cellular differentiation needs to experience an intermediate double low state ( both stem cell marker genes and differentiation marker genes have low expression level ) , and the cellular reprogramming needs to experience an intermediate double high state ( both stem cell marker genes and differentiation marker genes have high expression level ) . We expect that these predictions can be tested by experiments in the future , as well as help to design the differentiation and reprogramming strategies . We also did a global sensitivity analysis of parameters for the stem cell network in order to discover the key parameters or connections in the network affecting the stability and kinetic transitions of both the stem cell state and the differentiation state . Giving parameters , here representing the strength of 123 links in the stem cell network at a perturbation level , we can explore the influence of these parameters on the stability of the system by comparing the change of landscape topography quantified by the barrier heights . We firstly exploited the self consistent approximation method [12] , [19] to obtain those most important parameters - that is , by finding those parameters affecting barrier heights of the system critically . Specifically , we changed the value of each of the activation and repression constant and ( Eq . ( 2 ) , the parameters and are only used for the global sensitivity analysis ) by giving a percentage ( here , represents parameter or , represents the change of parameter , the value of is controlled as between to ) as the degree to change . Then for every mutation of parameters we compared the change of the landscape topography in terms of the barrier heights for both differentiation ( ) and reprogramming ( ) . In this way , we acquired 20 most critical parameters or connections ( 14 of them are activation links and 6 of the others are repression links , see Text S1 and Figure S3 for details ) . In the following , we employed the Langevin dynamics to further obtain the change of barrier heights when these 20 parameters are changed , because by the Langevin dynamics the landscape of the system can be acquired directly by the statistics of the trajectories of the system - not through approximation . Figure 6 shows the results of the global sensitivity analysis for the 20 parameters or connections ( see Text S1 for details ) . Figure 6 ( A ) shows the results for 6 repression links , and Figure 6 ( B ) shows the results for 14 activation links . Blue bars represent the change of the barrier for stem cell state ( ) , and the red bars represent the change of the barrier for differentiation state ( ) . In Figure 6 ( A ) , x axis represent 6 parameters or connections . The 6 links are respectively corresponding to: ( link R1 , ) , ( link R2 , ) , ( link R3 , ) , ( link R4 , ) , ( link R5 , ) , ( link R6 , ) . Here , represents the repression regulation from gene CDX2 to gene OCT4 ( see Text S1 for the detailed relation of the order numbers of genes and the corresponding genes ) . We can see that when the repression of CDX2 to OCT4 increases , ( stem cell state barrier ) decreases significantly and ( differentiation state barrier ) decreases slightly , making it easier to jump from stem cell state attractor to differentiation state attractor . Some experimental results have showed the importance of CDX2 to cell differentiation , which indicate that at the blastocyst stage Oct4 is gradually downregulated in the outer trophectoderm ( TE , one cell type of differentiation state ) cells by Cdx2 through direct physical interaction and transcriptional regulation [26] . In addition , repression link ( representing gene PRDM14 represses gene GATA6 , here we defined a name for every repression and activation link , see Table S2 and Table S3 for the definition of link names ) represents the repression of gene PRDM14 to gene GATA6 . According to experimental results when PRDM14 is activated , the reprogramming is enhanced [27] , which is also reflected in our global sensitivity analysis results that strengthening repression link make increase and decrease . For the repression link R2 ( ) , R3 ( ) , R4 ( ) , R5 ( ) , they are all the repression regulations from either stem cell marker genes ( OCT4 , NANOG ) or other genes ( GATA4 , LMCD1 ) to the key differentiation marker gene . So , our global sensitivity analysis provide some predictions that increasing above 4 repression links will promote cellular reprogramming , since the increase of these 4 repression links make the stem cell barrier increase and differentiation barrier decrease . The repression of the differentiation marker gene will strength the stem cell maker genes due to the repression of to , and then promote the cellular reprogramming . We found some experimental evidences that indicated the forced expression of Gata6 in embryonic stem ( ES ) cells is sufficient to induce the proper differentiation program [28] . This can provide some confirmation for the above 4 repression links , since these links are all the repression links to GATA6 , and the global sensitivity analysis for these 4 links shows that increasing them will inhibit differentiation . Therefore , these predictions are reasonable and can be further validated by experiments . We can also see that among the 6 top important repression links , 5 of them are related to the repression of gene GATA6 ( when these 5 repression links are strengthened , reprogramming becomes easier in that increases and decreases ) , which should be due to the repression of to stem cell marker gene . Figure 6 ( B ) shows the global sensitivity analysis results for 14 activation links , in which x axis represents separately: A1: A1:3→1 ( NANOG→OCT4 ) ;A2 : 4→1 ( OCT4SOX2→OCT4 ) ;A3 : 3→2 ( NANOG→SOX2 ) ;A4 : 4→2 ( OCT4SOX2→SOX2 ) ;A5 : 3→3 ( NANOG→NANOG ) ;A6 : 4→3 ( OCT4SOX2→NANOG ) ;A7 : 5→3 ( KLF4→NANOG ) ;A8 : 7→3 ( ZIC3→NANOG ) ;A9 : 11→3 ( PBX1→NANOG ) ;A10 : 1→4 ( OCT4→OCT4SOX2 ) ;A11 : 2→4 ( SOX2→OCT4SOX2 ) ;A12 : 2→7 ( SOX2→ZIC3 ) ;A13 : 3→7 ( NANOG→ZIC3 ) ;A14 : 3→11 ( NANOG→PBX1 ) ( arrows represent activation regulation , represent the name of the activation links , see supporting information for details ) . It can be seen that increasing the strength of the 14 activation connections all increase the barrier heights of stem cell state and decrease the barrier heights of the differentiation state , which means that the stem cell state becomes more stable and the differentiation state becomes less stable and it's easier for the system to make a transition from differentiation state to the stem cell state , i . e . increasing these activation links promote reprogramming progression of the system . We can see that among these 14 activation links , the first 11 of them are all activation links from other genes to the key stem cell marker genes ( OCT4 , SOX2 , NANOG , OCT4SOX2 ) . Undoubtedly , strengthening these 11 activation links will promote the cellular reprogramming process . Especially , among the 4 activation links which influence barrier greatly ( link A3 , A5 , A7 , A10 , Figure 6 ( B ) ) , two of them are activation links to NANOG , this shows activating NANOG is a robust way for reprogramming [29] , [30] . For the last 3 activation links from global sensitivity analysis , we can see that they represent the activation of the key stem cell marker genes ( NANOG , SOX2 ) to other stem cell marker genes ( ZIC3 , PBX1 ) . The increase of these 3 activation links will increase the expression of , because of the activation of and to , and thus promote the cellular reprogramming process . We also found some experimental evidences supporting our global sensitivity results described above . Experiments show that the increase of Klf4 promote reprogramming [25] . Additionally , among the 14 the 14 activation links , 5 of them are the activation regulation to NANOG . Some experiments show that Activation of Nanog can overcome barriers to lead reprogramming [31] . And some other links among the 14 activation links are activation links to OCT4 or SOX2 . The experiments have confirmed that the activation of Oct4 and Sox2 can promote reprogramming [1] . Some recent recent experiments also indicate Zic3 can induce conversion to pluripotent stem cells [32] . Therefore , our global sensitivity analysis results are confirmed by some experiments . Our global sensitivity analysis results also provide the quantitative prediction about the effects of regulation links on the differentiation or reprogramming , which can be tested by further experiments . We need to emphasize that compared with the conventional sensitivity analysis which is usually local our sensitivity analysis is global since it is based on the global landscape topography quantified by the barrier height . Additionally , we also quantified the global sensitivity of parameters through MFPT ( mean first passage time ) , since MFPT reflects the average transition time from one basin of attraction to another , and therefore provides another quantitative measure for the stability of the system . Figure 6 ( C ) and ( D ) show the influence of parameter change on the MFPT respectively for 6 repression links and 14 activation links . Comparing Figure 6 ( A ) with ( C ) , and ( B ) with ( D ) , we can find that MFPT and barrier height give the consistent results on the global sensitivity analysis . Larger ( ) makes the transition from stem cell state to differentiation state harder , and thus means larger MFPT for differentiation . In contrast , larger ( ) makes the transition from differentiation state to stem cell state harder , and thus larger MFPT for reprogramming . Therefore , ( ) is corresponding to MFPT for differentiation , and ( ) is corresponding to MFPT for reprogramming . We also did the mutation for the knockdown of single nodes to see their influence to the landscape . Figure 6 ( E ) show the influence of knockdown of single genes on the barrier heights , ( only showing the genes having large influence on barrier heights ) . The genes whose knockdown affect barrier heights critically include: GATA6 , CDX2 , OCT4 , SOX2 , NANOG , KLF4 , ZIC3 , PBX1 . GATA6 and CDX2 are two key differentiation marker genes , so their knockdown promote reprogramming ( increase barrier of stem cell state and decrease barrier of differentiation state ) . OCT4 , SOX2 , NANOG , KLF4 , ZIC3 , PBX1 are key stem cell marker , and it's reasonable that their knockdown promote differentiation ( increase barrier of differentiation state and decrease barrier of stem cell state ) . These key gene markers have been highlighted in Figure 1 . We also did some mutations to see their effects on kinetic path based on path integral approach . Figure 7 show the influence of parameters on the kinetic paths separately for mutation 1 ( increase the repression of CDX2 to OCT4 ) , mutation 2 ( increase the repression of OCT4 to GATA6 ) , mutation 3 ( increase the repression of NANOG to GATA6 ) , mutation 4 ( increase the repression of GATA4 to GATA6 ) . The left attractor represent the stem cell state , and the right one represents the differentiation state . The blue paths are before mutations , the magenta paths are the results after mutations . The global sensitivity analysis ( Figure 6 ( A ) ( C ) ) of mutation 2 ( increase the repression of OCT4 to GATA6 ) show that mutation 2 can make stem cell state ( left attractor in landscape ) more stable , and differentiation state less stable , or the MFPT for differentiation become longer . While by comparison of paths in Figure 7 ( B ) , the differentiation path ( path from left attractor to right attractor ) becomes more deviating ( change from blue path to magenta path ) from the shortest path , or the differentiation path become longer ( so spending more time or MFPT increases ) , and the opposite results are for the reprogramming path . This is consistent with the barrier height and MFPT results in Figure 4 ( A ) ( C ) . In the same way , the path sensitivity for mutation 3 , and mutation 4 ( Figure 7 ( C ) ( D ) ) also show the differentiation path becomes more deviating ( change from blue path to magenta path ) from the shortest path , and thus spend more time , which are also consistent with the global sensitivity analysis results in terms of barrier height and MFPT in Figure 4 ( A ) ( C ) . In addition , for mutation 1 ( Figure 7 ( A ) ) , the path comparison shows that after mutation the differentiation path becomes closer to the shortest path , meaning that the differentiation process become easier . This is also consistent with the barrier and MFPT sensitivity for mutation 1 , which shows that the differentiation state become more stable , i . e . this mutation promotes the differentiation process . In summary , the global sensitivity analysis in terms of barrier , MFPT , and kinetic path provide a way to uncover the key factors critically determining the process of cellular differentiation and reprogramming ( highlighted in black solid links in Figure 1 ) . Some of our predictions are consistent with the experimental evidences . More importantly , we provided certain predictions about which regulation links in the stem cell network are critical to differentiation or reprogramming ( Figure 6 ) , which can be directly validated from relevant experiments in terms of both MFPT or the differentiation and reprogramming pathway . We uncovered the landscape of a stem cell developmental and differentiation network . Landscape shows that the stem cell gene regulatory network has two stable basins of attractions at specific parameter regions , one of which represents the pluripotent stem cell state and the other of which represents the differentiation state . In terms of the path integral approach , we acquired the kinetic paths for both development and reprogramming . Both landscape and curl flux determine the dynamics of the stem cell network . Flux leads the kinetic paths of the system deviating from the steepest descent path from gradient of potential , and the differentiation path and the reprogramming path are irreversible . Barrier heights based on landscape topography provide quantitative measures for the stability and kinetic transition of the two attractors . MFPT provide an avenue to acquire the information of transition rate or kinetic speed for the system to jump from an attractor to another . By the global sensitivity analysis in terms of barrier heights , MFPT , and kinetic path , we provided some predictions about the key genes and connections affecting differentiation and reprogramming significantly , which can be tested by experiments . Importantly , the key links and genes from global sensitivity analysis and biological paths we acquired can be used to guide the differentiation designs or reprogramming tactics . The current stem cell network we employed only provides some general biological markers and their interaction about stem cell differentiation and reprogramming . With more biological details added into the stem cell network , such as a network including certain differentiation marker genes representing some different differentiation states [33] , it can be anticipated that we can explore the landscape and paths not only for the differentiation or reprogramming process , but also for transdifferentiation process ( the transition between different differentiation state cell types ) . In addition , in our current model , we only simulated the single cell behavior , not considering the effects of cell division . We hope that we can absorb the cell division to the model , since the cell division rate can influence the stem cell or differentiation cell populations [34] . Our approach provides a general way to investigate the global properties—landscape topography , transition rate , kinetic path—of large gene regulatory networks which only have information on interaction directions ( activation or repression ) without interaction strength . In particular , we provide a approach to investigate biological paths of high dimensional systems . Our approach can be applied to other gene regulatory networks or protein networks .
A human stem cell network has been constructed by searching for literatures , which includes most of the main regulations in human embryonic stem cell ( hESCs ) as shown in Figure 1 [18] . This network includes 52 protein nodes ( Table S1 ) and their interactions ( total 123 links including 84 activation links and 39 repression links ) , in which red arrows represent activation and blue bars represent repression . There are 11 marker genes for Pluripotency state ( iPS state or stem cell state ) and 11 marker genes for differentiation state , which are separately colored in purple and cyan . The orange nodes represent genes that are activated by iPS marker genes , and the light red color nodes denote other genes . The iPS marker genes include OCT4 , SOX2 , NANOG , Oct4-Sox2 , KLF4 , FOXD3 , ZIC3 , ZFP42 , GDF3 , TDGF1 , PBX1 , and the differentiation marker include FOXA2 , AFP , SOX17 , GATA4 , GATA6 , T , GATA2 , GATA3 , hCGa , hCGb , CDX2 . Basically , the dynamics of the network is determined by the mutual repression of major ES marker genes ( NANOG , OCT4 , SOX2 ) and major differentiation marker genes ( GATA4 , CDX2 ) . When the ES markers are highly expressed , the system will be in ES state , and when the differentiation markers are highly expressed , the system will be in differentiation state . Specifically , the trophectoderm lineage is determined by the antagonism between Oct4 and Cdx2 ( mutual repression links in the network ) , whereas the mutually repressions between Gata6 and NANOG determine the primitive endoderm lineage [30] , [35] , [36] . Some other regulations of the network include the self-activation of some key marker genes ( NANOG , GATA6 , CDX2 ) , as well as the mutual activation between ES marker genes . So , the bistable regulatory dynamics for the full 52 gene network is mostly determined by the antagonism between Oct4 and Cdx2 , and the antagonism between GATA6 and NANOG . These major regulation links are for embryo stem cells [30] , [35] , [36] . These marker genes constitute a major stem cell gene regulatory network , which orchestrates some important cellular functions , such as the cell differentiation and reprogramming . For instance , transcription factors OCT4 , SOX2 and NANOG play important roles to the early development of cell and propagation of undifferentiated embryonic stem cell [36] , [37] . The protein OCT4 and the protein FOXD3 are transcriptional regulators expressed in embryonic stem cells . Down regulation of OCT4 is an essential requirement during gastrulation for proper endoderm development [38] . For the 52 node network , we constructed 52 corresponding ordinary differential equations describing dynamics of the system , in terms of Hill function representing their activation or repression interactions . The equation has the form as: ( 1 ) Here in Eq ( 1 ) , i = 1 , 2 , … , 52 , so totally there are 52 equations . represents the threshold ( inflection point ) of the explicitly sigmoidal functions , i . e . , the strength of the regulatory interaction , and is the Hill coefficient which determines the steepness of the sigmoidal function [22] . Here , parameters for Hill function are specified as: . In addition , is self-degradation constant , is repression constant , and is activation constant . In the above equation , the first term represents self-degradation , the second term represents activation from node to node ( m1 represents the number of activations to node , and this term represents self-activation when ) , and the last item denotes repression from node to node ( m2 represents the number of repressions to node , and this term represents self-repression when ) . Here firstly we designated parameters value uniformly ( Eq . ( 1 ) ) , i . e . all activation strength is same , and also for the repression strength , because so far we have no access for the information about the regulation strength — or the magnitude of activation and repression parameters — between different genes in the stem cell network . In the global sensitivity analysis section ( throughout the paper we use Eq ( 1 ) as the driving force , and the Eq ( 2 ) is only used in global sensitivity analysis section ) , we will change each specific activation strength ( representing the activation constant for the regulation from node j to node i ) and repression strength ( representing the repression constant for the regulation from node j to node i ) ( Eq . ( 2 ) ) to see their influence on the dynamics of the system . Throughout the paper , we use and to separately denote uniform activation constant and repression constant . The parameters and ( Eq ( 2 ) ) are only used in global sensitivity analysis section . The default parameter values ( Figure 2 ) are set as: . ( 2 ) About the value of parameters , we choose parameter values according to the following criteria: The time evolution the dynamical systems are governed by the diffusion equations . Given the system state , where is the concentration or populations of molecules or species , we expected to have N-coupled differential equations , which are difficult to solve . Following a self consistent mean field approach [8] , [12] , [19] , we split the probability into the products of individual ones: and solve the probability self-consistently . This effectively reduces the dimensionality from to , and thus makes the problem computationally tractable . However , for the multi-dimensional system , it is still hard to solve diffusion equations directly . We can start from moment equations and then simply assume specific probability distribution based on physical argument , meaning that we give some specific connections between moments . In principle , once we know all moments , we can construct the probability distribution . For example , Poisson distribution has only one parameter , so we may calculate all other moments from the first moment , that is the mean . Here we use gaussian distribution as approximation , then we need two moments , mean and variance . When diffusion coefficient is small , the moment equations can be approximated to [39] , [40]: ( 3 ) ( 4 ) Here , , and are vectors and tensors , and is the transpose of . The matrix elements of A is . In terms of this equation , we can solve and . Here , we consider only diagonal elements of from mean field splitting approximation . Therefore , the evolution of probabilistic distribution for each variable could be acquired using the mean and variance based on gaussian approximation ( see Text S1 for detailed deduction process of Gaussian Approximation method ) : ( 5 ) The probability obtained above corresponds to one fixed point or basin of attraction . If the system allows multistability , then there are several probability distributions localized at every basin of attraction , but with different variations . Therefore , the total probability is the weighted sum of all these probability distributions . The weighting factors are the size of the basin , representing the relative size of different basin of attraction . For example , for a bistable system , the probability distribution takes the form: , here . Here , we determine the weights by giving a large number of random initial conditions for ODEs to find solution , and then collect the statistics for different solution . For example , for a bistable system , if initial condition goes to the first steady state , and initial condition goes to the second steady state , then the weight for the first basin is 0 . 1 and for the second basin is 0 . 9 . The multistability comes from the solution of 52 ODEs giving a large number ( 100000 ) of random initial values . We give large number of random different initial conditions for ODEs for solution at a fixed parameter set . By collecting the statistics of the solution , we can determine if the system is monostable or bistable or mutistable at current parameter region . In our current work , for 52 dimensional system , we can acquire 52 dimensional probability distribution . To exhibit the results in a 2-dimensional space , we integrated out the other 50 variables and left two variables NANOG and GATA6 . Finally , once we have the total probability , we can construct the potential landscape by the relationship with the steady state probability: . In the gene regulatory network system , every parameter or link contributes to the structure and dynamics of the system , which is encoded in the total probability distribution , or the underlying potential landscape . For nonequilibrium gene regulatory systems , the driving force can not be written as the gradient of potential , like the equilibrium case . In general , can be decomposed into a gradient of the potential and a curl flux force linking the steady state flux and the steady state probability [10] , [12] ( ) . denotes steady state probability and potential U is defined as . The probability flux vector of the system in concentration or gene expression level space is defined as [32]: . In the 52-dimensional protein concentration space , it's hard to visualize 52-dimensional probabilistic flux . Approximately , we explored the associated 2-dimensional projection of flux vector: and . In addition , to validate the Gaussian approximation method , we provided the landscape results from Gaussian distribution approximation of the 2-dimensional case for GATA1/PU1 [11] , [13] , and made comparisons for this 2-dimension case between Gaussian approximation method and Langevin dynamics method ( Figure S4 ) . We can see that the landscapes from Gaussian approximation preserve the similar global properties ( the number of attractors , the relative stability of basin of attractions ) as the Langevin dynamics method . The landscape in Figure 2 only is the 2-dimensional projection of the whole 52 dimensional state space . In order to demonstrate the cell states and the transitions between different cell types in the complete state space , we projected the expression level of the 52 gene variables to binary states , and acquired discretized dynamics results of the network ( Figure 3 ) . We first used the Langevin dynamics to obtain the stochastic dimensionless trajectories of the 52 dimensional system . Then the trajectory is converted to discrete trajectories by setting the value ( ( maximum value - minimum value ) /2+minimum value ) of every variable as the cutoff ( cutoff is chosen so that two up/down states are well separated ) , i . e . the value higher than the cutoff is set to 1 ( indicating high expression ) , while the value lower than the cutoff is set to 0 ( indicating low expression ) . So , we can obtain the discrete trajectories for 52 variables of the system . For a 52 dimension system , there will be states even in discrete case ( every variable has two value , 1 represent high expression , 0 represent low expression ) , which cannot be handled computationally . So , we chose the major 22 marker genes to present the discrete system , which has states . For example , the stem cell state is represented by the binary number ( representing expression level from gene 1 to gene 22 , 1 for high expression , 0 for low expression ) , and for the differentiation state , it is represented by . By the statistics for the discrete trajectory , we can obtain the appearing probability separately for different states . To present the results , we set a probability cutoff 0 . 0002 ( only states with higher probability than 0 . 0002 are chosen , the cutoff is chosen so that the major states can be presented in a figure , not too many or too few states , i . e . we only demonstrate the states and paths with higher probability ) . Figure 3 shows the differentiation and reprogramming process represented by 313 cell states ( nodes ) and 329 transition jumps ( edges ) between the different cell states . We believe that these 313 states with higher probability can capture the major states and regulation dynamics of the system . The sizes of nodes and edges are separately proportional to the occurrence probability of the corresponding states and paths . Red nodes represent states which are closer to stem cell states , and blue nodes represent states which are closer to differentiation states . Especially , we acquired the dominant kinetic paths as the biological paths from path integral formulism ( see Path Integral section for detailed methods ) , which are shown as green and magenta paths ( Figure 3 ) separately for differentiation and reprogramming process . In the cell , there exist external noise and intrinsic noise , which can be significant to the dynamics of the system [41] , [42] . Therefore , a network of chemical reactions in noisy fluctuating environments can be addressed by: . Here , represents the vector of protein concentration or gene expression level . is the vector for the driving force of chemical reaction . is Gaussian noise term whose autocorrelation function is , and is diffusion coefficient matrix . The dynamics for the probability of starting from initial configuration at t = 0 and ending at the final configuration at time t , in terms of the Onsager-Machlup functional , can be formulated [13] , [43] as: . is the diffusion coefficient matrix . The integral over denotes the sum over all possible paths from the state at time to at time . The exponent factor gives the weight of each path . Therefore , the probability of network dynamics from initial state to the final state is equal to the sum of all possible paths with different weights . The is the action and is the Lagrangian or the weight for each path . The path integrals can be approximated with a set of dominant paths , since each path is exponentially weighted , and the other subleading path contributions are often small and can be neglected . Therefore , the dominant path with the optimal weights can be acquired through minimization of the action or Lagrangian . In our case , we identify the optimal paths as the biological paths , i . e . differentiation and reprogramming paths . A network of chemical reactions in noisy fluctuating environments can be addressed by . Here , represents the vector of protein concentration . is the vector for the driving force of chemical reaction . In the cell , there exist external noise and intrinsic noise , which can be significant to the dynamics of the system [42] , so the noise term is added to force item for which Gaussian distribution is assumed , since the force depict only the averaged dynamics of the system . The noise item is satisfied with: and ( for , and for ) . Here is the Dirac delta function , and is diffusion coefficient matrix . The noise term is associated with the intensity of cellular fluctuations either from the environmental external fluctuations or intrinsic fluctuations . Under large expansions , the process follows Brownian dynamics . Following the Brownian dynamical trajectories with multiple different initial conditions by solving the above SDE ( stochastic differential equations ) iteratively , we can obtain the steady state distribution function for the state variable ( relative gene expression value in the gene regulatory network ) , which is relevant to the potential energy function as . Here the partition function . In this way , we acquire the potential energy landscape . | Cellular differentiation and reprogramming have been extensively studied using experimental methods . We developed a landscape and kinetic path approach to explore the global stability of a stem cell developmental network . The cell fates are quantified by the basins of attractions of the underlying landscape . The developmental process can be quantitatively described and uncovered by the biological paths on the landscape from the progenitor state to the differentiation state . This allows us to trace the underlying detailed kinetic process and obtain the recipe for engineering differentiation and reprogramming . By quantifying the landscape topography by the barrier heights and dynamic transition speed , we can evaluate the stability and kinetics of cell fate decision making process of the development and reprogramming . The global sensitivity analysis provided predictions about the effects of the key genes and regulation links of the network on the stability of differentiation and reprogramming process . This can be tested in the experiments . Results from sensitivity analysis and biological paths acquired can be used to guide the differentiation designs or reprogramming tactics . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] | [
"physics",
"biophysics",
"biology",
"computational",
"biology"
] | 2013 | Quantifying Cell Fate Decisions for Differentiation and Reprogramming of a Human Stem Cell Network: Landscape and Biological Paths |
Studies of motor control have almost universally examined firing rates to investigate how the brain shapes behavior . In principle , however , neurons could encode information through the precise temporal patterning of their spike trains as well as ( or instead of ) through their firing rates . Although the importance of spike timing has been demonstrated in sensory systems , it is largely unknown whether timing differences in motor areas could affect behavior . We tested the hypothesis that significant information about trial-by-trial variations in behavior is represented by spike timing in the songbird vocal motor system . We found that neurons in motor cortex convey information via spike timing far more often than via spike rate and that the amount of information conveyed at the millisecond timescale greatly exceeds the information available from spike counts . These results demonstrate that information can be represented by spike timing in motor circuits and suggest that timing variations evoke differences in behavior .
The relationship between patterns of neural activity and the behaviorally relevant parameters they encode is a fundamental problem in neuroscience . Broadly speaking , a neuron might encode information in its spike rate ( the total number of action potentials produced per unit time ) or in the fine temporal pattern of its spikes . In sensory systems as diverse as vision , audition , somatosensation , and taste , prior work has demonstrated that information about stimuli can be encoded by fine temporal patterns , in some cases where no information can be detected in a rate code [1]–[11] . This information present in fine temporal patterns might be decoded by downstream areas to produce meaningful differences in perception or behavior . However , in contrast to the extensive work on temporal coding in sensory systems , the timescale of encoding in forebrain motor networks has not been explored . It is therefore unknown whether the precise temporal coding of sensory feedback could influence spike timing in motor circuits during sensorimotor learning or whether millisecond-scale spike timing differences in motor networks could result in differences in behavior . Although many studies have shown that firing rates can predict variations in motor output [12]–[14] , to our knowledge no studies have examined whether different spiking patterns in cortical neurons evoke different behavioral outputs even if the firing rate remains the same . The songbird provides an excellent model system for testing the hypothesis that fine temporal patterns in cortical motor systems can encode behavioral output . Song acoustics are modulated on a broad range of time scales , including fast modulations on the order of 10 ms [15] , [16] . Vocal patterns are organized by premotor neurons in vocal motor cortex ( the robust nucleus of the arcopallium [RA] ) ( Figure 1a ) , which directly synapse with motor neurons innervating the vocal muscles [14] , [15] , [17] . Bursts of action potentials in RA ( Figure 1b ) are precisely locked in time to production of vocal gestures ( “song syllables” ) , with millisecond-scale precision , suggesting that the timing of bursts is tightly controlled [18] . Similarly , the ensemble activity of populations of RA neurons can be used to estimate the time during song with approximately 10 ms uncertainty [15] . However , although these prior studies demonstrate that the timing of bursts is tightly aligned to the timing of song syllables , it is unknown how variations in the temporal pattern of spikes might encode the trial-by-trial modulations in syllable acoustics known to underlie vocal plasticity [19] . Significantly , biomechanical studies have shown that vocal muscles in birds initiate and complete their force production within a few milliseconds of activation ( far faster than in most mammalian skeletal muscles ) , suggesting that RA's downstream targets can transduce fine temporal spike patterns into meaningful differences in behavior [20] , [21] . However , while it is clear that trial-by-trial variation in spike counts within a 40 ms time window can predict variations in the acoustics of individual song syllables [14] , [22] , it is unknown whether the precise timing of spikes within bursts might be an even better predictor of vocal motor output than spike counts . To quantify the temporal scale of encoding in the vocal motor system , we adapted well-established mathematical tools that have previously been applied to measure information transfer in sensory systems . First , we used a spike train distance metric to quantify the differences between pairs of spike trains produced during different renditions of individual song syllables and a classification scheme to quantify whether distance metrics based on rate or timing yielded the best prediction of acoustic output [23] , [24] . Second , we used model-independent information theoretic methods to compute the mutual information between spike trains and acoustic features of vocal behavior [8] , [10] . Crucially , both techniques measure information present in the neural activity at different timescales , allowing us to quantify the extent to which spike timing in motor cortex predicts upcoming behavior .
We first used a version of the metric-space analysis established by Victor and Purpura to compare the information conveyed by spike rate and spike timing [24] , [25] . As described in Materials and Methods , this analysis quantifies how mutual information between neural activity and motor output depends on a cost parameter q , which quantifies the extent to which spike timing ( as opposed to spike number ) contributes to the dissimilarity , or “distance , ” between spike trains ( Figure 2a ) . The distance between two spike trains is computed by quantifying the cost of transforming one spike train into the other . Here , parameter q , measured in ms−1 , quantifies the relative cost of changing spike timing by 1 ms , as compared to the fixed cost of 1 . 0 for adding or subtracting a spike . Spike train distances are then used to classify iterations of each song syllable into behavioral groups , and the performance of the classifier I ( GP , G ) is used to quantify the mutual information between neural activity and vocal output . Figure 2b shows a representative “rate case , ” where qmax = 0 ( that is , information is maximized at q = 0 , where spike train distances are computed based solely on spike counts ) . As q increases , the performance of the classifier decreases from its maximal value . This means that the best discrimination between behavioral groups ( Figure 1c and 1d ) occurs when only spike counts are used in calculating the distances between pairs of spike trains . In contrast , Figure 2c illustrates a “temporal case . ” In temporal cases , mutual information between neural activity and vocal motor output reaches its peak when q>0 . This indicates that there is better discrimination when spike timings are taken into consideration . Note that in the case shown in Figure 2c , the rate code does not provide significant information about behavioral output ( empty symbol at q = 0 ) . Across all analyses in cases where information was significant at any value of q , including cases where qmax = 0 , the median value of qmax was 0 . 3 , suggesting a high prevalence of temporal cases . Figure 2d shows the prevalence of rate cases and temporal cases in our dataset . As described in Materials and Methods , we assigned the iterations of each song syllable to behavioral groups based either on a single acoustic parameter ( e . g . , pitch , Figure 1c ) or using multidimensional clustering ( 3D acoustics ) ( Figure 1d ) . The different grouping techniques yielded similar results . When syllable acoustics were grouped by clustering in a three-dimensional parameter space ( Figure 2d , blue bars ) , the fraction of temporal cases was significantly greater than the fraction of rate cases ( blue asterisk; p<10−8 , z-test for proportions ) . Similarly , temporal cases significantly outnumbered rate cases when acoustics were grouped using only a single parameter ( pitch , amplitude , or spectral entropy , shown by green , yellow , and red asterisks respectively; p<10−8 ) . Note that in approximately 25% of cases ( between 31 and 36 out of 125 cases across the four analyses shown in Figure 2d ) these analyses did not yield a significant value of I ( GP , G;q ) for any value of q; the fractions in Figure 2d therefore do not sum to unity . In such cases variations in a neuron's pattern of neural activity during a particular syllable are not predictive of variations in the particular acoustic parameter being analyzed . Furthermore , note that an alternate version of this analysis in which the spike train distance measurement was not normalized by the total number of spikes ( see Materials and Methods ) yielded nearly identical results , as shown in Figure S1 . Additionally , we asked whether the proportions of temporal cases shown in Figure 2d were significantly greater than chance by randomizing the spike times in each trial ( Poisson test; Materials and Methods ) . This analysis revealed a significant proportion of temporal cases when vocal acoustics were measured by multidimensional clustering ( 3D acoustics , p<0 . 05 after Bonferroni correction for multiple comparisons indicated by cross in Figure 2d ) , but the same measure fell short of significance when the three acoustic parameters were considered individually ( p = 0 . 06–0 . 24 after Bonferroni correction ) . To measure the maximum information available from the metric-space analysis , we computed Īmax , the average peak information available across all cases ( see Materials and Methods ) . Across all metric-space analyses Īmax was 0 . 10 bits out of a possible 1 . 0 bit . As discussed below , this value suggests that additional information might be available in higher-level spike train features that cannot be captured by metric-space analyses . Additionally , since the proportion of rate and temporal cases did not differ significantly when computed from single- or multiunit data ( p>0 . 07 in all cases; z-tests for proportions ) , we combined data from both types of recording in this as well as subsequent analyses . The similarity between the single- and multiunit datasets likely results from multiunit recordings in this paradigm only reflecting the activity of a single or a very small number of neurons , as discussed previously [14] . Finally , the results of the metric-space analysis were not sensitive to the number of behavioral groups used to classify the iterations of each song syllable . Although our primary analysis uses two behavioral groups ( Figures 1c and 2 ) , as shown in Table 1 we found a similar prevalence of rate and temporal cases when the trials were divided into three , five ( Figure 1d ) , or eight groups . Our metric-space analysis therefore indicates that in most RA neurons , taking the fine temporal structure of spike trains into account provides better predictions of trial-by-trial variations in behavior than an analysis of spike rate alone ( asterisks , Figure 2d ) . Furthermore , at least when vocal outputs are grouped in three-dimensional acoustic space , spike timing can predict vocal acoustics significantly more frequently than would be expected from chance ( cross , Figure 2d ) . However , it remains unclear whether spike timing can provide information about single acoustic parameters ( beyond the 3-D features ) . Finally , since not all cases were temporal , it is also unclear how important this information is on average , rather than in special cases . Answering these questions necessitates the direct method of calculating information , as described below . In the metric-space analysis , not all cases were classified as temporal . Further , when behavior was grouped by a single acoustic parameter rather than in multidimensional acoustic space , the number of temporal cases was not significantly larger than by chance ( Figure 2d , green , yellow , and red plots ) . Thus it still remains unclear to what extent spike timing is important to this system overall , rather than in particular instances . Additionally , a drawback of metric-space analyses is that they assume that a particular model ( metric ) of neural activity is the correct description of neural encoding . As discussed more fully in Materials and Methods , metric-space approaches therefore provide only a lower bound on mutual information [23] , [25] . Put another way , metric-space analyses assume that the differences between spike trains can be fully represented by a particular set of parameters , which in our case include the temporal separation between nearest-neighbor spike times ( Figure 2a ) . However , if information is contained in higher order aspects of the spike trains that cannot be captured by these parameters ( e . g . , patterns that extend over multiple spikes ) , then metric-space analyses can significantly underestimate the true information contained in the neural code . We therefore estimated the amount of information that can be learned about the acoustic group by directly observing the spiking pattern at different temporal resolutions ( Figure 3a ) , without assuming a metric structure , similar to prior approaches in sensory systems [8] , [10] . We used the Nemenman-Shafee-Bialek ( NSB ) estimator to quantify the mutual information [26] , [27] . As described in Materials and Methods , this technique provides minimally biased information estimates , quantifies the uncertainty of the calculated information , and typically requires substantially less data for estimation than many other direct estimation methods [26] . Nevertheless , the NSB technique requires significantly larger datasets than metric-space methods . We therefore directly computed mutual information using the subset ( 41/125 ) of cases where the recordings were long enough to gather sufficient data to be analyzed with this method . We found that the mutual information between neural activity and vocal behavior rose dramatically as temporal resolution increased . As shown in Figure 3b , when averaged across all 41 cases analyzed using the NSB technique , mutual information was relatively low when only spike counts were considered ( i . e . , for ms ) . Across the four methods of grouping trials based on syllable acoustics , mutual information between spike counts and acoustic output ranged from 0 . 007 to 0 . 017 bits ( with standard deviations of ∼0 . 012 ) , which is not significantly different from zero given that all mean information estimates were within ∼1 SD of zero bits . If information about motor output were represented only in spike counts within the 40 ms premotor window , then mutual information at dt<40 would be equal to that found at dt = 40 ( dashed lines in Figure 3b ) ; note that this is true despite the increase in word length at smaller dt [8] , [10] . However , in all analyses mutual information increased as time bin size dt decreased and reached a maximum value at dt = 1 ms , the smallest bin size ( and thus greatest temporal resolution ) we could reliably analyze . At 1 ms resolution , mutual information ranged from 0 . 131 to 0 . 188 ( with standard deviations of ∼0 . 06 ) bits across the four analyses performed . These values of mutual information correspond to d′ values near zero at dt = 40 ms and to d′ values between 0 . 9 and 1 . 1 at 1-ms resolution ( Figure 3b , right-hand axis ) . Additionally , although Figure 3b shows the results of analyzing data from single-unit and multiunit recordings together , we found very similar results when single- and multiunit data were considered separately ( see Figure S2 and Data S1 and S2 ) . These results indicate that far more information about upcoming vocal behavior is available at millisecond timescales and suggest that small differences in spike timing can significantly influence motor output . Therefore , although in some individual cases more information may be available from a rate code in the metric-space analysis ( Figure 2d , empty bars ) , across the population of RA neurons much more information is present in millisecond-scale spike timing . Similarly , note that although in the direct information calculations ( Figure 3 ) mutual information is averaged across all neural recordings , the information at different timescales varied across different neurons ( e . g . , information at dt = 5 ms in some recordings was greater than information at dt = 1 ms in other recordings ) . The low mutual information present at dt = 40 ms , for example , therefore reflects the fact that datasets with higher ( relative to other datasets ) information in the spike count are greatly outnumbered by cases with very low information at dt = 40 . We performed further analysis to investigate whether the information present at dt = 1 ms reflects differences in burst onset times or differences in the pattern of spikes within bursts ( either of which could account for our results ) . To do so , we performed an alternate analysis ( see Materials and Methods , “Inter-Spike Interval Analysis” ) in which we calculated the mutual information conveyed by sequences of inter-spike intervals ( at a temporal resolution of 1 ms ) rather than by absolute spike times . Representing neural activity in this way removes all information about burst onset time and instead quantifies only the mutual information between inter-spike intervals and the acoustic output . In this alternate analysis , information estimates ranged from 0 . 148 to 0 . 172 ( with standard deviations of ∼0 . 06 ) bits across the four different behavioral groupings , and thus were nearly identical to those obtained at dt = 1 ms in our primary analysis . This finding suggests that the information contained in spike timing patterns is carried primarily through the structure of spike timing within bursts rather than by burst onset times . The results shown in Figure 3 demonstrate that millisecond-scale differences in spike timing can encode differences in behavior . To highlight these timing differences , we examined particular “words” ( spike patterns ) and considered how different timing patterns could predict vocal acoustics . Figure 4a and 4b each show eight different words from two different single-unit recordings , color-coded according to the behavioral group in which each word appears most frequently . All words shown in Figure 4 contain the same number of spikes , and thus are identical at the time resolution of dt = 40 ms ( Figure 3a ) . In the example shown in Figure 4a , a distinct set of spike timing patterns predicts the occurrence of low-pitched ( group 1 ) or high-pitched ( group 2 ) syllable renditions . In Figure 4b , behavioral groupings are performed in the three-dimensional acoustic space and similarly show that distinct spike timing patterns can predict vocal acoustics . In some cases , the timing patterns associated with behavioral groups share intuitive features . For example , the words associated with higher pitch in Figure 4a ( blue boxes in grid ) have shorter inter-spike intervals ( but the same total number of spikes ) compared with words associated with lower pitch ( Figure 4a , red boxes ) , suggesting that fine-grained interval differences drive pitch variation . However , in other cases ( e . g . , Figure 4b ) no such common features were apparent . Future studies incorporating realistic models of motor neuron and muscle dynamics are therefore required to understand how the precise timing patterns in RA can evoke differences in vocal behavior . We compared the maximum information available from the metric-space analysis ( see Materials and Methods ) , which is Īmax = 0 . 10 , to the information available at the smallest dt = 1 ms in the direct information calculation , MINSB = 0 . 16 bits . Reassuringly , the peak information available from the direct method is of the same order of magnitude but somewhat larger than that computed independently in the metric-space analysis . This finding points at consistency between the methods and yet suggests that additional information may be present in higher order spike patterns that cannot be accounted for by a metric-space analysis , namely in temporal arrangements of three or more spikes . ( Note , however , that in a small number of cases we were unable to compute mutual information at the finest timescales using the direct method , possibly leading to small biases in our estimates of mutual information at dt = 1 ms; see Materials and Methods ) . Similarly , a common technique in metric-space analysis is to estimate the “optimal time scale” of encoding as 1/qmax ( although other authors suggest that such estimates may be highly imprecise [25] ) . In our dataset , the median value of qmax was 0 . 3 ms−1 , suggesting that spike timing precision is important down to 1/qmax∼1 ms , which is again in agreement with the direct estimation technique .
We computed the mutual information between premotor neural activity and vocal behavior using two well-established computational techniques . A metric-space analysis demonstrated that spike timing provides a better prediction of vocal output than spike rate in a significant majority of cases ( Figure 2 ) . A direct computation of mutual information , which was only possible in the subset of recordings that yielded relatively large datasets , revealed that the amount of information encoded by neural activity was maximal at a 1 ms timescale , while the average information available from a rate code was insignificant ( Figure 3 ) . It also suggested that information in the spike trains may be encoded in higher order spike patterns . Although previous studies have shown that bursts in RA projection neurons are aligned in time to the occurrence of particular song syllables [15] , [18] , ours is the first demonstration to our knowledge that variations in spike timing within these bursts can predict trial-by-trial variations in vocal acoustics . These acoustic variations are thought to underlie vocal learning ability in songbirds . A number of studies have demonstrated that nucleus LMAN ( the lateral magnocellular nucleus of the anterior nidopallium ) , the output nucleus of the anterior forebrain pathway ( AFP ) and an input to RA ( Figure 1a ) , both generates a significant fraction of vocal variability and is required for adaptive vocal plasticity in adult birds [28]–[30] . A significant question raised by our results therefore concerns the extent to which LMAN inputs can alter the timing of spikes in RA . Recent work has shown that spike timing patterns in LMAN neurons encode the time during song [31] . Future studies might address whether the observed patterns in LMAN spiking can also predict acoustic variations , and lesion or inactivation experiments could quantify changes in the distribution of firing patterns in RA after the removal of LMAN inputs [32] . Our results indicate that spike timing in cortical motor networks can carry significantly more information than spike rates . Equivalently , these findings suggest that limiting the analysis of motor activity to spike counts can lead to drastic underestimates of information . This contrast is illustrated by a comparison of the present analysis and our prior study examining correlations between premotor spike counts and the acoustics of song syllables [14] . In that earlier study , we found that spike rate predicted vocal output in ∼24% of cases , a prevalence similar to the proportion of rate cases observed in the metric-space analysis and far smaller than the prevalence of temporal cases ( Figure 2 ) . Similarly , direct computations of mutual information ( Figure 3 ) show that a purely rate-based analysis would detect only a small fraction of the information present in millisecond-scale timing . Therefore our central finding—that taking spike timing into account greatly increases the mutual information between neural activity and behavior—suggests that correlation and other rate-based approaches to motor encoding might in some cases fail to detect the influence of neural activity on behavior . As shown in Figure 3 , we found that spike timing at the 1 ms timescale provides an average of ∼0 . 16 bits out of a possible 1 . 0 bit of information when discriminating between two behavioral groups . While this value is of course less than the maximum possible information , it is important to note that this quantity represents the average information available from a single neuron . A number of studies in sensory systems have demonstrated that ensembles of neurons can convey greater information than can be obtained from single neurons [33] . While our dataset did not include sufficient numbers of simultaneous recordings to address this issue , future analyses of ensemble recordings could test the limits of precise temporal encoding in the motor system . Temporal encoding in the motor system could also provide a link between sensory processing and motor output . Prior studies have shown that different auditory stimuli can be discriminated on the basis of spike timing in auditory responses [11] , [34] , [35] , including those in area HVC , one of RA's upstream inputs [36] . Our results demonstrate that in songbirds , temporally precise encoding is present at the motor end of the sensorimotor loop . Propagating sensory-dependent changes in spike timing into motor circuits during behavior might therefore underlie online changes in motor output in response to sensory feedback [37] , [38] or serve as a substrate for long-term changes in motor output resulting from spike timing-dependent changes in synaptic strength [19] , [39]–[41] . While the existence of precise spike timing is strongly supported for a variety of sensory systems , a lingering question is how downstream neural networks could use the information that is present at such short timescales , and hence whether the animal's behavior could be affected by the details of spike timing . Although theoretical studies have suggested how downstream neural circuits could decode timing-based spike patterns in sensory systems [42] , the general question of whether the high spiking precision in sensing , if present , is an artifact of neuronal biophysics or a deliberate adaptation remains unsettled [43] . In motor systems , in contrast , spike timing differences could be “decoded” via the biomechanics of the motor plant , thereby transforming differences in spike timing into measureable differences in behavior . In a wide range of species [44]–[47] , the amplitude of muscle contraction can be strongly modulated by spike timing differences in motor neurons ( i . e . , neurons that directly innervate the muscles ) owing to strong nonlinearities in the transformation between spiking input and force production in muscle fibers . Furthermore , biomechanical studies have shown that vocal muscles in birds have extraordinarily fast twitch kinetics and can reach peak force production in less than 4 ms after activation [20] , [21] , suggesting that the motor effectors can transduce millisecond-scale differences in spike arrival into significant differences in acoustic output . Finally , in vitro and modeling studies have quantified the nonlinear properties in the songbird vocal organ , demonstrating that small differences in control parameters can evoke dramatic and rapid transitions between oscillatory states , suggesting again that small differences in the timing of motor unit activation could dramatically affect the acoustics of the song [48] , [49] . Future studies that model the dynamics of brainstem networks downstream of RA as well as the mechanics of the vocal organ could address how particular spiking patterns in RA ( such as those shown in Figure 4 ) might drive variations in acoustic output . Our results demonstrate that the temporal details of spike timing , down to 1 ms resolution , carry about ten times as much information about upcoming motor output compared to what is available from a rate code . This is in marked contrast to sensory coding [8] , [10] , where the information from spike patterns at millisecond resolution is often about double that available from the rate alone . For this reason , the most striking result of our analysis might be that precise spike timing in at least some motor control systems appears to be even more important than in sensory systems . In summary , although future work in both sensory and motor dynamics is needed to fully explicate how differences in spike timing are mapped to behavioral changes , our findings , in combination with previous results from sensory systems , represent the first evidence , to our knowledge , for the importance of millisecond-level spiking precision in shaping behavior throughout the sensorimotor loop .
All procedures were approved by the Emory University Institutional Animal Care and Use Committee . To measure the information about vocal output conveyed by motor cortical activity at different timescales , we recorded the songs of Bengalese finches while simultaneously collecting physiological data from neurons in RA . We then quantified the acoustics of individual song syllables and divided the iterations of each syllable into “behavioral groups” on the basis of acoustic features such as pitch , amplitude , and spectral entropy . Mutual information was then computed using two complementary techniques . First , we used a metric-space analysis [23] to quantify how well the distance between pairs of spike trains can be used to classify syllable iterations into behavioral groups . Second , we used a direct calculation of mutual information [10] , [26] , [27] , [50] to produce a minimally biased estimate of the information available at different timescales . Single-unit and multiunit recordings of RA neurons were collected from four adult ( >140 days old ) male Bengalese finches using techniques described previously [14] . All procedures were approved by the Emory University Institutional Animal Care and Use Committee . Briefly , an array of four or five high-impedance microelectrodes was implanted above RA nucleus . We advanced the electrodes through RA using a miniaturized microdrive to record extracellular voltage traces as birds produced undirected song ( i . e . , no female bird was present ) . We used a previously-described spike sorting algorithm [14] to classify individual recordings as single-unit or multiunit . In total , we collected 53 RA recordings ( 19 single-unit , 34 multiunit ) , which yielded 34 single-unit and 91 multiunit “cases , ” as defined below . Based on the spike waveforms and response properties of the recordings , all RA recordings were classified as putative projection neurons that send their axons to motor nuclei in the brainstem [14] , [15] , [51] . A subset of these recordings has been presented previously as part of a separate analysis [14] . We quantified the acoustics of each song syllable as described in detail previously [14] . Briefly , for each syllable we measured vocal acoustics at a particular time ( relative to syllable onset ) when spectral features were well defined ( Figure 1b , red line ) . Syllable onsets were defined on the basis of amplitude threshold crossings after smoothing the acoustic waveform with a square filter of width 2 ms; therefore the measurement error of our quantification of syllable onset time is on the order of milliseconds . Note that this uncertainty cannot account for our results , since millisecond-scale jitter in syllable onset ( and thus burst timing ) will decrease , rather than increase , the amount of information present at fine timescales . We quantified the fundamental frequency ( which we refer to here as “pitch” ) , amplitude , and spectral entropy by analyzing the acoustic power spectrum at the specified measurement time during each iteration of a song syllable . We selected these three acoustic features because they capture a large percentage of the acoustic variation in Bengalese finch song [14] . In the example syllable illustrated in Figure 1b ( top ) , the band of power at ∼4 kHz is the fundamental frequency . Furthermore , each sound recording was inspected for acoustic artifacts unrelated to vocal production and these trials , which constituted less than 1% of the total data , were discarded to minimize potential measurement error . For each iteration of each syllable , we analyzed spikes within a temporal window prior to the time at which acoustic features were measured . The width of this window was selected to reflect the latency with which RA activity controls vocal acoustics . Although studies employing electrical stimulation have produced varying estimates of this latency [52] , [53] , a single stimulation pulse within RA modulates vocal acoustics with a delay of 15–20 ms [54] . We therefore set the premotor window to begin 40 ms prior to the time when acoustic features were measured and to extend until the measurement time ( Figure 1b , red box ) . This window therefore includes RA's premotor latency [14] , [22] and allows for the possibility that different vocal parameters have different latencies . While grouping spike trains is straightforward in many sensory studies , where different stimuli are considered distinct groups , we face the problem of continuous behavioral output in motor systems . We took two approaches to binning continuous motor output into discrete classes . First , we considered only a single acoustic parameter and divided the trials into equally sized groups using all of the data . For example , Figure 1c shows trials divided into two behavioral groups based on one parameter ( pitch ) . In addition to pitch , separate analyses also used sound amplitude or spectral entropy to divide trials into groups . In the second approach ( which we term “3D acoustics” ) ( Figure 1d ) , we used k-means clustering to divide trials into groups . Clustering was performed in the three-dimensional space defined by pitch , amplitude , and entropy , with raw values transformed into z-scores prior to clustering . Note that both approaches allow us to divide the dataset into an arbitrary number of groups ( parameter N , see “Discrimination analysis” below ) . Our primary analysis divided trials into N = 2 groups since a smaller N increases statistical power by increasing the number of data points in each group . However , alternate analyses using greater N yielded similar conclusions ( see Results ) . In previous studies , metric-space analysis has been used to probe how neurons encode sensory stimuli ( for a review , see [55] ) . The fundamental idea underlying this approach is that spike trains from different groups ( e . g . , spikes evoked by different sensory stimuli ) should be less similar to each other than spike trains from the same group ( spikes evoked by the same sensory stimulus ) . In the present study , we adapt this technique for use in the vocal motor system to ask how neurons encode trial-by-trial variations in the acoustic structure of individual song syllables . To do so , we divide the iterations of a song syllable into “behavioral groups” based on variations in acoustic structure ( Figure 1c ) . We then construct a “classifier” to ask how accurately each spike train can be assigned to the correct behavioral group using a distance metric that quantifies the dissimilarity between pairs of spike trains [24] . As described in detail below , the classifier attempts to assign each trial to the correct behavioral group on the basis of the distances between that trial's spike train and the spike trains drawn from each behavioral group . Crucially , the distance metric is parameterized by q , which reflects the importance of spike timing to the distance between two spike trains . This method therefore allows us to evaluate the contribution of spike timing to the performance of the classifier , and thus to the information contained in the spike train about the behavioral group . In addition to the metric-space analysis described above , we also directly calculated the mutual information between song acoustics and neural activity [10] . Whereas metric-space analysis makes strong assumptions about the structure of the neural code , the direct approach is model-independent [10] , [56] . Specifically , spike train distance metrics assume that spike trains that have spike timings closer to each other are linearly more similar than spike trains whose timings are more different . As with all assumptions , the methods gain extra statistical power if they are satisfied , but they may fail if the assumptions do not hold . The direct method simply considers distinct patterns of spikes at each timescale ( which can vary in total spike number , the pattern of inter-spike intervals , burst onset time , etc . ) , without assigning importance to specific differences . Crucially , direct methods allow us to estimate the true mutual information , whereas the mutual information computed from a metric-space analysis represents only a lower bound on this quantity [3] . However , because the direct method is a model-independent approach that does not make strong assumptions about the neural code , it requires larger datasets to achieve statistical power . To determine whether there is information about acoustics in the precise timing of spikes , we compared the information between neural activity and behavioral group following discretization of the spike trains at different time resolutions . For a time bin of size dt , each ms-long spike train was transformed into a “word” with 40/dt symbols , where different symbols represent the number of spikes per bin . The mutual information is simply the difference between the entropy of the total distribution of words and the average entropy of the words given the behavioral group : ( 3 ) could be quantified exactly if the true probability distributions , , and were known: ( 4 ) However , estimating these distributions from finite datasets introduces a systematic error ( “limited sample bias” or “undersampling bias” ) that must be corrected [57] . There are several methods to correct for this bias , but most assume that there is enough data to be in the asymptotic sampling regime , where each typical response has been sampled multiple times . As we increase the time resolution of the binning of the spike train , the number of possible neural responses increases exponentially , and we quickly enter the severely undersampled regime where not every “word” is seen many times , and , in fact , only a few words happen more than once ( which we term a “coincidence” in the data ) . We therefore employed the NSB entropy estimation technique [26] , [27] , which can produce unbiased estimates of the entropies in Equation 3 even for very undersampled datasets . The NSB technique uses a Bayesian approach to estimate entropy . However , instead of using a classical prior , for which all values of the probability of spiking are equally likely , NSB starts with the a priori hypothesis that all values of the entropy are equally likely . This approach has been shown to reliably estimate entropy in the severely undersampled regime ( where the number of trials per group is much less than the cardinality of the response distribution ) provided that the number of coincidences in that data is significantly greater than one . This typically happens when the number of samples is only about a square root of what would be required to be in the well-sampled regime [26] , [50] . This method often results in unbiased estimates of the entropy , along with the posterior standard deviation of the estimate , which can be used as an error bar on the estimate [50] . On the other hand , we know that no method can be universally unbiased for every underlying probability distribution in the severely undersampled , square-root , regime [58] . Thus there are many underlying distributions of spike trains for which NSB would be biased . Correspondingly , the absence of bias cannot be assumed and must instead be verified for every estimate , which we do as described below . A priori , we restricted our analysis to cases in which the number of trials was large enough ( >200 ) so that the number of coincidences would likely be significantly greater than 1 . Of our 125 datasets , 41 passed this size criterion . We emphasize that no additional selection beyond the length of recording was done . Since recording length is unrelated to the neural dynamics , we expect that this selection did not bias our estimates in any way . The NSB analysis was performed using N = 2 behavioral groups , since increasing the number of groups greatly decreased the number of coincidences and increased the uncertainty of the entropy estimates ( not shown ) . Additionally , because NSB entropy estimation assumes that the words are independent samples , we verified that temporal correlations in the data are low . To do this , we used NSB to calculate the entropy of four different halves of each dataset: the first half of all trials , the second half , and the two sets of every other trial , where the second set is offset from the first set by one trial . We found that the difference in mean entropy between the first half and second half data was very similar to the difference between the two latter sets . Any temporal correlations in our neural data are therefore very low and thus unlikely to affect entropy estimation , and the information at high spiking precision that we observe cannot just be attributed to modulation occurring on a longer time scale . To make sure that the NSB estimator is unbiased for our data , we estimated each conditional and unconditional entropy from all available N samples , and then from αN , α<1 , samples . Twenty-five random subsamples of size αN were taken and then averaged to produce . We plotted versus 1/α and checked whether all estimates for 1/α→1 agreed among themselves within error bars , indicating no empirical sample-size dependent bias [8] , [10] . At temporal resolutions in the tens of milliseconds , virtually all cases showed no sample size-dependent drift in the entropy estimates , and hence the estimates from full data were treated as unbiased . As the temporal resolution increased to dt = 1 , bias was visible in some cases . However , the bias could often be traced to the rank-ordered distribution of words not matching the expectations of the NSB algorithm . Specifically , some of the most common words occurred much more often than expected from the statistics of the rest of the words . Since NSB uses frequencies of common , well-sampled words to extrapolate to undersampled words , such uncommonly frequent outliers can bias entropy estimation [27] . To alleviate the problem , we followed [8] and partitioned the response distribution in a way such that the most common word was separated from the rest when it was too frequent ( with “too frequent” defined as >2% of all words ) . We use to denote the frequency of the most common word and to denote the frequency of all other words . We then used the additivity of entropy , ( 5 ) to compute the total entropy by first estimating the entropy of the choice between the most common word and all others , , and the entropy of all of the data excluding the most common word , S2 , independently using the NSB method ( the entropy of the single most common word , S1 , is zero ) . The error bars were computed by summing the individual error bars in Equation 5 in quadratures . Once the most common word was isolated in the cases in which it was “too frequent , ” the resulting entropies were checked again for bias using the subsampling procedure explained above . In the majority of cases in which sample-size dependent bias was detected , this bias was removed through the partition-based entropy correction and the resulting entropy estimates were therefore empirically verified as being unbiased . In other biased cases , there were no uncharacteristically common words , and we could not perform the partition-based entropy correction . Furthermore , at the highest temporal resolutions , there were a few cases that did not have enough coincidences for the NSB algorithm to produce an estimate of entropy at all . These cases were excluded from the following steps . The total fraction of entropy estimates found to be unbiased ( i . e . , was either unbiased to begin with or bias was successfully corrected using partitioning ) was 100% at dt = 40 ms and decreased monotonically to 72% at dt = 2 ms and 58% at dt = 1 ms . Thus , we were unable to correct entropy biases in a minority of entropy measurements at fine timescales . However , as described below ( and illustrated in Figure S2 ) , our results were nearly identical when we removed all cases with biased entropy from our dataset , demonstrating that any residual biases cannot account for our results . We then averaged the mutual information between the spike train and the acoustic group over all cases , weighing contribution of each case by the inverse of its respective posterior variance . The variance of the mean was similarly estimated . As an example , the ten biased cases at dt = 1 for syllables grouped by pitch that could not be corrected had few coincidences and hence large error bars , so that the average value of the inverse variance for these ten cases was 14% of the average inverse variance for the 20 cases that were originally unbiased , or corrected to unbiased . Therefore , since 14% of ten cases is approximately one case , these ten biased cases together contributed about as much as one of the 20 unbiased or bias-corrected cases in the final calculation of the average mutual information . These biased cases thus cannot contribute significantly to bias in the average mutual information . This is true for the other behavioral groupings we performed as well . To confirm this , we further performed an alternate analysis ( Figure S2d ) in which we excluded the biased cases that could not be corrected from the final calculation of mutual information . This alternate analysis yielded very similar results as the original analysis , with a large increase in mutual information at finer temporal resolution . Our results therefore cannot be ascribed to the inclusion of some cases in which entropy biases could not be corrected . Finally , note that as described above we of necessity excluded from our analysis cases in which no coincidences occurred ( since the NSB algorithm estimates entropy by counting the number of coincidences ) . This occurred in a small fraction of cases ( 13% of entropy estimates at dt = 1 ms , 5% of estimates at dt = 2 ms , <1% of estimates at dt = 5 ms , and 0% at dt = 10 ms or greater ) . Since cases in which no coincidences occur will likely be those with low mutual information , excluding these cases may have introduced a slight upward bias to our information estimates at fine timescales . However , information in such cases would be likely to have large uncertainties and thus would make insignificant contributions to the weighted average , and furthermore the number of excluded cases at timescales greater than 1 ms ( crucially , including dt = 2 ms ) is negligible . In any event , these excluded cases cannot account for either the non-zero information at fine timescales or for the dramatic increase in information observed as temporal resolution increases . As discussed above , the metric-space and direct methods of computing mutual information differ in their underlying assumptions about the statistical structure of the neural code , and the metric-space method can only produce lower bounds on the signal-response mutual information . Therefore , comparing the values of information computed by the two methods is prone to various problems of interpretation . It is nevertheless instructive to ask whether the direct method estimates greater mutual information than the metric-space analysis , and thus if patterns of multiple spikes carry additional information beyond that in spike pairs , which is discoverable by the metric-space method . To answer this , we calculated the peak metric space information Īmax which is the mean of Imax across all cases . This is the upper bound on the information detectable through the metric-space method , as the information is maximized for each case independently , rather than finding a single optimal q for all cases . | A central question in neuroscience is how neurons use patterns of electrical events to represent sensory information and control behavior . Neurons might use two different codes to transmit information . First , signals might be conveyed by the total number of electrical events ( called “action potentials” ) that a neuron produces . Alternately , the timing pattern of action potentials , as distinct from the total number of action potentials produced , might be used to transmit information . Although many studies have shown that timing can convey information about sensory inputs , such as visual scenery or sound waveforms , the role of action potential timing in the control of complex , learned behaviors is largely unknown . Here , by analyzing the pattern of action potentials produced in a songbird's brain as it precisely controls vocal behavior , we demonstrate that far more information about upcoming behavior is present in spike timing than in the total number of spikes fired . This work suggests that timing can be equally ( or more ) important in motor systems as in sensory systems . | [
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] | 2014 | Millisecond-Scale Motor Encoding in a Cortical Vocal Area |
Neutrophils and macrophages provide the first line of cellular defence against pathogens once physical barriers are breached , but can play very different roles for each specific pathogen . This is particularly so for fungal pathogens , which can occupy several niches in the host . We developed an infection model of talaromycosis in zebrafish embryos with the thermally-dimorphic intracellular fungal pathogen Talaromyces marneffei and used it to define different roles of neutrophils and macrophages in infection establishment . This system models opportunistic human infection prevalent in HIV-infected patients , as zebrafish embryos have intact innate immunity but , like HIV-infected talaromycosis patients , lack a functional adaptive immune system . Importantly , this new talaromycosis model permits thermal shifts not possible in mammalian models , which we show does not significantly impact on leukocyte migration , phagocytosis and function in an established Aspergillus fumigatus model . Furthermore , the optical transparency of zebrafish embryos facilitates imaging of leukocyte/pathogen interactions in vivo . Following parenteral inoculation , T . marneffei conidia were phagocytosed by both neutrophils and macrophages . Within these different leukocytes , intracellular fungal form varied , indicating that triggers in the intracellular milieu can override thermal morphological determinants . As in human talaromycosis , conidia were predominantly phagocytosed by macrophages rather than neutrophils . Macrophages provided an intracellular niche that supported yeast morphology . Despite their minor role in T . marneffei conidial phagocytosis , neutrophil numbers increased during infection from a protective CSF3-dependent granulopoietic response . By perturbing the relative abundance of neutrophils and macrophages during conidial inoculation , we demonstrate that the macrophage intracellular niche favours infection establishment by protecting conidia from a myeloperoxidase-dependent neutrophil fungicidal activity . These studies provide a new in vivo model of talaromycosis with several advantages over previous models . Our findings demonstrate that limiting T . marneffei’s opportunity for macrophage parasitism and thereby enhancing this pathogen’s exposure to effective neutrophil fungicidal mechanisms may represent a novel host-directed therapeutic opportunity .
Pathogenic fungal infections represent an important but widely overlooked human disease burden [1] . Invasive fungal infections , primarily affecting immunocompromised individuals , carry a high rate of mortality , despite the availability of antifungal drugs [2] . A uniting biological feature of a number of pathogenic fungi is dimorphism: modulation of morphological form in response to environmental cues . Most dimorphic fungal pathogens , such as Blastomyces dermatitidis , Histoplasma capsulatum , Paracoccidioides brasiliensis and Talaromyces marneffei ( formerly Penicillium marneffei ) , exist in the environment in hyphal form , but convert to yeast growth during human infection [3–6] . Conversely , some dimorphic fungal pathogens , such as Candida albicans , exist as commensal yeasts , but under permissive conditions can cause invasive infection upon extension of germ tubes and subsequent hyphal growth [7] . Other common opportunistic fungal pathogens including Cryptococcus neoformans , which causes disseminated intracellular yeast infection leading to meningoencephalitis [8] , and Aspergillus fumigatus , which causes serious pulmonary infections in immunocompromised patients [9] maintain a single morphological state despite having the capacity to change under certain circumstances , such as mating or development [10 , 11] . Professional phagocytes of the innate immune system ( neutrophils and macrophages ) provide the first line of defence against fungal infection [12] . For T . marneffei , initial interactions are characterised by phagocytosis of conidia by leukocytes in the lung , followed by leukocyte-facilitated hematogenous dissemination [13] . For Cryptococcus , which infects as a yeast , macrophages also may play a role in pathogen dissemination [14] . Many fungal pathogens , such as H . capsulatum and T . marneffei , proliferate within macrophages as yeast [15–17] , while invasive hyphae formed by A . fumigatus cannot be phagocytosed and elicit a neutrophil-dominated response , including generation of reactive oxygen species and formation of neutrophil extracellular traps ( NETs ) [18] . Although much has been learnt from mammalian infection models regarding disease progression , these models are inherently limited with regards to observing host-pathogen interactions in vivo and in real-time . Zebrafish embryos and larvae provide an excellent platform for high-content imaging of early host-pathogen interactions , especially since the generation of transgenic strains labelling neutrophils [19–21] and macrophages [22–24] . The zebrafish toolbox has proven useful for modelling bacterial infection [25] , particularly tuberculosis [26 , 27] . Zebrafish have been utilised for modelling and imaging infections with the human fungal pathogens Candida spp . [28–31] , Aspergillus spp . [32–35] , Cryptococcus spp . [36–38] , and Mucor spp . [39] . Such studies have provided significant new insights into the molecular and cell biology of host-pathogen interaction during these infections . Here we present a new in vivo zebrafish model of talaromycosis ( formerly called penicilliosis ) that is caused by T . marneffei , a thermally dimorphic , opportunistic pathogen of humans . T . marneffei is capable of switching between a saprophytic hyphal growth form and a pathogenic yeast form in response to temperature and host cues including factors such as pH , salt concentration , calcium signalling and iron availability [40–43] . In the host , it primarily occupies the phagocyte niche but extracellular fungal cells are also evident , presumably due to host cell death . For these studies , we have exploited the ectothermic nature of the zebrafish host . Although zebrafish are customarily held at 28°C in the laboratory , their normal development is documented for temperatures up to 33°C [44] , and in the wild populations are found at temperatures up to 38 . 6°C [45] . We model an invasive hyphal form of T . marneffei infection unique to this zebrafish model ( at 28°C ) and a disseminated intracellular yeast form of infection resembling the human disease ( at 33°C ) . These studies show that thermal dimorphism can be overridden by intracellular cues . We demonstrate a protective , G-CSFR-dependent expansion of neutrophils during protracted infection . Our studies particularly focused on the initial period of infection establishment , when fungal spores first encounter host leukocytes . These studies show that macrophages provide a protective niche for fungal conidia during infection establishment , whereas neutrophils exhibit a strongly fungicidal activity towards conidia that is myeloperoxidase dependent .
To optimise modelling of talaromycosis in zebrafish larvae , a variety of pathogen inoculation approaches and larval culture conditions were tested . At 28°C , immersion of 24 hpf embryos in T . marneffei conidia delivered within the chorion did not result in infection ( 0% ( 0/55 ) infected and 2% ( 1/56 ) death after 24 hours of co-incubation ) . However , reproducible local infection was established by intramuscular or 4th ventricle injections , while systemic infection was initiated by intravenous inoculation into the Duct of Cuvier at 52 hpf . Histology demonstrated fungal conidia in close proximity to vascular walls immediately following inoculation ( Fig 1A ) , their immediate phagocytosis by leukocytes ( Fig 1B ) and their germination within 1 day post infection ( dpi ) , including within intravascular leukocytes ( Fig 1C ) . From these initial dose-finding studies , it was established that intravenous inoculations of 100–150 viable conidia established a systemic infection that resulted in <25% mortality during the course of the experiment , with stable fungal colony-forming unit ( CFU ) counts until 4 dpi , when CFU counts declined ( Fig 1D and 1E ) . This indicates that for infective challenges in this dose range , the zebrafish innate immune system alone is capable of controlling a T . marneffei infection . As expected , considering the thermal dimorphism of T . marneffei [3] , during the later days of infection extension of fungal germ tubes within phagosomes elongated some leukocytes ( Fig 1C and S1D Fig ) . In some embryos , there was also invasive filamentous growth within various tissues ( S1C and S1H Fig ) , including the brain ( S1E Fig ) . By 3–4 dpi , accumulation of leukocytes at infection foci resulted in abscess and/or a tight collection of cells reminiscent of the “early granuloma” observed in larval M . marinum infection [46] ( S1C and S1F–S1H Fig ) . Although this form of invasive filamentous infection digressed from the predominantly yeast-form infection observed in human talaromycosis , it did confirm that in vivo infection per se did not provide sufficient cues for T . marneffei to switch to its yeast form . To model infection with yeast morphology T . marneffei , as observed in human disease [47] , infection was also established at 33°C [3] ( Fig 2 ) . This is a temperature which zebrafish tolerate well [26 , 29 , 44 , 48–51] , and at which the yeast morphological switch occurs in vitro [40] . Furthermore , it reflects the temperatures of human torso and extremity skin ( 31–34°C ) [52–54] , a tissue characteristically involved in disseminated talaromycosis and where intracellular yeast forms are readily found on biopsy [55 , 56] . Despite an extensive literature of zebrafish experimentation at 33°C , we specifically verified experimentally that zebrafish phagocyte function is intact at 33°C . We conducted comparative experiments at 28°C and 33°C with Aspergillus fumigatus , a fungal pathogen that is not thermally dimorphic , and has been previously studied in zebrafish at 28°C [32–35] . We compared four parameters of phagocyte function at the two temperatures: ( 1 ) the initial migratory response of neutrophils and macrophages to the site of conidial inoculation ( S2A Fig ) ; ( 2 ) the initial phagocytic response of neutrophils and macrophages to A . fumigatus conidia following their arrival at the site of conidial inoculation ( S2B Fig ) ; ( 3 ) the myelopoietic response to inoculation with A . fumigatus conidia over a 4-day period ( S3 Fig ) ; ( 4 ) the impact of morpholino-induced perturbations of leukocyte abundance on the germination of A . fumigatus 24 hpi ( S4 Fig ) . Furthermore , for scenarios ( 1–3 ) , we conducted these experiments with both live conidia and dead conidia ( killed by γ-irradiation ) to address the possibility that , had any variation been observed between temperatures , that this might be due to different rates of conidial germination and/or proliferation at the two temperatures . For each of these 13 experimental scenarios , no consistent significant difference was observed in any endpoint ( S2–S4 Figs ) . 7/53 pairwise comparisons testing the null hypothesis “that there was no temperature-dependent difference in the endpoint” generated a p-value ( corrected for multiple comparisons ) that was <0 . 05 . We therefore concluded that phagocyte numbers and function were intact at 33°C and that experiments conducted at 33°C would make a valid contribution to an exploration of the role of phagocyte function in the pathogenesis of T . marneffei infection establishment . For infections that proceeded at 33°C , histology again demonstrated leukocyte conidia phagocytosis ( Fig 2Ai ) and T . marneffei growth within leukocytes and tissues ( Fig 2Aii–2Aiv ) . Cytospun leukocyte preparations at 4 dpi of 33°C infections demonstrated germinated , proliferating fungal cells within leukocytes . Strikingly , at this temperature , T . marneffei assumed an elongated , septate filamentous form within all observed neutrophils , whereas within macrophages , fission yeast morphology ( recognized as characteristic elongated forms with a medial septum [57] ) predominated ( 7/9 macrophages ) ( Fig 2B ) . We confirmed that also in mammalian macrophages at 33°C , T . marneffei assumed this characteristic fission yeast morphology ( S5 Fig ) . Collectively , these morphologically-based observations indicate that the different intracellular milieu of each phagocyte type can differentially influence T . marneffei form , and that at 33°C , the macrophage milieu favours the transition to the yeast form . At 33°C , despite a vigorous granulopoietic response to infection ( Fig 2E ) , fungal CFU numbers did not decrease at 4 dpi ( Fig 2C ) , and death from infection was increased ( Fig 2D ) , suggesting enhanced fungal viability at this temperature . To facilitate live imaging of host-pathogen interactions , infection was performed using larvae expressing neutrophil- and/or macrophage-specific fluorescent transgenes . Quantification of leukocyte numbers over 4 days of infection at 28°C revealed a dramatic increase from 2–4 dpi in both neutrophil and macrophage numbers in response to active infection ( Fig 3A–3C ) . By 4 dpi , macrophage and neutrophil numbers had increased to levels 150% and 260% respectively of those in uninfected controls , while pathogen numbers decreased by 65% over the same period ( Fig 1D ) , indicating that the vigorous myelopoietic response was a component of an effective host response that controlled the infective burden . A vigorous granulopoietic response was also seen over 4 days of infection at 33°C ( Fig 2E ) . Amplification of leukocyte numbers during zebrafish bacterial infection has been shown to depend on signalling through the Csf3-Csf3r pathway [58] , while both Csf3 and Interleukin-6 are known to be important in the response to yeast infection in mammalian systems [59 , 60] . To interrogate these pathways in the context of talaromycosis , Csf3 signaling was intercepted by knocking down the single chain of the homodimeric csf3 receptor ( csf3r ) , and interleukin-6 signalling was intercepted by knockdown of each subunit of the heterodimeric interleukin 6 receptor ( il6ra and gp130 ) . To quantify granulopoiesis , infections were performed in Tg ( mpx:EGFP ) larvae at 28°C and quantified as previously described [61] . Csf3r knockdown significantly reduced baseline neutrophil numbers and also significantly reduced the increase in neutrophil population size at 4 dpi ( Fig 3D ) . The relative increase in neutrophil abundance in both control and csf3r-knockdown infected embryos was 2 . 2-fold . Hence the significant difference in the absolute increase is likely to have in part reflected the significantly lower basal neutrophil abundance in csf3r-knockdown embryos . This difference was not due to a different pathogen burden following infection establishment in the face of lower basal neutrophil abundance , since csf3r knockdown did not alter fungal survival/proliferation as reflected by 24 hours post infection ( hpi ) CFU numbers ( Fig 5B ) . By 4 dpi , the significantly reduced granulopoietic response of infected csfr3-knockdown embryos resulted in impaired survival from infection ( Fig 3E ) . In contrast , il6r subunit knockdown did not impair the infection-driven granulopoietic response ( Fig 3D ) . The effect of il6r subunit knockdown on basal neutrophil population size was also modest . Collectively , these data indicate that the large expansion of neutrophil numbers during sustained T . marneffei infection is dependent on an interaction between pathogen and host , which mounts a cytokine-driven granulopoietic response that is in part csf3r- but not il6r-dependent . Intact basal csfr3 signalling is required for effective , protective , host-defences to talaromycosis . Since leukocyte/conidia interactions were a prominent histological feature of the initial host response to T . marneffei conidia inoculation , they were examined in detail at 28°C ( Fig 4 ) . For these experiments , we used intramuscular inoculations , as we were not testing hypotheses about the normal route of infection , but rather about the direct interaction between conidia and leukocytes . These studies were conducted at 28°C , as the conidial state of T . marneffei at the time of inoculation is not temperature dependent , and this is the temperature at which zebrafish leukocyte function is usually characterised . We confirmed that leukocytes actually phagocytosed fungal conidia , rather than merely associated with them . Fig 4A shows a neutrophil with a calcofluor-stained , germinated conidium , proven by z-stack analysis to be within an intracellular vacuole . Fig 4B shows a GFP-low macrophage in the Tg ( mpx:EGFP ) line ( as previously described [62] ) containing multiple germinated conidia . Using reporter lines in which red and green fluorophore expression is driven in macrophages and neutrophils respectively , active interaction of both neutrophils and macrophages with calcofluor-stained conidia was documented in the initial stages of infection ( Fig 4C and 4D and S1–S3 Movies ) , with apparent phagocytosis . Using new reporter lines in which reporter fluorophore expression was targeted to membranes by linkage to the CAAX prenylation signal , Z-stack profiling confirmed that conidia associated with leukocytes were intracellular and within membrane-lined phagosomes , as demonstrated by cross-sectional fluorescence intensity profiles ( Fig 4E–4G ) . Although both neutrophils and macrophages were capable of phagocytosing T . marneffei conidia , following both somitic ( Fig 4G ) and intravascular inoculation ( S6 Fig ) , T . marneffei conidia were phagocytosed almost exclusively by macrophages ( Fig 4H ) . To quantify the various leukocyte-pathogen interactions at the inoculation site , fluorescence signal colocalization were analyzed based on the different leukocyte-associated reporter fluorophores and blue fluorescence of calcofluor-labelled conidia . Analysis of the caudal hematopoietic tissue of Tg ( mpx:EGFP/mpeg1:mCherry ) embryos 2 hours following intravenous calcofluor-labelled conidia inoculation showed that , while the neutrophil:macrophage voxel ratio was 1:2 . 6 ( S6A Fig ) , reflecting the relative population sizes of the two phagocyte types , the ratio of conidia associated with these neutrophils and macrophages was 1:60 ( S6B Fig ) . In contrast , following intramuscular inoculation , although T . marneffei conidia were still preferentially phagocytosed by macrophages , neutrophils also actively engaged in conidial phagocytosis ( Fig 4Hi ) . Hence , although both neutrophils and macrophages phagocytose T . marneffei conidia during the initial stages of infection , macrophage phagocytosis predominates . Despite a common function as professional phagocytes , neutrophils and macrophages utilize distinctive antimicrobial arsenals [63] , and hence display diverse antimicrobial properties that are often microbe-specific . Given the specific effects of the neutrophil and macrophage intracellular milieu on T . marneffei form at later stages of infection ( Fig 2B ) , we hypothesized that neutrophils and macrophages might also play divergent roles during infection establishment . To determine the different roles of neutrophils and macrophages during the initial stages of T . marneffei infection , CFU counts were compared at 0 and 24 hpi in embryos at 28°C that had been experimentally manipulated to modulate the relative numbers of neutrophils and macrophages ( Fig 5 ) . Viable fungal cell number did not change between 0 and 24 hpi in leukocyte-replete control embryos ( 109 . 7% of baseline on average at 24 hpi ) ( Fig 5Ai and 5B ) , indicating either that conidia are both non-proliferative and resistant to killing , or that fungal death and proliferation were balanced in this context . In embryos depleted of both neutrophils and macrophages by spi1+csf3r-knockdown using antisense morpholino oligonucleotides [64] , 24 hpi T . marneffei CFU counts were significantly reduced to 64 . 4±6 . 7% of baseline ( Fig 5Aiii and 5B ) . This demonstrated both that the presence of leukocytes during infection establishment enhanced conidial survival and/or fungal proliferation , and indicated the existence of a leukocyte-independent fungicidal activity . In csf3r morphant embryos , which are depleted of neutrophils with little effect on macrophages [65 , 66] , 24 hpi CFU counts were 101 . 8±10 . 3% of baseline ( Fig 5Aii and 5B ) , indicating that the presence of normal numbers of macrophages alone is sufficient to protect T . marneffei conidia from the leukocyte-independent fungicidal activity observed in spi1+csf3r morphants . To complement the phagocyte depletion experiments , the effects of expansion of neutrophil populations on T . marneffei viability was examined in scenarios of different macrophage abundance . As previously reported [67] , overexpression of csf3b from mRNA injection relatively selectively increased neutrophil numbers . In our hands , it resulted in approximately twice as many neutrophils at 2 dpf ( S7Ai and S7Aii Fig ) compared to controls , but only an increase of 39% in macrophages ( S7Aiii and S7Aiv Fig ) [68] . No significant change in fungal viability was observed over the first 24 hpi for embryos overexpressing csf3b ( S7B Fig ) , indicating that the slightly increased macrophage population present in these embryos is sufficient to provide for conidial viability , even when neutrophil populations are expanded . Knockdown of irf8 expands neutrophil numbers , but at the expense of macrophage numbers [61 , 69] . In irf8 morphants , CFU counts at 24 hpi were 37 . 1±6 . 7% of baseline , which was significantly lower than for both control animals and spi1+csf3r morphants ( Fig 5B ) , pointing to a potent fungicidal activity of the expanded neutrophil population when availability of the protective macrophage niche is reduced . Collectively , all four scenarios support the hypothesis that macrophages provide a protective niche for T . marneffei conidia during infection establishment , shielding conidia from both neutrophil-dependent and leukocyte-independent fungicidal mechanisms . In agreement with the important role played by neutrophils in controlling infection at later timepoints ( Fig 3E ) , neutrophils were found to be strikingly fungicidal . As neutrophils were clearly the more fungicidal leukocyte during establishment of T . marneffei infection , we examined potential mechanisms that might determine neutrophil response to this pathogen and its outcome . Myeloperoxidase is an abundant neutrophil enzyme important for generating potent antimicrobial radicals , and is critical for defence against other fungal pathogens in mammals [70–72] . We therefore hypothesized that neutrophil-dependent fungicidal activity against T . marneffei was dependent on myeloperoxidase activity . We tested the requirement for myeloperoxidase ( mpx ) using an mpx-deficient zebrafish mutant that is neutrophil-replete but lacks enzymatic Mpx activity [73] . During the establishment phase of infection , there was no difference in T . marneffei CFU counts at 24 hpi between WT and mpx-/- larvae for infections at both 28°C and 33°C ( Fig 5B and S8A Fig ) despite equivalent neutrophil populations over this period ( S8B Fig ) . However , the enhanced fungicidal activity of the expanded neutrophil population in irf8-MO embryos , which reduced CFU counts at 24 hpi , was lost in mpx-/- embryos ( Fig 5B ) . During sustained infection , mpx-/- embryos mounted a vigorous granulopoietic response which is >2 . 5-fold higher than that of WT embryos at 3 dpi ( S8B Fig ) . However , despite this expanded neutrophil population , mpx-/- embryos carried a fungal CFU burden similar to WT embryos ( S8A Fig ) . Collectively , these data indicate that the fungicidal activity of neutrophils against T . marneffei is in part myeloperoxidase-dependent , and that this component of neutrophil antifungal activity becomes most critical when the neutrophil population is expanded and macrophage abundance is depleted . During infection establishment , our results support a model in which T . marneffei preferentially parasitizes macrophages , which provide an intracellular niche that shields conidia from both neutrophil-dependent and neutrophil-independent anti-fungal mechanisms . We therefore hypothesized that limiting access to the macrophage niche would enhance conidial clearance during infection establishment . To test this hypothesis , macrophages were ablated prior to infection at 28°C using the metronidazole-dependent nitroreductase system [74 , 75] . Metronidazole treatment of Tg ( mpeg1:Gal4FF/UAS-E1b:Eco . nfsB-mCherry ) embryos reduced macrophage abundance by 70% ( Fig 6A , S9A Fig and S4 Movie ) , which resulted in a significant ( 35% ) reduction of T . marneffei CFU counts at 24 hpi , compared to control sibling embryos that were metronidazole-treated but did not carry the UAS-E1b:Eco . nfsB-mCherry transgene ( Fig 6B ) . Conversely , selective ablation of neutrophils by metronidazole treatment of Tg ( mpx:KalTA4/UAS-E1b:Eco . nfsB-mCherry ) embryos resulted in a 20% increase in fungal viability compared to controls ( Fig 6C and 6D , S9B and S9C Fig ) , further supporting our findings that neutrophils exhibit fungicidal properties during infection establishment . Taken together , these results suggest that limiting access to the macrophage intracellular niche may be one targetable pathway for restricting establishment of infection for the benefit of the host .
This new model of Talaromyces marneffei infection in larval zebrafish holds many advantages over current murine in vivo models [76 , 77] . It combines a replete vertebrate innate immune system with transgenically labelled lineages and optical transparency to facilitate detailed live imaging of host-pathogen interactions . Additionally , the ectothermic biology of zebrafish allows experimental separation of thermal and host-dependent influences on fungal morphology . While this can also be achieved using the recently developed Galleria mellonella and Caenorhabditis elegans models [78 , 79] , the invertebrate hemocyte provides only limited insight into complex innate immune responses such as those described here . Combining an ectothermal host and a thermally dimorphic pathogen in an experimental system modelling a human infection provides both opportunity and raises some technical questions . Although zebrafish experiments are normally conducted at 28–28 . 5°C , zebrafish physiology is robust at 33°C . Evidence for this is: existence of wild D . rerio populations in waters up to 38 . 6°C [45]; normal embryological development over this temperature range [44]; experimental studies involving human xenotransplantation [49]; conditional temperature sensitive mutant alleles [26 , 50 , 51]; and even temperature shifts employed in previous infection modelling studies [26 , 29] . We provide new experimental evidence showing that the myeloproliferative and functional phagocyte response to A . fumigatus , a non-thermally dimorphic fungal pathogen , is similar at 28°C and 33°C . T . marneffei exhibits thermal dimorphism , but even so , the fungal to yeast shift is not absolute at 37°C . At 37°C in vitro , the rate of hyphal conversion to yeast varies from 40% to 90% between the lowest and highest tolerated pHs , and NaCl concentrations ≥4% suppress hyphal to yeast conversion almost completely [40] . Even in human infection at 37°C , extracellular forms characteristically assume an elongated shape [47 , 80] . As is uniquely possible in an ectothermic model , we exploited the ectothermic biology of the zebrafish host to identify a divergence between fungal morphology and temperature , specifically in response to the leukocyte intracellular milieu . The phase transition to a yeast morphology during human T . marneffei infection at 37°C is widely regarded to be triggered primarily by the ambient temperature , with the thermal dimorphism of the pathogen seen as an evolved pathogenic trait [42] . At 33°C , we observed filamentous forms in neutrophils and tissues ( Fig 2Aii , 2Aiv , 2Bi and 2Bii ) , and predominantly yeast forms within macrophages ( Fig 2Biii and 2Biv ) , providing strong evidence that the intracellular milieu of the phagocytosing leukocyte type may be a determinant of T . marneffei form in vivo that can override the influence of ambient temperature . We therefore hypothesize that one factor contributing to the macrophage being the preferred infectious niche for T . marneffei is its temperature-independent support of a phase transition to the more pathogenic yeast form . In response to prolonged infection , we observed expansion of leukocyte populations , particularly neutrophils , and demonstrated that this protective response was at least in part dependent on signalling through G-CSFR . This demand-driven hematopoietic response to infection appears to be conserved , and has been since reported by others in response to Salmonella enterica infection [58] . Overexpression of the zebrafish Csf3r receptor ligand Csf3b by mRNA overexpression phenocopied the granulopoietic response observed during T . marneffei infection , further supporting our findings . Genetic manipulation of leukocyte lineage numbers prior to infection demonstrated that macrophages provide a protective niche for fungal conidia , while neutrophils are fungicidal . Further , using an existing mutant , we demonstrated that the fungicidal activity of neutrophils was myeloperoxidase-dependent . Myeloperoxidase deficiency is a prevalent congenital human disorder of neutrophils with an incidence of 1:2000 [81] . Epidemiological studies associate myeloperoxidase-deficiency with increased risk of fungal infection [82] , myeloperoxidase-deficient mice are vulnerable to Candida infection [83] , and myeloperoxidase-deficient neutrophils display reduced ability to produce fungicidal neutrophil extracellular traps ( NETs ) [84] . Our demonstration of myeloperoxidase deficiency as a talaromycosis disease-enhancer in zebrafish suggests it may be an unrecognized disease-modifier of human talaromycosis and invites an evaluation of myeloperoxidase status in such patients . Selective depletion of macrophages using the metronidazole system demonstrated that removal of this protective niche exposes T . marneffei conidia to neutrophil-dependent and independent antifungal mechanisms . Temporary depletion of macrophages in patients that transiently selectively reduce access of conidia to the macrophage niche might provide a novel therapeutic strategy to restrict infection establishment . We also hypothesize that transient macrophage ablation therapy in established talaromycosis may facilitate talaromyces infection eradication by exposing the organism to fungicidal neutrophils . One limitation of this zebrafish model is that the infection is established by inoculation , rather than via alveolar macrophages which is presumed to be that natural route of infection [85] . However , in human T . marneffei infection , fungal forms are found in tissue macrophages throughout the body , including particularly in skin [56] , lymph nodes [86] and bone marrow [87] , and so our observations of the behaviour of tissue macrophages provide relevant insights to understanding the pathogenesis of the human disease . Findings from this new in vivo zebrafish T . marneffei infection model have implications not only for talaromycosis but also for the pathogenesis of infections with other fungal and dimorphic pathogens including histoplasmosis , blastomycosis and coccidioidomycosis . Furthermore , it is a unique resource for exploring in detail the in vivo cell biology of leukocyte-pathogen interactions during this infection .
Zebrafish strains were: wildtype ( AB* ) ; durif ( mpx-/- ) gl8 [73]; Tg ( mpx:EGFP ) i113 [19]; Tg ( mpeg1:Gal4FF ) gl25 [22]; Tg ( mpeg1:mCherry ) gl23 [22]; Tg ( mpx:Kal4TA4 ) gl28 [88]; Tg ( UAS-E1b:Eco . nfsB-mCherry ) c264 ( Zebrafish International Stock Centre , Eugene , OR ) . The new Tg ( mpeg1:mCherryCAAX ) gl26 and Tg ( mpx:EGFPCAAX ) gl27 lines were generated using Multisite Gateway cloning ( Invitrogen ) in combination with 1 . 87 kb of mpeg1 promoter [22] and 8 . 35 kb of mpx promoter [20] . Fish were held in the Walter and Eliza Hall Institute and FishCore ( Monash University ) aquaria using standard practices . Because zebrafish exhibit juvenile hermaphroditism , gender balance in embryonic and larval experiments was not a consideration [89] . Embryos were held in egg water ( 0 . 06 g/L salt ( Red Sea , Sydney , Australia ) ) or E3 medium ( 5 mM NaCl , 0 . 17 mM KCl , 0 . 33 mM CaCl2 , 0 . 33 mM MgSO4 , equilibrated to pH 7 . 0 ) ; from 12 hpf , 0 . 003% 1-phenyl-2-thiourea ( Sigma-Aldrich ) was added . Animal experiments followed appropriate NHMRC guidelines and were conducted under protocols approved by Ethics Committees of the Walter and Eliza Hall Institute ( 2007 . 004 and 2009 . 031 ) and Monash University ( MAS/2010/18 ) . In accordance with the approved protocol requirements , all zebrafish embryos and larvae used in experiments were younger than 7 dpf ( i . e . experiments were concluded on the 6th dpf which was the 4th day after infection ) . We performed the experiments under Institution Biosafety Committee Notifiable Low Risk Dealing ( NLRD ) approvals 2007 . 01 ( Walter and Eliza Hall Institute ) and PC2-N23-10 ( Monash University ) . T . marneffei was assigned to Risk Group 2 at the time these approvals were granted . In most jurisdictions , including endemic regions , T . marneffei is a risk group 2 organism . T . marneffei strains , derived from the FRR2161 type strain , were: acuD:RFP strain , which expresses RFP on germination and the control strain SPM4 [90] . To prepare cells for injection , T . marneffei conidia were inoculated onto Sabouraud Dextrose ( SD ) medium and cultured at 25°C for 10–12 days when the cultures were conidiating . Conidia were washed from the plate with 0 . 005% Tween 80 solution , filtered , sedimented ( 6000 rpm , 10 min ) , resuspended in dH2O and stored at 4°C . For inoculation , conidia were resedimented and resuspended in PBS . Heat-inactivation and calcofluor staining was as described previously [22] . Calcofluor staining did not affect conidial viability , as evidenced by their subsequent germination in vivo ( S1A Fig ) . T . marneffei colony forming unit ( CFUs ) numbers per embryo were determined by thorough homogenization of individual embryos in 500 μL of dH2O using a Dounce homogenizer . 250 μL of homogenate was cultured on SD medium agar with 1% ampicillin for 3–4 days at 37°C . Plates were then incubated overnight at room temperature , and colonies that underwent yeast to hyphal morphological switching were scored as T . marneffei colonies . For inoculation , 52 hpf tricaine-anesthetized embryos were mounted on an agar mould with head/yolk within the well and tail laid flat on the agar . The T . marneffei conidial suspension was inoculated using a standard microinjection apparatus and large-bore needle via the common cardinal vein for systemic infection , or the 4th ventricle or a somite aligned to the yolk extension tip for local infection [22 , 91] . Inoculated embryos were held at 28°C or 33°C according experimental design . The delivered conidial dosage was determined by immediate CFU enumeration on a group of injected embryos . Following initial dose-finding experiments that established an intravascular inoculum of 100–150 CFU/embryo achieved <25% mortality for 28°C infections ( Fig 1D and 1E ) , this was the target inoculum dose . Freshly-prepared A . fumigatus conidia stocks ( strains CEA10 and 295 ) for these experiments were stored at 4°C for < 2 months . Fresh aliquots were prepared for microinjection as described for T . marneffei spores , delivering a target inoculum of 50–150 live spores , verified by back-plating as described above for T . marneffei . To prepare dead A . fumigatus conidia for microinjection , they were γ-irradiated with 10kGy ( delivered over 207 hr 27 min 10 sec ) from the Monash University Gammacell 40 Exactor ( Theratronics ) with two Caesium-137 sources ( dose selected based on [92] ) . Irradiated spores were verified as dead by plating and incubation for 5 days: no growth occurred . The dead spores were microinjected at the same dilution of stock as used for live spores . Irradiated spores still stained well with calcofluor . For intravascular delivery experiments , microinjection of conidia utilised polydimethylsiloxane ( PDMS ) microstructured surface arrays [93] , while imaging was performed following mounting in PDMS imaging devices , as previously described [94] . Leukocyte numbers were determined by two techniques . For direct enumeration , manual counting of neutrophil numbers was assisted by the brush tool in Paintbrush 2 . 1 . 2 ( Soggy Waffles ) , which records clicks to avoid duplicate counting . Alternatively , “Leukocyte Units” ( LUs ) , a surrogate parameter proportional to leukocyte numbers determined by analysis of digital images , were computed as previously described and validated [61] . LUs incorporate an internally-controlled correction for cell size , and can be independently applied to the signal from fluorescent neutrophils and macrophages . Where appropriate , LUs are called “Neutrophil Units” or “Macrophage Units” . In some cases as indicated , leukocytes were enumerated in the tail region distal to the tip of the yolk extension , in order to score cells in a representative part of the whole animal where overlap and anatomical shape did not interfere with accurate scoring . Migrating phagocyte numbers and their phagocytosis of conidia were counted manually in reconstructed 3-dimensional imaged volumes using Imaris v5 ( Bitplane ) . Antisense morpholino oligonucleotides ( MOs ) were purchased from Gene Tools , LLC ( Eugene , OR ) ( S1 Table ) . New MOs were demonstrated to target their intended sequence using EGFP reporter constructs engineered to contain target sequences ( S10 Fig ) . MO-csf3rATG specificity was controlled by MO-csf3rsplice; only MO-csf3rATG data are presented . MO-il6raATG and MO-gp130ATG served as specificity controls for each other as they targeted separate components of the same heterodimeric receptor complex . Tg ( mpeg1:Gal4FF/UAS-E1b:Eco . nfsB-mCherry ) or Tg ( mpx:Kal4TA4/UAS-E1b:Eco . nfsB-mCherry ) embryos generated by intercrossing were treated with 10 mM metronidazole ( Sigma M3761 ) from 28–52 hpf . The efficiency of macrophage ablation assessed using live imaging ( S4 Movie ) and LU at 24 h after treatment was ~70% ( S9A Fig ) , comparable with the experience of others [25] . The efficiency of neutrophil ablation at 24 h after treatment was ~60% of Sudan Black positive neutrophils ( S9B and S9C Fig ) . In infection experiments , metronidazole treatment was continued throughout the infection time course to restrict leukocyte recovery . Capped csf3b mRNA was transcribed from pCS2+ plasmids containing the cDNA [67] linearized by Not1-HF using the mMessage mMACHINE kit followed by RNA cleanup using RNeasy Mini Kit ( Qiagen ) according to manufacturer’s instructions . 5 μL was subjected to RNA electrophoresis for a quality check and stock aliquoted and stored at -70°C . 500–1000 pg of capped RNA was microinjected directly into the cell of 1-cell embryos . Control embryos received diluent alone . J774 murine macrophages were seeded at a concentration of 1x105 conidia/mL into a 6 well microtitre tray containing sterile coverslips and 2 mL of DMEM medium . Macrophages were incubated at 33°C or 37°C for 24 hours followed by the addition of 0 . 1 μg/mL lipopolysaccharide ( LPS ) and incubation for a further 24 hours . The cells were washed in PBS and 2 mL of complete DMEM medium containing 1x106 conidia was added . A control lacking conidia was also performed . Macrophages were incubated for 2 hours at 37°C to allow conidia to be engulfed , washed once in PBS to remove non-phagocytosed conidia and incubated a further 24 hours at 33°C or 37°C . Macrophages were fixed in 4% paraformaldehyde and stained with 1 mg/mL fluorescent brightener 28 ( calcofluor ) to observe fungal cell walls . Mounted coverslips were examined using differential interference contrast and epifluorescence optics for cell wall staining and imaged on an Olympus IX70 microscope . Standard 4% paraformaldehyde-fixed , paraffin-embedded sections were stained by hematoxylin and eosin or Grocott methanamine silver stains by the Walter and Eliza Hall Institute of Medical Research Histology Department . FACS-sorted leukocytes from infected Tg ( mpx:EGFP ) embryos were collected based on EGFP fluorescence and cytospun preparations stained with Grocott methanamine silver / Nuclear Fast Red . Routine brightfield and fluorescence imaging used a Zeiss Lumar V12 stereo dissecting microscope with an AxioCam MRm camera running AxioVision 4 . 8 software . Images were 1388x1040 pixels . Compound microscopy used an upright Nikon Optiphot-2 microscope with 40x and 100x objectives and a Zeiss AxioCam MRc5 Camera running AxioVision AC ( Release 4 . 5 ) software . Images were 1292x968 pixels . Confocal microscopy used a Zeiss LSM 5 Live with a Plan-Apochromat 20x , 0 . 8 NA objective . Software was Zen ( Version 4 . 0 ) . Images were 16-bit 512 x 512 pixels . Z-depth ranged from 0–90 slices with Z-intervals optimized for 1:1:1 X:Y:Z reconstruction . Time intervals were optimized for each experiment and ranged between 30–200 s . Excitatory laser wavelengths were 405 nm for calcofluor , 489 nm for EGFP and 561 nm for mCherry . Emission detection used a BP495-555 filter for calcofluor and EGFP emission and a LP575 filter for mCherry emission . Image processing was performed using Fiji ( ImageJA 1 . 45b ) and Imaris v5 ( Bitplane ) . Colocalization analysis was performed using the Coloc function in Imaris , with thresholds set such that internal colocalization of each channel corresponded to the cell-specific fluorescent signal . Figures were constructed using Adobe CS5 Photoshop and Illustrator . Descriptive and analytical statistics were prepared in Prism 5 . 0c ( GraphPad Software Inc ) . Unless otherwise stated , data are mean±SEM , with p-values generated from two-tailed unpaired t-tests . | For people with compromised immune systems , such as those suffering from AIDS , fungal infections are difficult to treat and often deadly . Different fungal species have different ways of avoiding destruction by the immune system during infection . For Talaromyces marneffei , the ability to infect host macrophages and replicate within them as yeast is thought to be key to their survival and spread throughout the human body . Here , we use a larval zebrafish infection model to study interactions between T . marneffei and cells of the innate immune system in greater detail than previously possible . We show that during early infection , T . marneffei spores are taken up primarily by macrophages . Limiting access to this macrophage niche enhanced engulfment and destruction of spores by neutrophils , another innate immune cell type important in host defences . This neutrophil antifungal activity was reduced in animals lacking Myeloperoxidase , an abundant antimicrobial enzyme of neutrophil granules , proving that myeloperoxidase is crucial for host defence against T . marneffei . These studies suggest that blocking access of infective T . marneffei spores to their macrophage niche , thereby exposing them to neutrophil fungicidal activity , may be therapeutically effective in T . marneffei infection . | [
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"developme... | 2018 | Macrophages protect Talaromyces marneffei conidia from myeloperoxidase-dependent neutrophil fungicidal activity during infection establishment in vivo |
Recent sequencing projects have provided deep insight into fungal lifestyle-associated genomic adaptations . Here we report on the 25 Mb genome of the mutualistic root symbiont Piriformospora indica ( Sebacinales , Basidiomycota ) and provide a global characterization of fungal transcriptional responses associated with the colonization of living and dead barley roots . Extensive comparative analysis of the P . indica genome with other Basidiomycota and Ascomycota fungi that have diverse lifestyle strategies identified features typically associated with both , biotrophism and saprotrophism . The tightly controlled expression of the lifestyle-associated gene sets during the onset of the symbiosis , revealed by microarray analysis , argues for a biphasic root colonization strategy of P . indica . This is supported by a cytological study that shows an early biotrophic growth followed by a cell death-associated phase . About 10% of the fungal genes induced during the biotrophic colonization encoded putative small secreted proteins ( SSP ) , including several lectin-like proteins and members of a P . indica-specific gene family ( DELD ) with a conserved novel seven-amino acids motif at the C-terminus . Similar to effectors found in other filamentous organisms , the occurrence of the DELDs correlated with the presence of transposable elements in gene-poor repeat-rich regions of the genome . This is the first in depth genomic study describing a mutualistic symbiont with a biphasic lifestyle . Our findings provide a significant advance in understanding development of biotrophic plant symbionts and suggest a series of incremental shifts along the continuum from saprotrophy towards biotrophy in the evolution of mycorrhizal association from decomposer fungi .
Plants in natural ecosystems often display a high degree of colonization by endophytic fungi . Since these fungi colonize their hosts without causing visible disease symptoms , they have often been overlooked and little attention has been paid to their impacts on plant communities . Endophytes exhibit a broad range of lifestyles along the saprotrophy-biotrophy continuum , depending on the fitness benefits conferred to their host , secondary metabolites production and their colonization strategies [1] , [2] , [3] , [4] , [5] . The filamentous fungus Piriformospora indica belongs to the order Sebacinales which represents the earliest diverging branch of the Agaricomycetes and the most basal basidiomyceteous order with mycorrhizal abilities [1] , [6] , [7] . Taxa within this fungal group are either facultative or , as in the more derived species , obligate biotrophs . P . indica , which was originally isolated from soil of the Indian Thar desert [8] is the asexual model organism for experimental studies in the Sebacinales . P . indica displays an endophytic lifestyle and has the ability to colonize the roots of a wide range of mono- and dicotyledonous plants , including members of the Brassicaceae ( e . g . Arabidopsis thaliana ) which are known as non-host plants for ecto- and arbuscular mycorrhiza [9] . Plants colonized by P . indica display a wide range of beneficial effects including enhanced host growth and resistance to biotic and abiotic stresses [10] , [11] , [12] , [13] , promotion of adventitious root formation in cuttings [14] and enhanced nitrate and phosphate assimilation [15] , [16] . P . indica extensively colonizes the differentiation and the root hair zones inter- and intracellularly , while it is rarely detectable in the elongation and meristematic zones [17] . This colonization pattern distinguishes it from ecto- and arbuscular mycorrhizal fungi , which either grow only intercellularly or colonize predominantly the deeper cortex layers of younger parts of the root [18] . An additional difference between mycorrhiza fungi and P . indica is its dependence on host cell death for successful colonization [17] . In barley , the host cell death related growth phase is associated with the down regulation of the endoplasmic reticulum membrane-localized cell death regulator BAX INHIBITOR-1 ( BI-1 ) . Consistent with this , transgenic barley plants that express the barley BAX INHIBITOR gene under a constitutive promoter , show increased cell viability and reduced colonization [17] . Recent studies revealed a complex interplay between the plant root and P . indica , involving suppression of microbe-associated molecular pattern ( MAMP ) -triggered root innate immunity , modulation of secondary metabolism ( including plant hormone biosynthesis ) , induction of cell death , and elicitation of systemic resistance responses [19] , [20] , [21] , [22] , [23] , [24] . However little information is available on the fungal genes and pathways involved in the establishment and maintenance of the symbiosis [15] , [25] . In this study we report on the genome of P . indica and provide a global characterization of fungal transcriptional responses to colonization of dead and living root tissues . Data from recent sequencing projects have provided novel insights into genomic traits associated with various lifestyles in fungi , including ectomycorrhizal fungi [26] , [27] , [28] , [29] , [30] . Cytological investigation and comparative analysis of P . indica genomic traits and gene expression profiles revealed substantial differences in colonization strategies compared to known ectomycorrhizal fungi providing first insights into root endophytic life strategies in the Basidiomycota .
A detailed knowledge about the fungal colonization strategy is a prerequisite for the interpretation of transcriptome changes in response to endophytic root colonisation . To generate this information , roots from 3-day-old barley seedlings and autoclaved roots of the same age were inoculated with 500 , 000 chlamydospores/ml under sterile conditions and the colonization pattern was documented over a period of 7 days by fluorescence and confocal microscopy . Fungal growth in autoclaved barley roots , which retained their macroscopic structure and texture , was characterized by a massive intracellular development with highly branched hyphae from 3 days post inoculation ( dpi ) onwards ( Figure 1 ) . Newly produced chlamydospores were detected on the root surface at 5 dpi whereas intracellular chlamydospores were observed at 7 dpi . The early extensive intracellular hyphal development in dead cells resembled the colonization pattern of cells in living roots at later stages ( >7 dpi ) , which prompted us to assess the viability of host cells during the symbiotic colonization . Colonized living roots were treated with both the fungal cell wall stain WGA-AF488 and the membrane stain FM4-64 that is commonly used for dissecting vesicles trafficking in living plant cells [31] , [32] , [33] . In agreement with a previous study [17] , P . indica was confined to the cortex layer whereas the root tips and the central meristematic tissue were free of hyphae . Living cells , identified by the internalization of FM4-64 into endomembrane structures , were intracellularly colonized by a single hypha with no or limited branching from 3 dpi onwards ( Figure 2 ) . The failure of the WGA-AF488 to stain the hyphae inside living cells ( Figure 2 ) strongly suggests that the fungus remained enveloped in an intact plant-derived membrane throughout intracellular growth . Formation of cell wall appositions ( papillae ) was observed sporadically during penetration attempts of living cortex cells . Presence of papillae , visualized with ConA-AF633 staining , correlated with the biotrophic phase of this fungus ( Figure S1A ) . Closer inspection of the papillae showed accumulation of plant vesicles and glycoproteins at the penetration zone ( Figure 3 ) . These papillae were not always effective in stopping fungal penetration , indicating that P . indica is able to overcome plant cell wall-mediated defense in barley . At later colonization stages ( >4 dpi ) P . indica was more frequently detected in moribund or dead host cells which were extensively colonized by fungal hyphae . This cytological analysis revealed a mixture of colonized dead and living cells from 4 dpi onwards ( Figure S1C and 1D ) . Pyrosequencing of the P . indica genome was performed in parallel to RNA-Seq of cDNA pooled from different fungal developing stages . The genome was assembled into 1 , 884 scaffolds ( size: >1 kb; N50: 51 . 83 kb ) containing 2 , 359 contigs with an average read coverage of 22 and a genome size of 24 . 97 Mb . 11 , 769 gene models were identified using various ab-initio gene prediction programs and the open reading frames were validated by mapping unique expressed sequence tags ( EST ) to the scaffolds ( Table S1 ) . To assess the genome completeness of P . indica a blast search was performed with highly conserved core genes present in higher eukaryotes [34] , [35] . From the expected 246 single-copy orthologs extracted from 21 genomes [35] , 245 are present in the P . indica genome draft , indicating that >99% of the gene space is covered by the assembly . Protein blast searches ( eVal: 10−3 ) showed that a large number of P . indica's predicted genes have closest matches for the ectomycorrhizal fungus Laccaria bicolor ( 3 , 109 , 26 . 42% ) and the saprotrophic fungus Coprinopsis cinerea ( 2 , 381 , 20 . 23% ) , which therefore represent the closest related organisms sequenced at the present time . In addition a large number of genes have no orthologs in other genomes ( 3 , 286 , 27 . 92% ) ( Figure S2 ) . Synteny analysis showed only a minor number of conserved syntenic gene blocks between the genome of P . indica and those of L . bicolor , C . cinerea and Ustilago maydis ( Figure S3 ) . In comparison to the genome of related fungi P . indica has a significantly higher gene density with 471 ORFs/Mb ( 39% more ORFs/Mb than the average gene density of 338 ORFs/Mb calculated from 9 genomes , Table 1; S2 ) , a low number of transposable elements ( 4 . 68% ) , and an absence of LTR gypsy elements in the repeat library , which are frequently found in other fungal genomes ( Table S3 ) . A specific identification of the reverse transcriptase 1 ( RVT1 ) found in LTR gypsy confirmed that this elements are rare in the P . indica genome since only three RVT1 sequences were identified ( data not shown ) . A relative abundance of 24 simple sequence repeats ( SSRs ) /Mb was identified in the P . indica draft genome which is in the lower range of fungal genomes . Additionally , with only 58 identified genomic tRNA genes P . indica has an unusual low number of these genes ( Table S4 ) . The codon usage preference of P . indica is comparable to that of other fungi ( Figure S4 ) . P . indica possesses multinucleate hyphae , but the failure to detect clamp connections or sexual reproduction has impaired the determination of ploidy [36] . We detected two allelic mating type loci with two genes encoding for homeodomain proteins in the P . indica genome ( Figure S5 ) . This finding is consistent either with a diploid nucleus or with a dikaryotic mycelium . To determine ploidy level , P . indica nuclei were stained with the DNA intercalating dye syto9 and fluorescence intensity ( measured by CLSM ) was compared to that of known DNA content from haploid and diploid forms of the reference organism Saccharomyces cerevisiae . The estimated DNA content of P . indica nuclei ranged from 15 . 3 to 21 . 3 Mb ( Figure S6 ) . This range is consistent with the genome size estimated by the pyrosequencing approach ( Table 1 ) , suggesting that P . indica nuclei are haploid . Single nucleotide polymorphisms ( SNP ) with two variants , were identified in about 92% of the contigs ( 23 . 15 Mb ) with a frequency of 2 . 6 SNPs per kb and a total of 60 , 493 polymorphisms for the entire genome ( Table S5 ) . This value is similar to that observed in the diploid genome of Candida albicans [37] . Based on DNA content and SNP analysis , we conclude that P . indica is most likely a heterokaryon containing two genetically distinct nuclei . Average read coverage analysis of the contigs highlighted the presence of a group of genomic segments with half as many reads compared to the rest ( Figure S7 ) . A correlation between the occurrence of polymorphisms and sequence depth was found with no SNP observed for the contigs with an average read coverage of about 10 ( Table S5 and Figure S8 ) . These contigs probably represent highly polymorphic regions in the genome and account for 1 . 87 Mb sequence data with 1 , 056 predicted ORFs ( Table S5 and Figure S8 ) . The occurrence within these regions of the two highly syntenic contigs representing the two putative allelic mating type loci , which were not homologous enough to be assembled in one scaffold , further supports this conclusion ( Figure S5 ) . To gain an overview of the biological processes and pathways that contribute to symbiosis , we compared the presence and abundance of individual protein functional regions in the P . indica predicted ORFs with the corresponding domain number in a broad range of fungal species using the Pfam database [38] ( Table S6 ) . The overall number of different domains represented in the P . indica genome ( 2 , 785 ) is comparable to that of other fungi ( with an average value of 2 , 840 calculated from 10 genomes ) , but marked differences are present in terms of protein abundance per functional domain . Thirty-two protein domains are significantly expanded in the P . indica genome with fourteen of these exhibiting greater abundance than in any other genome analyzed in this study ( Table S6 ) . Expanded domains include proteins that are predicted to be involved in plant cell wall degradation ( e . g . , glycoside hydrolase families GH10 , GH11 and GH61 ) ; proteolysis ( e . g . , metallopeptidases families M36 and M43 ) ; carbohydrate binding ( e . g . protein containing LysM , WSC or CBM1 domains ) ; protein binding ( WD domain , G-beta repeat - WD40; NACHT domain; tetratricopeptide repeat - TPR_4 domain ) together with proteins most probably involved in signaling and regulation of cellular responses to stress and nutrient availability ( NB-ARC , G-alpha protein , F-box , RAS and RHO families ) ( Table S6 and S7 ) . The expansion of protein binding motifs together with domains involved in signaling is evidence that P . indica owns a complex regulatory machinery that helps to sense and couple signals received from the external environment with the intracellular signaling pathways . These traits are shared by the ectomycorrhizal fungus L . bicolor but not by the saprotrophic fungus C . cinerea ( Table S6 ) , supporting the contention that some of these proteins are candidates for the regulation of a complex communication system between the mycobiont and its host [30] . In particular , the expansion of genes encoding NWD proteins , associating the NACHT and the WD-repeat domains ( with 99 ORFs ) in the P . indica genome ( Table S6 ) is significant . WD-repeat proteins are found in all eukaryotes and coordinate multi-protein complex assemblies . Their combination with the NACHT NTPases , which share similarities in domain architecture with AP-ATPases , is found in a variety of proteins controlling programmed cell death , known as the incompatibility reaction , ensuring innate immunity in plants and animals towards microbial pathogens . It is therefore possible that this expansion of NWD genes might reflect the evolution of systems that function in non-self recognition and fungal innate immunity [39] , [40] . Additional analyses included clustering of protein families using Tribe-MCL [41] and the estimation of evolutionary changes in the size of these families using CAFE [42] ( Table S8 and Figure 4 ) . A total number of 4 , 458 multigene families were identified in the P . indica genome by Tribe-MCL analysis with an average of 2 . 26 proteins per family , which is in the expected range for the Basidiomycota ( with average size of 4 , 488 and 2 . 2 , respectively ) and which correlated with the genome size ( Table S2 and Figure S9; [30] ) . From the CAFE analysis , 421 families proved to be expanded in P . indica , 2 , 711 showed no change , and 529 had undergone contraction ( Figure 4 ) . In general , the domains identified by the Pfam analysis as being significantly expanded were found to be predominant in the expanded protein families , showing an overall good congruence of both methods ( Table S6 and S8 ) . Gene families that had undergone contraction account for proteins coding for amino acid and ABC transporters ( e . g . nitrate transporters , amino acid permeases , transmembrane amino acid transporter proteins , nucleobase cation symporters , ABC-2 type transporters , and CDR ABC transporters ) ( Table S6 ) and proteins involved in primary and secondary metabolism such as those for nitrate and nitrite reductase , polyketide synthase and non-ribosomal peptide synthetase ( PKS , NRPS ) ( Table S9 ) . Based on this data P . indica is predicted to experience nitrogen deficiency during growth on nitrate as sole N source . In order to test this hypothesis P . indica was grown on buffered minimal medium either containing no nitrogen or supplemented with N in the form of nitrate , ammonium or glutamine ( Figure S10 ) . As anticipated P . indica growth on nitrate is comparable to its growth on medium without N source . How the nitrogen sources impact the interaction of P . indica with the host is unknown and needs to be analyzed in the future . Altogether , 121 P . indica proteins contain either one or a combination of the following carbohydrate binding motifs: LysM , WSC or CBM1 . Of these , 94 proteins are predicted to be secreted , with 21 proteins smaller than 300 aa in size . The LysM domain is a widely distributed peptidoglycan/chitin binding motif present in secreted proteins , membrane proteins , lipoproteins or proteins bound to the cell wall [43] . In bacteria the majority of the LysM containing proteins are peptidoglycan hydrolases involved in cell surface adhesion and virulence . In plants the LysM containing proteins have been found in pattern recognition receptors ( PRRs ) that enable the plant to identify microbial symbiotic partners or pathogens [43] . In fungi , the LysM domains are mainly associated with hydrolytic enzymes acting on fungal cell wall , but they are also present in proteins lacking other conserved domains . A lectin-like LysM protein from Cladosporium fulvum was found to inhibit chitin oligosaccharide triggered and PRR-mediated activation of host immunity [44] . In contrast , little information about the functions of WSC containing proteins is available [45] , [46] , [47] . They are thought to bind glucan and were first described in yeast as cell wall integrity sensors involved in mediating intracellular responses to environmental stress [47] . The CBM1 domain has cellulose-binding function and is almost exclusively found in fungal hydrolytic enzymes acting on plant cell walls [48] . A lectin-like CBM1 containing protein , named CBEL , was described to be involved in cell wall deposition and adhesion to cellulosic substrates in Phytophthora parasitica [49] , [50] . The majority of P . indica's LysM ( 11 of 18 ) , WSC ( 28 of 36 ) , and some of the CBM1 ( 14 of 67 ) containing proteins are devoid of other conserved domains , resembling lectins . The rest of them are associated with different domains , which are predicted to possess plant or more rarely fungal cell wall hydrolytic activities . Figure 5 shows a schematic representation of domain combinations for the P . indica LysM , WSC and CBM1 containing proteins . LysM , WSC and CBM1 are short domains , containing consensus cysteine residues ( [43] and Figure S11 ) , and they are present as single or multiple repeats . Most of these proteins are predicted to be secreted , yet forms that lack a signal peptide sequence and/or have one predicted transmembrane domain were identified in the P . indica genome ( e . g . PIIN_02781 and PIIN_07931 , Figure 5 ) . Phylogenetic analysis of concatenated LysM domains shows a strong P . indica-specific expansion , which include 15 of the 18 LysM proteins ( Figure 6 , clade D ) , symptomatic of a rapid evolution . Genes coding for proteins from this clade are found in clusters ( of 2 to 6 genes ) within the genome . The remaining 3 LysM containing proteins are distributed in 3 different clades containing Basidiomycota ( A and C ) and Ascomycota taxa ( B ) . All of the LysM proteins from clade C contain one LysM domain and one transmembrane domain with no SP predicted , strongly suggesting similar functionality . Phylogenetic analysis of single LysM domains suggests that some of the domain repeats were created by sequential duplications of an ancestral domain or by the duplication of a tandem repeat ( Figure 7 ) . P . indica predicted ORFs containing LysM domains could be amplified by PCR from cDNA showing that all of these proteins are expressed , while pseudogenes were not found in the genome ( data not shown ) . Furthermore transposable elements were not found in the proximity of these proteins , suggesting that unequal recombination events have contributed to gene duplication in this family . The occurrence of a protein that combines 4 WSC and 2 LysM domains ( PIIN_06786 ) supports the hypothesis of domain reshuffling in P . indica ( Figure 5 ) . This domain combination is not found in closely related fungal genomes ( one such protein was found in Chaetomium globosum , uniprot entry Q2HEN7_CHAGB ) and the LysM and WSC domains present in PIIN_06786 are more closely related to other P . indica LysM or WSC domains respectively . These observations suggest that the most recent common ancestor of both LysM and WSC proteins most likely did not possess a protein with a combination of both domains , and the structural similarities between these proteins in bacteria , green algae , and fungi are likely due to convergent evolution . An Agilent customized microarray was designed to monitor P . indica gene expression during colonization of living and autoclaved barley roots from seedlings grown on sugar-free plant minimal medium ( PNM ) from 36–48 hpi , 3 and 5 dpi . Fungal mycelium grown on complete medium ( CM ) was used as a control , because P . indica grew poorly on the PNM medium . Despite the fact that in young barley roots a mixture of living and dead cells were colonized by P . indica ( Figure S1D ) , we found 579 genes in the pre-penetration phase ( 36–48 hpi ) , 397 genes in the early colonization phase ( 3 dpi ) , and 641 genes at 5 dpi that were differentially regulated compared to autoclaved roots ( Figure 8; Table 2 and S10 ) . These differences in gene expression are consistent with a diversified colonization strategy for living and dead roots ( supported by enrichment analysis , Table S11 ) . An interesting observation based on results from blastx searches against the NCBI nr-database ( eVal: 10−3 ) emphasizes the existence of transcriptionally defined gene sets for biotrophic and saprotrophic lifestyles . Genes induced during symbiosis exhibited higher amino acid sequence similarity to those of L . bicolor ( 18% of the total induced genes ) . In contrast , genes induced during colonization of autoclaved roots exhibited higher amino acid similarity to those of C . cinerea ( 23% ) . Additionally , most of the symbiosis induced genes ( 40% ) were non orthologous to either species but specific to P . indica ( Table 3 ) . Genes predicted to be involved in plant cell wall degradation were highly expressed at 3 dpi and remained induced or showed an even higher induction at 5 dpi on autoclaved roots ( Figure 9 ) . The high number of up-regulated genes encoding hydrolytic enzymes ( including a pectin lyase , PIIN_04321 , a pectin esterase , PIIN_04734 and a pectate lyase , PIIN_00890 ) during saprotrophic growth is consistent with the observation that colonized autoclaved roots were macerated at later stages ( in contrast to non-colonized dead material ) . This suggests that dead tissue is subjected to intense hydrolytic activity which is not observed in colonized living roots . Nineteen genes encoding putative hexose transporters are annotated in the P . indica genome . Many of these genes were induced during colonization of dead roots , including a physical cluster of 3 hexose transporters with closest homology to C . cinerea ( PIIN_03367 , PIIN_03368 , PIIN_03369; Figure 10 ) . The up-regulation of genes related to carbohydrate transport and metabolism together with the induction of plant cell wall degrading enzymes ( Table S10 ) indicate that a state of glucose depletion exists during growth of P . indica on dead root tissue at 5 dpi . Consistent with the existence of this state is the observation that genes for lipid metabolism were induced at this later time , while those for mitochondrial activity and biogenesis were repressed ( Table S11 ) . Enzymes predicted to be involved in proteolysis are well represented in the P . indica draft genome . In particular two families of metallopeptidases , M36 ( fungalysin ) and M43 ( cytophagalysin ) , are present in expanded forms . Members of these two families , together with members of the M28 ( aminopeptidase Y ) and M35 ( deuterolysin ) families , were greatly induced upon colonization of dead roots ( Figure 11 ) . The presence of a great number of metalloproteases that closely match the M36 peptidase family in C . cinerea ( [51] and Table S6 ) suggests that these enzymes are involved in plant tissue degradation for nitrogen assimilation . Fungal transporter genes , involved in the uptake of different nitrogen forms , such as a urea permease ( DUR3 ) , uracil permeases , purine permeases , a high-affinity ammonium transporter , and amino acid transporters displayed a similar expression profile with increased induction over time ( Figure 10 ) . Stress response to C and N depletion may therefore be responsible for the high number of hydrolytic enzymes ( CWDE and peptidases ) induced at 5 dpi on dead root material . During colonization of living roots , genes predicted to be involved in plant cell wall degradation were induced at the pre-penetration stage with a reduction in number and expression intensity at 3 and 5 dpi ( Figure 9 ) . These results suggest a tightly controlled expression of a defined set of symbiosis-related CWDE at the onset of the biotrophic phase . Production of cell wall degrading enzymes ( CWDE ) by plant colonizing fungi is often inhibited by glucose or other simple sugars in a well studied metabolic process known as catabolite or glucose repression [52] , [53] , [54] . The opposite trends observed in the expression profiles of the hydrolytic enzymes in living and in dead roots could , therefore , be partially explained by plant carbon allocation during symbiosis . Members of the expanded glycoside hydrolase GH61 enzyme family were almost solely responsive to living roots at the pre-penetration stage . Expression of GH10 , GH11 , GH18 , and GH62 was induced at all 3 time points and may be involved in the local secretion of enzymes at the penetration site in living roots . Differences in expression of genes coding for CWDE between living and dead roots may also be explained as response to papillae formation ( Figure 3 and S1A ) . Expression of genes involved in protein degradation and nitrogen transport showed an increased induction over time . The expression profile for these genes resembled that observed during colonization of dead roots , although lower gene inductions were recorded for the peptidases in response to colonization of living roots ( Figure 10 , 11 , S12 and S13 ) . The increasing number of non-vital plant cells over time in living roots could account for this similarity in expression profile between living and autoclaved root substrate . In general , expression levels of various key genes affected by starvation , such as those involved in autophagy or coding for metacaspases , acetyl-CoA synthetase and enoyl-CoA hydratase [55] , [56] , were unaffected or even down regulated during symbiosis ( Table S10 ) consistent with nutrient availability during early biotrophic interaction . Fungal genes annotated in the functional categories of cell rescue and stress response were prevalent among those induced in living roots ( Figure 12 ) . An increased expression of genes involved in oxidative stress , flavonoid and phenolic compounds reduction ( including a dj-1 family protein putatively identified as a catalaseA-like ) and an extracellular dioxygenase was observed at the pre-penetration phase . Genes for siderophore transcription factors and a thaumatin-like protein were also up-regulated . In contrast , at later time points the fungus appears to be engaged in chemical detoxification , which involved the increased expression of genes for DHA14 and other ABC transporters , cytochrome P 450 , glutathione S-transferase , isoflavone- , thioredoxin- and quinine-reductases . In addition genes with strong amino acid sequence similarity to the gliotoxin biosynthetic gene cluster of Aspergillus fumigatus were identified as responsive to the living substrate ( e . g . gliotoxin biosynthesis protein , gliK , PIIN_08979 and thioredoxin reductase , gliT , PIIN_07313; Figure 12 [57] ) . Closer inspection of the microarray data showed that four additional genes ( PIIN_10069 related to aflatoxin efflux pump , PIIN_10416 related to cytochrome P450 , PIIN_05842 related to methyltransferase , and PIIN_08304 related to cytochrome P450 ) with amino acid sequence similarity to gliA , gliF , gliN , and gliC from the Aspergillus gene cluster were induced in colonized living roots ( Figure 12 ) . These genes were not clustered in the P . indica genome and the absence of a NRPS related to gliP , the key enzyme for gliotoxin production in A . fumigatus [57] ( Table S9 ) , suggest that the respective P . indica genes are involved in protection against host antibiotic compounds rather than in production of mycotoxins . It is accepted that most phytopathogenic fungi are able to reprogram plant defense and cell metabolism through the secretion of small proteins called effectors ( for review see [58] , [59] , [60] ) . Recently it has been shown that effector-like proteins exist also in mutualistic fungi [61] , [62] . About 10% of the genes induced during P . indica colonization of living barley roots encoded putative small secreted proteins ( SSP , <300 aa; Table 2 ) . Increased expression of these SSPs suggests that they are likely to play a role in determining the success of endophytic interactions that involve penetration , suppression of plant immunity and growth within living cells . Intriguingly , some of the lectin-like proteins identified in P . indica genome were represented within this group ( Figure 13 ) . Yet , the role played by these lectin-like proteins during symbiosis remains unclear . Since these proteins are expressed at a higher level in living roots at the pre-penetration stage , we can speculate that they are involved in modulating recognition in host-microbe interaction . This could be achieved either through mediation of adherence to host cells or , alternatively , by masking of microbe-associated molecular patterns ( MAMPs ) and thus avoiding recognition by the host plant . Beside these proteins , other P . indica-specific plant responsive SSPs with no known domains were found . A search for motifs ( Table S12 and S13 ) in the amino acid sequences of these heterogeneous proteins identified a group of 25 proteins with a highly conserved pattern of seven amino acids “RSIDELD” at the C-terminus ( named DELD ) ( Figure 14 ) . Extension of this search to the genome draft recognized 4 truncated ORFs . Three of these putative genes were predicted to be pseudogenes ( PIIN_10706; PIIN_10879 and PIIN_10960 ) and they had a higher mutation rate compared to the other DELD-encoded genes . We therefore assume that most , if not all of the DELD proteins are secreted . In total , 17 proteins containing a RSIDELD motif showed increased expression during symbiosis ( Figure 15 and S12 ) . All DELD proteins have a similar size ranging between 101 and 135 aa with no known functional protein domain . A multiple protein sequence alignment identified a conserved and regular distribution of histidine and alanine residues within the DELD proteins ( Figure 14 ) . Searches of public fungal genome databases revealed that the RSIDELD motif is present at the C-terminus in other fungal proteins but , when present , the proteins bearing this motif are not highly enriched in histidine and alanine residues ( Table S12 ) . Interestingly , two ectomycorrhiza-regulated small secreted proteins from L . bicolor possess a DELD motif at the C-terminus but lack a high content of histidines . This observation supports the notion that the central part of the protein and the C-terminal tail are functionally distinct entities . Secondary structure prediction shows that the DELD proteins most probably form a two-helix bundle interrupted by a central conserved glycine residue ( Figure 14 ) . Amino acid sequence similarity searches with the central part of the DELD proteins revealed a ∼30% sequence identity with HRPII , a protein family from Plasmodium falciparum . This similarity was primarily due to the high histidine and alanine content ( Figure S14 ) . HRPII is an abundant protein released during erythrocyte infection by the malaria parasite and was reported to be localized in several cell compartments including the cell membrane and the cytoplasm of the host cells [63] . HRPII has been implicated in the detoxification of heme [64] , in cytoskeleton modification by actin binding [65] and in inhibition of antithrombin ( AT ) by selectively binding to coagulation-active glycosaminoglycans ( such as dermatan sulfate , heparin sulfate and heparin ) in a Zn2+ dependent manner [66] . Further this protein was shown to be able to bind to phosphatidylinositol 4 , 5-bisphosphate ( PIP2 ) and erythrocyte ghosts by undergoing a coil-to-helix transition [65] . Although the function of the HRPII seems to be still controversially discussed , this is one of the best studied histidine rich protein at the present time . The function of histidine and alanine rich proteins in fungi is not known . We investigated the association between the DELD gene family and transposable elements by assessing the extent to which they occurred together in the P . indica draft genome sequence . In contrast to the LysM protein-coding genes , the occurrence of DELD sequences strongly correlates with the presence of flanking transposable elements in gene-poor genomic regions ( Figure S15 ) . Similar to effectors found in other filamentous organisms [67] , [68] , [69] , the expansion of DELD genes in P . indica may be accounted for by transposition activity . These findings suggest that the DELDs represent a new gene family with a conserved domain of unknown function secreted during symbiotic root colonization . P . indica possesses a small genome that is gene dense with few repetitive DNA sequences . Despite the unusual low number of transposable elements in the P . indica genome compared to known plant pathogens and symbionts [28] , [67] , [30] , a high number of expanded gene families exist , which are typically present in clusters ( of 2 to 7 genes ) within the genome . Expansion of these families is likely to be due to local duplication events caused by unequal recombination , rather than retrotransposition . An exception to this is the expansion of the P . indica-specific DELD protein-coding gene family . All members of this novel family occurred in the proximity of transposable elements strongly suggesting a significant co-expansion between DELD paralogs and transposon sequences that benefited P . indica in some way during adaptation to the endophytic growth . This gene family expansion together with the combined rapid evolution of different types of plant responsive lectin-like proteins and different classes of secreted CWDE must have provided important functional advantages in the colonization of different plant hosts , e . g . by overcoming host inhibitors and by minimizing MAMP-triggered immunity ( MTI ) induction . Consistent with this hypothesis , recent work has shown that P . indica has evolved an extraordinary capacity for plant root colonization that has been attributed to its potential to suppress host MTI [19] . Future research is required to elucidate the contribution of these protein families to P . indica's colonization strategy . The facts that P . indica can grow readily on synthetic media and can colonize a wide range of mono- and dicotyledonous plants , indicate that its genome did not undergo host driven specialization as observed in typical obligate biotrophs [28] . Further , the observed dual ability of P . indica to colonize living and dead cortex cells point to a widening of the symbiotic lifestyle , i . e . implementing , maintaining or enforcing properties of biotrophy and saprotrophism , which maybe a reason leading to a broader host range . In agreement with this hypothesis , extended comparative analysis of P . indica genomic and transcriptomic traits with those of other Ascomycota and Basidiomycota taxa with different lifestyles decoded features typically associated with biotrophism [26] , [28] . These were the presence of small secreted proteins during symbiosis and the absence of genes encoding for nitrate uptake and reduction , as well as those for secondary metabolism , such as polyketide synthase and non-ribosomal peptide synthetase . On the other side , the genome sequence uncovered saprotrophic features uncommon to symbionts , i . e . expansions in cell wall degrading enzymes and metallopeptidases [70] . The tightly controlled expression of CWDE and the identification of different lifestyle-associated genomic traits argue for a biphasic lifestyle . This interpretation of the genomic information is supported by microscopic data that revealed an early biotrophic growth followed by a cell death-associated phase . In contrast to hemibiotrophic pathogens , such as Magnaporthe oryzae , where the switch from an initial biotrophic growth to necrotrophy leads to disease symptoms [31] , [55] , the interaction of P . indica with plant roots has a beneficial outcome for its host . It remains to be clarified whether the beneficial effects produced by P . indica on its host are merely attributable to the biotrophic phase or to a yet unknown mechanism associated with the lifestyle switch . The finding of a mutualistic symbiont with a biphasic lifestyle support the idea that the evolution of diverse mycorrhizal associations present in the order Sebacinales have begun with saprotrophic fungi that became endophytic , and then progressed to obligate biotrophic forms . Genome sequencing of other sebacinoid species is ongoing and will help clarifying , at least for this group of fungi , the evolutionary steps involved in mycorrhizal symbiosis . The availability of the genome and the genetic tractability of P . indica will provide powerful experimental advantages for investigating fundamental aspects of symbiosis , including functional analyses of the effector-like proteins and symbiosis determinants , identification of novel symbiosis/pathogenicity genes by genome comparison , population genomics , and SNP polymorphism of symbiosis-regulated genes .
Total RNA was extracted with TRIzol reagent ( Invitrogen , Darmstadt , Germany ) from germinating Piriformospora indica ( DSM 11827 , DSMZ , Braunschweig , Germany ) chlamydospores ( 24h ) and from 3 days old mycelium grown in liquid complete medium ( CM ) [36] and pooled together . Messenger RNA ( mRNA ) containing poly-A tails were isolated from 500 µg of this pool using MN-Nucleotrap mRNA Kit ( Macherey-Nagel , Düren , Germany ) . After a precipitation step with isopropanol and dilution in milliQ water , first strand cDNA was prepared using a SMART RACE cDNA amplification Kit ( Clontech/Takara Bio Europe , Saint-Germain-en-Laye , France ) according to the manufacturer's protocol . SMART oligo II and 3′ RACE CDS primers ( Clontech ) were used for first strand cDNA synthesis . The cDNA reaction mixture was precipitated with isopropanol and dissolved in milliQ water to a final concentration of 100 ng/µl . A 1 . 5 µl aliquot was used for first strand cDNA normalization using the Evrogen JSC Kamchatka crab duplex specific nuclease , DSN ( BioCat GmbH , Heidelberg , Germany ) as described before [71] . After DSN inactivation long distance PCR with primers compatible to the adapters using a proofreading taq polymerase was performed as follows: 95°C for 1 , min , twenty-seven PCR cycles at 95°C for 15 s , 65°C for 30 s , 72°C for 3 min and one cycle at 72°C for 7 min . Finally 40 µl of the solution ( 255 ng/µl ) were sent to Roche Diagnostics Corporation ( 454 Life Sciences ) for pyrosequencing using the 454 platform . Genomic DNA was extracted from 10 g fungal material grown in CM liquid culture using the CTAB protocol of Doyle and Doyle [72] . Sequencing of the genome of P . indica was performed by Eurofins MWG operon , Ebersberg , Germany , using the 454 GS FLX Titanium platform . The performed paired-end pyrosequencing resulted in 1 . 406 . 954 reads with 45 . 392 mate pair candidates . Assembling of the data was accomplished by using the Celera Assembler ( version 5 . 3 , [73] ) and the CABOG pipeline [74] to reduce assembly problems caused by long homopolymeric stretches in the reads . An additional assembly of these contigs was performed by making use of the mate pair information . The final genome set consists of 1 . 884 scaffolds . 7945 degenerate contigs were excluded from the assembly because they failed different quality criteria , e . g . they had low sequence support ( high proportion of bases with low PHRED value [75] ) or a length below 1 kb . GC-content , length and average coverage of both scaffolds and contigs were analyzed by plotting GC-content and average coverage against the contig length using gnuplot ( version 4 . 4 patchlevel 2 , Williams and Kelley , Figure S7 ) . Most of the contigs share a coverage of about 22 fold ( 21 . 74 for contigs ) and a GC-content of about 50% ( 49 . 78% for contigs , 50 . 68% for scaffolds ) . Additionally , 15 contigs had a high coverage of 200 fold and a low GC-content of about 26% . These contigs could be assembled into the circular mitochondrion of P . indica ( see material and methods , analysis of the mitochondrion ) . Contigs with a lower coverage of about 10 fold were also identified . The high number of scaffolds despite use of deep sequencing and the differences in the coverage of the contigs resemble the assembly challenges and coverage differences in the genome project of the diploid human pathogen Candida albicans [37] giving a first indication for the presence of two genomes in P . indica . The presence of two haploid genomes was bioinformatically verified by searching for single nucleotide polymorphisms ( SNPs ) using the swap454 program from the Broad Institute [76] . According to the protocol ( http://www . broadinstitute . org/science/programs/genome-biology/computational-rd/454-help ) a new standard flowgram format ( SFF ) file was created from the raw read sequence fasta and quality files . For the creation of a coverage map the Celera-assembled contig sequences were used as reference . The SNP calling parameters were chosen in such a way that at least 10% of the reads had to differ from the reference sequence in order to be counted as a SNP . With this procedure a total of 61 . 532 SNPs could be identified in the genome ( Table S5 ) of which 1 . 039 ( 1 . 7% ) were identified on degenerate contigs and therefore discarded from further analysis . For the validation of the prediction , the number of SNPs per contig was plotted against its size using gnuplot 4 . 4 . 2 . ( Figure S8 ) . The plot shows a proportional relation between the number of SNPs in a contig to the size of the contigs ( R2 = 0 . 8625 ) which is a first hint of a good reliability of the prediction . Additionally , the predicted number and position of SNPs in the contigs was manually validated in ∼100 randomly chosen contigs using the assembly viewer eagleview [77] with a high degree of consistency ( ∼95% ) . Further , the SNP prediction from the contigs was mapped onto the scaffolds . By doing so , few problems were encountered . First , small contigs without SNPs were occasionally assembled together with contigs with SNPs resulting in a mixture of both datasets . Second , the scaffolds contain a significantly higher number of unknown nucleotides ( “N's” ) than contigs ( 212090 vs 270 ) because of the performed mate pair assembly . These nucleotides could not be considered in the SNP calling . These data are therefore not shown . From all genes that were predicted from the P . indica genome , 1056 ( 8 . 97% ) were found in contigs that did not contain any SNPs . 110 of these genes ( 10 . 42% ) had a signifcant hit against the NCBI nr-database ( eVal: 10−3 ) . RepeatScout [78] was used to identify de novo repetitive DNA in the P . indica genome draft . The default parameters ( with l = 15 ) were used . RepeatScout generated a library of 913 consensus sequences . This library was then filtered as follows: 1 ) all the sequences less than 100 bp in size were discarded; 2 ) repeats having less than 5 copies in the genome were removed ( as they may correspond to protein-coding gene families ) and 3 ) repeats having significant hits to known proteins in Uniprot [79] other than proteins known as belonging to TEs were removed . The 227 consensus sequences remaining were annotated manually by a tblastx search [80] against RepBase ( http://www . girinst . org/repbase/index . html ) . Five sequences have homologies with Class 1 retrotransposons LINE and three with Class 1 LTR retrotransposons copia . Since Class 1 retrotransposons gypsy was not identified in the RepeatScout repeat library and such elements are largely represented in fungi , a rpsblast search [80] with the reverse transcriptase 1 ( RVT1 ) motif ( pfam00078 ) found in Class 1 retrotransposons gypsy was preformed . The 21 putative RVT1 sequences obtained with the rpsblast search were compared by a tblastn search against RepBase . Sixteen sequences have homologies with Class 1 retrotransposons LINE , three with Class 1 retrotransposons Gypsy , one with Class 1 retrotransposons copia and one did not have homology . To identify full length LTR retrotransposons , a second de novo search was performed with LTR_STRUC [81] . No full length LTR retrotransposons were identified . The number of TE occurrences and the percent of genome coverage were assessed by masking the P . indica genome assembly using RepeatMasker [82] ( www . repeatmasker . org ) with the 227 consensus sequences coming from the RepeatScout pipeline . RepeatMasker masked 4 . 68% of the P . indica genome assembly . 4 . 12% of the genome was masked by repeated elements belonging to unknown/uncategorized families ( Table S3 ) . MISA ( http://pgrc . ipk-gatersleben . de/misa/download/misa . pl ) was used to identify mono- to hexanucleotide Simple Sequence Repeat ( SSR ) motifs using default parameters . A total of 602 SSRs have been identified in the P . indica genome corresponding to 213 mono- , 154 di- , 218 tri- , 4 tetra- , 2 penta- and 11 hexanucleotide motifs . The relative abundance of SSRs was calculated as the number of SSRs per Mb . For all 602 SSRs , the relative abundance was 24 SSRs/Mb . For the prediction of tRNAs the program tRNAscan-SE ( version 1 . 23 , [83] ) was used . The prediction was performed on the nucleotide sequences of scaffolds and contigs with the default search mode and eukaryotic gene model . In total 52 standard proteinogenic tRNAs could be identified from which 37 contained introns . Additionally , 2 tRNAs of an unknown isotype and 4 pseudo-tRNAs were predicted by tRNAscan ( Table S4 ) . Codon triplets and a corresponding codon table of A . bisporus , A . nidulans , C . cinerea , C . neoformans , F . oxysporum , H . annosum , L . bicolor , M . larici populina , P . crysoporium , P . indica , P . ostreatus , P . placenta , P . graminis , S . commune , S . lacrymans , S . roseus , T . atroviride , T . mesenterica , T . reesei and U . maydis were calculated from nucleotide sequences of the predicted genes using the programming language JAVA ( http://www . java . com/en/ ) . The codon triplets were then used to calculate frequency plots using WebLogo [84] . The plots show which nucleotide is preferred in each position of the codon triplets and indicate that despite the low number of tRNAs P . indica has very similar codon-usage preferences to those of C . cinerea , P . ostreatus , T . atroviride and A . nidulans . ( Figure S4 ) . A list of all reference genomes used in this study can be found in Table S14 . Gene modelling for P . indica was done by applying 3 different gene prediction programs: 1 ) Fgenesh [85] with different matrices ( trained with Aspergillus nidulans , Neurospora crassa and a mixed matrix based on different species ) ; 2 ) GeneMark-ES [86] and 3 ) Augustus [87] with P . indica ESTs as hints and default gene models for C . neoformans , U . maydis , C . cinerea and L . bicolor . In addition , 857 yeast proteins from CYGD [88] were mapped to the P . indica contigs using Exonerate [89] to help define genes . The mapped genes were used to retrain Augustus ( starting with parameters from the default L . bicolor model ) and subsequently predict new genes . Putative genes were also considered by first mapping annotated proteins from U . maydis , L . bicolor and C . cinerea onto the P . indica genome using Exonerate and then accepting only those P . indica genes that could be mapped back to the original gene structure from the homologous organism . The different gene structures were displayed in GBrowse [90] allowing manual validation of all coding sequences ( CDSs ) . Annotation was aided by blastx hits between the P . indica genome and those from L . bicolor , C . cinerea and U . maydis , respectively . The best fitting model per locus was selected manually and gene structures were adjusted by manually splitting them or redefining exon-intron boundaries based on EST data where necessary . A final set of 11769 protein coding genes were predicted from the P . indica genome . 10350 ESTs were assembled from 454 generated RNA-Seq reads . ESTs were mapped onto the genome using Blat [91] . Evaluation of annotated introns was done against introns defined by ESTs . For 100% identity mapped ESTs without gaps , the sensitivity is ∼89% and specificity is ∼97% . The performance drops to 87 and 95% sensitivity and specificity , respectively for imperfectly mapped ESTs ( Table S1 ) . Furthermore , the predicted protein set was searched for highly conserved single ( low ) copy genes to assess the genome completeness . Ortholog genes to 245 of 246 single copy genes could be identified by blastp comparisons ( eVal: 10−3 ) against the single-copy families from all 21 species available from the FUNYBASE [92] . Additionally , 245 of 248 core-genes commonly present in higher eukaryotes ( CEGs ) could be identified by blastp comparisons ( eVal: 10−3 ) [34] , [35] . The 11769 protein coding genes of P . indica were analyzed and functionally annotated using the PEDANT system [93] , accessible at http://pedant . helmholtz-muenchen . de/genomes . jsp ? category=fungal . The corresponding GBrowse set is located at http://mips . helmholtz-muenchen . de/gbrowse/fungi/cgi-bin/gbrowse/piindica/ . The genome and annotation was submitted to the EBI ( http://www . ebi . ac . uk/GOA/RGI/index . html ) and can be found under the accession numbers listed in Table S17 . For comparative analysis the P . indica proteome and those of four related basidiomycetes , L . bicolor , C . cinerea , U . maydis and C . neoformans , were analyzed using the following tools . 1 ) Secreted proteins were predicted using TargetP and SignalP as described in material and methods , amino acid motifs in P . indica; 2 ) Gene ontologies ( GO ) were assigned using Blast2GO [94]; 3 ) The percentages of assigned GOs in level 4 of molecular function were calculated for the secretome of each of the four related fungi and used for comparative analysis . Cellular targets of the P . indica proteins were predicted by WoLF PSORT ( version 0 . 2 , [95] ) . To improve the accuracy of the program the final output was filtered by allowing predictions only if the “first neighbour” was more than 50% higher than the “second neighbour” . A putative subcellular localization could be assigned to 6 . 341 proteins ( Table S15 ) . The prediction of secreted proteins was performed by using the TargetP software package v1 . 1 [96] ( including cleavage site predictions by SignalP , [97] ) with standard settings for non-plant networks . 1 . 846 proteins were predicted to contain a signal peptide which targets them to the secretory pathway . This set was further refined by excluding all proteins with a low reliability class from the TargetP prediction ( 3–5 ) as well as proteins which contain more than one transmembrane domain according to TMHMM v2 . 0 [98] prediction with standard settings . In total 867 proteins were assigned to the secretome of P . indica . In order to screen the genome of P . indica for known and unknown motifs in the amino acid sequence , a self-written JAVA program based on regular expressions was used which was initially trained on the frequently described oomycetes effector motif “RXLR…EER” [99] . Including three different derivatives of this motif 321 ( 309 degenerated ) RXLR-like motifs could be found in the genome of P . indica . However , only 5 proteins with a degenerated motif possess a signal peptide and none of them were found to be up-regulated during colonization of barley roots ( Table S13 and S10 ) . Further a yet undescribed C-terminal motif with the strongly conserved consensus sequence “RSIDELD” motif could be identified in 29 proteins annotated in P . indica . All of these proteins are less than 135 amino acids in size and contain a significantly increased number of regular distributed alanines and histidines but no cysteines ( compared to the whole proteome; p<0 . 01 ) . To confirm the uniqueness of this motif to P . indica , a psi-blast [100] against the NCBI nr-database as well as a screening against all reference genomes was performed ( Table S12 ) . While the blast search produced only a few hits of low reliability , the motif search identified 43 putative RSIDELD motifs in all genomes of the reference set within 20 bp of the C-terminal region . However , several of the identified motifs differ , in contrast to those from P . indica , significantly from the consensus and none of the proteins showed the regular histidine/alanine distribution or even the increased concentration of these amino acids compared to the DELD proteins from P . indica ( Table S12 ) . LysM and WSC protein domains were identified in the genome of P . indica and all other fungi from the reference set by using the PfamScan perl-script [38] , ftp://ftp . sanger . ac . uk/pub/databases/Pfam/Tools/PfamScan . tar . gz ) and the results were validated with the SMART [101] analysis pipeline using standard settings . The 18 LysM and 36 WSC proteins from P . indica were grouped based on their domain structure and visualized using DOG ( version 1 . 0 , [102] , Table S7 , Figure 5 ) . Because the combination of LysM with other domains is unusual in P . indica the prediction of all 18 genes was verified by PCR on genomic DNA and cDNA . For phylogenetic analysis the LysM and WSC domains of each protein were extracted and concatenated by a self-written JAVA program . LysM and WSC nucleotide ( nt ) and deduced aa sequences were aligned in 4 datasets together with publicly available sequences obtained from GenBank ( http://www . ncbi . nlm . nih . gov ) , PFAM ( http://pfam . sanger . ac . uk/ ) or individual genome sequencing projects . All alignments were constructed at the nt and aa level using ClustalX version 1 . 83 [103] and then manually corrected as needed using BioEdit ( http://www . mbio . ncsu . edu/bioedit/bioedit . html ) . Phylogenetic analyses were performed in two steps . First all available sequences were included in neighbour joining ( NJ ) ( nt and aa ) and maximum parsimony analysis ( nt ) using the program PAUP [104] . The LysM alignments contained data from 186 taxa whereas the WSC alignments contained data from 126 taxa . Parsimony search consisted of 1 , 000 rounds of random stepwise sequence addition with all changes weighted equally and bootstrap analyses consisting of 1 , 000 replicates in heuristic search with random sequence addition ( 10 replicates ) . Heuristic searches were performed using random sequence addition ( up to 50 replicates ) and the tree-bisection reconnection ( TBS ) branch-swapping algorithm . A consensus of multiple trees was performed by majority role and collapsed when conflict present . NJ ( nt and aa ) analyses were conducted utilizing the GTR + I + G model with parameters estimated by the program and 10 , 000 bootstrap replicates or mean character difference . A selection of the closest related sequences was done based on the results obtained from the PAUP phylogenetic analysis of nt and aa alignments . Selected aa sequences were used in a final analyses of single and concatenated domains performed with MrBayes with the fixed ( Wag ) aamodel and a sample frequency of 50 with 500000 and 1000000 generations starting the tree randomly ( Figure 6 and 7 ) . The aa alignment of concatenated LysM sequences contained data from 40 taxa and a data matrix of 306 characters whereas the aa alignment of individual LysM domains contained data from 50 P . indica domains and 3 plant domains and a data matrix of 59 characters . The aa alignment of concatenated WSC sequences contained data from 50 taxa and a data matrix of 794 characters , whereas the aa alignment of individual WSC domains contained a selection of 44 domains and a data matrix of 93 characters . Clustering of proteins was performed using mcl ( version 10–201 , [41] ) according to the online available workflow protocol ( http://micans . org/mcl/man/clmprotocols . html#blast ) . The inflation parameter was defined by clustering with increasing inflation parameters going from 1 to 4 in steps of 0 . 2 . All results were compared with respect to their ability to group LysM and WSC proteins seperately while clustering only P . indica proteins . Based on these results an optimal inflation parameter of 1 . 4 was used for all further clustering procedures . To identify P . indica specific protein families in the basidiomycetes group , a blastp ( eVal: 10−3 ) “all vs all” comparison of the proteomes of P . indica , L . bicolor , C . cinerea , U . maydis and C . neoformans was performed and used as input for the mcl workflow . Within this group , 6704 protein families were identified containing at least two proteins . 355 of these clusters were P . indica specific . The P . indica specific protein families containing 10 or more proteins were manually revised in terms of secretion , regulation during colonization of barley roots and amino acid composition . Almost all of these protein families consisted of moderately to strong plant responsive genes . All 29 DELD proteins occurred in cluster 144 ( 37 proteins in total ) . Additional analysis of the remaining 8 proteins in the group showed either a similar expression pattern or a similar amino acid composition in comparison to the DELD proteins but they did not possess the 7 aa conserved motif . It is still possible that these proteins have a similar function as the DELD proteins and share therefore a certain degree of similarity which groups them together . Clustering of proteins was performed based on predicted functional domains . Protein domains were predicted on the proteomes of C . cinerea , L . bicolor , U . maydis , C . neoformans , P . graminis , T . reesei , A . nidulans , F . oxysporum and T . melanosporum using the PfamScan perl-script . To determine decreased/increased number of proteins in comparison to the other genomes , chi-square-statistics were applied using R ( http://www . R-project . org ) and the whole dataset was filtered for domains with an adjusted significance value of p<0 . 005 ( Table S6 ) . All clusters with a domain number below 5 were discarded . In the resulting data set P . indica protein domains were considered to be enriched when they had the highest number in comparison to the other genomes or to a subset of genomes grouped by lifestyle or phylum . On the contrary , P . indica protein domains were considered to be constraint when P . indica had the lowest or second lowest number of protein members in comparison to the other genomes ( Table S6 ) . Evolutionary changes in protein family size were analyzed using CAFE ( version 2 . 2 , [42] ) . For the identification of protein expansions/contractions , all protein families from the MCL analysis were used that contained at least 5 proteins . From this set , all protein families that are unique to one of the analyzed genomes were excluded . A phylogenetic tree was constructed based on 98 single copy genes from P . indica , L . bicolor , C . cinerea , C . neoformans and U . Maydis , predicted as described in material and methods , evaluation of gene modelling . The CAFE analysis included 3 , 661 protein families ( from 4 , 458 ) . From these , 421 families were expanded in P . indica , 2 , 711 showed no change and 529 families had undergone contraction . Table S8 shows the 62 largest expanded protein family clusters in P . indica . A comparison of the CAFE results to those from the Pfam domain clustering shows the overall good agreement of both methods but reveals also the drawbacks and the necessity to use both methods . The Pfam domain clustering uses no phylogenetic information and counts proteins with different domains multiple times . The MCL/CAFE approach used phylogenetic information and protein similarities but is unable to successfully cluster all functionally related proteins into distinct families . For the assembly of the P . indica mitochondrion , all contigs with either a high coverage or a low GC-content ( Figure S7 ) were assembled in a single scaffold with a length of 63 . 682 bp and a GC-content of 26 . 29% , using the contig assembler seqMan [105] . Circularity was verified by PCR with primers designed at the beginning and at the end of the scaffold . Genes on the mitochondrion were predicted using a program pipeline with different bioinformatical tools . 1 ) Different in silico sheared fragments were analyzed by Blast2GO to identify all genes on the mitochondrion of P . indica . The exon/intron structure of these genes was then refined by building consensuses from multiple sequences alignments produced by the program protein2genome of the Exonerate package . A manual revision of the predictions resulted in a full set of proteins that are commonly present in fungal mitochondrions ( Figure S16 ) . P . indica is able to colonize living plant roots as well as dead plant material . In order to address the fungal gene expression in these two unequal environments , experiments were performed with P . indica growing on living and dead barley roots . P . indica was cultivated on complete medium agar plates or liquid medium as described before [36] . Barley seeds ( Hordeum vulgare L . cv . Golden Promise ) were surface sterilized with 3% sodium hypochlorite , rinsed in water and pregerminated for 3 days in dark . For inoculation of barley roots with P . indica , the roots were dipped in a chlamydospore suspension ( 500 , 000/ml in 0 . 05% Tween water ) or mock inoculated and grown in sterile culture on a minimal medium ( 1/10 PNM ) and under same growth chamber conditions as described in [22] . To address the experimental design four different treatments were done ( P . indica on barley roots on 1/10 PNM medium , P . indica on autoclaved barley roots on 1/10 PNM medium , P . indica on 1/10 PNM medium and P . indica on CM medium ) , each in three independent biological replications . Root and fungal material was harvested in liquid nitrogen after 24 , 36 , 48 , 72 , 120 and 168 hpi . For each time point roots from 15 to 20 living plants or 21 to 36 autoclaved plants were pooled . Total RNA was extracted with TRIzol ( Invitrogen , Karlsruhe , Germany ) following the manufacturer's instructions . RNA quality was analyzed with a 2100 Bioanalyzer ( Agilent , Santa Clara , USA ) . Two independent biological replicates for each treatment were labelled for microarrays analysis . RNA from the time points 36 and 48 hpi of P . indica colonizing roots were pooled together and referred to as the pre-penetration sample . Two more time points were selected for the hybridization , 72 hpi ( early colonization ) and 120 hpi ( late colonization ) . Further RNA from 36 , 48 , 72 and 120 hpi of P . indica grown on CM or PNM were pooled together and used as controls , giving a total of 16 samples . The labelling preparation was performed according to Agilent's One-Color Microarray-Based Gene Expression Analysis ( Quick Amp Labeling ) with Tecan HS Pro Hybridization protocol ( version 6 . 0 ) . For each reaction 500 ng of total RNA from each experiment was used . Cye-3-labeled probes were afterwards hybridised to 2×105k custom-designed Agilent microarrays according to Agilent's One-Color Microarray-Based Gene Expression Analysis ( Quick Amp Labeling ) protocol ( version 5 . 7 ) . The microarray design was performed using eArray ( https://earray . chem . agilent . com/earray/ ) . Up to six 60-mer probes were calculated with the best distribution methodology . Additionally , probes for 265 barley genes ( including genes involved in defense and transport ) , 158 A . tumefaciens genes ( bacterial control ) and 11 P . indica housekeeping genes ( positive control ) were generated . To evaluate the hybridization efficiency within one array , probes from 10 P . indica genes were hybridised randomly in 10 replicates . Microarray image files were analyzed using Agilent's Feature Extraction software v . 10 . 5 . For each spot , signal and background intensities were obtained . To allow for comparison of expression levels across experiments , the raw data were standardized by quantile normalization . To assess the quality of the slides diagnostic plots were generated . Intensities from same-nucleotide probes were averaged . In each group-comparison the log2-ratio between corresponding intensities was calculated and averaged over all probes of an ORF . The Students t-statistic was applied to test ORF signal averages for significant differences between groups . Probes with low reproducibility in the two experiments were discarded from further analysis . The selection of differentially expressed genes is based on a fold change of 2 and an absolute t-statistic of 1 . 96 . Preliminary analysis of the microarrays data indicated that P . indica grown on 1/10 PNM was under conditions of severe starvation , therefore the data from this control were not further used in our study . Gene annotations and expression data from P . indica grown on complete medium and from living and autoclaved barley roots colonized by P . indica are stored in Gene Expression Omnibus ( http://www . ncbi . nlm . nih . gov/geo/ ) under the accession number GSE31266 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=hdabzwmswaqkmxs&acc=GSE31266 ) , which complies with MIAME ( minimal information about a microarray experiment ) guidelines . The R environment and the Bioconductor package ‘Limma’ was used for quality control and normalization of the data . Microarray data were verified by quantitative real-time PCR ( qRT-PCR ) ( Figures S12 and S13 ) . 1 µg of total RNA from all time points ( 24 , 36 , 48 , 72 , 120 and 168 hpi ) and all three independent biological replications was transcribed into cDNA with the First Strand cDNA synthesis kit ( Fermentas , St . Leon-Rot , Germany ) . 10 ng of cDNA was used as template for qRT-PCR using specific primers ( Table S16 ) . Primer design of all primers used in this study were based on Primer3 . Specific primers for the constitutively expressed P . indica Tef gene [106] were used as reference gene . qRT-PCRs were performed in 20 µl iQ SYBR Green Super Mix ( Bio-Rad , München , Germany ) using a iCycler ( Bio-Rad , München , Germany ) and the following amplification protocol: initial denaturation for 10 min at 95°C , followed by 40 cycles with 30 s at 95°C , 30 s at 60°C , 30 s at 72°C and a melt curve analysis . Ct values were determined with the software supplied with the cycler . Relative expression values were calculated using the 2−ΔΔCt method [107] as described previously by [22] . The absence of contaminating genomic DNA was confirmed by performing a control PCR on RNA not reverse transcribed . To identify significantly enriched gene ontology ( GO ) terms from the microarray hybridization experiments the Gene Ontology Enrichment Analysis Software Toolkit ( GOEAST ) was used ( http://omicslab . genetics . ac . cn/GOEAST/index . php ) with settings for customized microarray platform . For the enrichment analysis the probe annotation file for gene ontology terms produced by Blast2GO was used . Induced genes during symbiosis or during growth on autoclaved root material were analyzed using the recommended parameter settings . A table summarizing all enriched GO terms was prepared from the GOEAST output and is shown in Table S11 . To visualize the papillae and the hyphal adhesion zone the carbohydrate binding lectin concanavalinA ( ConA ) coniugated with Alexa Fluor 633 ( ConA-AF633 , Molecular Probes , Karlsruhe , Germany ) , was used . ConA selectively binds to α-mannopyranosyl and α-glucopyranosyl residues found in various sugars , glycoproteins , and glycolipids and it is generally used to visualize glycoproteins . Barley seeds ( Hordeum vulgare L . cv . Golden Promise ) were surface sterilized as described in microarray experimental design . Three days old roots were inoculated with 3 ml of P . indica spore suspension ( 500 , 000 chlamydospores/ml ) . Incubation was performed in a Conviron phytochamber ( 8 h 18°C dark , 16 h 22°C light ) . Two , three , four , five , seven and ten days post inoculation the second cm of the roots below the seed ( differentiation zone ) was excised and stained by infiltration ( two times 4 minutes at 260 mbar ) with ConA-AF633 and wheat germ agglutinin ( WGA ) Alexa Fluor 488 conjugate ( WGA-AF488 , Molecular Probes , Karlsruhe , Germany ) each 10 µg/ml in 1x PBS buffer . 6×1 cm root fragments of independent biological material were analyzed for the presence of ConA stained papillae . Counting of papillae was performed by confocal microscopy ( TCS-SP5 confocal microscope , Leica , Bensheim , Germany ) . Excitation of ConA-AF633 was done at 633 nm and detection at 650–690 nm . Root colonization and barley cortex cells viability were analyzed by confocal microscopy . Colonized roots were stained by infiltration for 10 min with 10 µg/ml WGA-AF488 to visualize fungal structures and 1 µg/ml propidium iodide ( Sigma ) for plant cells in PBS buffer . Membranes were stained with 3 µM FM4–64 ( Molecular Probes , Karlsruhe , Germany ) for 5 min . For imaging of living cells with fluorescein diacetate ( FDA , Sigma ) roots were incubated for 15 min in 1 µg/ml FDA . Root samples were imaged with a TCS-SP5 confocal microscope ( Leica , Bensheim , Germany ) using an excitation at 488 nm for WGA-AF488 and FDA and detection at 500–540 nm . propidium iodide and FM4-64 were excited at 561 nm and detected at 580–660 nm . To determine the nuclear ploidy level of P . indica , chlamydospores were collected from 4-week-old CM-agar plates with 0 . 002% Tween water . Chlamydospores were washed 3 times with 0 . 002% Tween water and resuspend in 0 . 9% NaCl to the final concentration of 1010 spores/ml . The haploid Saccharomyces cerevisiae genotype BY4741 , MATa ( ACC . No . Y02321 , Euroscarf , Frankfurt ) , and the diploid S . cerevisiae genotype FY1679 , MATa/MATa ( ACC . No . 10000D , Euroscarf , Frankfurt ) were used as standards . Yeast cells were collected by centrifugation from 4 days old liquid culture , washed three times with 0 . 9% NaCl and resuspended in the same buffer to a final concentration of 1010 cells/ml . The same volume ( approx . 250 µl ) of P . indica spore-suspension and 1n or 2n S . cerevisiae cells suspensions were mixed together and incubated for 15 minutes in darkness on ice with 0 . 5 µl of Syto 9 and propidium iodide . Excess stain was removed by washing 3 times with 0 . 9% NaCl . Fungal spores and cells suspensions were spread onto glass slides , covered with cover glass and analyzed under confocal laser scanning microscope , Leica TCS SP2 ( Leica , Bensheim , Germany ) . A series of optical sectioning images were taken ( set manually at 0 . 10 µm steps ) for both P . indica and S . cerevisiae after marking the area of each nucleus . Fluorescence of each section of the nucleus was measured using the software provided with the microscope ( LCS , Leica Confocal Software ) . At least seven nuclei were measured for each fungal strain . Based on the assumption that the amount of DNA per cell is directly proportional to the fluorescence intensity [108] the DNA content of the P . indica nucleus was estimated by comparing the histogram mean of the fluorescence intensity with that of the S . cerevisiae standards . | Plant-associated fungi comprise a wide range of lifestyles , such as biotrophy , necrotrophy and hemibiotrophy . Biotrophic fungi require actively metabolizing plant tissues and avoid extensive damage while keeping their host alive . They include pathogenic as well as mutualistic forms . Necrotrophic fungi , which kill host cells in advance of their own growth and obtain nutrients from the dead cells , comprise only pathogenic forms . An intermediate category is represented by the hemibiotrophic fungi . These require living host cells during part of their life cycles , but switch to necrotrophy at later colonization stages with detrimental effects on host survival and fitness and have therefore so far been classified as pathogens . Our study reveals that the mutualistic symbiont Piriformospora indica possesses biotroph-associated genomic adaptations , such as lack of genes involved in nitrogen metabolism and is therefore predicted to suffer from some metabolic deficiencies . In line with biotrophy , P . indica has a limited potential for damage and destruction shared by symbiotic fungi and obligate biotrophic pathogens , i . e . absence of genes potentially involved in biosynthesis of toxic secondary metabolites and cyclic peptides . On the other hand , P . indica shares genomic traits with saprotrophic and hemibiotrophic phytopathogenic fungi , such as the presence of an expanded enzyme arsenal which is weakly expressed during the initial biotrophic phase . Cytological evidence for biotrophic growth followed by a cell death-associated phase that results in a mutually beneficial outcome , supports the idea that P . indica represents a missing link between decomposer fungi and obligate biotrophic mutualists . | [
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] | 2011 | Endophytic Life Strategies Decoded by Genome and Transcriptome Analyses of the Mutualistic Root Symbiont Piriformospora indica |
Polyunsaturated fatty acids ( PUFA ) have a role in many physiological processes , including energy production , modulation of inflammation , and maintenance of cell membrane integrity . High plasma PUFA concentrations have been shown to have beneficial effects on cardiovascular disease and mortality . To identify genetic contributors of plasma PUFA concentrations , we conducted a genome-wide association study of plasma levels of six omega-3 and omega-6 fatty acids in 1 , 075 participants in the InCHIANTI study on aging . The strongest evidence for association was observed in a region of chromosome 11 that encodes three fatty acid desaturases ( FADS1 , FADS2 , FADS3 ) . The SNP with the most significant association was rs174537 near FADS1 in the analysis of arachidonic acid ( AA; p = 5 . 95×10−46 ) . Minor allele homozygotes had lower AA compared to the major allele homozygotes and rs174537 accounted for 18 . 6% of the additive variance in AA concentrations . This SNP was also associated with levels of eicosadienoic acid ( EDA; p = 6 . 78×10−9 ) and eicosapentanoic acid ( EPA; p = 1 . 07×10−14 ) . Participants carrying the allele associated with higher AA , EDA , and EPA also had higher low-density lipoprotein ( LDL-C ) and total cholesterol levels . Outside the FADS gene cluster , the strongest region of association mapped to chromosome 6 in the region encoding an elongase of very long fatty acids 2 ( ELOVL2 ) . In this region , association was observed with EPA ( rs953413; p = 1 . 1×10−6 ) . The effects of rs174537 were confirmed in an independent sample of 1 , 076 subjects participating in the GOLDN study . The ELOVL2 SNP was associated with docosapentanoic and DHA but not with EPA in GOLDN . These findings show that polymorphisms of genes encoding enzymes in the metabolism of PUFA contribute to plasma concentrations of fatty acids .
Polyunsaturated fatty acids ( PUFA ) refer to the class of fatty acids with multiple desaturations in the aliphatic tail . Short chain PUFA ( up to 16 carbons ) are synthesized endogenously by fatty acid synthase . Long chain PUFA are fatty acids of 18 carbons or more in length with two or more double bonds . Depending on the position of the first double bond proximate to the methyl end , PUFA are classified as n-6 or n-3 . Long chain PUFA are either directly absorbed from food or synthesized from the two essential fatty acids linoleic acid ( LA; 18:2n-6 ) and alpha-linolenic acid ( ALA; 18:3n-3 ) through a series of desaturation and elongation processes [1] . The initial step in PUFA biosynthesis is the desaturation of ALA and LA by the enzyme d6-desaturase ( FADS2; GeneID 9415 ) ( Figure 1 ) . PUFA modulate inflammatory response through a number of different mechanisms including modulation of cyclooxygenase and lipoxigenase activity [2] . Cyclooxygenase and lipoxigenase are essential for production of eicosanoids and resolvins [2]–[4] . Since n-3 and n-6 fatty acids compete for the same metabolic pathway and produce eicosanoids with differing effects , it has been theorized that the balance of the two classes of PUFA may be important in the pathogenesis of inflammatory diseases . Epidemiological studies have shown that fatty acid consumption and plasma levels , in particular of the n-3 family , are associated with reduced risk of cardiovascular disease [5]–[7] , diabetes [8]–[10] , depression [11] , [12] , and dementia [13] . However , not all studies show significant associations and there has been inconsistencies in the direction of the associations especially for the n-6 acids [14] , [15] . The different methods ( dietary questionnaire or biomarkers ) for accessing PUFA status may contribute to discrepant results [16]–[18] . The disadvantage of using dietary PUFA intake is the evidence of inaccuracies intrinsic in any reporting methods of dietary intake that plasma levels would circumvent . In addition , direct measures of PUFA reflect the cumulative effects of intake and endogenous metabolism . Dietary fatty acids can be converted into longer chain PUFA or stored for energy thus another reason for inconsistent results may be due the general lack of control for individual differences in metabolism once fatty acids are consumed . Previous studies have examined the association of genetic variants , especially polymorphisms in the FADS genes , on fatty acid concentrations in plasma and erythrocyte membranes [19]–[21] . There are 3 FADS ( FADS1 [GeneID 3992] , FADS2 , and FADS3 [GeneID 3992] ) clustered on chromosome 11 . Variants in FADS1 and FADS2 have been consistently shown to be associated with PUFA concentrations . It is unknown whether other loci also determine fatty acid concentrations . To address this question , we conducted a genome-wide association study of plasma fatty acid concentration in participants in the InCHIANTI study .
Linoleic acid ( LA ) constituted the highest proportion of total fatty acids followed by arachidonic acid ( AA ) ( Table 1 ) The narrow heritability was highest for AA ( 37 . 7% ) followed by LA ( 35 . 9% ) , eicosadienoic acid ( EDA , 33 . 3% ) , alpha-linolenic acid ( ALA , 28 . 1% ) , eicosapentanoic acid ( EPA , 24 . 4% ) , and docosahexanoic acid ( DHA , 12 . 0% ) . For EDA , AA , and EPA , genome-wide significant signals fell in the FADS1/FADS2/FADS3 region on chromosome 11 ( Figure 2 , Figure 3 , Table S1 ) . Of these , the most significant SNP was rs174537 for AA ( P = 5 . 95×10−46 ) , where the variant explained 18 . 6% of the additive variance of AA concentrations . This SNP was significantly associated with EDA ( P = 6 . 78×10−9 ) , and EPA ( P = 1 . 04×10−14 ) . The association with LA ( P = 5 . 58×10−7 ) and ALA ( P = 2 . 76×10−5 ) did not reach genome-wide significance , and there was no association with DHA ( P = 0 . 3188 ) . Presence of the minor allele ( T ) was associated with lower concentrations of longer chain fatty acids ( EDA , AA , EPA ) , but with higher concentrations of LA and ALA ( Table 2 ) . With the exception of DHA , the SNPs exhibiting the strongest evidence of association with the fatty acids examined in this study mapped to the FADS1 , FADS2 , and FADS3 cluster . The most significant SNP for DHA was on chromosome 12 within the SLC26A10 gene ( GeneID 65012 , rs2277324; PDHA = 2 . 65×10−9 ) . In all cases , inclusion of the most significant SNP as a covariate in the model resulted in attenuation of the effect of the other SNPs in the region ( Figure S1 ) . Accordingly , associated SNPs in this region were in significant linkage disequilibrium with each other in the InCHIANTI sample ( Figure S2 ) . To investigate whether this SNP has an effect on other cardiovascular disease risk factors , we examined the association of rs174537 with plasma lipid parameters . Significant association was observed with total cholesterol ( P = 0 . 027 ) and LDL-cholesterol ( P = 0 . 011 ) , but not with either HDL-C ( P = 0 . 775 ) or triglycerides ( P = 0 . 862; Table 2 ) . The minor allele homozygotes ( TT ) had 8 mg/dL lower total cholesterol and 9 mg/dL lower LDL-C compared with GG subjects . To identify other putative chromosome regions associated with fatty acid concentrations beyond the FADS cluster , we examined the top 3 non-redundant ( r2<0 . 2 ) SNPs from the analysis of each fatty acid and selected the SNPs that mapped to candidate gene regions ( defined as 20kb upstream of intron 1 , or downstream of last exon ) for replication in an independent study ( Table S1 ) . These included rs16940765 ( HRH4 [GeneID 59340] , chr18 , PEDA = 2 . 18×10−6 ) , rs17718324 ( SPARC [GeneID 6678] , chr5 , PAA = 7 . 64×10−7 ) and rs953413 ( ELOVL2 [GeneID 54898] , chr6 , PAA = 1 . 1×10−6 ) . In the InCHIANTI study , these four SNPs most strongly associated with a specific fatty acid , unlike FADS cluster that was associated with multiple fatty acids . We noted , however that rs16940765 ( HRH4 ) , rs953413 ( ELOVL2 ) and rs2277324 ( SLC26A10 ) showed significant association at the 0 . 05 level for AA ( P = 0 . 003 ) , DHA ( 0 . 004 ) , and EPA ( P = 0 . 004 ) respectively . In addition , there were four SNPs ( rs953413 , rs1570069 , rs3798719 , rs7744440 ) in the ELOVL2 gene were associated with EPA with p values ranging from 9 . 51×10−5 to 1 . 10×10−6 ( Figure S3 ) . In total , 5 SNPs ( rs174537 , rs2277324 , rs16940765 , rs17718324 , rs953413 ) were selected for replication . In the GOLDN study , there were significant associations of FADS SNP , rs174537 , with ALA , LA , AA , EPA and DHA ( P<0 . 001 ) and marginal association with docosapentaenoic acid ( DPA ) ( P = 0 . 068 ) ( Table 2 ) . As with the InCHIANTI study , presence of the T allele was associated with higher ALA and LA concentration and lower AA , EPA , DPA and DHA concentrations . Consistent with the InCHIANTI study , this SNP was associated with total cholesterol and LDL-C but not triglycerides or HDL-C . We also observed strong associations of rs953413 with docosapentanoic acid ( DPA; P = 0 . 002 ) and DHA ( P<0 . 001 ) . The presence of the minor allele ( A ) was associated with lower DHA and higher DPA and higher AA compared to the minor allele carriers ( Table 2 ) . The remaining 3 SNP ( rs2277324 , rs16940765 , rs17718324 ) were not associated with fatty acid concentrations in the GOLDN study ( data not shown ) .
The genome-wide association approach enables comprehensive examination of the genome to identify novel loci contributing to PUFA homeostasis . In addition , the significance of the genes previously reported in association with PUFA can be assessed relative to other regions in the genome . Here , we demonstrated that polymorphisms in the FADS cluster are the strongest determinants of plasma and erythrocyte fatty acid concentrations , explaining up to 18 . 6% of the additive variance in plasma AA levels . Consistent with prior reports , the greatest evidence of association was observed in the region containing FADS1 , FEN1 ( flap structure specific endonuclease , GeneID 2237 ) , two hypothetical proteins ( C11orf9 [GeneID 745] , C11orf10 [GeneID 746] ) , and the promoter region of FADS2 [20] , [21] . With the exception of EDA , the direction of the association of rs174537 with plasma and erythrocyte fatty acids is consistent with previous reports . We find that there are higher levels of ALA and LA which is suggestive of an accumulation of the initial products of the PUFA metabolic pathway . The cluster of SNP ranging from rs174537 to rs509360 showed the strongest evidence for association , and contains the haplotype block previously examined in relation to plasma and erythrocyte fatty acids [20] , [21] . Based on the HapMap CEU data , the r2 between rs174537 and previously reported SNPs were ≥0 . 8 . If functional polymorphisms exist within this region , it could affect the expression of both desaturases . To this end , in a recent report of genome-wide association of global gene expression , the rs174546 that is in LD with the rs174537 ( r2 = 0 . 99 ) was associated with FADS1 expression ( LOD = 5 , P = 1 . 6×10−6 ) but not FADS2 ( LOD = 0 . 7 , P = 0 . 07 ) in lymphoblastoid cells [22] , [23] . The allele associated with higher AA showed greater expression of FADS1 , consistent with our results . Since FADS1 and FADS2expression varies by tissue type , it would be of interest to examine the effect of the variant on gene expression in other cell types [24] . The T allele associated with lower AA was also associated with decreased LDL-C and total cholesterol . The association with LDL-C was also observed in a large meta-analysis of plasma lipid concentrations in ∼8500 subjects [25] , [26] . In this meta-analysis , there was stronger evidence of association with high density lipoprotein ( HDL-C ) and triglycerides ( TG ) , where the T allele displayed lower HDL-C and higher triglyceride concentrations . Finally , in the Welcome Trust Case-Control Consortium coronary artery disease ( CAD ) study , the T allele was associated with increased prevalence of CAD ( P = 0 . 0375 ) [27] . The increased prevalence of CAD , low HDL-C and high TG is consistent with lower AA concentrations in the T allele carriers . Endogenous PUFA are natural ligands of peroxisome proliferator activating receptor alpha ( PPARA ) [28] . PPARA activation has been shown to elevate HDL-C and lower TG by inducing the expression of ApoA1 , Apo-AII , lipoprotein lipase and suppressing ApoCIII [29]–[32] . Thus the low AA , EPA and EDA in the T allele carriers will results in lower PPARA activation . Under this hypothesis , we would expect the T allele carriers to display higher LDL-C since PPARA is known to enhance LDL-C clearance [33] . However , in both the InCHIANTI and GOLDN study , lower concentrations of LDL-C are observed . It is likely that there are other mechanisms by which fatty acids regulate lipoprotein homeostasis , for example through membrane fluidity . It may be possible , that the higher concentrations of linoleic and linolenic acid in the T allele carriers results in increased membrane fluidity , thus increasing LDL-receptor recycling leading to lower LDL-C . The elongation of very long chain fatty acid ( ELOVL ) family genes are elongases that catalyze the rate-limiting condensation reaction resulting in the synthesis of very long chain fatty acids ( VLCFA ) [34] . To date , six ELOVL genes have been described . The ELOVL1 , 3 and 6 are involved in synthesis of monounsaturated and saturated long chain fatty acids while ELOVL2 , 4 and 5 elongate polyunsaturated fatty acids [35] . In this study , rs953413 in the ELOVL2 was the third most significant SNP in the analysis of EPA , with strong , although not genome-wide level significant association with long chain fatty acids EPA and DHA . In GOLDN , there were no significant associations of this SNP with EPA , but a significant association was observed with DPA . In mammals , two elongation steps are required for the synthesis of DHA from EPA . First , EPA is elongated to DPA , then to 24:5n-3 followed by a desaturation and retroconversion step to form DHA [1] ( Figure 1 ) . The two initial elongation steps of 20 and 22-C fatty acids are mediated by ELOVL2 [36] . The rs953413 is associated with substrate EPA ( InCHIANTI ) , and product DPA ( GOLDN ) and DHA ( both studies ) of the EVOLV2 pathway . Plasma DPA levels were not measured in InCHIANTI , thus whether this association is also observed in this population cannot be investigated . Why the ELOVL2 SNP was not associated with EPA in GOLDN is not clear , however it may reflect the differences in fatty acid metabolism in erythrocytes versus plasma as they reflect two slightly different pools of fatty acids [37] . Plasma fatty acids reflect short term intake of fatty acids whereas erythrocyte levels reflect long term intake . Thus the different results between the plasma and erythrocyte fatty acids may reflect dietary differences between subjects in the GOLDN ( USA ) and the InCHIANTI study ( Italy ) . Regardless of these differences , the results of this study suggestive of the role of ELOVL2 in the conversion of EPA to DHA in humans . The presence of the minor ( A ) allele was associated with higher EPA/DPA and lower DHA . If rs953413 , located in intron 1 , is the functional SNP ( or is in LD with the functional SNP ) , this variant would likely be associated with lower expression of the ELOVL2 or result in a less efficient variant of the elongase resulting in decreased elongation of EPA to DHA . In lymphoblastoid cells , this SNP was not associated with ELOVL2 expression ( LOD = 0 . 4 , P = 0 . 2 ) [22] , [23] . Further investigation in other cell lines and functional analysis of the different variants is warranted . In summary , we have shown that the major loci for fatty acid concentrations in both plasma and erythrocyte membranes are in genes involved in the metabolism of PUFA . The FADS locus on chromosome 11 was the major contributor of plasma fatty acid concentrations , and thus may have implications for cardiovascular disease . In addition , we have identified a second promising locus in ELOVL2 that is involved in the homeostasis of longer chain n-3 fatty acids . Future studies should investigate the interactions between dietary intake , circulating levels of fatty acids and genetic variants on risk of diseases such as cardiovascular disease .
The InCHIANTI study is a population-based epidemiological study aimed at evaluating factors that influence mobility in the older population living in the Chianti region of Tuscany , Italy . Details of the study have been previously reported [38] . Briefly , 1616 residents were selected from the population registry of Greve in Chianti ( a rural area: 11 709 residents with 19 . 3% of the population greater than 65 years of age ) and Bagno a Ripoli ( Antella village near Florence; 4704 inhabitants , with 20 . 3% greater than 65 years of age ) . The participation rate was 90% ( n = 1453 ) and participants ranged between 21–102 years of age . Overnight fasted blood samples were collected for genomic DNA extraction and measurement of plasma fatty acids . Genotyping was completed for 1231 subjects using the Illumina Infinium HumanHap 550 genotyping chip ( ver1 and ver3 chips were used ) . The study protocol was approved by the Italian National Institute of Research and Care of Aging Institutional Review . There were 85 parent-offspring pairs , 6 sib-pairs and 2 half-sibling pairs documented . We investigated any further familial relationships using IBD of 10 , 000 random SNPs using RELPAIR and uncovered 1 parent-offspring , 79 siblings and 13 half-sibling [39] . We utilized the correct family structure inferred from genetic data for all analyses . In addition , we identified 2 duplicated samples and removed these from the study . Sample quality was assessed using the GAINQC program ( http://www . sph . umich . edu/csg/abecasis/GainQC/ ) . The average genotype completeness and heterozygosity rates were 98% and 32% respectively . We excluded subjects that had less than 97% of genotyped completeness ( n = 12 ) , heterozygosity rate of less than 30% ( n = 5 ) and misspecified sex based on heterozygosity of the X chromosome SNPs ( n = 1 ) . The final sample size used for SNP quality control was 1210 . The confirmation study population consisted of 1120 white men and women in the United States participating in the Genetics of Lipid Lowering Drugs and Diet Network ( GOLDN ) Study . The majority of participants were re-recruited from the ongoing National Heart and Lung and Blood Institutes ( NHLBI ) Family Heart Study ( FHS ) [40] in two genetically homogeneous centers ( Minneapolis , MN and Salt Lake City , UT ) . GOLDN is part of the Program for Genetic Interactions ( PROGENI ) Network , a group of NIH-funded intervention studies of gene-environmental interactions . The primary aim of the GOLDN study was to characterize the genetic components of triglycerides response following a high fat meal and hypolipedemic drug , fenofibrate . Detailed study design and methodology has been previously described [41] , [42] . In the replication sample , we excluded persons with missing genotypes or extreme fatty acid values . The final data set consists of information on 1076 individuals . The protocol for this study was approved by the Human Studies Committee of Institutional Review Board at University of Minnesota , University of Utah and Tufts University/New England Medical Center . Written informed consent was obtained from all participants . InCHIANTI: Plasma fatty acid measurement methods has been described previously [43] . Briefly , blood samples were collected in the morning after a 12-hr overnight fast . Aliquots of plasma were immediately obtained and stored at −80 C . Fatty acid methyl esters ( FAME ) were prepared through transesterification using Lepage and Roy’s method with modification [44] , [45] . Separation of FAME was carried out on an HP-6890 gas chromatograph ( Hewlett- Packard , Palo Alto , CA ) with a 30-m fused silica column ( HP-225; Hewlett-Packard ) . FAMEs were identified by comparison with pure standards ( NU Chek Prep , Inc . , Elysian , MA ) . For quantitative analysis of fatty acids as methyl esters , calibration curves for FAME ( ranging from C14:0 to C24:1 ) were prepared by adding six increasing amounts of individual FAME standards to the same amount of internal standard ( C17:0; 50xg ) . The correlation coefficients for the calibration curves of fatty acids were in all cases higher than 0 . 998 in the range of concentrations studied . Fatty acid concentration was expressed as a percentage of total fatty acids . The coefficient of variation for all fatty acids was on average 1 . 6% for intraassay and 3 . 3% for interassay . HDL-C , total cholesterol and triglycerides were determined using commercial enzymatic tests ( Roche Diagnostics , Mannheim , Germany ) . Serum low-density lipoprotein cholesterol ( LDL-C ) was computed with the Friedewald formula ( LDL-C = total cholesterol − HDL-C − triglicerides/5 ) . GOLDN: Fatty acids ( FA ) in erythrocyte membrane were measured following procedures described previously [46] Briefly , lipids were extracted from the erythrocyte membranes with a mixture of chloroform:methanol ( 2:1 , v/v ) , collected in heptanes and injected onto a capillary Varian CP7420 100-m column with a Hewlett Packard 5890 gas chromatograph ( GC ) equipped with a HP6890A autosampler . The GC was configured for a single capillary column with a flame ionization detector and interfaced with HP chemstation software . The initial temperature of 190°C was increased to 240°C over 50 minutes . Fatty acid methylesters from 12:0 through 24:1n9 were separated , identified and expressed as percent of total fatty acid . Triglycerides were measured using a glycerol blanked enzymatic method ( Trig/GB , Roche Diagnostics Corporation , Indianapolis , IN ) and cholesterol was measured using a cholesterol esterase , cholesterol oxidase reaction ( Chol R1 , Roche Diagnostics Corporation ) on the Roche/Hitachi 911 Automatic Analyzer ( Roche Diagnostics Corporation ) . For HDL-cholesterol , the non-HDL-cholesterol was first precipitated with magnesium/dextran . LDL-cholesterol was measured by a homogeneous direct method ( LDL Direct Liquid Select Cholesterol Reagent , Equal Diagnostics , Exton , PA ) . In the InCHIANTI , dietary intake was assessed using a food-frequency questionnaire ( FFQ ) created for the European Prospective Investigation into Cancer and nutrition ( EPIC ) study , and has previously been validated to provide good estimates of dietary intake in this study population [47] , [48] . In GOLDN , habitual dietary intake was estimated using the validated dietary history questionnaire ( DHQ ) developed by the National Cancer Institute [49] . We excluded subjects that reported <800 kcal and >5500 kcal in men and <600kcal and >4500kcal in women . InCHIANTI: Genome-wide genotyping was performed using the Illumina Infinium HumanHap550 genotyping chip ( chip version 1 and 3 ) as previously described [50] . The SNP quality control was assessed using GAINQC . The exclusion criteria for SNPs were minor allele frequency <1% ( n = 25 , 422 ) , genotyping completeness <99% ( n = 23 , 610 ) and Hardy Weinberg-equilibrium ( HWE ) <0 . 0001 ( n = 517 ) . GOLDN: Five SNPs were selected for replication in the GOLDN study: rs953413 , rs2277324 , rs16940765 , rs17718324 and rs174537 . One of these , rs2277324 , failed genotyping and therefore another SNP in high LD , rs923838 ( r2 = 0 . 89 in hapmap ) , was used as a proxy for this SNP . DNA was extracted from blood samples and purified using commercial Puregene reagents ( Gentra System , Inc . ) following manufacturer’s instructions . SNPs were genotyped using the 5’nuclease allelic discrimination Taqman assay with allelic specific probes on the ABI Prism 7900HT Sequence Detection System ( Applies Biosystems , Foster City , Calif , USA ) according to standard laboratory protocols . The primers and probes were pre-designed ( the assay -on -demand ) by the manufacturer ( Applied Biosystem ) ( Assay ID: FEN_rs174537: C___2269026_10 , HRH4_rs16940765: C__32711739_10 , SPARC_rs17718324: C__34334455_10 , ELOVL2_rs953413: C___7617198_10 , rs923828: C___2022671_10 ) . InCHIANTI GWAS: Inverse normal transformation was applied to plasma fatty acid concentrations to avoid inflated type I error due to non-normality [51] . The genotypes were coded 0 , 1 and 2 reflecting the number of copies of an allele being tested ( additive genetic model ) . For X-chromosome analysis , the average phenotype of males hemizygous for a particular allele was treated assumed to match the average phenotype of females homozygous for the same allele . Association analysis was conducted by fitting simple regression test using the fastAssoc option in MERLIN [52] . Narrow heritability reflects the ratio of the trait’s additive variance to the total variance [51] , [53] . In all the analyses , the models were adjusted for sex , age and age squared . The genomic control method was used to control for effects of population structure and cryptic relatedness [54] . An approximate genome-wide significance threshold of 1×10−7 ( ∼0 . 05/495343 SNPs ) was used . For each fatty acid concentration , a second analysis included the most significant SNP from the first pass analysis as a covariate . Linkage disequilibrium coefficints within the region of interest were calculated using GOLD [55] . For the other phenotypes ( total cholesterol , triglycerides , LDL-cholesterol , HDL-cholesterol and BMI ) , the traits were normalized either by natural log or square root transformation when necessary . Associations for each SNP were investigated using the general linear model ( GLM ) procedure in SAS . GOLDN: Inverse normal transformation was applied to erythrocyte membrane fatty acid concentration to achieve approximate normality . For the additive model , genotype coding was based on the number of variant alleles at the polymorphic site . With no significant sex modification observed , men and women were analyzed together . We used the generalized estimating equation ( GEE ) linear regression with exchangeable correlation structure as implemented in the GENMOD procedure in SAS ( Windows version 9 . 0 , SAS Institute , Cary , NC ) to adjust for correlated observations due to familial relationships . Potential confounding factors included study center , age , sex , BMI , smoking ( never , former and current smoker ) , alcohol consumption ( non-drinker and current drinker ) , physical activity , drugs for lowering cholesterol , diabetes and hypertension and hormones . A two-tailed P value of <0 . 05 was considered to be statistically significant . | Polyunsaturated fatty acids ( PUFA ) have a number of beneficial effects on human health . Plasma PUFA concentrations are determined by a combination of dietary intake and metabolic efficiency . To determine the genes involved in PUFA homeostasis , we scanned the genome for genetic variations associated with plasma PUFA concentrations . The fatty acid desaturase gene , studied in previous candidate gene association studies , was the strongest determinant of plasma PUFA . A second gene encoding a fatty acid elongase was associated with long chain PUFA . The results of this study contribute to our understanding of the genetics of PUFA homeostasis . These genetic markers may be useful tools to examine the inter-relationship between diet , genetics , and disease . | [
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Nucleic acid amplification is a powerful molecular biology tool , although its use outside the modern laboratory environment is limited due to the relatively cumbersome methods required to extract nucleic acids from biological samples . To address this issue , we investigated a variety of materials for their suitability for nucleic acid capture and purification . We report here that untreated cellulose-based paper can rapidly capture nucleic acids within seconds and retain them during a single washing step , while contaminants present in complex biological samples are quickly removed . Building on this knowledge , we have successfully created an equipment-free nucleic acid extraction dipstick methodology that can obtain amplification-ready DNA and RNA from plants , animals , and microbes from difficult biological samples such as blood and leaves from adult trees in less than 30 seconds . The simplicity and speed of this method as well as the low cost and availability of suitable materials ( e . g . , common paper towelling ) , means that nucleic acid extraction is now more accessible and affordable for researchers and the broader community . Furthermore , when combined with recent advancements in isothermal amplification and naked eye DNA visualization techniques , the dipstick extraction technology makes performing molecular diagnostic assays achievable in limited resource settings including university and high school classrooms , field-based environments , and developing countries .
The ability to amplify and detect specific DNA sequences is a powerful tool routinely used for a wide variety of applications including disease diagnostics , qualitative trait loci ( QTL ) selection and mutant screening . In diagnostic applications , nucleic acid-based analysis has many advantages over more traditional methods such as enzyme or antibody-based assays offering increased sensitivity , faster sample-to-answer results , and flexibility as it can be rapidly modified to meet new challenges as they arise [1] . However , the major bottleneck preventing the widespread adoption of molecular diagnostics outside the modern laboratory environment is the requirement to purify nucleic acids from samples , which is a complex task that traditionally requires trained technicians and involves many liquid handling steps [2–4] . The demand for simpler and more rapid nucleic acid purification methods resulted in the expansion of commercially available solid-phase extraction kits . A large majority of these kits are based on the binding of nucleic acids to a solid silica support in the presence of a chaotropic salt [5–8] . Contaminants are then removed by a series of wash and centrifugation steps before finally eluting the nucleic acids from the silica in a low salt solution . Commercially available paramagnetic beads with a variety of different functionalised surface chemistries designed to capture and purify nucleic acids have become available , removing the need for centrifugation [9–11] . In these systems , a magnet is used to attract and hold the paramagnetic beads to the side of the tube to allow supernatant removal during the wash and elution steps . Even though paramagnetic particle-based nucleic acid purification is relatively fast ( approximately 10 minutes ) and does not require electrical equipment , it is still too complicated for applications that are performed outside the modern laboratory environment such as field-based point-of-need ( PON ) assays . Recent publications have reported rapid nucleic acid extraction using different types of membranes including aluminium oxide , the cellulose-based Flinders Technology Associates ( FTA ) cards ( GE Healthcare , USA ) , and the silica-based Fusion 5 filters ( GE Healthcare , USA ) [12–18] . These new methods simplified the nucleic acid purification process by eliminating the need for a separate nucleic acid elution step by directly amplifying the nucleic acid off the membrane . This is an advantage over many of the other solid-phase extraction techniques as either the surface chemistries of the matrix or the residual reagents attached to them ( e . g . , ethanol , chaotropic salts ) inhibit DNA amplification [17 , 19] . However , despite eliminating the elution step , all of these methods require relatively complex fabrication or experimental set ups , multiple pipetting steps , or electrical equipment to help purify the nucleic acids , which , again , limit their usefulness for field-based assays . Cellulose-based DNA binding is ideal for molecular diagnostics as it is inexpensive , portable , disposable , and easily modified [20–22] . Therefore , we set out to develop a nucleic acid purification method using cellulose paper that does not require any complex fabrication or specialized equipment such as pipettes and centrifuges . Herein , we describe a simple , equipment-free method that can purify nucleic acids from a wide range of plant , animal , and microbe samples within less than 30 seconds , and is therefore equally suited to nucleic acid-based applications both within and outside the modern laboratory environment .
To develop a simple nucleic acid purification method that does not require modern laboratory facilities , we first investigated the ability of a number of cationic chemicals that could potentially help to capture anionic DNA and RNA by spotting them onto a piece of Whatman No . 1 paper ( GE Healthcare , USA ) . We found that a number of compounds , including chitosan and polyethylenimine ( PEI ) , showed a strong ability to bind nucleic acids ( S1A Fig ) and were further tested for their ability to capture genomic DNA that could be directly amplified from the modified cellulose in a PCR reaction . In these experiments , none of the chemical treatments examined produced reproducible amplification . However , we noted that the control , unmodified Whatman No . 1 paper , consistently resulted in strong amplification ( S1B Fig ) . The ability of cellulose-based paper to entrap or adsorb DNA under specific conditions has been extensively reported , but its use has been limited to storage or transport and not for nucleic acid purification purposes under nonprecipitating conditions [10 , 11 , 23–25] . We further examined the efficiency at which Whatman No . 1 can capture DNA and retain it during a brief ( one minute ) wash prior to DNA amplification directly from the paper . Our results show that the Whatman No . 1 has a relatively high efficiency , as the amplification results were comparable to that observed when an identical amount of DNA template was added directly to the PCR reaction ( Fig 1A ) . These results suggest that cellulose can efficiently bind , or at least entrap , DNA and does not inhibit the amplification reaction . We then devised a simple nucleic acid purification method ( outlined in Fig 1B and described in detail in the Material and methods section ) to test whether it was possible to remove PCR-inhibiting chemical/biological contaminants present in a plant crude extract while retaining enough DNA for amplification . A 7 mm2 ( 3-mm diameter ) disc of Whatman No . 1 paper was added to an A . thaliana leaf extract for one minute before transferring it to a tube containing wash buffer for one minute and finally transferring it to the PCR reaction tube , where it remained for the entire PCR process . The primers used in the PCR were designed to amplify a 262-bp fragment of the Arabidopsis G-protein gamma-1 subunit gene ( At3g63420 ) . No amplification occurred when either 1 μl of extract or a Whatman No . 1 filter soaked in extract was directly added to the amplification mix ( Fig 1C ) . However , briefly washing the extract-soaked filter paper once prior to using the filter directly in a PCR reaction was sufficient to remove amplification inhibitors while retaining the captured plant DNA ( Fig 1C ) . Performing a second wash did not enhance or diminish the amplification efficiency . Although the method was successfully applied to the model plant species A . thaliana , our aim was to develop it further so that it would be suitable for DNA-based diagnostics of commercially important crops capable of deployment in challenging field environments . We successfully applied our cellulose-based DNA purification method to a number of agriculturally important species including wheat , barely , rice , soybean , tomato , and sugarcane ( Fig 2A ) . The method was also successfully used to produce PCR-ready DNA from mature leaves of a number of citrus tree species ( mandarin , lime , and lemon ) ( Fig 2A ) , which are notoriously difficult to extract nucleic acids from due to their high levels of lignin , phenolics , and polysaccharides [26] . Important human diseases such as HIV and Hepatitis can be diagnosed using nucleic acid-based tests from blood samples , although it is essential to remove several inhibitory compounds prior to DNA amplification [27] . For example , reagents such as proteinase K are used to help increase the efficiency of DNA extraction but can themselves inhibit DNA amplification . There are both commercial kits and published methods able to extract DNA from blood , but they require relatively extensive sample manipulation , which makes them suboptimal for PON applications and other limited resource environments ( e . g . , university classrooms and developing countries ) [28–30] . We therefore tested whether cellulose filter paper is capable of purifying DNA from whole blood samples in order to amplify a fragment of the human V-raf murine sarcoma viral oncogene homolog B1 ( BRAF ) gene by PCR ( Fig 2B ) . Cell lysis was achieved by diluting the blood samples 1 in 5 ( v/v ) in an extraction buffer containing proteinase K . Direct addition of the sample to the PCR reaction did not result in detectable amplification . In contrast , immersing the filter paper in the sample followed by a one-minute wash allowed amplification while removing inhibiting compounds from the sample , resulting in a clear amplification product . The cellulose disc method was also successfully used to amplify genomic DNA from melanoma cell line cultures while direct addition of lysate to the PCR reaction mix did not produce any amplicons ( Fig 2C ) . In our opinion , one of the ultimate applications for our method is in molecular diagnostic assays at the PON outside the modern laboratory , replacing the current labor-intensive procedures . To test the ability of the method to detect plant pathogens , we infected Arabidopsis plants with the bacterial pathogen Pseudomonas syringae . Our method successfully extracted and amplified pathogenic DNA even before the symptoms were visible to the human eye ( Fig 3A ) . Furthermore , as the size of the cellulose disc and the tissue to extraction buffer ratio were kept constant between samples , the disease progression could be quantified by PCR as the band intensities increase with increasing disease severity . The cellulose disc method was also successfully used in the detection of an animal bacterial pathogen , Actinobacillus pleuropneumoniae , in a lung swab from an infected pig ( Fig 3B ) . Finally , we tested if the method could be used for the extraction of RNA as many plant and animal pathogens have RNA genomes . Tomato plants infected with cucumber mosaic virus ( CMV ) were tested using the filter paper DNA extraction method without any modifications . In this case , we used recombinase polymerase amplification ( RPA ) ( isothermal ) with the addition of reverse transcriptase to the reaction mix in order to perform reverse transcription and amplification simultaneously in a single tube . An amplification product was obtained in reactions containing reverse transcriptase , while no amplification was observed on uninfected samples or reactions lacking reverse transcriptase ( Fig 3C ) . The cellulose disc method also works in conjunction with other isothermal methods including Loop-mediated amplification ( LAMP ) , which detected the CMV RNA without requiring a reverse transcriptase due to the intrinsic reverse transcriptase activity of the Bst 2 . 0 enzyme [31] ( Fig 3D ) . To explore the mechanism behind DNA capture by Whatman No . 1 paper , we examined whether we could replace it in our simple extraction method with different types of solid supports . Our results show that other cellulose-based papers , including a common hand drying paper towel ( Scott brand “Optimum towel” ) can be used to purify crude plant extracts ( S2A Fig ) . However , not all cellulose papers can be used to purify nucleic acids as common photocopy paper , either bleached or unbleached , failed to amplify a product . We successfully used nylon membranes ( Qiagen Qiabrane , Amersham Hybond-N ) to purify DNA from the plant extract revealing that this method is not limited to cellulose-based supports . Positively charged supports failed to produce amplicons , independently of whether they were nylon- or cellulose-based ( Amersham Hybond-N+ , Qiabrane nylon plus , Hybond-C extra ( nitrocellulose ) and DEAE cellulose ) . This result indicates that materials that are ideal for DNA capture are not necessarily adequate for use in DNA amplification . We determined whether the amount of the cellulose used in the DNA extraction has an effect on the amplification yield . After using 1 , 2 , or 3 discs in the extraction procedure , we observed an inverse relationship between the number of cellulose or nylon discs used and the amount of DNA amplified . It is plausible that increasing amounts of cellulose could result in greater sequestration of primers , deoxynucleotide triphosphates ( dNTPs ) or other reagents in the amplification reaction . Consistent with this , Scott paper towels which have 24% less cellulose by weight compared to Whatman No . 1 resulted in stronger amplification when an equal number of discs were used ( S2B Fig ) . We hypothesised that the mechanism underlying our rapid purification method is based on differences in the kinetics of nucleic acid binding to , and release from , the cellulose . In our model , nucleic acids are able to rapidly bind to the cellulose fibres but are released at a much slower rate . Of note , other components present in the sample extracts , such as amplification inhibitors , either do not bind to the cellulose or are rapidly released and subsequently removed from the cellulose matrix during the brief washing step . As predicted , we found that DNA binds to Whatman No . 1 and rapidly reaches an equilibrium with the surrounding liquid ( Fig 4A ) . Briefly exposing the cellulose disc to a 1 ng/μl DNA solution for seven seconds , followed by a wash step , resulted in strong amplification that could not be improved by longer incubation times . In contrast , during the washing step , we found that DNA was released from the cellulose at a relatively slow rate ( Fig 4B ) . Whatman No . 1 discs with 10 ng of purified genomic DNA directly added to them were washed for varying lengths of time by gently rocking in 10 ml of water before being dropped in the PCR amplification mix . As expected , the disc that was not washed , and therefore contained the entire DNA sample , gave the highest band intensity after amplification . Also , consistent with our hypothesis , Whatman No . 1 washed for up to 24 hours still retained enough DNA to give a positive amplification product . Most importantly , a brief one-minute wash did not greatly affect the amount of DNA retained in the filter as can be seen by the similar intensity of the amplification bands when compared to the no wash control . To better understand the nature of the interaction between the nucleic acids and the Whatman No . 1 , we examined the paper’s surface charge by measuring the streaming potential . We found that the surface of Whatman No . 1 has a negative zeta potential that remains relatively constant across the range of pHs tested ( pH 5 to pH 10 ) ( Fig 5A , S1 Data ) . Similarly , the nylon-based Hybond N membrane , which can also be used to purify nucleic acids , also shows a net negative zeta potential that decreases in absolute value with increasing pH . These results are consistent with previous studies that reported a negative surface charge for cellulose and nylon due to the presence of acidic groups , such as carboxyl groups , on their surface [32 , 33] . As DNA also carries a net negative charge , largely due to its electronegative phosphate backbone , the like charges between the DNA and the Whatman No . 1 surface will result in a repulsive force that will hinder DNA binding . We therefore predicted that the addition of salt could increase the binding of DNA to the Whatman No . 1 paper by counteracting the electrostatic repulsion . Our results support our hypothesis as we observed a 30%–46% increase in DNA amplification from samples diluted in 150 mM NaCl compared to those diluted in water ( Fig 5B ) . Based on the information gained in this study , we aimed to further simplify the nucleic acid extraction method by using a cellulose-based dipstick in order to streamline the handling and eliminate the need to transfer the cellulose disc between tubes . To this end , we designed dipsticks made from Whatman No . 1 with a small 8 mm2 DNA binding surface and a long water repellent handle made by impregnating the filter paper with paraplast wax ( Fig 6A ) . Using these dipsticks , we developed an improved method in which all reagents can be prepared in advance and stored for a long period of time at room temperature . When needed , a nucleic acid extraction can be performed rapidly in three easy steps and less than 30 seconds without a pipette or any electrical device ( Fig 6B , S1 Movie ) . Tissue is first homogenised in a tube containing the appropriate lysis buffer and ball bearings to help macerate the tissue . The cellulose dipstick is used to capture nucleic acids by dipping it into the lysate three times . Contaminants are removed from the dipstick by dipping it up and down in a wash solution three times . Finally , the bound nucleic acids are eluted from the cellulose by dipping the dipstick directly into the amplification mix three times . Using this method , we have successfully demonstrated that it is possible to rapidly purify DNA from plant leaves infected with the fungus Fusarium oxysporum or the bacteria P . syringae ( Fig 6C ) . Additionally , this method works equally well in extracting viral RNA from plant leaves that is suitable for use in reverse transcription PCR amplification ( Fig 6D ) . To validate our newly developed nucleic acid purification method , we compared it with a popular commercial rapid paramagnetic bead DNA extraction method ( Beckman coulter , AMPure ) . We found that our method can purify amplifiable DNA significantly faster: under 30 seconds for our method versus 14 . 5 minutes for AMPure purification when following the manufacturers’ recommended instructions ( Fig 7A ) . Importantly , the method achieves this speed and simplicity without the need for any pipetting . Our method is significantly cheaper , with consumables costing four times less than those required by the AMPure system and does not require the initial investment of USD $685–$876 for the specialized magnet plate . The sensitivity of our method was comparable to the commercial system , as they could both extract amplifiable DNA from initial concentrations of 0 . 1 ng/μl genomic DNA and above ( Fig 7B ) . In some experimental settings , there is a limited supply of sample tissue and it is therefore critical to be able to extract DNA from small volumes of tissue extract . We found that our method was again comparable the commercial system in its ability to purify nucleic acids from tissue extract volumes as low as 0 . 5 μl ( Fig 7C ) .
The development of new molecular technologies for PON diagnostics is now proceeding and an unprecedented pace . Nucleic acid-based ( molecular ) assays offer greater sensitivity , specificity and speed over other technologies including enzyme-linked immunosorbent assay ( ELISA ) , lateral flow strips and cell culture/analysis [1 , 34 , 35] . As such , molecular assays have the potential to revolutionize the early detection and continual monitoring of human , plant , and animal diseases . However , a major bottleneck in molecular diagnostics is that they rely on nucleic acid purification , which is a relatively time consuming and laborious procedure that is not easily performed outside the modern laboratory environment ( e . g . , field-based testing ) [36–38] . Our study reveals that a small ( approximately 8 mm2 ) piece of cellulose-based paper is capable of purifying nucleic acids away from inhibitors in a wide range of plant , animal , and microbial samples including whole blood and mature tree leaves ( Figs 2 and 3 ) . To make the method suitable for field-based testing , we incorporated the knowledge gained in this study to create a cellulose-paper-based dipstick that can bind , wash , and elute purified nucleic acids in under 30 seconds without requiring any pipetting or electrical equipment ( S1 Movie ) . A significant advantage of the method presented here is that the amount of nucleic acid transferred to the amplification reaction will be similar between samples of the same type because the size of the DNA binding surface on the cellulose dipstick remains constant . Furthermore , the system can be fine-tuned by altering the size of the DNA binding surface in the dipstick , thus optimising the amount of nucleic acid transferred for downstream applications . This is an important feature as it provides flexibility to adapt the method to different tissues ( e . g . , plant leaves , blood , and saliva ) depending on the intended application . We have demonstrated that , despite its speed and simplicity , this method is comparable to a commercially available nucleic acid purification method in its ability to prepare amplification-ready template nucleic acids ( Fig 7 ) . Therefore , we believe that the speed , simplicity , and universality of this method makes it an attractive option in a broad range of diagnostic applications both within and outside the laboratory environment . As this study was focused on creating a simple , pipette-free nucleic acid purification method and not a complete molecular diagnostic system , a mains powered thermocycler was used for most reactions , which is obviously not ideal for research outside the laboratory environment . We have demonstrated that our method can be coupled with isothermal DNA amplification technology ( Fig 3C and 3D ) , which have previously been successfully performed using simple portable devices that generate heat using either a small battery or chemical heat pad [39–42] . Therefore , it is conceivable that a simple molecular diagnostic assay that requires minimal equipment and no pipetting can be created by coupling our dipstick nucleic acid purification system with isothermal DNA amplification and equipment-free naked eye visualisation methods [16 , 21 , 43–48] . Such a system would not only be ideal for field-based PON diagnostics but would make molecular-based testing more accessible to a greater spectrum of the community including those in university and high school classrooms , farmers , biosecurity , and remote/resource-limited environments . Overall cost , including the equipment required to perform the procedure , is a major determinant in the likelihood of broad scale adoption of new diagnostic technologies [49 , 50] . The cellulose dipstick purification method described here significantly increases the affordability of nucleic acid purification , with the price per sample being $US 0 . 15 including plasticware and reagents , with the ball bearings being the major contributor to the final price ( Fig 7A ) . If the ball bearings used to homogenize the tissue are washed and reused , the cost can be further reduced to just $US 0 . 06 per sample . Whatman No . 1 paper is cheap and easy to obtain but not absolutely necessary as common paper towels proved to be equally efficient , providing an even cheaper alternative . The dipstick-purification system presented here has numerous advantages over the vast array of commercially available and published nucleic acid extraction procedures . The most obvious advantage is that it is significantly faster and has fewer steps than common liquid-based DNA extraction methods ( e . g . , phenol/chloroform , hexadecyltrimethylammonium bromide [CTAB] , or guanidinium salts ) or solid-phase DNA extraction methods involving silica or paramagnetic beads ( Fig 7 ) [5 , 9 , 51–53] . The method is more closely related to a number of recently developed DNA extraction methods that utilize commercially available filters , including the silica-based Whatman Fusion 5 and the cellulose-based Whatman FTA cards [12 , 14 , 15 , 54] . However , our method is much simpler and faster than any of the available membrane-based procedures . For example , FTA cards contain chemicals that lyse cells and protect the DNA from degradation and have been used for over a decade as a means to store and preserve DNA samples before processing [55–57] . These chemicals are inhibitory to DNA amplification and therefore must be removed through a number of washing and drying steps [14] before the DNA can be amplified from the FTA card . Additionally , unlike the 2-minute Fusion-5-based purification method , which can only capture DNA [13 , 15] , our method using Whatman No . 1 can also be used to extract RNA suitable for reverse-transcription and subsequent DNA amplification ( Figs 3C , 3D and 6D ) . The use of cellulose for DNA purification is commercially available and is claimed to have improved performance over silica-based DNA purification methods [58 , 59] . DNA purification using cellulose has been previously reported to be achieved by coaggregating or adsorbing the DNA to the cellulose in the presence of various chemicals , including chaotropic salts [60] , ethanol [23 , 61] , and high salt concentrations and/or crowding agents such as polyethylene glycol [11 , 62 , 63] , which destabilise the DNA structure and facilitate its interaction with the cellulose fibres . In these systems , water or a low salt solution is then required to elute the DNA from the cellulose . In contrast , we found that cellulose can capture DNA in pure water ( Fig 5B ) and , moreover , retain the DNA in the presence of a large volume of water for over 24 hours ( Fig 4B ) . Furthermore , we demonstrated that the method is not dependent on a specific buffer but could successfully purify nucleic acids from crude extracts with either guanidine hydrochloride-based ( e . g , . Figs 2B and 3B ) , SDS-based ( Fig 6C and 6D ) , or Tween 20-based ( e . g . , Fig 1C ) extraction buffers . Although the exact mechanism of nucleic acid binding remains unclear , we observed that the amount of nucleic acid bound can be enhanced in the presence of salts ( Fig 5B ) . This is likely due to the neutralization of the negative charges on the surfaces of both the cellulose and nucleic acids thereby eliminating the repellent electrostatic forces between them . Our cellulose-based method for nucleic acid purification takes advantage of four key cellulose characteristics . First , cellulose paper is capable of rapidly absorbing a relatively large amount of DNA and/or RNA relative to its mass through capillary action [64] . Second , nucleic acids are either rapidly entrapped by , or bind to , the cellulose fibers ( Fig 4A ) . Third , a sufficient amount of nucleic acid is retained on the cellulose even after extended incubation in a large volume of water , while inhibitors including Proteinase K and cellulosic and phenolic compounds are rapidly eluted ( Figs 1C , 2B and 4B ) . Lastly , unlike positively charged membranes ( S2A Fig ) , cellulose enables rapid elution of a sufficient quantity of bound nucleic acids into the amplification mix ( Fig 6C and 6D ) . This rapid elution from the cellulose is likely catalysed by dNTPs present in the amplification mix as has been reported for other systems [65] . Collectively , these characteristics make cellulose an ideal material for a rapid and simple nucleic acid purification system that easily separates unwanted contaminants and inhibitors away from nucleic acids while also transferring a reproducible amount of nucleic acids into the amplification mix . We have presented here a simple and rapid technique that allows researchers to obtain nucleic acid at a suitable purity and concentration for DNA amplification . We have reduced a complicated process to three simple steps that do not require any specialized equipment ( e . g . , pipettes or centrifuges ) and takes less than 30 seconds to perform ( S1 Movie ) . As such , the simplicity and speed of this method as well as the low cost and availability of suitable dipstick materials means that nucleic acid extraction is now more accessible and affordable for a wide variety of people and applications both inside and outside the laboratory environment .
Human research ethics approval was obtained from The University of Queensland Institutional Human Research Ethics Committee ( Approval No . 2004000047 ) . Animal research ethics approval was obtained from The University of Queensland Animal Ethics Committee ( Approval No . AE14701 ) . Plant materials used in this study included A . thaliana ecotype Columbia , tobacco ( Nicotiana benthamiana ) , tomato ( Solanum lycopersicum cv . MicroTom ) , sugarcane ( Saccharum officinarum cv . Q208 ) , sorghum ( Sorghum biocolor cv . IS8525 ) , soybean ( Glycine max cv . Bunya ) , rice ( Oryza sativa cv . Topaz ) , barley ( Hordeum vulgare line 21:ZIB14 ) , wheat ( Triticum aestivum line S19-49 ) , mandarin ( Citrus reticulata ) , lime ( C . aurantiifolia ) , lemon ( C . limon ) , passion fruit ( Passiflora edulis ) . Diseased plant materials included A . thaliana leaf tissue infected with P . syringae pv tomato strain DC3000 or F . oxysporum f . sp . conglutinans , and tomato leaf tissue infected with cucumber mosaic virus . Human samples included melanoma cell line LM-MEL-70 and blood . Diseased animal material was harvested by a swab from a pig lung infected with Act . pleuropneumoniae . To investigate nucleic acid—cellulose interaction , A . thaliana ( ecotype Columbia ) DNA was extracted by modified CTAB DNA extraction [66] . A . thaliana leaves were finely ground using liquid nitrogen , and approximately 100 mg of leaf powder was mixed with 500 μl of extraction buffer ( 2% w/v CTAB , 1 . 42 M NaCl , 20 mM EDTA , 100 mM Tris HCl [pH 8 . 0] , 1% w/v polyvinylyrilodone [PVP 40] ) that was preheated 60°C . After 45 minutes at 60°C , 500 μl of chilled chloroform: isoamyl alcohol ( 24:1 , v/v ) was added into the mixture and rocked gently at room temperature for 15 minutes , followed by centrifugation at 15 , 000 xg for 10 minutes . 200 μl supernatant was transferred to a new tube and mixed gently with 400 μl chilled ethanol . After incubation at −20°C for 1 hour , the sample was centrifuged at 15 , 000 xg for 10 min to pellet the DNA . The pellet was washed with 80% ethanol , followed by 100% ethanol . DNA was suspended with 100 μl of H2O and 50 μg RNase A and followed by incubation at 37°C for 20 minutes to degrade RNA . 10 μl of 3 M sodium acetate and 100 μl of isopropanol were added into sample and incubated at -20°C for 10 minutes . DNA was pelleted by centrifugation at 15 , 000 xg for 2 minutes and washed with 80% ethanol . After air drying , the DNA was suspended with 50 μl of H2O and quantified with NanoDrop ND-1000 spectrophotometer . A number of chemicals with potential DNA binding capability were selected including spermine , PVP 40 and the cationic polymers: PEI , dopamine , 3-aminopropyl trimethoxysilane ( APTMS ) , and chitosan . Solutions were made containing the chemicals at either 1 . 25% ( w/v ) ( Chitosan , APTMS , PEI ) or 2 . 5% ( w/v ) ( dopamine , spermine , PVP-40 ) . 1 μl of each solution was carefully added to two 70-mm Whatman No . 1 discs approximately 10 mm from the centre of the disc . The chemicals were allowed to fully dry onto the paper before viewing the filter under UV light to assess the amount of fluorescence each chemical induces in the absence of DNA . 150 μl of 500 ng/μl salmon sperm DNA ( Sigma ) labelled with 0 . 5% ( v/v ) GelRed ( Biotum ) and buffered in either 50 mM MES ( pH 5 ) or 50 mM Tris ( pH 8 . 5 ) was added to the centre of each Whatman No . 1 disc . After approximately 5 minutes , the movement of DNA by capillary action had stopped and the cellulose disc was viewed under UV light . DNA binding by the chemical was indicated by brighter fluorescence over the background . 3-mm diameter discs were cut from a piece of Whatman No . 1 using a hole puncher , to which 1 μl of 1 . 25% ( w/v ) chitosan in 50 mM acetic acid was added . After the chitosan had dried , the cellulose discs were incubated for one minute in 20 μl of 100 pg/μl purified Arabidopsis DNA buffered with 10 mM MES ( pH5 ) . The cellulose pieces were transferred into a tube containing 200 μL of 10 mM MES ( pH5 ) or 10 mM Tris ( pH8 . 5 ) , briefly agitated by pipetting up and down and then incubated for one minute prior to transferring into a PCR reaction . The results of the amplification reaction were visualised by agarose gel electrophoresis . For nucleic acid purification from plant tissues , 5–10 mg of leaf tissue was ground in a 1 . 5-ml tube with a plastic pestle in presence of 50 μl extraction buffer #1 ( 50 mM Tris [pH 8 . 0] , 150 mM NaCl , 2% PVP , 1% Tween-20 ) for approximately 30 seconds . A 3-mm diameter disc was cut from a piece of Whatman No . 1 using a hole puncher and transferred into the tissue extract for a minimum of three seconds . The disc was then transferred to 200 μl of wash buffer ( 10 mM Tris [pH 8 . 0] , 0 . 1% Tween-20 ) using a pipette tip to remove contaminants including amplification inhibitors . After one minute , the disc containing nucleic acid was then transferred into an amplification reaction using a pipette tip . For RNA purification from CMV-infected tomato leaves , samples were prepared as described above with the exception that 50 μl of extraction buffer #2 ( 800 mM guanidine hydrochloride , 50 mM Tris [pH 8] , 0 . 5% Triton X100 , 1% Tween-20 ) was used to lyse the samples instead of extraction buffer #1 . For DNA purification from blood , samples were mixed with four volumes of extraction buffer #2 with the addition of 40 μg/ml Proteinase K to aid DNA extraction . For DNA purification from human cell lines , 100 μl of cultured LM-MEL-70 cells were gently spun down ( 500 xg , 5 minutes ) , washed in 1XPBS buffer , and lysed by adding 200 μl extraction buffer 2 and vortexing for 10 seconds . A 3 mm Whatman No . 1 disc was incubated in cell lysate for one minute . The cellulose disc was transferred to 200 μl of wash buffer for one minute before transferring to PCR reaction mix . For DNA extraction from the pig lung , the surface of the lung was sterilised by cauterization with a hot spatula . An incision was made in the cauterized area of the lung and a cotton swab inserted into the inner lung tissue and then dropped into 500 μl of extraction buffer #3 ( 1 . 5 M guanidine hydrochloride , 50 mM Tris ( pH 8 ) , 100 mM NaCl , 5 mM EDTA , 1% Tween-20 ) . A 3 mm Whatman No . 1 disc was incubated in the extract for one minute . The cellulose disc was transferred to 200 μl of wash buffer for one minute before transferring to PCR reaction mix . Unless otherwise stated , experiments that investigated DNA binding or release from cellulose were performed as follows . A hole puncher was used to generate 3-mm diameter cellulose discs from a piece of Whatman No . 1 filter paper . Purified Arabidopsis genomic DNA was pipetted onto individual cellulose discs that were subsequently transferred into a tube containing 200 μl of wash buffer ( 10 mM Tris ( pH 8 . 0 ) , 0 . 1% Tween-20 ) using a pipette tip . After one minute in wash buffer , the cellulose discs were transferred into a PCR amplification reaction and the amplifications including control reactions , performed in a thermocycler as described below . The same method was used to compare the performance of various commercially available membranes including Whatman No . 1 and No . 4 ( GE Life sciences ) , Whatman DEAE-cellulose ( GE Life sciences ) , filter paper ( Invitrogen ) , blotting filter paper ( Immobilon ) , FL PVSF ( Immobilon ) , Hybond N and N+ ( Amersham ) , nylon and nylon plus ( Qiabrane ) , hybond-C extra ( Amersham ) , bleached and unbleached photocopy paper ( Australian paper ) , Scott Optimum hand towel ( Kimberly-Clark ) . For analysis of DNA release from the cellulose , the disc containing 10 ng of purified DNA was then transferred into 10 mL of wash buffer ( 10 mM Tris [pH 8 . 0] , 0 . 1% Tween-20 ) and placed on a Mini rocker ( Bio-Rad ) and agitated gently for 0 to 24 hours . After agitation , the cellulose disc was transferred into a PCR reaction and the amplification performed in a thermocycler . An electrokinetic analyzer for solid surface analysis ( SurPASS , Anton Paar ) was used to determine surface charge of Whatman No . 1 and Hybond-N filters . Two pieces of each filter ( approximately 15 mm x 25 mm ) were fixed on opposite sample holders by double-sided adhesive tape with an approximately 100 μm gap between each other . The streaming potential resulting from pressure-driven 10 mM NaCl flowing through the gap was measured . The pH of the solution was automatically altered by the addition of 100 mM NaOH . The streaming potential of paper as well as pH of the NaCl solution was recorded at set intervals . Dipsticks were created by dipping half of a Whatman No . 1 filter into molten wax ( Paraplast Plus , Fluka ) to create a region that is impervious to water . After the wax had set , the partially wax-coated filter paper was cut into a 44 mm-wide rectangle , of which approximately 40 mm was coated in wax and 4 mm was uncoated . This rectangle was then cut into approximately 2 mm-wide strips to create dipsticks with a 2x4 mm nucleic acid binding area and a 2x40 mm handle . For nucleic acid purification using the dipsticks , leaf tissue ( approximately 200 mm2 ) was added to a 2 mL tube containing 500 μl cell lysis buffer ( 20 mM Tris [pH 8 . 0] , 25 mM NaCl , 2 . 5 mM EDTA , 0 . 05% SDS ) , and two ball bearings . The plant tissue was macerated by shaking the tube for approximately eight seconds . The dipstick was dipped into extract to bind nucleic acids then dipped into 1 . 75 mL of wash buffer ( 10 mM Tris [pH 8 . 0] , 0 . 1% Tween-20 ) and then finally the bound nucleic acids were eluted by dipping the dipstick directly into amplification reaction . Each time the dipstick was dipped up and down in each solution three times , taking approximately three seconds . After elution , the dipstick was discarded and the DNA amplification reaction transferred to a thermocycler . Agencout AMPure XP PCR Purification kit ( Beckman Coulter ) was used to purify DNA following the manufacturer’s recommendations . Briefly , one volume of sample was mixed with 1 . 8 volumes of paramagnetic particles . The mixture was incubated at room temperature for five minutes and then placed onto magnetic plate ( Life technologies ) for two minutes to pull down DNA-bound paramagnetic particles . After supernatant was removed , paramagnetic particles were washed twice with 70% ethanol . After the 70% ethanol from the last wash was removed , the paramagnetic beads were air dried for five minutes . The bound DNA was eluted by resuspending the particles in 40 μl of water and incubating at room temperature for one minute before pulling the particles down to the bottom of the tube by magnet . The supernatant containing purified DNA was transferred to a new tube and was later used as template DNA in PCR amplifications . Nucleic acid amplification was performed by either PCR , LAMP , or RPA . For PCR amplification , 15 μl reactions were performed using 7 . 5 μl of GoTaq Green Master Mix ( Promega ) , 15 pmol of both forward and reverse primers ( S1 Table ) , and template DNA . Unless otherwise stated , PCR cycling parameters were as follows: 95°C for two minutes , 35 cycles of 95°C for 20 seconds , 55–60°C for 20 seconds , 72°C for 40 seconds , followed by final extension of 72°C for one minute . Quantification of PCR band intensities was performed by using the “Analyse: Gels” function within ImageJ ( V1 . 48 ) software [67] . For reactions involving RPA , either the TwistAmp Basic RPA or Basic RT-RPA kits ( Twist DX ) were used as per manufacturer’s recommendations . Briefly , each RPA pellet was resuspended with 29 . 5 μl of rehydration buffer and 0 . 8 μM of both forward and reverse primers ( S1 Table ) . The mix was then aliquoted evenly into four 0 . 2 ml tubes into which the template DNA , water , and 0 . 625 μl of 280 mM magnesium acetate are added to make a final volume of 12 . 5 μl . RPA or RT-RPA reactions were performed at 37°C or 42°C for 20 minutes , respectively , and the results visualised by agarose gel electrophoresis . For reactions involving LAMP , cellulose discs containing extracted nucleic acids were added to 15 μl reactions containing: 20 mM Tris ( pH 8 . 8 ) , 10 mM ( NH4 ) 2SO4 , 50 mM KCl , 0 . 1% ( v/v ) Tween-20 , 0 . 8 M betaine , 8 mM MgSO4 , 1 . 2 mM dNTPs , 4 . 8 U Bst2 . 0 warmstart ( NEB Biolabs , USA ) , 0 . 8 μM of FIP and BIP primers , and 0 . 2 μM of F3 and B3 primers . Reactions were incubated at 63°C for 50 minutes , followed by a five-minute incubation at 80°C to denature the enzyme . | Nucleic acid amplification has proven to be indispensable in laboratories around the world for a myriad of applications from diagnostics to genotyping . The first step in any application aiming to amplify DNA or RNA is the extraction of nucleic acids from a complex biological sample; a task traditionally requiring specialised equipment , trained technicians , and multiple liquid handling steps . It is this complexity of current nucleic acid isolation methods that limit the use of many DNA amplification technologies outside of the modern laboratory environment . Therefore , in this study , we investigated new materials and approaches to simplify nucleic acid extraction . We found that cellulose-based filter paper can be used to rapidly bind nucleic acids , retain them during a short washing step to remove contaminants , and then elute them directly into the amplification reaction . We then adapted the cellulose filter to create a dipstick that can be used to purify nucleic acids from a wide range of plant , animal , and microbe samples in less than 30 seconds without the need for any specialised equipment . The speed and simplicity of our method makes it ideally suited for nucleic acid amplification-based applications both within and outside the laboratory , including limited resource settings such as remote field sites , developing countries , and teaching institutions . | [
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... | 2017 | Nucleic acid purification from plants, animals and microbes in under 30 seconds |
The pathways that comprise cellular metabolism are highly interconnected , and alterations in individual enzymes can have far-reaching effects . As a result , global profiling methods that measure gene expression are of limited value in predicting how the loss of an individual function will affect the cell . In this work , we employed a new method of global phenotypic profiling to directly define the genes required for the growth of Mycobacterium tuberculosis . A combination of high-density mutagenesis and deep-sequencing was used to characterize the composition of complex mutant libraries exposed to different conditions . This allowed the unambiguous identification of the genes that are essential for Mtb to grow in vitro , and proved to be a significant improvement over previous approaches . To further explore functions that are required for persistence in the host , we defined the pathways necessary for the utilization of cholesterol , a critical carbon source during infection . Few of the genes we identified had previously been implicated in this adaptation by transcriptional profiling , and only a fraction were encoded in the chromosomal region known to encode sterol catabolic functions . These genes comprise an unexpectedly large percentage of those previously shown to be required for bacterial growth in mouse tissue . Thus , this single nutritional change accounts for a significant fraction of the adaption to the host . This work provides the most comprehensive genetic characterization of a sterol catabolic pathway to date , suggests putative roles for uncharacterized virulence genes , and precisely maps genes encoding potential drug targets .
Mycobacterium tuberculosis ( Mtb ) is a deadly human pathogen , which is estimated to infect one third of the world's population and killed 1 . 7 million people in 2009 alone [1] . This bacterium must adapt to a number of different microenvironments over the course of a chronic infection [2] , and understanding the physiological state of the pathogen in these sites is central to the design of more effective antitubercular drugs . Defining these metabolic adaptations requires a holistic understanding of the cellular pathways that are critical for survival in each specific environment . While whole-genome methods to profile mRNA , protein , or transcription factor binding are valuable for understanding cellular responses , gene regulation has proven to be a poor predictor of essentiality [3] , [4] . Therefore , genome-wide approaches to accurately and directly assess the relative contribution of each gene to the growth and survival of M . tuberculosis are needed . The genome-scale methods initially used to predict gene essentiality in bacteria employed either microarray hybridization [5] , [6] or conventional sequencing [7] , [8] , [9] to map the sites of transposon insertions in large libraries of random mutants and identify regions of the chromosome that were unable to sustain mutation . Both of these methods suffer from significant limitations . Microarrays are not able to precisely map insertions , resulting in ambiguity regarding the precise location of an essential region . While conventional sequencing can surmount this problem , it is both cumbersome and expensive to apply this method to the very complex libraries necessary for the unambiguous identification of essential genes . Finally , neither of these approaches can be used to quantitatively compare the compositions of mutant pools over more than a very small dynamic range . In order to precisely define the genes that are important for the growth of Mtb , we used highly parallel Illumina sequencing to characterize transposon libraries . This approach achieves single base pair resolution of insertion sites in very complex libraries , resulting in the precise identification of essential open reading frames . Furthermore , the depth of sequencing that is possible allowed the relative abundance of individual mutants to be accurately assessed in libraries grown under different conditions . Using the latter approach , we comprehensively defined the pathways required for the bacterium to grow on cholesterol , a critical nutrient during infection . These previously ill-defined pathways account for an unexpectedly large fraction of the genes required for survival in animal models of TB , providing new functional insight into the adaptation to the host environment .
A library of transposon mutants consisting of approximately 105 independent insertion events was generated in the H37Rv strain of Mtb using a modified himar1 based transposon [5] . Replicate portions of this library were grown for 12 generations in defined media containing different carbon sources , and chromosomal DNA was extracted from each pool . This DNA was randomly fragmented , ligated to asymmetric adapters , and transposon chromosome junctions were amplified using PCR . Illumina sequencing was then used to determine the sequences of 6 to 8 million transposon:chromosome junctions per library . 95–99% of these sequences represented the specific amplification of transposon insertions ( see methods ) and were used for subsequent analyses . The transposon used in these studies requires a TA dinucleotide insertion site . We identified transposon insertions at 44 , 350 of the 74 , 605 possible TA sites in the genome ( Table S1 ) . This corresponded to approximately one insertion every 100 base pairs on average and was consistent with the previously described random insertion of this element [9] , [10] , [11] . The identified insertions were distributed throughout the chromosome ( Figure 1A ) , with the exception of small gaps , many of which corresponded to known essential genes in which we expected insertions to be lethal . For example , a small region of the chromosome encodes several of the enzymes required for the synthesis of the essential cofactor , heme , ( Figure 1B ) and we found that gaps in transposon coverage precisely mapped to these open reading frames ( ORFs ) . In contrast , we found that other likely essential genes could harbor a small number of insertions ( not shown ) , consistent with previous observations [12] . Therefore , we globally defined essential ORFs by searching for genes with statistically significant gaps in transposon insertion coverage , instead of simply compiling those that were absolutely devoid of insertions . We identified the longest sequence of TA sites lacking insertions within a given gene , and determined the likelihood of a non-essential gene harboring a gap of that length ( modeled as a Bernoulli process ) as a function of the total number of TA sites in the ORF . We then calculated the probability of the longest observed gap arising by chance , relative to the Extreme Value Distribution ( Table S2 ) . This analysis method exploited the value of the high-resolution data provided by deep sequencing , while allowing for permissive insertion sites in essential genes . While the himar1 transposon used in these studies has been shown to integrate relatively randomly , it remained possible that some regions of the chromosome were resistant to transposition and genes in these regions would erroneously appear to be required for viability . To address this possibility we determined if the identified gaps in transposon coverage corresponded to protein-coding regions . As shown in Figure 1C , the probability of identifying a transposon insertion in or near a putative essential region is highly correlated with the position of the corresponding predicted ORFs . This probability increased at the extreme ends of the gene , likely because many insertions at these sites do not disrupt gene function . Together , these data indicate that regions devoid of sequenced transposon insertions represent protein-coding genes that are important for the growth or survival of the organism and not regions that are less accessible to transposition . Not surprisingly , we were also able to identify significant gaps in transposon coverage that did not correspond to predicted ORFs . Determining which of these regions correspond to unannotated protein-coding genes , essential untranslated RNAs , or regulatory motifs will require further study . This deep sequencing-based approach for identifying growth-attenuated mutants both validates and improves upon our previous microarray-based studies . About 15% of the Mtb genome , or 614 genes , were previously predicted to be required for optimal growth in vitro using microarray hybridization to map insertions sites [6] . Our current deep sequencing approach identified a somewhat larger number of genes , 774 , that contain statistically-significant gaps in coverage ( p<0 . 05 ) . These gene sets largely overlap and encode a similar distribution of cellular functions , which are quite different from the genome as a whole ( Figure 2A , B ) . Despite the gross similarities between the two gene sets , we also found differences ( Figure 2A , C ) . Some of these differences were attributable to minor alterations in growth conditions . The current study used a more defined media than the standard 7H10 agar employed previously . However , it is likely that most discrepancies are due to the increased resolution and dynamic range of the current method . For example , approximately one half of the genes that deep sequencing identified as essential ( p< . 05 ) could sustain a small number of insertions ( Table S2 ) . The presence of these permissive insertion sites likely caused many of these genes to be deemed nonessential by the previous lower resolution method ( Figure 1C ) . The second major difference between these datasets is a result of the sequencing depth that was employed . Because of the limited dynamic range of the microarray method ( typically 3–10 fold ) , it was difficult to differentiate between mutants that were truly nonviable and those that were merely significantly underrepresented . The increased dynamic range of deep sequencing-based mapping allowed for this differentiation . Indeed , the insertions we identified in genes previously predicted to be essential were associated with a significantly lower number of sequence reads than the genome as a whole ( Figure 2D ) . This suggested that these mutants suffered from a fitness defect but remained viable . Based on the average number of sequence reads we detected in nonessential ORFs ( 173 reads/ORF ) , we estimate that genes containing statistically significant gaps in coverage correspond to mutants that are more than 100 fold underrepresented in the pool and are therefore essentially nonviable . This method provided a quantifiable assessment of transposon library composition , indicating that the comparison of mutant pools selected under different conditions should be possible . We compared pools grown in glycerol , a standard in vitro carbon source , with pools grown in cholesterol , a critical carbon source during infection [13] , [14] , [15] , in order to define the genes and pathways required for the catabolism of cholesterol . In principle , the number of sequence reads associated with a specific insertion should be proportional to the relative abundance of the corresponding mutant in the library . We predicted that the sequencing depth used in our study would allow the relative abundance of each mutant in different libraries to be compared over a 100–1000 fold range . To verify this , we specifically analyzed the genes encoding the previously characterized cholesterol uptake system encoded by the ten-gene mce4 operon . Disruption of Mce4 function by deleting an essential transmembrane component has been shown to cause specific defects in cholesterol uptake and growth in this carbon source [14] . Consistent with these observations , we found that mutations in every gene of this operon , as well as the associated ATPase [16] , resulted in severely impaired growth in cholesterol relative to glycerol ( Figure 3A ) . The degree of this selective underrepresentation predicted that these mutants suffered a 31% growth disadvantage per generation , which is very similar to the experimentally observed doubling time of an isolated Mce4 mutant in these media ( Figure 3B ) . The predicted cholesterol-specific growth defects of three additional transposon were experimentally verified ( Figure 3C ) , further validating the method . We concluded that the number of sequence reads associated with a given mutant could be used to accurately predict its growth rate . Using this metric as an estimate of relative abundance , we compared the genome-wide data sets . As expected , we found that the representation of most mutants was similar in both pools ( Figure 3D ) . Due to the pre-selection of the library on complex glycerol-containing media before passage in single carbon sources , relatively few mutants were found to be specifically required for growth in glycerol . In contrast , a much larger number of mutants were underrepresented in the cholesterol-grown pool . To define differentially represented mutants , we employed a cutoff using a continuous function that equally weighted statistical significance and the magnitude of the change in representation ( Figure 3D ) . To reduce the potential impact of biases introduced during PCR amplification and sequencing , statistical significance was calculated using each individual insertional mutant in the replicate libraries as an independent data point . Ninety-six genes met these statistical criteria and were therefore predicted to be important for growth on cholesterol ( Table S3 and 4 ) . While the cholesterol catabolic pathway of Mtb has only been partially defined , all of the known and predicted catabolic enzymes are encoded in a distinct ∼50 kb region . This “Cho region” comprises over 80 genes and includes the mce4 operon [15] , [17] . While the genes we found to be required for growth in cholesterol were enriched in this region , the majority ( >60% ) were distributed elsewhere in the chromosome ( Figure 3D ) . These genes encoded a wide variety of predicted enzymatic activities that could be categorized into the following three functional groups associated with cholesterol utilization . Following import , cholesterol is degraded via β-oxidation of the side-chain , and ring cleavage to open the steroid nucleus ( Figure 4 ) . Our phenotypic data supports and augments the current predicted pathway for sterol ring degradation . The first step in cholesterol catabolism requires the oxidation of the 3β-hydroxyl group and isomerization of the resulting cholest-5-en-3-one to cholest-4-en-3-one . Two distinct enzymes have been suggested to be involved in this transformation; a ketosteroid dehydrogenase , Rv1106c , and a cholesterol oxidase , ChoD [18] , [19] . We found that only mutations in the ketosteroid dehydrogenase gene caused a significant growth defect , whereas choD mutants appeared to grow at a similar rate in both carbon sources ( Figure 4A and Table S3 ) . While all of the subsequent predicted steps of sterol ring degradation were critical for growth in cholesterol , the degree of importance varied depending on the position of the enzyme in the pathway . We found that hsaEFG mutants , which are predicted to be unable to further degrade 2-hydroxyhexa-2 , 4-dieneoic acid ( HHD ) to propionate and pyruvate [15] , were only modestly defective in cholesterol media , in contrast to the drastic growth attenuation of mutants lacking functions at earlier steps . This is likely because HsaEFG act distal to a branchpoint in the pathway and the mutant bacteria could grow , albeit slowly , by fully catabolizing the portion of the molecule containing rings C and D . In the related actinomycete , Rhodococcus jostii RHA1 , the initial hydroxylation of C26 during side-chain degradation is mediated by the cytochrome P450 encoded by the cyp125 gene [20] . In Mtb , the Cyp125 ortholog serves a similar function [21] , [22] , although Cyp142 has been shown to serve a functionally redundant role [23] . Here , we identify Cyp125 as the sole monooxygenase significantly required for growth on cholesterol ( Table S3 and S4 ) . However , the modest 5 . 5 fold underrepresentation of cyp125 mutants in cholesterol supports the previously observed redundancy between cytochrome P450 enzymes . Many enzymes resembling those predicted to be required for the β-oxidation of the cholesterol side-chain are encoded in the Cho region . Surprisingly , we found significant functional clustering even within this region . None of the predicted β-oxidation genes encoded within the first half of the Cho region were required for growth on cholesterol , except hsd4A ( Table S3 ) . Instead , we found that almost all of those encoded in the second half were required ( including ltp2 , fadE29 , fadE28 , fadA5 , fadE30 , FadE32 , fadE33 , fadE34 , and hsd4B ) . Notably , we also identified predicted β-oxidation genes encoded outside of the Cho region that had not yet been implicated in cholesterol degradation ( including fadE5 , echA9 , fadD36 , and fadE25 ) ( Figure 4B ) . Together , these genes could perform the three rounds of β-oxidation predicted to be required for full catabolism of the C17 side-chain , although we cannot exclude the participation of other functionally-redundant enzymes , or the possibility that some of these genes participate in degradation of the steroid nucleus . Cholesterol catabolism is thought to result in the production of a mixture of metabolites including the C2 unit acetyl CoA , the C3 unit propionyl CoA , and pyruvate [14] , [15] , [24] . The incorporation of acetyl- and propionyl-CoA into the TCA cycle of Mtb likely requires the coordinated activity of the glyoxylate- and methylcitrate-cycles . The icl gene of M . tuberculosis H37Rv encodes a multifunctional protein with both isocitrate lyase and methylisocitrate lyase activity , which is required for both pathways [25] , [26] . We found icl mutants to be 125-fold underrepresented in the cholesterol-grown pool ( Table S3 ) , verifying an important functional role . In addition , our data suggests that the expression of this multifunctional enzyme may be rate limiting during growth on cholesterol , as the mutation of RamB , a negative regulator of icl expression [27] , resulted in the opposite effect ( a nine-fold overrepresentation in the cholesterol-grown pool ) . Growth on lipids as a primary carbon source requires gluconeogenesis , and different metabolites can be used to fuel this pathway . Following growth on cholesterol , we found that ppdK mutants were severely underrepresented ( Table S3 ) . This gene encodes pyruvate phosphate dikinase , which can mediate the conversion of pyruvate to phosphoenolpyruvate ( PEP ) , the first committed step of gluconeogenesis . Together , these observations define pathways required for the assimilation of the three metabolites that most likely result from cholesterol catabolism .
In this work , we have significantly refined our understanding of the cellular functions necessary for the viability of Mtb . While others have used similar deep sequencing methods to map insertion sites in other organisms [28] , [29] , [30] , [31] , [32] our new analytical tools allowed the statistically rigorous prediction of the genes essential for the viability of Mtb . The majority of these essential genes are consistent with those found by previous microarray-based methods . However , significant differences in these predictions were also noted , and the majority of these are attributable to technical and analytical refinements . As a result , this work provides a much more precise and statistically rigorous global assessment of essentiality than previously possible . A few genes that we found to contain very significant gaps in transposon coverage have been successfully deleted , and produce strains that grow normally under similar conditions to those used in our study [33] , [34] , [35] , [36] , [37] , [38] . The reasons for these apparently contradictory results likely vary for each gene . It is possible that some of these genes are truly dispensable for in vitro growth , and the identified gaps in transposon coverage are either due to unappreciated transposon specificity , or selection against specific protein truncations . Conversely , it is possible that deletion mutants lacking these genes appear to grow normally because extragenic suppressor mutations have accumulated . This phenomenon is well-documented for mutants generated by homologous recombination [39] , but is very unlikely to affect transposon-mapping studies . These differences highlight the advantages and limitations of different genetic approaches for defining essential genes . In addition to this qualitative analysis , we also quantitatively compared mutant pools grown under different conditions to understand how Mtb metabolizes cholesterol . A number of recent studies have demonstrated that Mtb mutants lacking the capacity to acquire or degrade cholesterol are defective for growth in animal models of TB [13] , [14] , [17] , [40] , [41] . In order to define a discrete set of ORFs required for growth on this carbon source , we applied a continuous function cutoff to our comparative data , which placed equal importance on fold change in representation and statistical significance . More traditional one-dimensional cutoffs would have excluded genes that were only moderately underrepresented but exceptionally significant , which our analysis includes . Nonetheless , in order to avoid false positive predictions , we set a relatively stringent cutoff , and it is likely that even more than the 96 identified genes contribute to growth in cholesterol . In addition to the dedicated cholesterol catabolic functions , we found that the use of this compound as a source of both cellular energy and biosynthetic carbon requires a variety of central metabolic pathways . Some of these requirements are likely due to the simple shift from a glycolytic substrate to one that relies heavily on β-oxidation . However , the precise pathways we identified appear specific to the mixture of metabolites derived from cholesterol . For example , gluconeogenesis under these conditions is likely to be initiated by the conversion of pyruvate to PEP via the action of pyruvate phosphate dikinase ( PpdK ) , whereas this pathway relies exclusively on phosphenolpyruvate carboxykinase ( PckA ) during growth on acetate [42] . In other bacteria , PpdK-mediated gluconeogensis is favored during growth in the presence of pyruvate [43] , [44] . As pyruvate is produced both as a direct product of sterol catabolism and through the activity of the methylcitrate cycle , we speculate that the relative abundance of cholesterol-derived pyruvate favors the PpdK-mediated pathway . These observations indicate that different gluconeogenic pathways may be preferentially used by Mtb depending on the relative abundance of precursor metabolites . Cholesterol acquisition is predominantly required for bacterial persistence during the chronic stage of Mtb infection in mice and for growth in the cytokine-stimulated macrophages that characterize this stage of infection [14] , [17] . While cholesterol is metabolized by the bacterium throughout infection [13] , [41] , we have shown that this ability is not required for the initial growth of the pathogen in acutely-infected animals or in the resting macrophages that model this early pre-immune period . A recent study demonstrating that cholesterol catabolism is not necessary for bacterial growth in acutely-infected guinea pigs or a macrophage cell line confirms these observations [45] . Thus , it appears that a mixture of carbon sources , including cholesterol , fuel the initial growth of the bacterium , and this sterol becomes a uniquely essential nutrient in chronically-infected animals . Based on these observations , we predict that the requirements for both the dedicated sterol catabolic enzymes , as well as the central metabolic pathways we have defined , are likely to change as infection proceeds . Identifying the full complement of cellular functions necessary for cholesterol utilization has also revealed the scale of this nutritional adaptation during infection . When we compared these data to previous genome-wide screens for mutants attenuated in mouse models of infection [46] we found that a full ten percent of genes specifically required for bacterial growth in vivo are also required for the utilization of cholesterol in vitro ( Table S3 ) . These genes encode both dedicated sterol catabolic functions , as well as enzymes involved in central metabolism . Thus , while it is clear that Mtb must adapt to a variety of host-specific conditions to sustain a productive infection , our data suggest that a single nutritional change is responsible for a significant portion of this adaptation . These cholesterol catabolic functions , in conjunction with the hundreds of other genes that we found to be essential for bacterial viability , both expand and refine the repertoire of targets for new TB therapies . Whole genome profiling techniques have proven to be useful tools for understanding complex pathways , such as those required for cholesterol utilization . Most of these approaches rely on determining the relative transcript or protein abundance . However , these strategies make a major assumption – that critical genes are tightly regulated in response to metabolic changes . While this may be true in many cases , it is often not . In order to avoid this assumption , we have directly identified the genes required for growth . While every approach has its own inherent strengths and weaknesses , the phenotypic profiling strategy described here is a powerful complementary tool for understanding bacterial physiology .
The transposon library was generated in the H37Rv background as previously described [46] , and was comprised of approximately 105 independent insertion events . 106 colony forming units ( cfu ) of library were inoculated into 200 ml of minimal media ( asparagine 0 . 5 g/L , KH2PO4 1 . 0 g/L , Na2HPO4 2 . 5 g/L ferric ammonium citrate 50 mg/L , MgSO4 ·7H20 0 . 5 g/L , CaCl2 0 . 5 g/L , ZnSO4 0 . 1 mg/L ) , 25 µg/ml Kanamycin , 0 . 2% tyloxapol , 0 . 2% ethanol and either 0 . 1% glycerol or 0 . 01% cholesterol . Selections were carried out in duplicate for glycerol and triplicate for cholesterol . Libraries were grown in roller bottles at 37°C . Cultures were diluted as necessary to maintain the optical density at 600 nm below 0 . 2 . The number of cell generations was monitored by cfu enumeration . Transposon mutants shown in Figure 3C were obtained from the Biodefense and Emerging Infections Research Resources Repository and are in the CDC1551 background . Genomic DNA isolation , partial restriction digestion , ligation to asymmetric adapters , and transposon junction amplification were performed as described [5] . An additional nested PCR with the following oligonucleotides was used to incorporate Illumina attachment and sequencing sites AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTCGGGGACTTATCAGCCAACC and CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCTGTCCAGTCTCGCAGATGATAAGG . Standard PCR ( denaturation at 94°C for 30 seconds , annealing at 57 . 5°C for 30 seconds and extension at 72°C for 30 seconds ) was performed for 9 cycles . Amplified fragments between 250–400 bp were purified and sequenced using either the primer: CCGGGGACTTATCAGCCAACC ( complementary to the transposon inverted terminal repeat , ITR ) or ACACTCTTTCCCTACACGACGCTCTTCCGATCT ( complementary to the Illumina adapter ) using an Illumina GA2 instrument . Illumina attachment and sequencing oligonucleotide sequences © 2006 Illumina , Inc . The sequencing reads that contained the Himar1 ITR sequence and the adjacent TA insertion site were identified in the raw fasta files and trimmed of the ITR sequence . The sequences were aligned to the M . tuberculosis H37Rv reference genome [47] using SOAPv1 . 11 alignment software [48] at default settings ( 2 mismatches allowed per read ) . A custom PERL script was used to extract the TA dinucleotide insertion site coordinates from the SOAP output file . For reads aligning to the plus strand of the genome , the genome coordinate at position 1 of the trimmed read was determined . For reads aligning to the minus strand , the genome coordinate at position 2 of the read was calculated to represent the TA coordinate position with respect to the plus strand . For each TA insertion site detected by alignment , the total number of reads and the strand orientation was determined . Sequence reads that aligned to more than one chromosomal position were randomly assigned to one of the positions . If less than 10% of the reads corresponding to an insertion site could be assigned to this single position , the TA site was removed from all further analyses . Less than 0 . 01% of TAs were excluded from any analysis . Insertion site coordinates were mapped to positions within protein coding genes annotated in RefSeq file NC_000962 . ptt ( from the National Center for Biotechnology Information: ftp://ftp . ncbi . nih . gov/ ) . Genes were scored for essentiality based on the length of the longest contiguous run of TA dinucleotide sites lacking observed insertions within the coding region . Read counts at each TA site were pooled from both strands and summed over all the sequencing runs for each library/growth condition ( 2 runs for growth on glycerol , 3 runs for growth on cholesterol ) , and sites with 5 or fewer observed reads were treated as non-insertions . The longest consecutive sequence of non-insertion sites , k , in each gene was identified . The expectation for the length of the longest run of TA sites lacking insertion in a gene with n TA sites is given by the following statistics for a sequence of Bernoulli trials with success probability θ: µ = log ( 1/θ ) ( n ( 1−θ ) ) +γ/ln ( 1/θ ) , σ = [π2/ ( 6 ln2 ( 1/θ ) +1/12 ) ]−1/2 , where γ = 0 . 577 . . . is the Euler-Mascheroni constant . In this case , θ represents the probability of insertions being observed at TA site in non-essential genes , which was conservatively estimated to be the fraction of TA sites containing insertions in the entire genome . To assess the statistical significance of the observed maximum run of sites without insertions , this was compared to the expected length of the longest run using the cumulative function of the Extreme Value Distribution to calculate a p-value: p ( k;n , µ σ ) = 1−exp ( −exp ( − ( k−𝛔 ) σ ) , where µ and σ are the statistics of the expected distribution given above . For a given protein coding gene , only detected insertion sites in the 5′ 5–80% of gene were considered . Samples were normalized such that the mean number of sequence reads per insertion site were equal . The relative representation of each mutant was determined by calculating the fold change ( sequence reads/insertion in cholesterol divided by sequence reads/insertion in glycerol ) for each gene . Statistical significance was determined by t-test . Each insertion site in each replicate sample was treated as a separate data point . The hyperbola used for defining genes specifically required for growth in cholesterol was defined by the formula , y = 3 . 8/x+0 . 7 . Genes above this line are annotated as required for growth on cholesterol in Table S4 . | The adaptation of a bacterial pathogen to the environments encountered during infection requires the wholesale remodeling of cellular physiology . One of the major changes encountered upon infection is nutritional , as the bacterium is forced to utilize compounds scavenged from the host . Mycobacterium tuberculosis has the unusual ability to use host cholesterol as a source of carbon and energy , and this capacity is required for persistence in animal models . However , our ignorance of the biochemical pathways involved in sterol degradation has limited our ability to assess the importance of this carbon shift in shaping the metabolic state of the bacterium . In this work , we developed a new method to quantitatively profile the fitness of thousands of mutants simultaneously . This allowed the identification of each bacterial gene that is required for the bacterium to grow using cholesterol as a carbon source . Reconstruction of the pathways comprised by these essential gene products revealed that adaptation to cholesterol required widespread metabolic changes . These genes account for a significant fraction of the bacterial functions important for growth in animal tissues , suggesting that the physiology of this intracellular pathogen is shaped by carbon sources available in this environment . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"systems",
"biology",
"medicine",
"infectious",
"diseases",
"biology",
"microbiology",
"genetics",
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] | 2011 | High-Resolution Phenotypic Profiling Defines Genes Essential for Mycobacterial Growth and Cholesterol Catabolism |
Animals live together with diverse bacteria that can impact their biology . In Drosophila melanogaster , gut-associated bacterial communities are relatively simple in composition but also have a strong impact on host development and physiology . It is generally assumed that gut bacteria in D . melanogaster are transient and their constant ingestion with food is required to maintain their presence in the gut . Here , we identify bacterial species from wild-caught D . melanogaster that stably associate with the host independently of continuous inoculation . Moreover , we show that specific Acetobacter wild isolates can proliferate in the gut . We further demonstrate that the interaction between D . melanogaster and the wild isolated Acetobacter thailandicus is mutually beneficial and that the stability of the gut association is key to this mutualism . The stable population in the gut of D . melanogaster allows continuous bacterial spreading into the environment , which is advantageous to the bacterium itself . The bacterial dissemination is in turn advantageous to the host because the next generation of flies develops in the presence of this particularly beneficial bacterium . A . thailandicus leads to a faster host development and higher fertility of emerging adults when compared to other bacteria isolated from wild-caught flies . Furthermore , A . thailandicus is sufficient and advantageous when D . melanogaster develops in axenic or freshly collected figs , respectively . This isolate of A . thailandicus colonizes several genotypes of D . melanogaster but not the closely related D . simulans , indicating that the stable association is host specific . This work establishes a new conceptual model to understand D . melanogaster–gut microbiota interactions in an ecological context; stable interactions can be mutualistic through microbial farming , a common strategy in insects . Moreover , these results develop the use of D . melanogaster as a model to study gut microbiota proliferation and colonization .
Animals live with microbial communities that have a strong impact on their physiology , including their development , nutrition , immunity , and behavior [1] . These effects may be partially explained by adaptation of animals to the ubiquitous presence of microbes and integration of this cue in their developmental and physiological programs . However , association with specific microbes may increase their fitness in their environment or provide the capacity to explore new niches . For instance , many endosymbionts in insects provide essential metabolites , allowing hosts to explore food sources deficient in some nutrients , such as plant sap and blood [2–6] . A primary organ for animal–microbe interactions is the gut , which is an interface between the external environment and the animal body . The gut microbiota can be very complex and comprised of up to 1 , 000 different bacterial species , as in humans [7] . Its composition varies to different degrees between and within host species . Moreover , even within the same host it can be very dynamic and fluctuate with host age and health , diet , and other environmental conditions [8–11] . Understanding the composition of the gut microbiota , which factors regulate it , and how these interactions impact both the host and the microbes are , therefore , major research questions . Drosophila melanogaster has been used as model system to study host interaction with gut bacteria [12 , 13] . Besides the host genetics , it has the advantages of having a simpler bacterial community , when compared with mammals , and of being relatively simple to produce axenic and gnotobiotic animals . D . melanogaster raised in axenic conditions have a delayed development and are not viable under certain nutritional conditions , and bacteria can rescue these developmental problems [14–16] . Bacteria also affect the fly life span , gut homeostasis , interaction with pathogens , and behavior [17–23] . All these phenotypes demonstrate the importance of bacteria to this host and the need to understand these interactions for a comprehensive view of D . melanogaster biology . Despite the recognized importance of gut-associated bacteria to D . melanogaster , what constitutes its gut microbiota is still an open question . Laboratory D . melanogaster is associated with few bacterial species , which belong mainly to Acetobacter and Lactobacillus genera [20 , 22 , 24–27] . This contrasts with data from flies sampled in their natural environment , which have a more diverse population of bacteria . In addition to Acetobacter and Lactobacillus , they are also enriched in bacteria from other families and genera [25 , 28] . Because D . melanogaster feeds on fermenting and rotten fruits containing many microbes , it is , however , difficult to understand which of the bacteria are colonizing the host gut and which are transiently passing with the food . Likewise , a similar problem is present in laboratory conditions , where flies live in a relatively closed environment . The bacteria found in their gut could simply correspond to food growing bacteria ingested by the flies . This hypothesis is supported by the fact that frequent transfer of adult flies to clean food vials strongly reduces their gut bacterial loads [20 , 27] . Consequently , the current working model is that the gut-associated bacteria in D . melanogaster are environmentally acquired and do not constitute bona fide gut symbionts . Most functional studies in D . melanogaster , however , have been performed with bacterial isolates from lab stocks . The properties of bacterial isolates from wild-caught D . melanogaster could differ . Bacteria found in the gut of some other Drosophila species differ from the bacteria present in their food source , suggesting that they can be gut symbionts [29 , 30] and raising the possibility of these also existing in D . melanogaster . Moreover , a recent study compared the ability of different Lactobacillus plantarum strains to colonize the gut and found that one wild strain was able to colonize flies more frequently than strains isolated from laboratory flies [31] . Therefore , it is possible that natural populations of D . melanogaster have stable colonizing bacterial communities in their guts . Here , we analyzed bacterial isolates from the gut of wild-caught D . melanogaster and compared it to bacteria from lab stocks . Using a protocol that avoids reinfection of flies with bacteria growing on the food , we identified bacterial species that are stably associated with the gut of wild D . melanogaster . Moreover , these isolates can stably associate and proliferate in the gut of lab flies . We further analyze the specificity of these interactions and fitness advantage of stable associations . Our results lead to the identification of gut symbionts in D . melanogaster and demonstrate fitness advantages for both partners in an ecological context .
In order to analyze the diversity and stability of gut bacteria in D . melanogaster , we used culture-dependent techniques . We plated single gut homogenates in five different culture media . This approach allowed us to determine the absolute number of bacteria present in each gut and isolate bacteria for follow-up experiments . We started by analyzing levels of bacteria in the gut of flies from our standard laboratory stock w1118 DrosDel isogenic strain ( w1118 iso ) [32 , 33] . We assessed these levels in young conventionally raised flies ( Day 0 ) and after these flies were maintained singly for 10 days either in the same vial or passed to a new vial twice a day ( similarly to the protocol in [20] ) . The latter protocol was designed to decrease the probability of flies getting reinfected with their own bacteria or bacteria growing on fly food and , therefore , allowed us to test if there was a resident gut bacterial microbiota in this D . melanogaster lab stock ( stability assay ) . In flies kept in the same vial for 10 days , bacterial levels in the gut increased approximately 17-fold ( Fig 1A and S1A Fig , linear mixed model [lmm] fit , p < 0 . 001 ) . In contrast , flies that were passed twice a day had an approximately 2 , 200-fold decrease in their gut bacterial levels ( Fig 1B and S1A Fig , lmm , p < 0 . 001 ) . A sharp decrease in bacterial loads was confirmed by quantitative PCR ( qPCR ) , a culture-independent method , using universal primers for the 16S rRNA gene ( Fig 1C and S1B Fig , lmm , p < 0 . 001 ) . These results show that bacterial levels in the gut of these flies are dependent on fly husbandry and suggest that these bacteria are transient , similarly to what was previously shown with a different laboratory stock [20] . Because these bacteria are associated with the lab stock , and bacterial loads in the gut of these flies actually increase over time if they are kept in the same vials for 10 days , we tested their growth on fly food ( Fig 1D ) . We placed single flies per vial ( Day 0 ) , discarded them after 24 hours ( Day 1 ) , and kept the vials for a further nine days ( Day 10 ) . Bacterial levels on the surface of the fly food increased 7 . 6×108-fold from Day 1 to Day 10 , clearly showing their capacity to grow on fly food ( Fig 1D , linear model [lm] , p < 0 . 001 ) . Therefore , the bacteria associated with this lab stock grow on the fly food and are only transiently associated with the gut of adult flies . We next asked if we could find stable bacteria in the gut of D . melanogaster collected from natural populations . We captured D . melanogaster from a population growing on fallen figs and quantified their gut bacterial levels at the time of collection ( Day 0 ) and 10 days after , using the same stability assay designed to avoid reinfection ( Day 10 ) ( Fig 1E , S1C and S1D Fig ) . Although there is a statistically significant change in the bacterial levels in the gut with time ( lmm , p = 0 . 004 ) , the bacterial levels only decreased 4 . 8-fold in 10 days . Moreover , at Day 10 , wild flies maintained 2 . 9×104 colony-forming units ( CFUs ) per gut , while w1118 iso flies only had 100 CFUs per gut . Also , even after 20 days of this protocol , wild flies still maintained approximately 6 . 1×103 CFU per gut ( S1D Fig ) , showing long-term stability of their microbiota . These results show that wild flies carry bacteria that are stably associated with their gut . In order to identify and isolate the bacteria that can stably interact with the gut of D . melanogaster , we analyzed the bacterial composition of the cultured gut extracts of w1118 iso and wild flies represented in Fig 1B and 1E . For each fly gut homogenate , in each of the five media , we distinguished colonies by morphology , determined CFUs per gut of each morphological type , and isolated two colonies of each morphological type . For each isolate , we sequenced by Sanger a fragment of the 16S rRNA gene , which included the V2 to V4 hypervariable regions . After sequencing , we classified morphological types into operational taxonomic units ( OTUs ) , based on Greengenes alignment tool and database [34] , and determined the number of CFUs of each OTU in each fly gut ( Fig 2 ) . In general , we could assign each morphological type to one OTU . However , in samples from wild flies we could not distinguish by morphology the colonies of different Lactobacillus species , different Acetobacteraceae ( genera Acetobacter and Gluconobacter ) species , and several genera of Enterobacteriaceae . We therefore calculated CFUs per fly for each of these groups of bacteria and not individual OTUs ( Fig 2 ) . The frequencies of the different OTUs belonging to these groups , in the different conditions , are shown in Fig 3B , 3D , 3F and 3H and S3 Fig . Laboratory flies were mainly found associated with two OTUs , Acetobacter OTU2753 and Lactobacillus OTU1865 , and accumulation curves indicate that we sampled most of the diversity present in these flies ( S2 Fig ) . On Day 0 , all the flies were associated with high levels of Acetobacter OTU2753 ( Fig 3A and 3B ) , while Lactobacillus OTU1865 was only present in some individuals ( Fig 3C and 3D ) . After 10 days of the stability assay , Acetobacter levels decreased ( lm , p = 0 . 001 ) , while Lactobacillus levels are not significantly different ( p = 0 . 635 ) ( Fig 3A and 3C ) . Importantly , when we analyzed the bacterial species that were capable of growing on fly food in Fig 1D , we found these two same OTUs , with Acetobacter OTU2753 being the most abundant . Altogether , these results show that this D . melanogaster laboratory stock has very low bacterial diversity , as previously reported in other stocks [25 , 26 , 28] . In contrast , wild-caught flies were associated with a higher diversity of bacterial species ( Fig 2 , Fig 3F and 3H and S3B Fig ) . From each gut of wild flies we isolated 9–16 different OTUs at Day 0 , and 3–14 different OTUs at Day 10 . In total , we isolated 35 and 31 different OTUs at Day 0 and Day 10 , respectively ( S2 Fig ) . Moreover , it seems that we are not close to saturation with these samples and that further sampling would allow the identification of more OTUs associated with the gut of D . melanogaster from this wild population ( S2 Fig ) . The individual characterization of bacterial species present in each gut allowed us to discriminate between OTUs that were only present in one or few individuals , albeit at higher levels , and OTUs associated with most individuals . At the day of collection ( Day 0 ) , 50% or more of the flies had in their gut Bacillus OTU1570 , Leuconostoc pseudomesenteroides OTU1934 , Acetobacter OTU2753 , Acetobacter ghanensis OTU2757 , A . lovaniensis OTU2759 , A . orientalis OTU2760 , A . syzygii OTU2762 , Gluconobacter OTU2781 , Enterobactereaceae OTU3529 , Tatumella OTU3635 , and Kluyvera ascorbata OTU3643 ( Fig 2 , Fig 3F and S3B Fig ) . Ten days after the stability assay , only a few bacteria remained associated with the gut of most individuals . One of these bacteria was L . pseudomesenteroides , which was present in 6 out of 10 flies and did not show a significant reduction in levels between Day 0 and Day 10 ( Fig 2 , S4 Fig , lm , p = 0 . 372 ) . Bacteria from the Acetobacteraceae family also remained associated with the gut of most wild flies at an estimated 1 . 3×103 CFU per gut at Day 10 , despite a significant reduction of approximately 100-fold in their levels between Day 0 and Day 10 ( lm , p = 0 . 002 ) ( Fig 2 , Fig 3E ) . However , the frequencies of different OTUs of Acetobacteraceae changed significantly between Day 0 and Day 10 ( Fig 3F , Pearson’s chi-squared with Monte Carlo simulation , p < 0 . 001 ) . At Day 10 , all the OTUs that were dominant at Day 0 became present at lower frequencies , and A . cibinongensis OTU2755 and A . thailandicus became the dominant bacteria ( Fig 3F ) . These two bacteria were present in at least 7 and 9 individuals out of 10 , respectively , and together represented 76% of the sequenced colonies . Overall , this analysis identified three species that seem to be stably associated with the gut of wild flies in this population: L . pseudomesenteroides , A . cibinongensis , and A . thailandicus . To study the interaction of these bacteria with D . melanogaster , we generated stocks of w1118 iso flies monoassociated with each of these bacteria and we tested their persistence using the stability assay . In agreement with our previous observations , the laboratory isolate of Acetobacter OTU2753 did not persist in the gut and disappeared from the majority of the flies ( lmm , p < 0 . 001 ) ( S5A and S5E Fig ) . On the other hand , the wild isolates of A . cibinongensis , A . thailandicus , and L . pseudomesenteroides persisted in the gut of flies until Day 10 , showing a more stable association with the host ( S5B–S5D and S5F–S5H Fig ) . L . pseudomesenteroides levels did not significantly change with treatment ( p = 0 . 96 ) and , although A . cibinongensis and A . thailandicus levels significantly decreased in the 10 days ( p < 0 . 001 for both ) , both remained in the gut at approximately 100 and 3 , 800 CFUs , respectively . To better assess the bacterial dynamics within the gut , we developed a stricter protocol to avoid reinfection . We maintained single flies in cages with a larger food surface ( 382 cm2 compared with 3 . 8 cm2 in vials ) , which was changed daily ( Fig 4A ) . We assessed gut bacterial levels at the beginning of the experiment and after 1 , 2 , 5 , and 10 days of this treatment . In accordance with previous data , Acetobacter OTU2753 levels rapidly decreased and most flies had no detectable bacteria in their gut after 5 days of treatment ( Fig 4B ) . A . cibinongensis and A . thailandicus also presented an initial decrease in bacterial levels in the gut , but these seemed to stabilize after 2 days of treatment , confirming their stability in the gut ( Fig 4C and 4D ) . However , and contrary to what was observed in vials , L . pseudomesenteroides was not stable when the protocol was performed in cages ( Fig 4E ) . After 2 days , approximately 50% of flies lost L . pseudomesenteroides from their gut . An independent experimental replicate with data from only Day 0 and Day 5 showed similar results for all bacteria ( S5E–S5H Fig ) . We compared the dynamics of the gut levels of the four bacteria by fitting the data of Fig 4B–4E to an exponential decay model ( S5I Fig ) . This model estimates the exponential decay rate , which corresponds to the rate of bacterial loss from the gut , and an asymptote that corresponds to the levels at which the bacteria tend to stabilize after this loss . The simplest model that explains the data has the same estimate for the exponential decay rate for all the bacteria . There are , however , significant differences between the asymptotes of all the bacteria ( contrasts between nonlinear least-squares estimates , p < 0 . 014 ) , except between Acetobacter OTU2753 and L . pseudomesenteroides ( p = 0 . 116 ) . Overall , an interpretation of this fit is that , in all cases , most of the bacterial population is in an unstable compartment at the beginning of the experiment , from where they tend to disappear , with similar dynamics . However , A . cibinongensis and A . thailandicus are also present in a stable compartment at levels that correspond to the calculated asymptotes ( approximately 300 and 1 , 300 CFU per gut , respectively ) . In order to identify in which gut region bacteria could be stably associated with the host , we analyzed A . thailandicus levels present in different gut regions before ( Day 0 ) and after 5 days of the stability protocol in cages ( Day 5 ) ( Fig 4F ) . At Day 0 , A . thailandicus was distributed along the gut , being present at lower levels in the midgut , compared with crop and hindgut ( Fig 4G and S6A Fig ) . After 5 days , bacteria were found in two anterior gut sections , one comprising the crop and the other comprising the anterior midgut and the proventriculus ( Fig 4H and S6B Fig ) . Fluorescent in situ hybridization ( FISH ) with a universal probe for 16S rRNA confirmed and refined these results . At Day 0 , A . thailandicus is present in all gut compartments , but after 5 days of the stability protocol , the bacteria persist almost exclusively in the foregut of D . melanogaster . A . thailandicus was consistently found in the crop ( mostly at the anterior part of the crop ) , the crop duct , and the proventriculus in both males and females ( Fig 4I and 4J , S6C–S6D Fig , S7A–S7C Fig , S8 Fig ) . Interestingly , the bacteria seem to be aggregated in clusters ( Fig 4I′ , 4I″ and 4J′ ) . In the crop these bacteria are mainly found at the periphery , not the lumen , and close to chitin ( Fig 4I and 4I″ ) . Bacteria were rarely found in the midgut and rectum and , when present , are also in small clusters ( S7D and S7E Fig ) . We performed a Live/Dead staining on the stable A . thailandicus population in order to analyze the proportion of live bacteria in these clusters . Most of the bacteria in the crop and proventriculus are alive ( Fig 4K and 4L ) . Electron microscopy in the crop and proventriculus confirmed that A . thailandicus is present in clusters and bacterial cells seem to be connected by external appendages , probably fimbriae ( Fig 4M and 4N and S9 Fig ) [35] . In the crop , the clusters of bacterial cells were found within folds and , therefore , are close to chitin ( S9A and S9B Fig ) . Importantly , several bacteria presented membrane invaginations compatible with cell division in gram-negative bacteria ( Fig 4M , S9H Fig ) [36] , indicating that these cells are proliferating . Interestingly , we also observed extracellular vesicles associated with the bacterial clusters , and structures resembling pili between bacteria ( Fig 4M and 4N , S9H and S9I Fig ) . Altogether , these analyses show that the stable population of A . thailandicus persists in aggregates in the crop and proventriculus of D . melanogaster . Their niche has a clear boundary at the end of the proventriculus because they are rarely found from the beginning of the midgut on . We next asked to which extent these bacteria had the capacity to proliferate in the gut of D . melanogaster , because stability in the gut could be achieved through other mechanisms ( e . g . , bacteria could be simply attaching to the gut and avoiding elimination ) . Thus , we developed a protocol to analyze proliferation based on giving a small inoculum of bacteria and test if bacterial loads increase over 24 hours . We raised flies in axenic conditions and exposed 3–6-day-old males to different doses of bacteria . After 6 hours of feeding on the bacteria inoculum , flies were either collected to dissect and assess bacterial levels in the gut ( 0 hours ) or placed singly in cages , as described above , and collected 24 hours later ( Fig 5A ) . In this assay , Acetobacter OTU2753 did not colonize the gut of adult flies , and at the higher inoculum titers , the levels decreased between 0 and 24 hours ( lmm , p < 0 . 001 ) ( Fig 5B , S10A and S10E Fig ) , indicating that these bacteria cannot proliferate in the gut of D . melanogaster . On the other hand , the levels of A . cibinongensis and A . thailandicus increased in 24 hours ( p = 0 . 024 and p < 0 . 001 , respectively ) ( Fig 5C and 5D , S10B , S10C , S10F and S10G Fig ) , showing that these bacteria can proliferate in the gut of D . melanogaster . A . thailandicus proliferate more and reached higher levels than A . cibinongensis ( p = 0 . 019 ) . Interestingly , in flies exposed to A . thailandicus inocula superior to 102 CFU/μL , these bacteria reach between 600 and 1 , 900 CFU per gut ( Fig 4D , S10C and S10G Fig ) . These levels are similar to the stable compartment population size estimated above ( 1 , 300 CFU per gut ) , indicating that A . thailandicus can rapidly colonize a fly . As all these Acetobacter species were able to grow on fly food ( S11 Fig ) , it was still possible that the increase in the levels of A . thailandicus in the proliferation assay was due to a very fast growth on the fly food and re-acquirement by feeding . To test this possibility , we placed axenic ( chaser ) flies in cages simultaneously with the flies that had fed on A . thailandicus at time 0 hours of the experiment . At 24 hours , none of the axenic chaser flies had bacteria in their gut ( Fig 5E and S10G Fig ) . This demonstrates that the levels measured in the inoculated fly were due to proliferation in the gut and not due to bacteria acquired from the food . L . pseudomesenteroides levels did not significantly increase or decrease over 24 hours ( lmm , p = 0 . 158 ) ( Fig 4F and S10D Fig ) . At inocula superior to 102 CFU/μL , L . pseudomesenteroides levels at 24 hours are between 150 and 550 CFU per gut . These results fail to show proliferation of L . pseudomesenteroides but indicate that this bacterium is not eliminated at the same rate as the unstable Acetobacter OTU2753 . Because Lactobacillus species are commonly found associated with D . melanogaster and shown to impact its physiology [25 , 26 , 28 , 37–39] , we also tested isolates of Lactobacillus paraplantarum OTU1905 and Lactobacillus brevis OTU1870 in this assay ( Fig 5G , S10H–S10J Fig ) . These Lactobacillus were isolated from the gut of wild flies at Day 0 of the stability assay ( Fig 3G and 3H ) . L . paraplantarum levels do not change over 24 hours ( lmm , p = 0 . 65 ) and can be sustained at 200–800 CFU per gut ( similarly to L . pseudomesenteroides ) ( S10H and S10I Fig ) . On the other hand , the levels of L . brevis increase in 24 hours ( p = 0 . 046 ) , showing that this bacterium proliferates in the gut of D . melanogaster ( Fig 5G and S10J Fig ) . Overall , these assays show that A . cibinongensis , A . thailandicus , and L . brevis isolates proliferate in the gut of D . melanogaster . On the contrary , the transient Acetobacter OTU2753 cannot proliferate and is rapidly lost . L . pseudomesenteroides and L . paraplantarum have an intermediate phenotype in which proliferation is not shown , but the bacteria can sustain themselves in the gut over a period of 24 hours after oral inoculation . To test if proliferation of A . thailandicus in the gut is host specific , we compared its proliferation in D . melanogaster and D . simulans . These two species share the same habitat , feed on the same substrate , and are frequently captured together [40] . We used a proliferation protocol similar to the one described above ( see figure legend , S12A and S12B Fig ) to test three different genetic backgrounds of each host species . These included one isofemale line of each species that were collected simultaneously , from the same place as the initial collection of wild D . melanogaster . There is a significant difference in the colonization by A . thailandicus in these two host species ( Fig 5H , S12C–S12E Fig , lmm , p < 0 . 001 ) , with the levels increasing over 24 hours in D . melanogaster but decreasing in D . simulans . These results suggest that D . melanogaster and A . thailandicus interaction is host specific . Interestingly , although A . thailandicus colonizes all strains of D . melanogaster tested ( Fig 5H , S12C–S12E Fig ) , there is variation in the growth at 24 hours , indicating modulation of this process by the host genotype ( lmm , p = 0 . 002 ) . Symbiotic associations can range from pathogenic to mutualistic . Because Acetobacter species have been previously described as beneficial to D . melanogaster [16] , we tested if the stable association between D . melanogaster with A . thailandicus could be advantageous for both . We started to test this hypothesis by comparing fitness parameters of flies monoassociated with A . thailandicus , Acetobacter OTU2753 , and axenic flies , raised in standard fly lab food , by measuring time to pupariation and adulthood and total number of its progeny . Both A . thailandicus and Acetobacter OTU2753 monoassociated stocks had a much higher fertility than axenic flies and there was no significant difference between them ( S13A and S13B Fig , lm , p < 0 . 001 for the comparisons of each Acetobacter monoassociation with axenic flies , in number of pupae or adults , p > 0 . 968 for the comparisons between Acetobacter monoassociated stocks ) . Flies monoassociated with either Acetobacter also developed until pupariation or adulthood approximately 3 days faster than axenic flies ( S13C and S13D Fig , lm , p < 0 . 001 for each Acetobacter monoassociation comparison with axenic flies ) . Flies monoassociated with Acetobacter OTU2753 developed slightly faster to pupae ( 0 . 38 days ) and adults ( 0 . 57 days ) ( p < 0 . 001 for each comparison ) . These results show that , in this setup , the association with either Acetobacter is clearly advantageous when comparing with axenic conditions and that the stable A . thailandicus does not provide a greater benefit than the lab isolate Acetobacter OTU2753 . However , the advantage of a stable association may not be revealed by directly studying monoassociated D . melanogaster stocks . In these conditions the bacteria are continuously associated with D . melanogaster , even if they are only present in the food or transiting through the gut . But in the wild , D . melanogaster adults freely move in space and can explore a continuously changing environment , a situation in which a stable association could be important . Therefore , we established a protocol to test the fitness benefits of the stable interaction in a scenario that simulates this changing environment . After 6 hours of feeding on an inoculum of bacteria , one female and two males were placed per cage and maintained there for 10 days , with food being changed daily ( Fig 6A ) . After 10 days of this protocol , males exposed to A . thailandicus have a median of 6 , 800 CFU per gut ( Fig 6B and S14A Fig ) , showing that colonization can be sustained for a long time . In females , A . thailandicus grows in the gut between the beginning of the experiment and 10 days in the cage ( Wilcoxon rank sum test , p < 0 . 001 ) and reaches a median of 17 , 500 CFU per gut . These results show that A . thailandicus also colonizes and proliferates in female D . melanogaster . On the other hand , Acetobacter OTU2753 levels decrease between Day 0 and Day 10 in females ( p = 0 . 048 ) and both sexes have a median of 0 CFU per gut at Day 10 , confirming that flies are not colonized by these bacteria ( Fig 6B and S14A Fig ) . As a measure of the fitness benefit for the bacteria , we tested if they could be transmitted to the food . We analyzed bacterial transmission by flies during the experiment at days 1 , 3 , 5 , 7 , and 9 . Flies associated with A . thailandicus transmitted bacteria to the food with a much higher frequency than flies associated with Acetobacter OTU2753 , in which transmission occurred only once ( Fig 6C , S14B Fig , generalized linear model with binomial distribution [glm-binomial] , p < 0 . 001 ) . Moreover , the probability of transmission of A . thailandicus to the food was independent of the day of the experiment ( ANOVA on glm-binomial models , p = 0 . 811 ) . These results show that , upon gut colonization , A . thailandicus can be continuously transmitted by D . melanogaster . The stable association may be advantageous to the bacteria and mediate their dispersal in the environment . To compare the effect of this association on host fitness , we started by analyzing the fertility of the flies in terms of number of eggs laid and adult progeny , during the experiment . The number of eggs or adult progeny were not significantly different between axenic flies and flies exposed to either bacteria ( Fig 6D and 6E , S14C and S14D Fig , lmm , p > 0 . 484 for all comparisons ) . However , the time that these embryos took to reach adulthood was different . Progeny from flies colonized by A . thailandicus developed 2 or 3 days faster than progeny from flies previously exposed to Acetobacter OTU2753 or axenic flies , respectively ( Fig 6F , S14E Fig , lmm , p < 0 . 001 for both comparisons ) . However , the progeny of flies exposed to Acetobacter OTU2753 developed only 0 . 6 days faster than axenic flies ( p < 0 . 001 ) . Moreover , the fertility of this progeny was strongly influenced by the interaction of their parents with bacteria . The progeny from flies previously colonized by A . thailandicus had a much higher fertility than the progeny from flies previously exposed to Acetobacter OTU2753 or axenic flies ( Fig 6G , S14F Fig , lmm , p < 0 . 001 for both comparisons ) , while there was no difference between the progeny of flies exposed to Acetobacter OTU2753 or axenic flies ( p = 0 . 592 ) . These data show that the interaction of adult flies with stable bacteria does not affect their fertility but has a strong influence on the development and fertility of its progeny . This transgenerational effect could be due to an effect of the stable A . thailandicus gut population on the parents and a subsequent indirect effect on the progeny , or through the transmission of the bacteria to the next generation and its effect during larval development . We tested if the developmental time of the progeny was dependent on the bacterial association with either parent by analyzing the four possible couple combinations of flies raised axenically or monoassociated with A . thailandicus ( Fig 6H , S15 Fig ) . There is no difference in developmental time to pupariation or adulthood if either or both parents are from the monoassociated stock ( lmm , p > 0 . 412 for all these comparisons ) . The progeny of these three crosses develop , on average , 2 . 7–2 . 8 days faster than the progeny of crosses with both parents axenic ( p < 0 . 001 for all comparison ) . These results show that the transgenerational effect on developmental time is not specifically associated with the mother or the father . Also , adding A . thailandicus to the progeny of axenic flies rescues the developmental delay . When bacteria are added , these flies develop approximately 2 days faster ( p < 0 . 001 ) . This is not a full rescue because axenic eggs plus A . thailandicus still develop , on average , 0 . 5–0 . 8 days slower than flies with either or both parents from monoassociated stocks ( p < 0 . 001 for all comparisons ) . This may be explained by the fact that the bacteria are only added when the parents are removed from the vial , after 2 days of egg laying . These data are compatible with a scenario in which flies associated with A . thailandicus , either male or female , can transmit the bacteria to the next generation , which then plays an important role in its development . In agreement with this hypothesis , we have shown above that A . thailandicus can be continuously transmitted to the environment ( Fig 6C , S14B Fig ) . Moreover , we detected bacteria in the surface of 20 out of 20 eggs laid by flies monoassociated with A . thailandicus , by testing bacterial growth in medium . This demonstrates that A . thailandicus is efficiently transmitted from mothers to their progeny . We also observed that A . thailandicus affected the fertility of D . melanogaster in this assay . Similarly to the results above , there is no difference in total number of progeny if either or both parents are from the monoassociated stock ( S15B , S15D , S15F and S15G Fig , pupae or adult number , lm , p > 0 . 180 for all these comparisons ) . However , if both parents are axenic , the number of pupae or adults is lower ( p < 0 . 001 for all comparisons ) . This lower number of pupae or adults is not rescued by adding A . thailandicus to the axenic eggs ( p = 0 . 998 ) , indicating that these bacteria are not affecting egg to pupae or adult survival . Because exposing axenic adults to A . thailandicus does not alter their fertility ( Fig 6D and 6E , S14C and S14D Fig ) , this fertility effect may be dependent on either parent development in the presence of A . thailandicus or in the presence of A . thailandicus in the fly food for the 2 days of the egg laying . The results above suggest that a stable association with gut bacteria is beneficial to adult D . melanogaster , because it allows continuous transmission to the next generation , promoting its faster development and higher fertility . However , these experiments were performed by providing axenic food to flies , and in a natural scenario , flies are bound to encounter many other bacteria present in the food substrates . If all bacteria were equally beneficial for fly development , this stable association could be irrelevant . Therefore , we tested if different bacteria naturally encountered by D . melanogaster confer different fitness benefits to the flies . We sterilized eggs of w1118 iso and associated them with different bacteria found in the gut of flies from a natural population ( sampled from the isolates of Fig 2 , Fig 7A ) . We determined total number of adults that developed from these eggs , their developmental time , and their fertility . The number of adults that emerged ( G0 ) was not different between associations with different bacteria or in germ-free conditions ( S16A and S16B Fig , lmm , p > 0 . 282 for all pairwise comparisons ) . However , we did observe differences in the developmental time and fertility of these adults associated with different bacterial isolates , and found a negative correlation between these parameters ( Pearson correlation −0 . 91 , p < 0 . 001 ) ( Fig 7B , S16C–S16F Fig , S17 Fig ) . Flies associated with A . thailandicus developed faster and are more fertile than axenic flies and flies associated with most of the other tested bacteria . Flies associated with Acetobacter OTU2753 , L . brevis , and L . paraplantarum developed as fast and are as fertile as A . thailandicus ( p > 0 . 200 for these pairwise comparisons ) . While flies associated with A . cibinongensis developed slower than with A . thailandicus ( p = 0 . 023 ) , the developmental time of flies with L . pseudomesenteroides is not significantly different ( p = 0 . 224 ) . However , both have lower fertility than flies with A . thailandicus ( p < 0 . 001 ) . Flies associated with Bacillus flexus OTU1589 were not different from axenic flies in terms of developmental time or fertility ( p = 0 . 878 ) . Overall , these data demonstrate that different bacteria have a variable effect on the development and fertility of D . melanogaster , with some not conferring any advantage to the flies’ development or fertility . A . thailandicus seems particularly beneficial to D . melanogaster and , therefore , the stable association may be advantageous to the host . We also analyzed the impact of A . thailandicus on D . melanogaster fitness when they develop in fruit , a more natural food substrate , instead of standard fly food . We compared development from eggs to adults on a sterile fig homogenate , with the addition of A . thailandicus , Acetobacter OTU2753 , or sterile medium ( Fig 7C ) . The association with both Acetobacter strongly influenced the number of emerging adults , with very few flies reaching adulthood in axenic conditions ( Fig 7D , S18A–S18C Fig , lmm , p < 0 . 001 ) . Moreover , flies associated with A . thailandicus develop 1 . 5 days faster than flies associated with Acetobacter OTU2753 ( Fig 7E , S18F Fig , lmm , p < 0 . 001 ) . The few axenic flies that reach adulthood are slower and take on average 27 days ( Fig 7E , S18D–S18F Fig , lmm , p < 0 . 001 ) . This reflects a delay in growth because 5-day-old larvae in axenic conditions were much smaller than larvae with A . thailandicus ( Fig 7F ) . We subsequently tested the number of progeny of flies that developed in these three conditions . Interestingly , the number of progeny was much higher in adults that developed on figs in the presence of A . thailandicus than in the presence of Acetobacter OTU2753 or axenic adults ( Fig 7G , S18I Fig , lmm , p < 0 . 001 ) . These results show that the benefit of A . thailandicus for the development and fertility of flies is even more pronounced in a natural food substrate . Because , in nature , fruits are not sterile and flies develop in the presence of different microbial communities , we decided to test the potential benefit of A . thailandicus in freshly collected figs . We compared the development of sterilized eggs in natural collected figs in the presence or absence of A . thailandicus . Flies grown in the presence of A . thailandicus had approximately double the survival rate to adulthood of control flies with no bacteria added ( Fig 7I , S18J Fig , lmm , p = 0 . 010 ) . This is similar to the effect seen in sterile figs . The effect of A . thailandicus on the time to reach adulthood varies with replicate ( Fig 7J , S18K Fig , lmm , p < 0 . 001 ) . In one replicate , the presence of bacteria does not affect time of development ( S18K Fig , p = 0 . 557 ) , while in the other replicate , A . thailandicus decreases time of development by 3 . 5 days ( Fig 7J , p < 0 . 001 ) . This difference between replicates may reflect the variable bacteria consortiums in the figs collected from the tree at different times . These results support that the stable association between D . melanogaster and A . thailandicus is beneficial for the flies in their natural environment .
The several protocols we developed were mainly based in culture-dependent techniques , in order to quantify absolute levels of live bacteria in the gut . Gut bacteria of D . melanogaster previously identified through 16S rRNA gene sequencing [20 , 26 , 28 , 41–46] belong to genera that can also be identified by culture-dependent techniques; however , it is possible that our approach missed gut bacteria that do not grow in the media or conditions that we used . Additionally , our approach mainly identifies the bacterial strains that are more abundant in the gut , as there is a limited number of colonies in the plates analyzed . Because of these limitations , our analysis may be incomplete . Nonetheless , our approach managed to quantify overall gut bacterial numbers in different husbandry conditions , and , when tested , the results were confirmed by qPCR . Moreover , we were able to identify , isolate , and analyze bacteria that can stably associate with D . melanogaster gut . Our results show a striking difference in gut bacterial diversity between lab and wild-caught flies . Lab flies carry mainly two bacterial species corresponding to Acetobacter OTU2753 and Lactobacillus OTU1865 . This low diversity and dominance of Acetobacter and Lactobacillus species is in agreement with several previous studies on the gut-associated bacteria in lab flies [20 , 22 , 24–27] . On the other hand , we were able to identify 35 different OTUs in the 10 individual flies freshly collected from the wild , and the sampling did not seem close to saturation . This higher diversity is also in agreement with previous reports [25 , 28] . The characterization of individual flies allowed us to identify Enterobacteriaceae , Acetobacteriaceae ( mainly Acetobacter and Gluconobacter species ) , Leuconostocaceae , and Bacillaceae as the most prevalent families , present in over 50% of the flies . These families of bacteria have been identified before in wild-caught D . melanogaster , although Bacillaceae are found less frequently [25 , 28 , 41–43 , 46] . Lactobacillus was only found in 5 out of 20 wild-caught flies . Although the low prevalence of Lactobacillus could be a characteristic of this specific population , it is a general trend observed in other published surveys [25 , 28 , 41–43 , 46] . The Acetobacter and Lactobacillus species associated with our laboratory stock cannot stably persist in the gut in the absence of reinfection , and they grow on the fly food , similar to what was reported before [20] . Thus , these bacteria are only transiently passing through the gut . This result highlights how husbandry conditions can affect D . melanogaster gut bacterial levels and that these measured levels can be unrelated with gut colonization ( also shown in [20 , 27] ) . In contrast to lab flies , wild-caught flies carry bacteria that can persist in the gut of D . melanogaster . This shows that in its natural state D . melanogaster lives with gut-colonizing bacteria . L . pseudomesenteroides , A . cibinongensis , and A . thailandicus were each present in more than 50% of wild flies at the end of the stability protocol . They are , therefore , interesting bacteria to further characterize in their interaction with D . melanogaster . A . cibinongensis and L . pseudomesenteroides have been previously studied in wild and lab Drosophila species by culture-dependent and -independent techniques [31 , 47–55] . A . thailandicus was recently isolated from the gut of lab D . melanogaster by culture methods [56] . Association of this species with D . melanogaster may have been missed in previous studies because the A . thailandicus 16S rRNA gene sequence was only recently available [57] and is very similar to this gene in other Acetobacter species . Several bacteria were present in 50% or more of the flies when they were caught , but were severely reduced in frequency after the stability protocol . These species may be transient gut bacteria that were acquired from the environment . However , it is also possible that they are stable gut bacteria that cannot be sustained in the particular lab environment we used . For instance , in the fly food we used , there may be nutritional requirements missing for their maintenance or there could be compounds toxic to them ( e . g . , methylparaben ) . In the future , this protocol could be repeated using another food source , for example , the fruit matching the source of capture . However , the natural environment of D . melanogaster is very complex and includes decomposing and fermenting fruits replete with different microorganisms . Therefore , it will be difficult to study bacterial stability under conditions that completely match the ones found in nature . At the end of the stability protocol , there was still a high diversity of bacteria in the gut of D . melanogaster , even if most were present in less than 50% of the flies . These may represent rare but stable gut bacteria of D . melanogaster , as in the case of Lactobacillus species . A particular fly ( fly 39 in Fig 2 ) has an interesting pattern of microbiota composition , with six rare OTUs persisting at relatively high levels and without Lactobacillales or Acetobacteriaceae . This gut microbiota composition may represent a disease-related dysbiosis , and some of these bacteria could be pathogenic . In contrast to the lab isolate of Acetobacter OTU2753 , both wild isolates of A . cibinongensis and A . thailandicus persist in the gut of lab flies when monoassociated , until the end of the stability protocol . However , the levels of both bacteria decreased significantly in the first 2 days of this assay , indicating that the majority of the bacteria found in the gut of these flies at the beginning of the experiment were transient . Nevertheless , both bacteria have the autonomous property to persist in the host , independently of other microbiota members . Moreover , this property seems largely independent of host background , because it is observed in the w1118 iso lab flies and in several individuals of the natural outbred population . In addition to being able to stably colonize , both A . thailandicus and A . cibinongensis proliferate in the gut of D . melanogaster , showing that these bacteria are bona fide D . melanogaster gut colonizers . The niche of the stable population of A . thailandicus is the foregut of D . melanogaster . Live bacteria were consistently observed in the anterior part of the crop , crop duct , and proventriculus and absent in the midgut and hindgut . Light and electron microscopy show that these bacteria are present in clusters . The bacteria seem to be attached to each other by fimbriae , although the nature of these extracellular appendages needs future confirmation . This organization in clusters may contribute to the stability of the bacteria in the folds of the crop or in the proventriculus by physically decreasing their changes of being dragged through the gut . Additionally , the crop is a diverticulum of the esophagus that can store liquid food , and its lumen is not subject to the same linear flux as the rest of the gut [58 , 59] , which might facilitate bacterial persistence . A similar argument is made for the appendix and cecum in humans and other mammals , as a reservoir of microbiota [60 , 61] . Another possibility is that A . thailandicus attaches to the cuticle lining of the foregut . Fimbriae , or other appendages , can also be involved in adherence to the host [62] . However , we did not observe direct adherence to the host by electron microscopy . In some instances , we saw close proximity between bacterial clusters and chitin folds of the crop , but a more thorough analysis will be required to assess this . We observed a clear border in A . thailandicus colonization between the proventriculus and the midgut . This border may work as a physical or immunological barrier for microorganisms in insects [27 , 63–65] , and the acidic region in the anterior midgut may also contribute to bacteria killing [38 , 66] . Also , the continuous secretion and movement of the peritrophic matrix , which separates the gut lumen from epithelial cells , may hamper stable bacterial colonization [67 , 68] . The crop was identified 130 ago as the region where yeasts proliferate in flies [69] . Several bacteria have also been shown to colonize the foregut in D . melanogaster and other insects , including the human pathogen Yersinia pestis and the plant pathogen Xylella fastidiosa [70–74] . Regurgitation of the foregut content has been implicated in transmission of Y . pestis to humans and transmission of bacteria to plant surfaces by Bactrocera and Ceratitis fruit flies [59 , 72 , 73 , 75] . This process may also be involved in transmission of A . thailandicus by D . melanogaster . Given the stable association between D . melanogaster and A . thailandicus , we asked if there was any advantage for either partner in this interaction . Symbiosis between a host and a microbe does not necessarily signify mutualism , and the effect of host association on the microbial partner has been less frequently studied [76 , 77] . Our results indicate that the stable association of A . thailandicus to the gut of the adult fly is advantageous to this bacterium because it can promote its dispersal . The interaction with A . thailandicus is also advantageous to D . melanogaster in several scenarios . A . thailandicus shortens larvae developmental time , and this can contribute to an increased host fitness if there are no associated trade-offs [37 , 78] . Interestingly , adult flies that developed in the presence of A . thailandicus are also more fertile , a clear measure of fitness , when compared with flies that developed axenically . Other bacteria have been shown before to shorten the developmental time of D . melanogaster [15 , 16 , 79–82] and increase adult fertility when associated in larval stages [83] . However , out of the 15 bacteria isolated from wild flies , A . thailandicus induced the shorter development time and higher fertility . Therefore , out of the set of bacteria interacting with D . melanogaster in the wild , this stable gut symbiont is particularly beneficial . We do not know the mechanism through which A . thailandicus , or the other bacteria we tested , benefit D . melanogaster . The negative correlation that we observed between developmental time and fertility suggests a similarity in the mechanisms behind these phenotypes . Microorganisms have long been recognized as important for Drosophila development and as a source of food [14 , 84] . In fact , the standard Drosophila food used in the lab is partly composed of dead Saccharomyces cerevisiae [85] , which , in this diet , is required and sufficient for Drosophila development . Moreover , in lab diets the bacterial influence on host development is generally stronger the less yeast extract the food contains [15 , 16] . In D . suzukii , high doses of heat-killed bacteria and yeast can decrease the developmental time to the same extent as the same strains alive [86] . Also , in D . melanogaster adults , a constant supply of heat-killed yeast Issatchenkia orientalis can extend the life span of flies to the same extent as live yeast [19] . The nutritional value of these microorganisms may be based on supplying amino acids or vitamins to the host [14 , 19 , 49 , 84 , 87] . Other evidence indicates that the effect of microorganisms on development of D . melanogaster has a component independent of its nutritional value , and heat-killed bacteria are not sufficient to fully rescue the phenotype conferred by live bacteria [38] . Bacteria can directly impact host physiology by activating the insulin pathway via acetic acid production in the case of an A . pomorum , or gut proteases in the case of L . plantarum [16 , 39 , 88] . The benefit of A . thailandicus for D . melanogaster becomes even more evident when larvae develop in figs , a natural food substrate . On sterile figs homogenates , very few larvae reach adulthood in axenic conditions , and those that do are severely delayed in growth and are infertile as adults . These results show the insufficiency of fruit , or figs in this particular case , to support normal D . melanogaster development . A . thailandicus rescues these phenotypes and is , therefore , sufficient for D . melanogaster development on fruit , indicating a nutritional basis for the interaction . Interestingly , developmental time of flies is shorter and fertility is higher with the addition of A . thailandicus to figs than with the addition of Acetobacter OTU2753 , contrary to what happens in the laboratory food . This may indicate adaptation of these bacteria to their food source and consequent impact on the host [89] . An alternative hypothesis is that bacteria are detoxifying some toxic components present on the food . Detoxifying symbiosis is known to occur in many insects [90] . However , the fact that A . thailandicus is beneficial both in lab food and figs indicates that to a large extent its benefit is independent of food toxins . We did not see a direct effect of the stable A . thailandicus population on adults’ fertility . However , direct effects of bacteria on adults have been previously reported on oocyte development or fertility [83 , 91] . Many factors may explain the different results , including the identity of the bacteria tested and the relatively small bacterial stable population in the gut . Nonetheless , it will be interesting in the future to determine if the stable A . thailandicus population has any other effect on the adult physiology . Analysis of L . pseudomesenteroides stability and proliferation in D . melanogaster gut produced ambiguous results . This bacterium seemed very stably associated with the gut of wild and monoassociated lab flies when the stability protocol was performed in vials . When we implemented the protocol using cages , however , it disappeared from 50% of the flies . These results illustrate how sensitive to experimental conditions this assay is , and that stringency is crucial . The proliferation assay did not clearly show an increase or decrease in L . pseudomesenteroides at 24 hours , when compared to the beginning of experiment . These results could be the consequence of this bacterium being able to very rapidly proliferate in the gut of the fly but unable to attach to the host and , therefore , requiring a constant cycle of reinoculation . Maybe this cycle could be kept in vials but broken down in cages . Further experiments will be required to test this hypothesis and elucidate the interaction of L . pseudomesenteroides with D . melanogaster . Lactobacillus species were still present in wild flies at the end of stability protocol , although less frequently than A . thailandicus , A . cibinongensis , and L . pseudomesenteroides . Interestingly , the data indicate a negative interaction between Lactobacillus and Leuconostoc presence . Both are lactic acid bacteria ( order Lactobacilalles ) , and they may occupy the same niche and compete for resources . Of the many bacterial isolates from the gut of wild flies , L . brevis , and L . paraplantarum are the most beneficial in terms of development time and fertility of D . melanogaster , together with A . thailandicus . This contrasts with previous reports indicating a small or null effect of lab Lactobacillus isolates on fecundity [37 , 83] . L . brevis is present in 4 out of 10 wild flies after the stability protocol and proliferates in the gut of D . melanogaster . So , L . brevis may also be a beneficial bona fide gut symbiont of D . melanogaster , although not as frequent as A . thailandicus in this population . Our results indicate that the interaction between D . melanogaster and the gut symbiont A . thailandicus is especially beneficial for both partners in the wild ( Fig 8 ) . The small stable bacterial population in the gut serves as a reservoir for the inoculation of the environment that the adult fly explores and exploits . This is beneficial to the bacteria because it leads to their continuous dissemination . On the other hand , transmission of A . thailandicus to the food substrate of the next generation , concomitant with egg laying , benefits D . melanogaster development . This association is therefore a form of farming , a strategy adopted by several insects , including ants , termites , and ambrosia beetles with fungi [92] . The stability of the D . melanogaster–A . thailandicus interaction provides the host some independence from the local bacterial populations and enables it to explore and modulate bacterial populations in new locations . Besides the interaction with these stable bacteria in the wild , D . melanogaster also interacts with a plethora of environmental bacteria and yeasts that may be transiently associated with the gut . Many of these non-colonizing microorganisms probably positively impact D . melanogaster biology , and vice versa . D . melanogaster are attracted to feed on , or oviposit in , substrates with specific potential benefiting bacteria and yeasts [14 , 91 , 93–97] . Attraction to fermenting fruits enriched with beneficial microbes may be a strategy adopted by D . melanogaster to increase interactions with these bacteria . Furthermore , D . melanogaster most likely disperses them as they transit through its gut . However , if these bacteria or yeasts are not stably associated with the flies , this would be a transient phenomenon . D . melanogaster may also benefit bacteria by promoting their growth in the food substrate [38] , which could be advantageous for the host if biased towards beneficial bacteria . Despite all these potential mechanisms promoting beneficial interactions , relying on the immediate environmental and local microbial community may be suboptimal for D . melanogaster ( Fig 8 ) . In the future it will be interesting to address some questions relevant for this model . For instance , we do not know how stable A . thailandicus is in the gut of larvae or if this stability is important . It may be sufficient for the bacteria to grow on the food substrate , because larvae are less mobile and they will be in constant contact with the local external population of bacteria . Another important aspect is to understand how adult flies acquire A . thailandicus . This could be through constant association throughout the developmental stages , including from larvae to pupae to adult , or de novo acquisition after adult eclosion [79] . This farming interaction model may extend to other bacteria , including L . brevis . Moreover , our study focused on the gut-colonizing bacterial species in one D . melanogaster population . It will be important to analyze other natural populations , from other diets and geographical regions , and determine to what extent there is conservation of stably colonizing species . This analysis could elucidate if there is a core gut microbiota of D . melanogaster based on stable symbionts . It will also be important to extend this analysis to other microbes , such as yeasts , given that flies are constantly exposed to them in the natural habitat . Interactions between microbes may affect their colonization and their influence on host phenotypes . These may happen with other colonizing bacteria , with environmental bacteria on the food substrate , or while in transit through the gut . Our analysis of wild-caught flies incorporates , to a certain degree , this complexity . For instance , A . thailandicus that stably colonizes in monoassociation is also present in the gut of the majority of wild flies of the population we analyzed , showing that its association is robust in the face of rich bacterial communities . Moreover , the beneficial effect of this bacterium observed in monoassociation is also present in the context of complex and natural microbial communities of figs . On the other hand , the analysis of wild-caught flies also indicates a negative interaction between Lactobacillus and Leuconostoc species . We show that stable interactions are specific from both host and the bacterial perspectives . Subtle differences in the bacteria associated with D . melanogaster and D . simulans in the wild have been found before [28] , but differences may be clearer when looking into the stable gut symbionts of different Drosophila species . The presence of these species-specific mutualistic interactions of gut bacteria with D . melanogaster raises the possibility that these are long-term interactions and the result of adaptation . Therefore , they may be a good system to study host-symbiont evolution and even address questions of coevolution and cospeciation [30 , 98–100] . We still do not know the cause of the specificity of these colonizations . The interaction between the host immune system and different bacteria could be one of the mechanisms involved in this selection . Bacteria can down-regulate or escape from the host immune system to establish infection [101] , and in D . melanogaster , alterations in immunity have an impact on gut bacterial compositions or load [22 , 27 , 102] . Many innate immune genes in Drosophila species are under fast positive selection [103–105] , and differences in these genes could mediate association of different Drosophila species with different stable gut bacteria . Although the perspective of a transient microbiota has been dominant in most analyses of gut bacteria in Drosophila [20 , 27 , 38 , 43 , 106] , there is some evidence of stable gut bacteria in these flies . Recently it was shown that a wild isolate of L . plantarum has a higher frequency of gut colonization than a lab isolate [31] . These results are in agreement with a tendency for wild isolates of bacteria being better at colonizing D . melanogaster . However , in this study , once bacterial colonization was established , titers were constant over time in wild and lab isolates [31] . It will be interesting to also test these isolates with the proliferation and stability protocols that we describe here . On a different approach , analysis of wild-caught individuals from mushroom , and cactus-feeding Drosophila species have identified bacterial strains highly enriched in the gut but very poorly represented in matched substrate samples [29 , 30] . This indicates that these enriched bacteria are gut symbionts , and it will also be interesting to study them in more detail . The presence of stable associations in the wild raises the question of why these seem to have been lost in laboratory stocks . Part of the answer may be related to the fact that association with non-colonizing bacteria can be as beneficial as with colonizing bacteria in the lab ( e . g . , Acetobacter OTU2753 versus A . thailandicus ) . Fly husbandry conditions in the lab normally ensure transmission of bacteria from generation to generation , even if they do not stably colonize the gut . Therefore , under laboratory conditions , there may be a loss of selective pressure for stability . This can lead to loss of the capacity to stably colonize the gut by the bacteria , either by drift or by selection , if there is a cost associated with this capacity . Alternatively , colonizing bacteria may be replaced by non-colonizing strains in the lab . The lab diet is relatively uniform and different from the natural diet; therefore , bacteria better adapted to these conditions may outcompete wild isolates [47] . Moreover , use of antifungal antimicrobials , and sometimes antibiotics , may constantly or occasionally severely disrupt bacterial communities associated with the flies that are then replaced with local bacterial strains that do not have the capacity to colonize Drosophila . One or combinations of these factors may , over the long periods of time that flies are kept in the lab , lead to the loss of the original microbiota . From our experience , wild bacterial isolates seem to be easily outcompeted in lab conditions and replaced by other bacteria , because we needed to carefully handle the fly stocks to keep the monoassociations with wild isolates . Exploring the interactions between the host and its natural colonizing symbionts can uncover new phenotypes missed in laboratory experiments . Previous studies with other organisms have shown that indeed this can be the case . For instance , in the nematode Caenorhabditis elegans , bacteria isolated from natural habitats conferred higher fitness when compared with the standard Escherichia coli used in the laboratory [107 , 108] . Also , wild collected mice harbor a different microbiota than laboratory mice , which decreases inflammation and is protective upon infection and tumorigenesis [109] . The capacity to colonize and proliferate in the gut of D . melanogaster described in this study demonstrates different properties of lab and wild bacterial isolates . Moreover , other phenotypes associated with wild isolates may yet be identified . The stable interaction we found between D . melanogaster and gut bacteria will be useful to address important questions in the gut microbiota field using this model system . This includes identifying and characterizing , from the host and bacteria perspective , genes required for colonization and for the control of this interaction . Moreover , it will allow understanding determinants of specificity , which are largely unknown , although adhesion and biofilm formation are important in this process [110 , 111] . These questions are also relevant to better understand and manipulate insect gut symbionts . The release of insects with specific gut bacteria in interventions may be useful against pests ( e . g . , by increasing the fitness of sterile males [112] ) and against vectors of disease ( e . g . , by increasing resistance to pathogens [113 , 114] ) . Knowing what regulates gut stability may be important for the success of these approaches . Our work defines a new paradigm for the association between D . melanogaster and gut bacteria , in which stable associations exist and contribute to the fitness of both partners in an ecological context . Therefore , this new conceptual and experimental framework to study gut stable symbionts will contribute to the growing field of Drosophila–microbe interactions .
Wild flies were collected with traps , with fallen figs as bait , placed for 24 hours under a fig tree in Oeiras , Portugal ( GPS coordinates 38°41'32 . 1"N , 9°18'59 . 4"W ) . D . melanogaster and D . simulans males were identified according to [40] . All the material to collect and sort wild flies was sterilized prior to use . DrosDel w1118 isogenic stock ( w1118 iso ) [33] was used as a laboratory stock , unless otherwise indicated . The female lines D . melanogaster O13 and D . simulans O13 were established from single wild females collected in 2013 , and later identified to the species level . Other stocks used were D . melanogaster Canton-S ( Bloomington Drosophila Stock Center at Indiana University , stock #1 ) and D . simulans A07 and J04 ( Drosophila Species Stock Center from California University , stocks #14021–0251 . 260 and #14021–0251 . 187 , respectively ) . Unless otherwise indicated , flies were 3–6 days old in the beginning of experiments . The age of wild-caught flies is uncontrolled . Stocks were kept and experiments were performed at 25°C in standard Drosophila food composed of 1 . 05 L water , 80 g molasses , 22 g beet syrup , 8 g agar , 10 g soy flour , 80 g cornmeal , 18 g yeast . Food was autoclaved and cooled to 45°C before adding 30 mL of a solution containing 0 . 2 g of carbendazim ( Sigma ) and 100 g of methylparaben ( Sigma ) in 1 L of absolute ethanol . Analysis of bacteria present in the gut was performed by culture-dependent methods in order to isolate bacteria for further manipulations . From each fly , the gut ( including crop , midgut , and hindgut ) , together with the Malpighian tubules , was dissected in Tris-HCl 50 mM , pH 7 . 5 , and homogenized with a plastic pestle in an 1 . 5 mL microcentrifuge tube with 250 μL Lysogeny Broth ( LB ) . Each sample was serially diluted ( 1:10 factor ) and 30 μL from each dilution were plated in five different culture media: LB ( GRiSP ) , de Man , Rogosa and Sharpe broth ( MRS ) ( Merck ) , Liver Infusion Broth ( Becton Dickinson ) , Brain heart infusion ( BHI ) ( Sigma-Aldrich ) , and Mannitol ( 3 g of Bacto Peptone [Becton Dickinson] , 5 g of Yeast Extract [Sigma-Aldrich] , 25 g of D-Mannitol [Sigma-Aldrich] , 1 L of Milli-Q water ) . Plates were incubated at 25°C for 6 days and dilutions containing 30–300 CFUs were used to count and isolate bacteria . To analyze flies or food associated with only specific bacterial isolates , samples were plated on specific media to grow the correspondent bacteria ( Mannitol for Acetobacter and MRS for Leuconostoc or Lactobacillus ) . Plates were incubated at 25°C for 4 days . For quantification of total bacteria in each gut sample , we selected the data from the medium that presented the highest number of colonies . For a detailed analysis , bacterial colonies were assigned in each culture medium plate , per sample , to distinct morphological types , and their number determined . Two colonies of each morphological type , per culture medium plate , per sample , were re-streaked and , after growth , colonies were picked , resuspended in 500 μL LB containing 15% glycerol ( v/v ) , and frozen at −80°C . To identify each bacterial isolate , a PCR was performed to amplify the 16S rRNA gene . For most samples a bacterial colony , or part of it , was directly placed in the PCR reaction tube ( colony PCR ) . In the few cases in which amplification was unsuccessful by colony PCR , DNA extraction was performed with ZR Fungal/Bacterial DNA MiniPrep Kit ( Zymo Research ) according to the manufacturer's instructions . Primers used were: 27f ( 5′-GAGAGTTTGATCCTGGCTCAG-3′ ) and 1495r ( 5′-CTACGGCTACCTTGTTACGA-3′ ) , with the following PCR conditions: 94°C for 4 minutes; 30 cycles of 95°C for 30 seconds , 58°C for 1 minute , and 72°C for 2 minutes; 72°C for 10 minutes . PCR products were sequenced at Source Biosciences Sequencing Center . Sequences were trimmed to 800 bp of each sequence , including V2 to V4 hypervariable regions . These sequences were aligned against a core set aligned fasta file from Greengenes [34] , using PyNAST [115] , and classified into OTUs according to Greengenes taxonomy [34] . Sequences that matched Ralstonia OTU3005 , Novosphingobium stygium OTU2886 , and Novosphingobium OTU2881 were removed from the analysis because they were occasionally present on negative controls for PCR . In most cases , each morphological type corresponded to one OTU . However , three groups of bacteria had different OTUs commonly assigned to the same morphological type . Thus , these bacteria could not be distinguished within their group , based on colony morphology . These groups are composed of bacteria belonging to the Lactobacillus genus , the Acectobacteraceae family ( Acetobacter and Gluconobacter genera ) , or the Enterobacteriaceae family . The frequencies of the sequenced colonies from each group are represented in Fig 3 and S3 Fig . To determine CFUs per gut for each OTU , or group of bacteria , the data from the medium that presented the highest number of colonies was selected . Bacterial isolates used for phenotypic analysis ( Acetobacter OTU2753 , A . thailandicus , A . cibinongensis , L . pseudomesenteroides , and all isolates used in Fig 7B and S16 Fig ) were sequenced with both 27f and 1495r primers and analyzed at least from V2 to V8 hypervariable regions of the 16S rRNA sequence . Sequences were automatically edited with PhredPhrap , and consensus sequences were generated using BioEdit Sequence Alignment Editor Software . Sequences are in S1 Text and deposited in GenBank with the following accession numbers: MG808351 . 1 , MG808350 . 1 , MG808352 . 1 , MG808353 . 1 , MG808354 . 1 , MG808355 . 1 , MG808356 . 1 , MG808357 . 1 , MG808358 . 1 , MG808359 . 1 , MG808360 . 1 , MG808361 . 1 , MG808362 . 1 , MG808363 . 1 , MG808364 . 1 , and MG808365 . 1 . The A . thailandicus isolate was initially identified as A . indonesiensis OTU2758 based on the Greengenes analysis of the V2–V4 hypervariable regions . Later , the analysis of the full 16S rRNA gene sequence matched several different Acetobacter OTUs with 98% identity in the Greengenes analysis . Therefore , we used BLAST to analyze the full sequence against the NCBI 16S rRNA sequences ( Bacteria and Archaea ) database [116] . A . thailandicus 16S rRNA gene was the best hit and was 99% identical to the sequence of this isolate [57] . DNA was extracted from dissected single guts with QIAamp DNA Micro kit ( Qiagen ) as described in the protocol "isolation of Genomic DNA from Tissues . " To facilitate DNA extraction from gram-positive bacteria , the guts were homogenized in 180 μL of enzymatic lysis buffer with Lysozyme ( from DNeasy Blood & Tissue Kit , QIAGEN ) and incubated for 1 hour at 37°C before starting the protocol . DNA concentrations were determined with a NanoDrop ND-1000 Spectrophotometer . qPCR reactions were carried out in CFX384 Real-Time PCR Detection System ( BioRad ) . For each reaction in a 384-well plate ( BioRad ) , 6 μL of iQ SYBR Green supermix ( BioRad ) , 0 . 5 μL of each primer solution at 3 . 6 mM , and 5 μL of diluted DNA were used . Each plate contained three technical replicates of every sample for each set of primers . Primers used to amplify the 16S rRNA gene were: 8FM ( 5′-AGAGTTTGATCMTGGCTCAG-3′ ) and Bact515R ( 5′-TTACCGCGGCKGCTGGCAC-3′ ) [117] . Primers used to amplify Rpl32 were Rpl32 forward ( 5′-CCGCTTCAAGGGACAGTATC-3′ ) and Rpl32 reverse ( 5′-CAATCTCCTTGCGCTTCTTG-3′ ) . The thermal cycling protocol for the amplification was initially 50°C for 2 minutes , and denaturation for 10 minutes at 95°C , followed by 40 cycles of 30 seconds at 95°C , 1 minute at 59°C and 30 seconds at 72°C . Melting curves were analyzed to confirm specificity of amplified products . Ct values for manual threshold of 10 were obtained using the program SDS 2 . 4 or with Bio-Rad CFX Manager , with default threshold settings . Gene levels of 16S rRNA were calculated relative to the Day 0 sample with the Pfaffl method [118] using Drosophila Rpl32 as a reference gene . To develop axenic flies , embryos were sterilized with 2% sodium hypochlorite during 10 minutes , followed by 70% ethanol during 5 minutes , and washed with sterile water . Embryos were placed in sterilized food vials and maintained in axenic conditions or monoassociated with 40 μL of overnight bacterial culture of specific isolates . Monoassociated stocks were kept at 25°C and flipped every 20 days , using sterile gloves . We waited at least two generations in monoassociation before performing experiments . The gut stability protocol in vials was based on placing a single fly per vial , with a food surface of 3 . 8 cm2 , and changing it twice a day to new vials . The stability protocol in cages was based on placing a single fly per cage with six petri dishes , with a total fly food surface of 382 cm2 , and changing them daily . Bacterial levels were analyzed in single guts . To analyze the gut region where stable bacteria are present , individual guts were dissected into five different regions: crop , anterior midgut , mid midgut , posterior midgut , and hindgut . The proventriculus was included in the anterior midgut sample . Each gut region from a single fly was homogenized , plated , and quantified , as described above . The proliferation assay was based on providing an inoculum of bacteria to axenic male flies for 6 hours and measuring gut bacterial levels , by plating , immediately at the end of this period ( time 0 hours ) , and 24 hours later . Bacteria were grown in the Mannitol ( Acetobacter ) or MRS ( Leuconostoc or Lactobacillus ) liquid media in a shaker at 28°C overnight . Bacterial concentrations ( cell/mL ) were calculated based on OD600 using a spectrophotometer ( SmartSpec 3000 from Biorad ) , using the formula OD1 = 5 × 108 cell/mL . The inoculum was provided in vials by adding 180 μL of bacterial solution in 2 . 5% sucrose to a round filter paper placed on top of the fly food . After the inoculation period , flies were placed singly in cages or in bottles ( food surface: 382 cm2 and 28 cm2 , respectively ) for 24 hours . Bacterial levels were analyzed in single guts by plating . To confirm that the 24 hours data corresponded to bacteria growing in the gut and not bacteria growing on the fly food and in transit , we added an axenic fly to the cage or bottle at time 0 , in some experiments . Bacterial levels in the gut of these chaser flies were determined at time 24 hours , simultaneously with the cohabiting experimental fly . Chaser flies and experimental flies were distinguished by marking one or the other , in different experimental replicates , with a dot in the wing with a permanent pen . To analyze bacterial growth on food from bacteria associated with flies , conventionally reared 3–6-day-old males were placed singly in vials for 24 hours , in order to contaminate the food with bacteria . After that period , flies were discarded and vials were incubated for 9 days at 25°C . Bacterial levels were determined after discarding the flies ( Day 1 ) and after the 9 days of incubation ( Day 10 ) . Vials that never contained flies before were used as control vials and incubated also for 9 days ( Day 10 control ) . Of the top layer of food , 2 . 9 g were homogenized in 10 mL LB . This homogenate was plated in the five different media . To analyze growth of Acetobacter species on the fly food , 3–6-day-old males monoassociated with the different Acetobacter were singly placed in vials with 4 mL of fly food for 16 hours . After that period , males were discarded and bacterial levels were assessed at that time point ( Day 0 ) and after 1 or 5 days of incubating the vials at 25°C . All the food from the vial was homogenized in 4 mL LB . Mannitol plates were incubated at 25°C for 4 days . To determine fitness parameters in monoassociated stocks ( S13 Fig ) , one virgin female and three 0–3-day-old males were placed per vial for 3 days and then discarded . Time to pupariation and to adulthood was daily assessed , as well as total number of pupae and adults . To analyze fitness parameters of flies in a changing environment ( Fig 6 , S14 Fig ) , axenic 1–3-day-old females and males were in contact for 6 hours with an inoculum of 105 CFU/μL Acetobacter OTU2753 , A . thailandicus , or with sterile Mannitol . After this period , one female and two males were placed per cage for 10 days . Each cage contained six bottles with food that were changed every day ( total food surface of 170 cm2 ) . Single gut bacterial loads were analyzed in females 0 hours and 10 days postinoculation and in males 10 days after inoculation . From each cage , all six bottles were daily collected , the number of eggs was counted , and bottles were kept to daily assess adult emergence ( Fertility of G0 and Development of G1 ) . Transmission of bacteria to the food was analyzed in bottles without eggs at days 1 , 3 , 5 , 7 , and 9 . The food surface was washed with 1 , 000 μL of Mannitol , and 100 μL of this suspension was plated in Mannitol . As a control , food from axenic flies was also tested at Days 1 and 9 , and no bacteria were detected . To analyze fertility of G1 , bottles from day 9 and 10 from each condition were used to collect flies . One female and one male from the same condition were placed per vial and flipped to new ones every other day , during 10 days . Adult emergence was daily assessed to determine total number of adults ( Fertility of G1 ) . To analyze if the benefit of A . thailandicus was dependent on the association with either parent , we compared the four possible pairs of males and females from an axenic stock and a stock monoassociated with A . thailandicus ( Fig 6H , S15 Fig ) . We placed one female and one male , both 1–2 days old , per vial , and flies were passed to new vials every other day during 10 days . We also tested a condition in which 30 μL of an overnight A . thailandicus culture was added to the progeny of axenic parents immediately after emptying it of parents . We daily assessed developmental time to pupariation and adulthood . To analyze fitness parameters conferred by different natural bacterial isolates ( Fig 7B , S16 Fig ) , 50 sterilized eggs were placed per vial and inoculated with 40 μL of an overnight bacterial culture . All isolates were grown at 28°C in Mannitol , except L . brevis , L . paraplantarum , and L . pseudomesenteroides , which were grown in MRS . As controls , we analyzed sterilized eggs associated with only Mannitol or MRS , or with no medium added . Number of adults ( G0 ) and developmental time to adulthood ( G0 ) were assessed . One male and female of the first adults emerging from each condition were placed per vial and flipped every other day during 8 or 10 days . Adult emergence was daily assessed to determine total number of adults ( Fertility of G0 ) . To analyze the impact of A . thailandicus on fitness parameters in sterile figs homogenate ( Fig 7C–7G , S18A–S18I Fig ) , 30 or 50 sterilized eggs were placed per vial and inoculated with 40 μL of an overnight culture of A . thailandicus , Acetobacter OTU2753 , or sterile Mannitol . Adult emergence was daily assessed . For the analysis of this G0 fertility , one male and one female adult that emerged from these vials were placed per vial and flipped every other day during 8 or 10 days . Adult emergence was daily assessed to determine total number of adults ( Fertility of G0 ) . Bacteria presence was confirmed on the last vials of egg laying , by resuspending the food in 1 mL of PBS 1× and plating it in Mannitol . Both bacteria were present . The fig food homogenate was produced with 300 mL homogenized commercial frozen figs , 600 mL water , and 4 . 8 g agar . After autoclave , food was poured into each vial in sterile conditions , inside a laminar flow hood . To analyze the fitness impact of A . thailandicus in fresh figs , we collected these at the same location where the wild flies were collected . Figs were cut in quarters and placed in vials with sterilized agar ( 0 . 8% agar in water ) at the bottom to fix the fig . Thirty sterilized embryos were placed on the top of these figs and inoculated with 40 μL of an overnight culture of A . thailandicus or sterile Mannitol . Quarters originated from the same fig were distributed to the two conditions . Adult emergence was daily assessed . As a control , figs without the addition of eggs were kept , and no flies emerged from those ones . Also , all flies that emerged from the experimental conditions had white eyes , confirming that they developed from the sterilized eggs and not from a possible contamination with wild flies present in the figs . All the imaging analysis was performed in flies monoassociated with A . thailandicus . FISH was performed in flies at Day 0 and Day 5 of the stability protocol in cages , with daily changed food . Flies were dissected in cold PBS 1× , and guts were fixed and permeabilized in 4% paraformaldehyde ( Merck ) and 1% Triton-X-100 ( Sigma ) in PBS 1× ( PBST ) for 90 minutes at 4°C . Samples were washed once for 15 minutes with PBST and twice for 5 minutes with phosphate-buffered saline 1× ( PBS ) at room temperature ( RT ) . Dehydration was performed in an ascending series of ethanol concentrations ( 50% , 80% , and 100% ) for 3 minutes each , rinsed twice in PBS 1× , and incubated overnight at 4°C in 6% hydrogen peroxide ( Sigma ) and 80% ethanol . Samples were then washed twice for 5 minutes in PBS 1× , prehybridized for 30 minutes without the probe , and hybridized overnight at 37°C with the universal probe for 16S rRNA gene EUB338 ( 5′-Cy3-GCTGCCTCCCGTAGGAGT-3′ ) [119] ( Sigma ) in hybridization buffer ( 5× SSC [ThermoFisher] , 50% formamide [Sigma] , 20mM Tris-HCl , 8% Dextran Sulfate , 0 . 1% SDS [Sigma] , and 200 ng/mL of probe ) . Post-hybridization washes were performed at 55°C . Samples were rinsed once in 2× SSC , twice for 15 minutes with 20% formamide in 0 . 5× SSC , once for 15 minutes in 0 . 5× SSC , and once for 15 minutes with 2× SSC . Samples were counterstained with Hoechst for 20 minutes in PBS 1× to stain DNA . After washing in 1× PBS , guts were mounted in Vectashield Mounting Medium ( Vector laboratories ) . To observe live and dead bacterial cells in the gut , we used males after 9 days of the stability protocol . Live and dead bacteria were stained with SYTO9 and propidium iodide ( PI ) , respectively , using the Invitrogen LIVE/DEAD BacLight Bacterial Viability Kit ( L7012 ) , and the protocol was adapted from [27] . Males were fed with a 20 μL mix of 2 . 5% sucrose , 1 . 5 μL of SYTO9 , and 1 . 5 μL of PI , and after 1 hour , guts were dissected and counterstained with Hoechst ( Sigma ) for 10 minutes in 1× PBS . Samples were washed in 0 . 9% NaCl , mounted in Vectashield Mounting Medium , and immediately observed . Confocal images were taken with Leica SP5 and processed in Fiji [120] . For transmission electron microscopy ( TEM ) , flies were dissected 5 days after the stability protocol in cages and the guts were fixed with 2% formaldehyde ( EMS ) and 2 . 5% glutaraldehyde ( Polysciences ) in 0 . 1 M phosphate buffer ( PB ) ( pH 7 . 4 ) for 90 minutes . Three washes were performed for 10 minutes each in 0 . 1 M PB , and samples were embedded for support and orientation in 2% low melting point agarose ( OmniPur ) and stained with toluidine blue . After solidifying , guts were postfixed with 1% osmium tetroxide ( EMS ) in 0 . 1 M PB on ice for 1 hour . After three washes in dH2O for 10 minutes each , samples were en bloc stained with 0 . 5% uranyl acetate in dH2O for 1 hour in the dark . Dehydration was performed with ascending series of ethanol concentrations ( 30% , 50% , 75% , 90% , and 100% ) . Infiltration was performed with ascending concentrations of Embed-812 epoxy resin ( EMS ) ( 25% , 50% , and 75% , for 1 hour each , and 100% overnight ) and cured overnight at 60°C . Proventriculus and crop were cut at 70 nm for TEM using a Leica UC7/FC7 Ultramicrotome . Ultrathin sections were post-stained with 0 . 5% uranyl acetate and Reynolds lead citrate for 5 minutes each and observed in a Hitachi H-7650 electron microscope operating at 100 KeV . The statistical analysis was performed in R [121] and graphs were generated using the package ggplot2 [122] and GraphPad . The script of all the analyses is provided in S2 Text , where details can be found . Bacterial levels; number of eggs , pupae , and adults; and time to pupariation and adulthood were analyzed using linear models , or linear mixed-effect models ( package lme4 [123] ) if there were random factors . Significance of interactions between factors was tested by comparing models fitting the data with and without the interactions using analysis of variance ( ANOVA ) . Models were simplified when interactions were not significant . Pairwise comparisons of the estimates from fitted models were analyzed using lmerTest [124] , lsmeans [125] , and multcomp [126] packages . Time course analysis of bacterial stability in cages was performed fitting a nonlinear least-squares model with the parameters of an exponential decay curve . Model simplification was achieved through ANOVA and Akaike information criterion ( AIC ) of fitted models . Bacterial levels in flies in the changing environment cage assay were analyzed with the nonparametric Mann–Whitney test because some data points were high and not estimated precisely . Bacteria transmission to bottles in the changing environment cage assay was analyzed with a generalized linear mixed-effects ( lme4 package ) with a binomial distribution . Independence of Lactobacillus and Leuconostoc , or different Acetobactereaceae , presence in wild-caught flies was tested with the Pearson’s chi-squared test . Correlation between developmental time and fertility of flies that developed associated with different bacteria was tested through the Pearson correlation of the means of these parameters . | Animals , including humans , live together with complex bacterial communities in their gut that influence their physiology and health . The fruit fly Drosophila melanogaster is an excellent model organism to study host–microbe interactions and harbors a relatively simple gut bacterial community . The dominating hypothesis in the field is that gut bacteria in D . melanogaster are unstable and their constant ingestion with food is required to maintain their levels in the gut . Here , however , we show in D . melanogaster collected from a natural population , that stable gut bacteria do exist . We isolated specific species that can proliferate in the gut and form a stable association that is beneficial for both the bacteria and the flies . For the bacteria , they benefit from being constantly disseminated by the flies as they move around . For the flies , this is a form of farming , as the next generation of flies benefits from the association with these particular bacteria during development . Flies associated with these bacteria during the larval stages become adults faster and are more fertile than if they develop with other bacteria encountered in nature . Our findings show that D . melanogaster has stable colonizing bacteria in the gut , which are important for host development , establishing a new framework to study host–gut bacteria interactions . | [
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"re... | 2018 | Drosophila melanogaster establishes a species-specific mutualistic interaction with stable gut-colonizing bacteria |
In a previous paper we have shown that , when DNA samples for cases and controls are prepared in different laboratories prior to high-throughput genotyping , scoring inaccuracies can lead to differential misclassification and , consequently , to increased false-positive rates . Different DNA sourcing is often unavoidable in large-scale disease association studies of multiple case and control sets . Here , we describe methodological improvements to minimise such biases . These fall into two categories: improvements to the basic clustering methods for identifying genotypes from fluorescence intensities , and use of “fuzzy” calls in association tests in order to make appropriate allowance for call uncertainty . We find that the main improvement is a modification of the calling algorithm that links the clustering of cases and controls while allowing for different DNA sourcing . We also find that , in the presence of different DNA sourcing , biases associated with missing data can increase the false-positive rate . Therefore , we propose the use of “fuzzy” calls to deal with uncertain genotypes that would otherwise be labeled as missing .
Genome-wide association ( GWA ) studies are becoming more common because of rapid technological changes , decreasing costs and extensive single nucleotide polymorphism ( SNP ) maps of the genome [1 , 2] . However , a major technological challenge is the fact that this ever-increasing number of SNPs is necessarily reliant on fully automated clustering methods to call genotypes . Such methods will inevitably be subject to errors in assigning genotypes because the clouds of fluorescence signals are not perfectly clustered and vary according to many factors , including experimental variation and DNA quality [3] . As it is no longer practical to inspect each genotype call manually , identification of unreliable calls requires a measure of clustering quality . Failure to identify such SNPs leads to an increased false-positive rate and , if a crude quality score is applied , loss of data . Adapting the clustering algorithm to allow for clustering variation arising from the study design can reduce the number of unreliably called SNPs and can minimise the false-positive rate . The decreasing genotyping costs of GWA studies is permitting the use of larger sample sizes . An efficient design to limit the blood sample collection and genotyping costs is the use of a common control group for several case collections [2] . To this end , the 1958 British Birth Cohort ( 1958 BBC ) , an ongoing follow-up study of persons born in Great Britain during one week in 1958 ( National Child Development Study ) , has been used to establish a genetic resource [4] ( www . b58cgene . sgul . ac . uk ) . The Wellcome Trust Case-Control Consortium ( WTCCC ) has adopted such a design utilising the 1958 BBC and additional blood donors ( www . wtccc . org . uk ) as a common control group for case collections of seven different diseases . A drawback of this approach is that it can generate a differential bias in genotype calling between case and control DNA samples that originated from different laboratories [3] . This leads to an increased false-positive rate . In this paper , we compare the genotype calls of a type 1 diabetes ( T1D ) GWA study using the original clustering algorithm [5] implemented for this genotyping platform and a new algorithm adapted to take into account differential bias in genotype scoring . This study consists of 13 , 378 nonsynonymous SNPs ( nsSNPs ) in 3 , 750 T1D cases and 3 , 480 1958 BBC controls using the highly multiplexed molecular inversion probe ( MIP ) technology [6 , 7] . Previously , we found that the original clustering algorithm [5] performed well when the genotypes clouds were perfectly clustered . However , when variability in the fluorescent signal caused the clouds to be less distinct , we found that a differential bias between cases and controls increased the false-positive rate [3] . The cause of this problem was attributed to the different sources for controls and cases DNA samples that resulted in different locations for the genotyping clouds of fluorescent signal . We addressed this issue by scoring separately cases and controls [3] . We also explored surrogate measures of clustering quality and employed stringent cut-offs to reduce the false-positive rate and extended the concept of genomic control by applying a variable downweighting to each SNP . However , neither approach was optimal , particularly the use of stringent cut-offs , which resulted in a considerable loss of data . Here , we adapted the methodology to address differential bias between cases and controls in a GWA study . There are three main improvements . Two modifications concern the genotyping algorithm: we used a new scoring procedure that enables cases and controls to be scored together and we adopted a more robust statistical model . The third modification was to use “fuzzy” calls in association tests in order to deal appropriately with call uncertainty . This avoids bias introduced by treating uncertain calls as “missing” when the proportion of such missing calls vary between cases and controls . We also propose a quality-control score for the clustering . These improvements allowed us to significantly increase the number of SNPs available for analysis and to improve the overall data quality . These modifications are generic and can be incorporated into any clustering-based genotyping algorithm . We illustrate this point by applying our algorithm to score the WTCCC control samples ( www . wtccc . org . uk ) , which were generated using the Affymetrix 500K ( http://www . affymetrix . com ) .
Our genotyping procedure follows the original algorithm [5] in fitting a mixture model using the expectation maximization ( EM ) algorithm but we modified this approach to address the characteristics of our dataset . The original algorithm transformed the two-dimensional fluorescent signal intensity plot into a one-dimensional set of contrasts ( see Methods ) . A mixture of three Gaussian ( one heterozygous cloud and two homozygous clouds ) was fitted to this one-dimensional set of contrasts using the EM algorithm [8] and data points were assigned to clusters . Data points that could not be attributed to a cluster with high posterior probability were treated as missing data . In addition to the parameters that described the location of the genotyping clouds the model also estimated the a priori probabilities ( Φ1 , Φ2 , Φ3 ) for each cluster; these correspond to the genotype relative frequencies . As control and case DNA samples were processed in different laboratories , the location of the genotyping clouds for the fluorescent signal can differ between cases and controls ( see Figure 1 ) . Previously , we scored cases and controls separately to allow for such differences [3] . However , this solution is not ideal . While the location of the clusters can differ , the a priori frequencies should be identical in cases and controls under the null hypothesis of no association . Statistical theory shows that the most powerful test is obtained when the maximum likelihood for the nuisance parameters ( here the genotyping parameters ) is estimated under the null hypothesis . Letting these values differ between cases and controls resulted in overestimated differences in allele frequencies and increased over-dispersion of the test statistic . Our modified algorithm linked the clustering for cases and controls by assuming genotype frequency parameters to be identical but imposed no such restriction on the location of the genotyping clouds . Variability in allele frequencies across geographic regions is also allowed . We extended this approach to score nsSNPs on the X chromosome to account for male/female copy number differences ( see Methods ) . In the original algorithm , the a priori frequencies for the three clusters ( Φ1 , Φ2 , Φ3 ) are linked by the condition Φ1 + Φ2 + Φ3 = 1 , leaving two free parameters . We investigated the effect of further constraining these frequencies to be consistent with Hardy-Weinberg equilibrium ( HWE ) . In that version of the algorithm , the a priori frequencies ( Φ1 , Φ2 , Φ3 ) are parameterised as ( π2 , 2π ( 1 − π ) , ( 1 − π ) 2 ) using a unique parameter π . We also found that the statistical model for the fluorescent clouds was not robust to excessive variability of the fluorescent signal within a genotyping cloud . Because our association tests require that no data point is treated as missing ( see below ) , we needed a model robust to outliers . As the tails of the Gaussian distribution decay too fast , we replaced the Gaussian distributions with t-distributions . Our parameter inference procedure ( EM algorithm , see [8] ) uses a representation of the t-distributions as a Gaussian random variable with a variance sampled randomly from a Gamma distribution . Fortunately , the sample size of this study was sufficient to estimate these additional parameters . The nsSNPs were analysed using the one degree of freedom Cochrane-Armitage trend test [9] . In this statistical framework , the outcome variable is the disease phenotype and the explanatory variable is the genotype . The null hypothesis is the absence of effect of the genotype on the odds of developing the disease . This test statistic for association is a score test; the score statistic is the first derivative of the log-likelihood of the data at the null value of the parameter tested . The test statistic is obtained by dividing the score test by its variance under the null , derived using a permutation argument ( see Methods ) . We also used a stratified version of this test introduced originally by Mantel [10] that allows for variability in allele frequency and disease prevalence across 12 broad regions in Great Britain [3] . In this version of the test , the score and its variance are summed over the 12 strata to obtain the overall score and variance . The ratio of the square of the score statistic to its variance is asymptotically distributed as a χ2 random variable with one degree of freedom We explored how differential bias could affect the distribution of the test statistic . An aspect of the data that is affected by the differential bias is the frequency of missing calls and the way these missing calls affect the genotyping clouds . These differences increased the over-dispersion level ( see for example Figure 1 ) . We found that the best solution was to avoid the use of missing calls and call all available samples , making appropriate allowance for call uncertainty . This led us to modify the association test . To do so , we reformulated the association test as a missing data problem in which the distribution of the genotypes status is estimated conditionally on the fluorescent signal and the geographic origin of the sample ( see Methods ) . This modification of the test amounts to replacing the score statistic with its expectation under this posterior distribution of the genotype status . Similar ideas have been used in the context of haplotype phasing [11] . The elevated rate of false positives observed in the data resulted from an over-dispersion of the test statistic . We estimated the over-dispersion factor , λ , by calculating the ratio of the mean of the smallest 90% of the observed test statistics to the mean of the smallest 90% of the values expected under the null hypothesis of no association [3] . Using the smallest 90% is motivated , in a case-control framework , by the exclusion of the “true” associations that are caused by actual differences between cases and controls and that can significantly affect the mean value of the test statistic . To make the interpretation of the results easier , we report Δλ , the difference between the theoretical over-dispersion factor ( equal to 1 ) and the observed one: a value of 1% for Δλ means that the over-dispersion factor λ is 1 . 01 . We illustrate the impact of our modifications by analysing simulated fluorescent signal data . We used two models for the quality of the fluorescent signal ( high and low quality SNPs ) . We considered various scenarios for the minor allele frequency in cases and controls and simulated 100 , 000 SNPs for each scenario . The signals were scored in three different ways: ( 1 ) the full algorithm , as described above; ( 2 ) cases and controls were called separately; and ( 3 ) fuzzy calls were not used . In ( 3 ) , we assigned a probability 1 to the most probable call under the posterior distribution and we called a sample missing when the probability of this most probable call was less than 0 . 95 . For each version of the scoring algorithm , we report Δλ under the null hypothesis of no association ( i . e . , identical population frequencies in cases and controls ) . We also compared the power for the three versions of the algorithm . Following Neyman-Pearson's lemma [12] , the best test is the one that , for a given type 1 error ( the probability to reject the null when the null is true ) , has the lowest type 2 error ( the probability to accept the null when the alternate hypothesis is correct ) . In practice it implied correcting the test statistic for the over-dispersion and estimating the fraction of the SNPs simulated under the alternate hypothesis for which the null hypothesis was accepted . We set the type 1 error to 0 . 05 in our simulations . Results are reported in Table 1 . We found that , as expected , all three versions of the algorithm performed comparably well when the quality of the fluorescent signal was high . In that situation , the only situation where the level of over-dispersion was significant was the separate typing procedure combined with low minor allele frequency . Clustering based algorithms are not well suited to estimate parameters when the number of data points in a genotyping cloud is low and this weakness was amplified when cases and controls were called separately . However , strong differences appeared for the lower quality SNPs . We found that there was little over-dispersion when the full algorithm was used ( Δλ between −0 . 79% and 0 . 59% ) . However , when the split typing version was used , over-dispersion ranged from 7 . 04% to 14 . 7% , increasing as the minor allele frequency decreases . In addition , comparison between joint and split typing methods showed that the power of the study ( measured by the type 2 error ) was very similar . However , this observation is misleading , as when the data consists of a mixture of high and low quality SNPs , applying a constant correction factor independently of the fluorescent signal quality would result in a loss of power for the split typing method . For the full method , we found a near perfect agreement between theoretical and observed distributions , and the use of a correction factor was not necessary . Not using the fuzzy calls had a less obvious effect on the over-dispersion . As mentioned above , the high quality SNPs were not affected because the vast majority of calls was certain . For the low quality fluorescent signal model , we found that on average 1 . 2% of the individuals had a probability of the most likely call lower than 0 . 95 . Labelling these unclear calls as missing significantly affected the over-dispersion slope , which reached a maximum at intermediate frequencies ( Δλ = 5 . 34% at minor allele frequency 10% , see Table 1 ) . In addition , and unlike the split typing version of the algorithm , the type 2 error increased significantly ( between 1% and 5% ) . We also note that calls with a most likely probability lower than 70% were rare ( on average 0 . 4% of the calls ) . Therefore , replacing the fuzzy posterior distribution with the most likely call had almost no effect on the over-dispersion slope , indicating that for the range of model and data considered here the inclusion of fuzzy calls is not critical as long as missing calls are not used . The MIP data consisted of 13 , 378 nsSNPs typed in 3 , 750 cases and 3 , 480 controls . We analysed 11 , 579 nsSNPs with minor allele frequency estimated to be greater than 0 . 01 . We also excluded 281 nsSNPs in the HLA region that is known to be associated with T1D , leaving 11 , 298 nsSNPs . Initially , using the original calls , we employed stringent cut-offs for the surrogate measures of clustering quality: case and control call rates both greater than 95% , difference in call rates between controls and cases smaller than 3% and HWE χ2 < 16 . This resulted in 2 , 079 high-quality nsSNPs with an over-dispersion factor Δλ of 4 . 5% . We obtained a lower over-dispersion of 1 . 5% when these nsSNPs genotypes were called using the adapted algorithm . As expected , this difference in over-dispersion between algorithms became more marked as less stringent cut-offs were applied . For example , lowering the call rate cut-off to 90% resulted in 5 , 294 nsSNPs with an over-dispersion of Δλ = 17% using the original Moorhead et al . [5] scoring algorithm and 8 . 1% using the adapted algorithm on the same set of nsSNPs . We propose a measure of clustering quality that compares the variability of the signal within a cluster with the variability between clusters ( see Methods ) . The lower limit for the quality measure was set such that beyond this value the over-dispersion factor λ remained constant . When we selected the nsSNPs according to our quality-control measure this resulted in 7 , 446 nsSNPs with an over-dispersion slope of 7 . 5% using our improved algorithm ( Figure 2 ) . For the same set of SNPs the over-dispersion level was 21% using the original calls . We investigated the effect of our modifications by scoring the data using various configurations of the algorithm and the association test . The quality was measured using the level of over-dispersion Δλ of the test statistic for the stratified test ( see Table 2 ) . For the genotyping procedure , we found that the split clustering of cases and controls significantly increased the over-dispersion level: Δλ = 10 . 5% , +3% compared to the joint typing of cases and controls with a unique set of a priori frequencies ( Δλ = 7 . 5% ) . However , letting these a priori frequencies vary across geographic regions in the stratified version of the test did not change the results , although a stronger discrepancy might have been observed if cases had not been well matched geographically with the controls . Assuming a Gaussian model ( rather than t-distribution in the adapted algorithm ) also significantly increased the over-dispersion level ( +4 . 3% ) . Imposing the a priori frequencies to be consistent with HWE did not lower the over-dispersion level ( +1 . 5% ) , probably because this condition was too stringent . We investigated a weaker version of this constraint in the parameter estimation: we first estimated the parameters under the HWE constraint . Then we relaxed this assumption in the second step but used the parameter values estimated in the first step as a starting point for the iterative parameter estimation procedure . This modification also did not lower the over-dispersion level ( Δλ = 8 . 2% , +0 . 7% compared to the adapted calls ) . However , while this further constrain did not improve the over-dispersion overall , this two-step procedure helped with finding the global maximum of the likelihood function for a small fraction of nsSNPs for which the variance of the fluorescent signal was large . Therefore , it provided an alternative scoring method useful to maximise the number of typed nsSNPs while increasing the over-dispersion only slightly . Regarding the association test , we investigated the effect of missing calls . For each nsSNP , we called a sample missing when the probability of belonging to the most likely genotype cloud was less than 95% . The number of missing calls varied greatly across nsSNPs: the median of the average number of missing calls across the nsSNPs that pass the quality threshold is 0 . 2% but this median number is 1% among the 2 , 171 nsSNPs with the lowest quality score among the best set of 7 , 446 nsSNPs . We found that the use of missing calls slightly increased the level of over-dispersion ( +1 . 1% compared to the same algorithm in the absence of missing calls ) . However , missing calls have a larger effect on the quality scores: re-estimating a best set of 7 , 446 nsSNPs but computing the quality scores with missing data generated an over-dispersion of 10 . 5% . This larger over-dispersion is explained by the fact that introducing missing calls biased the computation of the quality scores and prevented us from identifying low quality nsSNPs ( see Discussion ) . However , once we avoided the use of missing calls and called all available samples , using the most likely call instead of the posterior distribution had little effect ( Δλ = 7 . 4% , 0 . 1% lower than our adapted calls ) . This limited effect is expected because split calls are rare for the range of models we considered . In this section , we show the result of our adapted algorithm applied to a different genotyping platform , the Affymetrix Mapping 500K array set . These data have been generated by the WTCCC ( www . wtccc . org . uk ) . The WTCCC is a GWA study involving seven different disease groups . For each disease , the WTCCC genotyped 2 , 000 individuals from England , Scotland , and Wales . Disease samples will then be compared to a common set of 3 , 000 nationally ascertained controls also from the same regions . These controls come from two sources: 1 , 500 are representative samples from the 1958 BBC and 1 , 500 are blood donors recruited by the three national UK Blood Service . Here , we compare the WTCCC control groups . This comparison is interesting because in a typical GWA study , we expect a fraction of the over-dispersion to reflect actual genetic differences between control and disease groups . However , when comparing two sets of healthy controls the interpretation of the results is easier , as both groups should be representative samples of the population . We first show that our quality measure was efficient at distinguishing poorly typed SNPs from correctly typed ones . We illustrate this point by showing the distribution of p-values on Chromosome 1 for three quality thresholds ( see Figure 3 ) . Because the distribution of the fluorescent signal differs between the MIP platform and the Affymetrix 500K , the optimum threshold also differs . We found that approximately 79% of the SNPs have a minor allele greater than 0 . 01 as well as a quality score greater that 1 . 9 . Given a total number of 40 , 220 SNPs on this chromosome , is approximately the level beyond which no p-value is expected . For that quality threshold of 1 . 9 , only four SNPs are obvious false positives with p-values beyond 1 × 10−5 . Visual inspection of the clusters confirmed that these were indeed clustering errors . When we increased the quality score to 2 . 2 , only one of the four SNPs remained ( with a quality score of 2 . 3 ) . As approximately 12% of the Affymetrix 500K SNPs are monomorphic in the British population , we found that only 9% of the SNPs did not pass our quality threshold , while keeping the false-positive rate close to zero . Similar numbers were found on other chromosomes . In addition , we compared our algorithm with the BRLMM calls , commonly used on this platform and provided by Affymetrix . For each autosomal chromosome we used BRLMM and our adapted algorithm to select the subset of SNPs with a quality score greater than 1 . 9 and a minor allele frequency greater than 0 . 01 . For both sets of calls , we computed the fraction of SNPs that pass that threshold . In order to make results comparable , we calculated the over-dispersion slope for the SNPs that passed both the BRLMM and the adapted calls threshold ( see Table 3 ) . We found that the percentage of SNPs that pass the quality threshold is typically 4% higher using our adapted algorithm , while the over-dispersion remained 2%–5% lower , indicating a significant improvement .
In this T1D nsSNP GWA study , the adapted algorithm was successful at scoring more nsSNPs confidently ( 7 , 446 nsSNPs instead of 5 , 294 nsSNPs ) and , as a consequence , reducing the false-positive rate: over-dispersion decreased from 17% to 7 . 5% . Rather than developing an entirely new genotyping algorithm we have adapted the current algorithm for GWA with the motivation of controlling the false-positive rate resulting from a cases/controls genotyping bias . Consequently , these modifications are relevant to all clustering based genotyping algorithms . Here , we considered the MIP genotyping technology [7] and the Affymetrix 500K array , but these modifications are also applicable to the Illumina platform ( http://www . illumina . com ) . Our results show that the most important recommendation consists of scoring the different datasets ( typically cases and controls ) in a centralised manner , when this is possible . Introducing fuzzy calls is less important as long as one avoids the use of missing calls . In practice , a key component of any genotyping algorithm is the ability to provide a single measure of clustering quality . Previously , we used surrogate measures of clustering quality ( such as call rate and deviation from HWE ) to identify unreliable SNPs , but this approach was not optimal [3] . Our measure of clustering quality compared the locations of the clusters of fluorescent signals with the variability of this signal within a cluster . However , to be really informative , this measure should be computed in the absence of missing calls . Excluding calls artificially reduces the variability of the signal within each cloud and biases the quality measure upward . Contrary to intuition , when using the calls provided by the original MIP algorithm [7] to compute both the quality measure and the association statistic , the over-dispersion level is higher for the nsSNPs that have the highest confidence value: Δλ = 26% for a confidence greater than 8 ( 1 , 116 nsSNPs ) and Δλ = 15% for a confidence level between 5 and 8 ( 2 , 393 nsSNPs ) . Visual inspection of the clustering for these nsSNPs showed that such high confidence levels were typically associated with small variability of the fluorescent signals within clouds . In that situation , the original algorithm called missing those data points located a few standard deviations away from the center of the cluster . When these missing calls occurred differently in cases and controls it resulted in an increased over-dispersion of the association statistic ( such as in Figure 1 ) . We note that in spite of our efforts a level of over-dispersion remains even for the 2 , 079 nsSNPs with near perfect clustering ( Δλ = 1 . 5% ) . This estimate is noisy and its significance or causes are difficult to assess . However , we note that in the larger set of 7 , 446 nsSNPs , the inclusion of 21 non-Caucasian samples increased the over-dispersion from 7 . 5% to 12 . 1% . Also , if there were any undetected close relations in the collections of cases and controls this could also increase the level of over-dispersion ( we did ensure that inadvertent or deliberate sample duplications were removed , and no first-degree relatives were included in the study ) The difference between the lower bound of 1 . 5% ( in the high quality set of 2 , 079 nsSNPS ) and our 7 . 5% level ( in the larger set of 7 , 446 nsSNPs ) is probably associated with remaining imperfections in our statistical model . As pointed out in the Results section , replacing the most likely call with its posterior distribution given the fluorescent signal had little effect on the level of over-dispersion . Indeed , when a data point was located between two clusters , the algorithm did not assign an intuitive 50%/50% probability on both adjacent clouds but rather put a weight close to one on the cloud with the largest standard deviation . This replacement of “grey” calls with “black or white” amplified the difference between cases and controls and contributed to the remaining level of over-dispersion .
The original algorithm is described in [5] . Genotypes are scored based on the contrast measure: for a SNP with alleles A and G and signal intensities IA and IB , respectively , S = IA + IB and contrast = sinh ( 2IAIG/S ) /sinh ( 2 ) . In this approach a mixture of three Gaussian is then fitted to the set of contrast values . Three parameters ( Φ1 , Φ2 , Φ3 ) with the constraint Φ1 + Φ2 + Φ3 = 1 represent the a priori probabilities to belong to each of the three clouds ( before knowing the value of the contrast ) . Parameters ( a priori frequency estimates location μ , and standard deviation σ of the three clouds ) are estimated using the EM algorithm [8] . This Gaussian mixture is replaced with t-distributions in our modified method . A possible representation of a t-distribution with n degree of freedom , variance parameter σ and mean μ is the following: This representation is used in the version of the EM algorithm we used to score the data [8] . It used a data augmentation procedure and treated the variables u as missing data . When controls and cases are typed separately each sample has its own set of parameters Θ: ( Φ1 , Φ2 , Φ3 ) that describe the a priori allele frequencies as well as that describe the location of the three genotype clouds . In the linked version of the scoring the a priori frequencies ( Φ1 , Φ2 , Φ3 ) are identical for both samples ( cases and controls ) . In the EM algorithm the set of parameters Θ is estimated iteratively . The estimator of Φi at step ( k+1 ) is where n is the number of observations and . When the scoring is done separately for cases and controls this estimator is computed separately for both samples . In the linked version of the scoring this sum is computed jointly for cases and controls . For the stratified association test , each geographic region s has its own set of parameters ( Φ1 , Φ2 , Φ3 ) that is estimated separately for each region , but jointly for cases and controls . The rest of the EM algorithm follows [8] . We extended our linked clustering approach to deal with nsSNPs on the X chromosome . Because of male/female copy number differences this situation is similar to differential genotyping bias as the location of the genotyping clouds can differ across samples . We extended our linked clustering approach to this situation: the location of the genotyping clouds could differ but the a priori frequencies were estimated jointly . In that case we denote and . Then for the female sample we have . For the male sample: The linked clustering approach can be extended to impose HWE for the a priori frequency estimates ( Φ1 , Φ2 , Φ3 ) . The frequencies are parameterised as ( π2 , 2π ( 1 − π ) , ( 1 − π ) 2 ) . Using the same notations the EM estimator becomes: . This approach can also be extended to X chromosome SNPs as presented above . We first consider the unstratified version of the test ( see Protocol S1 for a complete derivation of the test ) . We denote the disease status ( the outcome variable in our model ) as a vector of binary variables Y . The vector X of explanatory variables ( the genotypes ) can take three values ( 1 , 2 , 3 ) . We assume a logistic model: logit[P ( Y = 1 ) ] = α + βX . The score statistic can be written as: The score variance can be computed using a profile likelihood argument: where D and H are the numbers of cases and controls , is the sample variance of the expected value of the genotype variable X , and is the variance of Xi under the fuzzy distribution . The test statistic U2/V is χ2 with one degree of freedom under the null . The derivation of this test is available in Protocol S1 . In that version of the test the score statistic becomes: where Si is the strata for the individual i and the mean value of Y in that strata . Each strata has its own score variance ( computed as in the nonstratified situation ) and the contribution of each strata is then summed to obtain the overall score variance . The test statistic U2/V is still distributed as χ2 with one degree of freedom under the null . We designed a measure that captures the intuition that clouds of points are well separated for a given SNP . We use the difference between the centres of adjacent clouds divided by the sum of the standard deviation for these two clouds . Center and standard deviation of the clouds is computed based on the most likely calls . The final quality measure for a SNP is the minimum computed over each pair of clusters . This computation is done for cases and controls separately and the minimum over both samples is then computed . As expected , increasing that threshold is inversely correlated with over-dispersion . The over-dispersion stops decreasing at a threshold of 2 . 8 and we used this value to generate our set of 7 , 446 SNPs . When simulating SNPs we simulated directly the set of contrasts . For high quality SNPs , the centres of the three genotyping clouds are −0 . 9 , 0 , 0 . 9 . The three t-distributions have degree of freedom equal to ν = 10 and the scaling factor for the standard deviation is 0 . 03 . The standard error for each genotyping cloud is then equal to . For lower-quality SNPs , the centres of the three genotyping clouds are also −0 . 9 , 0 , 0 . 9 . The three t-distributions have degree of freedom equal to ν = 3 . 5 and the scaling factor for the standard deviation is 0 . 1 . The standard error for each genotyping cloud is then equal to . | Genome-wide disease association studies are becoming more common and involve genotyping cases and controls at a large number of SNP markers spread throughout the genome . We have shown previously that such studies can have an inflated false-positive rate , the result of genotype calling inaccuracies when DNA samples for cases and controls were prepared in different laboratories , prior to genotyping . Different DNA sourcing is often unavoidable in the large-scale association studies of multiple case and control sets . Here we describe methodological improvements to minimise such biases . These fall into two categories: improvements to the basic clustering methods for calling genotypes from fluorescence intensities , and use of “fuzzy” calls in association tests in order to make appropriate allowance for call uncertainty . | [
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] | 2007 | A Method to Address Differential Bias in Genotyping in Large-Scale Association Studies |
Sensing and response to changes in nutrient availability are essential for the lifestyle of environmental and pathogenic bacteria . Serine/threonine protein kinase G ( PknG ) is required for virulence of the human pathogen Mycobacterium tuberculosis , and its putative substrate GarA regulates the tricarboxylic acid cycle in M . tuberculosis and other Actinobacteria by protein-protein binding . We sought to understand the stimuli that lead to phosphorylation of GarA , and the roles of this regulatory system in pathogenic and non-pathogenic bacteria . We discovered that M . tuberculosis lacking garA was severely attenuated in mice and macrophages and furthermore that GarA lacking phosphorylation sites failed to restore the growth of garA deficient M . tuberculosis in macrophages . Additionally we examined the impact of genetic disruption of pknG or garA upon protein phosphorylation , nutrient utilization and the intracellular metabolome . We found that phosphorylation of GarA requires PknG and depends on nutrient availability , with glutamate and aspartate being the main stimuli . Disruption of pknG or garA caused opposing effects on metabolism: a defect in glutamate catabolism or depletion of intracellular glutamate , respectively . Strikingly , disruption of the phosphorylation sites of GarA was sufficient to recapitulate defects caused by pknG deletion . The results suggest that GarA is a cellular target of PknG and the metabolomics data demonstrate that the function of this signaling system is in metabolic regulation . This function in amino acid homeostasis is conserved amongst the Actinobacteria and provides an example of the close relationship between metabolism and virulence .
Mycobacterium tuberculosis is the causative agent of TB , and remains one of the world’s biggest health threats . Existing vaccination and drug treatment regimens may be circumvented by M . tuberculosis through sophisticated adaptation and resistance mechanisms . New insights into the regulatory and signal transduction networks and metabolism of M . tuberculosis are needed to better understand the biology of this outstanding pathogen . Genome analyses revealed that M . tuberculosis encodes 11 serine/threonine protein kinases ( STPKs ) , some of which play essential roles for viability or virulence [1 , 2] . There is wide interest in these kinases as routes to understand the virulence strategies of M . tuberculosis and as potential therapeutic targets [3] . For these reasons , M . tuberculosis has become a target organism for research into the general mechanisms of signaling by serine and threonine phosphorylation in bacteria [2 , 4] . PknG has been the focus of a number of studies because of its essentiality for virulence [5 , 6] and its involvement in regulating industrial glutamate production by Corynebacterium glutamicum [7] . The kinase substrate GarA is also essential in M . tuberculosis and has been strongly associated to metabolic regulation [8 , 9] . GarA controls the activities of three enzymes linking glutamate metabolism to the TCA cycle , and genetic disruption of garA leads to a distinctive nutrient-dependent phenotype in fast-growing non-pathogenic Mycobacterium smegmatis [9] . Although there is strong genetic evidence for the requirement of PknG for virulence , multiple alternative mechanisms have been proposed [10–12] . A major challenge for this and other bacterial STPKs is to determine the mechanisms underlying observed genetic essentiality , and to move beyond study of proteins in vitro to determine the physiological substrates and functions of kinases . We have previously presented evidence that phosphorylation of GarA switches off its regulatory functions , and that phosphorylated GarA can be found in M . tuberculosis and M . smegmatis [8] . However , the molecular or environmental signals that trigger GarA phosphorylation were unknown and the role of PknG in regulating metabolism ( via GarA ) has been controversial . Initially PknG was thought to inhibit phagosome-lysosome fusion [6] , and pknG disruption in Mycobacterium bovis BCG caused no growth defect [13] , raising the possibility that PknG played different roles in pathogenic Mycobacterium spp compared to non-pathogens [14 , 15] . Subsequent research has shown that pknG mutants in different Mycobacterium spp . have increased antibiotic sensitivity and reduced biofilm formation [11 , 12] . With the increasing number of examples of the complex interplay between bacterial physiology and virulence , we present here an investigation into the influence of PknG and GarA on virulence and metabolism of M . tuberculosis and M . smegmatis and identify stimuli for this signaling pathway . M . tuberculosis and M . smegmatis are metabolically versatile and are able to synthesise all twenty proteinogenic amino acids , which may be critical to combat host strategies to starve intracellular bacteria of amino acids [16] . Although able to utilize inorganic nitrogen sources , M . tuberculosis shows a preference for amino acids such as glutamate , and these are also co-catabolised along with other carbon sources in axenic culture and in macrophages [17–22] . Genome analysis suggests that catabolism of glutamate is carried out by glutamate dehydrogenase ( GDH ) and metabolism via TCA cycle ( alpha-ketoglutarate dehydrogenase , KDH ) , while glutamate synthase ( GltS , also known as GOGAT ) is the main route of glutamate biosynthesis [23] and this is supported by recent studies of M . tuberculosis and M . bovis BCG [24–26] . Co-catabolism of glutamate raises the challenge of maintaining the balance between carbon and nitrogen metabolism , particularly given host strategies to deprive intracellular bacteria of amino acids [16] . Glutamate is the major donor for transaminations so the intracellular glutamate pool must be preserved . A second challenge specific to the Actinobacteria is that the KDH complex ( alpha-ketoglutarate dehydrogenase or oxoglutarate dehydrogenase complex ) does not have a dedicated E2 subunit: the dihydrolipoyl transacetylase subunit is shared between the KDH and pyruvate dehydrogenase complexes [27] , potentially requiring an additional degree of regulation of carbon metabolism . Bacteria commonly have multiple mechanisms to sense carbon and nitrogen status ( starvation or sufficiency ) including sensing key intracellular metabolites including oxoglutarate , glutamine , ATP , cyclic AMP , ( p ) ppGppp [28 , 29] . The nitrogen sensor ( s ) of M . tuberculosis has yet to be identified [30] , but compared to Escherichia coli , M . tuberculosis has only a single PII protein ( nitrogen sensor ) rather than two , and lacks the nitrogen-sensing two component system NtrB/C . Given the direct effects of GarA on the relevant enzyme activities ( KDH , GDH and GltS ) , we reasoned that the system of PknG-GarA could fulfill this function in Mycobacterium spp . and other Actinobacteria and we set out to investigate the regulation of GarA by phosphorylation and its relationship to metabolism and virulence .
We have previously constructed a conditional gene disruption mutant of M . tuberculosis and demonstrated that garA was essential in standard Middlebrook medium , which contains 3 mM glutamate , but dispensable when additional amino acid supplements were used ( 10 mM asparagine , glutamate or glutamine [9] ) . Using this knowledge , we have now constructed an unmarked garA deletion mutant ΔgarAMt ( Fig 1 ) . This strain grew poorly on standard Middlebrook medium and growth was restored by addition of asparagine or reintroduction of garA ( Figs 1A and S1 ) . Asparagine was chosen rather than glutamate or glutamine because of good solubility and low influence on pH or buffering capacity . ΔgarAMt was tested for its ability to infect and replicate in THP-1 macrophages ( ATCC TIB-202 ) and was unable to replicate ( Fig 1B ) . Plasmid-encoded GarA complemented the defect ( Fig 1B ) but addition of 20 mM asparagine to the cell culture medium did not restore replication ( S2 Fig ) , suggesting that intracellular M . tuberculosis could be in an environment with non-permissive asparagine concentration ( below 10 mM ) . These results reinforce our earlier findings with the conditional mutant strain [9] , which suggest that garA is essential for M . tuberculosis in macrophages and that this essentiality could be due to amino acid deprivation inside the phagosome . Since ΔgarAMt shows auxotrophy in axenic growth and attenuation in macrophages , we predicted it would be avirulent . To compare the virulence of ΔgarAMt with parental H37Rv , equivalent inocula , as measured by colony forming units ( CFU ) , were used for intranasal infection of immune competent BALB/c mice . Virulent H37Rv and the complemented strain replicated in the lungs and disseminated to the spleen within 28 days , whereas ΔgarAMt failed to replicate within the lungs and no bacteria could be recovered from lungs or spleen after 21 days ( Fig 1C & 1D ) . Notably , a clear confirmation of the attenuation was observed between the macrophage infection model and the mouse infection model , which is also compatible with the observed auxotrophy in axenic media . We conclude that garA was essential for virulence of M . tuberculosis in mice , which may be due to amino acid deprivation in vivo or because of the impact of primary metabolism on other aspects of virulence , such as stress tolerance [25] . According to our model , GarA and PknG act in the same pathway but with opposite effects on metabolism . Interestingly , loss of garA caused severe attenuation in our study ( complete loss of replication and bacterial survival ) whereas loss of pknG caused only partial attenuation in a previous study using two different mouse models [5] . We next turned our attention to the role of phosphorylation of GarA and the putative responsible kinase PknG . The normal role of phosphorylation is to “switch off” the activity of GarA by preventing it from binding to its enzyme targets [8] . The phosphorylation sites are found in a conserved ETTS motif in an unstructured N-terminal extension distinct from the forkhead-associated domain [31] . Variants of GarA lacking phosphorylation sites are functional for enzyme binding [31] but cannot be “switched off” by kinase activity , and thus might have an opposite effect on bacterial metabolism from garA knockout . We used variants of garA with mutations in the phosphorylation motif to complement the knockout strain ΔgarAMt in order to examine the role of regulation of GarA by phosphorylation in M . tuberculosis . GarA variants lacking the first phosphorylation site , threonine 21 ( EATS or EAAS at the motif ) was less effective than normal GarA at restoring growth on Middlebrook medium ( Fig 1A ) , despite confirmed expression ( S3 Fig ) . In THP-1 macrophages GarA that lacks a single phosphorylation site ( EATS or ETAS at the motif ) restored growth of ΔgarAMt in THP-1 macrophages but GarA lacking both phosphorylation sites ( EAAS ) failed to restore growth ( Fig 1B ) . This suggests that the normal function of GarA requires regulation by phosphorylation . The single mutants EATS and ETAS led to slightly different levels of complementation in macrophages compared to axenic growth , which may reflect the different nutrient sources utilized in the two conditions . As PknG is the only kinase reported to phosphorylate GarA at T21 ( the first threonine in the GarA ETTS motif ) , disruption of pknG might be expected to alter phosphorylation of GarA , mimicking the effects of mutations to the GarA phosphorylation motif . ΔpknGMt has previously been reported to have a defect in survival in bone marrow derived mouse macrophages [11] . Unlike ΔgarAMt , ΔpknGMt was able to replicate in THP-1 macrophages , albeit 10-fold less than parental M . tuberculosis ( S4 Fig ) . Taken together , these results support the importance of phosphorylation in regulating the function of GarA in M . tuberculosis both in vitro and during infection . Phosphorylation at both sites ( T21 and T22 ) may be important , meaning that PknG and at least one other kinase may be involved , as is thought to be the case for the homologous system in C . glutamicum [32] . Having established the importance of GarA for virulence , we probed the reasons for this essentiality and the extent of conservation between slow-growing M . tuberculosis and fast growing non-pathogenic model M . smegmatis . GarA binds to the same enzyme targets to promote the same effects on enzyme activity in both organisms [8 , 31] . We predicted that pknG disruption ( or disruption of GarA phosphorylation sites ) would lead to an inability to catabolise glutamate ( since excess unphosphorylated GarA would inhibit GDH and KDH ) , while garA disruption would lead to uncontrolled glutamate catabolism ( since GltS would be less active and GDH and KDH would be uninhibited ) . We have previously studied the phenotype caused by garA deletion in M . smegmatis ( ΔgarAMs ) . This strain grew normally on standard mixed medium ( Fig 2A ) but relied on external glutamate or related amino acids for growth and also showed differences from wild type in the ability to utilize a range of carbon sources [9] . Here we examined the ability of truncated GarA , which lacks the phosphorylation motif , to complement the growth defect . Truncated GarA and EAAS GarA fully complemented the growth defect of ΔgarAMs on media lacking glutamate , indicating that these variant proteins are functional in stimulating glutamate production and preventing glutamate catabolism ( Fig 2B ) . However , when glutamate was the only source of carbon ( Fig 2C ) or nitrogen ( Fig 2D ) , strains lacking pknG or strains expressing non-phosphorylatable GarA formed clumps ( Fig 2E ) and grew poorly compared to the parent strain . The nutrient-specific growth phenotypes recorded in microplates ( Fig 2 ) were also apparent when the same strains were cultured in flasks ( S5 Fig ) . Similarly , ΔpknGMt showed no growth defect compared to parental M . tuberculosis on minimum medium supplemented with glycerol ( Fig 3A ) or glucose or acetate ( S6 Fig ) , but had a specific growth defect when asparagine or glutamate were used as the sole carbon source in liquid culture ( Fig 3B & 3C ) . Reintroduction of pknG restored PknG expression ( S7 Fig ) and improved growth on asparagine and glutamate ( Fig 3B & 3C ) , although full restoration of growth was not achieved , possibly due to deleterious effects of PknG over-expression . The nutrient-specific phenotypes of M . tuberculosis and M . smegmatis gene knockouts were both specific to amino acid metabolism: garA-disrupted strains required glutamate or asparagine for growth , while pknG-disrupted strains had a defect in utilization of glutamate or asparagine . To investigate the likely conservation of function of the regulatory pathway between M . tuberculosis and M . smegmatis we used M . tuberculosis pknG and garA to complement the growth defects of M . smegmatis mutants ( S8 Fig ) . Our results suggest conservation of function of PknG and GarA between a fast-growing saprophyte and a slow-growing pathogen ( other differences between these organisms have been reviewed [33–35] ) . In summary , the nutrient-specific phenotypes of pknG- and garA-disrupted M . smegmatis and M . tuberculosis support a role for these proteins in regulating amino acid metabolism . To investigate kinase ( s ) that phosphorylate GarA in live mycobacteria and the stimuli that lead to kinase activity , we developed methods to distinguish phosphorylated GarA from the unphosphorylated form in cell extracts . Hexahistidine-tagged GarA shows a shift in mobility in SDS PAGE upon phosphorylation [8] but for untagged GarA this shift was too minor for reliable separation ( for example S3 Fig shows a single band though Fig 4B demonstrates a mixture of phosphorylated and unphosphorylated GarA ) . Here we used three methods to determine whether GarA is phosphorylated in cells: ( i ) use of the Phos-tag reagent to retard the mobility of phosphorylated GarA in SDS PAGE of cell extracts from M . tuberculosis and M . smegmatis ( Fig 4A ) , ( ii ) development of LC-MS/MS protocols to detect GarA in cell extracts of M . tuberculosis ( Fig 4B ) , ( iii ) replacement of endogenous GarA with a hexahistidine-tagged version in M . smegmatis ( Fig 4C ) . M . smegmatis and M . tuberculosis growing in standard media contained two forms of GarA suggesting that cells contained a mixture of phosphorylated and unphosphorylated GarA ( Fig 4A ) . In M . smegmatis lacking pknG the upper band was missing but could be restored by the introduction of plasmid-borne GarA , showing that PknG was the main kinase responsible for GarA phosphorylation . In M . tuberculosis lacking pknG the upper band was also missing , suggesting that PknG could also be responsible for phosphorylating M . tuberculosis GarA . Reintroduction of pknG to ΔpknGMt did not restore GarA phosphorylation , despite strong overexpression of pknG ( S7 Fig ) . Non-physiological expression levels may influence GarA phosphorylation ( see below for investigation into the conditions and stimuli that provoke phosphorylation ) . To seek clarification about whether PknG may phosphorylate GarA in M . tuberculosis we decided to investigate the specific site of GarA phosphorylation . Several kinases have been reported to phosphorylate purified GarA at the second threonine ( T22 ) while PknG is the only kinase shown to phosphorylate the first threonine ( T21 ) [8 , 36] . LC-MS/MS can distinguish between phosphorylation at T21 or T22 ( S9 & S10 Figs ) . We have previously enriched phosphorylated GarA from cell extracts for detection by LC-MS/MS [8] . Here we developed a protocol avoiding enrichment to detect the various forms of GarA in cell extracts of M . tuberculosis . We used this protocol to determine the relative abundance of the three forms of GarA by comparison with peptide standards ( unphosphorylated , T21-phosphorylated , and T22-phosphorylated ) . In wild type cells all three forms were detected ( Figs 4B and S10 ) but the concentration of T22-phosphorylated form was always too low to quantitate reliably . However , in cell extracts of the M . tuberculosis pknG mutant strain there was no detectable T21-phosphorylated GarA , supporting the suggestion from Fig 4A that PknG may phosphorylate GarA in M . tuberculosis . The equivalent peptides of M . smegmatis GarA were less amenable to mass spectrometry and so we created a reporter strain: ΔgarAMs + His6garA , in which the garA deletion strain ΔgarAMs [9] is complemented by hexahistidine-tagged GarA ( S11 Fig ) . We also generated variants with mutations in the phosphorylation motif of GarA ETTS . Western blotting showed that only those GarA variants lacking the phosphorylation site for PknG were predominantly unphosphorylated ( Fig 4C ) . In summary , the data from Fig 4A–4C clearly demonstrate that PknG is the main kinase responsible for phosphorylation of GarA in M . smegmatis whereas phosphorylation by other kinase ( s ) may occur at lower levels , similar to the findings in C . glutamicum [32] . We also provide three independent lines of evidence to show that PknG may phosphorylate GarA in M . tuberculosis: site specificity ( T21 phosphorylation in cells ) , loss of phosphorylation in ΔpknGMt , and conservation of function since M . tuberculosis pknG was able to complement the growth defect of M . smegmatis pknG knockout ( S8 Fig ) . We next sought to identify the specific environmental signals that trigger phosphorylation or dephosphorylation of GarA . Since the active form of GarA is the unphosphorylated form , we predicted that this form would predominate in conditions where garA is essential , such as during amino acid deprivation . We used mass spectrometry to investigate GarA phosphorylation in M . tuberculosis and found only the unphosphorylated form in PBS-starved M . tuberculosis compared to a mixture in cells grown on standard media ( S12 Fig ) . This trend of GarA phosphorylation in optimal medium but a lack of phosphorylation upon amino-acid starvation is similar to observations made on the homologous protein in C . glutamicum [37] . We then used the reporter strain of M . smegmatis to analyse a range of carbon and nitrogen sources separately for their effects on GarA phosphorylation ( Fig 5A and 5B ) . Strikingly , the nutrients that led to the most phosphorylation ( Fig 5A ) , are the amino acids that rescue the growth defect of ΔgarAMt and ΔgarAMs: glutamate , aspartate , glutamine and asparagine . When carbon sources were compared there was least phosphorylation during growth on acetate or glucose ( Fig 5B ) . The correlation between the extent of GarA phosphorylation and the severity of growth phenotype of ΔgarAMs was weaker when comparing carbon sources , suggesting that there may be other sensory/regulatory input ( s ) that remain to be identified . Thus we conclude that nutrients are likely stimuli for PknG activity , and , at least in M . smegmatis , glutamate and related amino acids are the most important . Notably , the sensory mechanism remains to be identified and there are also likely to be additional stimuli influencing kinase activity . In principle , reversible phosphorylation of GarA could allow cells to respond rapidly to changing nutrient availability . Since GarA interacts directly with enzymes of central carbon and nitrogen metabolism this would allow a more rapid response than alterations in gene expression level . To investigate the dynamics of adaption we grew the reporter strain on media promoting low or high phosphorylation of GarA and then exchanged the medium at mid-log phase , monitoring GarA phosphorylation until it had stabilized . Addition of glycerol/asparagine to a culture grown in medium containing glucose/ammonium chloride led to GarA phosphorylation within the shortest time period that could be sampled with accuracy ( 15 minutes ) ( Fig 5C ) . By contrast , when cells were transferred from standard Sauton’s medium to minimal Sauton’s medium , reductions in GarA phosphorylation were only seen after three hours , suggesting that dephosphorylation occurred slowly if at all ( Figs 5D and S13 ) . Similarly to M . tuberculosis , we observed only unphosphorylated GarA in starved or stationary phase M . smegmatis ( Fig 5E ) . Our observations suggest that the PknG—GarA system may allow cells to adapt rapidly to an increase in amino acid availability: glutamate or related amino acids would stimulate PknG to phosphorylate GarA and hence enable glutamate catabolism . However , adaptation to nitrogen starvation may take longer and could involve new protein synthesis or protein dilution through cell division . Having established that GarA is predominantly in the active , unphosphorylated form during starvation and stationary phase in M . tuberculosis and M . smegmatis ( S12 Fig and Fig 5E ) , we used a metabolomics approach with M . smegmatis to test the specific effects of garA knockout on intracellular metabolites . Since GarA stimulates glutamate synthase activity and inhibits enzymes involved in glutamate catabolism , we predicted that ΔgarAMs might have a lower concentration of intracellular glutamate compared to wild type M . smegmatis . We used a targeted mass spectrometry approach to monitor the intracellular concentration of glutamate and 39 additional metabolites of central carbon metabolism in stationary phase cultures . Of the 40 metabolites analysed , glutamate and glutamine showed the greatest difference between ΔgarAMs compared to wild type . In wild type cells the concentration of glutamate was maintained at a relatively steady level throughout 28 days while the concentrations of glutamine and many other metabolites declined during the first 7 days and were then steady ( Figs 6A & S14 ) . As predicted , ΔgarAMs had lower intracellular glutamate in stationary phase ( from day 7 onwards ) . The intracellular concentration of glutamine was transiently elevated during entry of ΔgarAMs into stationary phase and then declined from day 7 onwards ( Fig 6A ) . The decline in glutamate and glutamine in extended phase could be due to catabolism through uninhibited GDH and KDH . After 28 days the ΔgarAMs strain began to show loss of viability . Metabolite sampling was discontinued and viability was monitored for 10 further weeks , by which point cultures of ΔgarAMs contained 100-fold fewer CFU ml-1 than wild type M . smegmatis ( Fig 6B ) . The depletion of intracellular glutamate in stationary phase ΔgarAMs and the defect in long-term survival provides a functional demonstration of our predicted model of metabolic regulation and highlights the importance of glutamate homeostasis for bacterial viability . Disruption of pknG in M . tuberculosis has previously been shown to perturb intracellular glutamate and glutamine levels [5] . To examine the effect of garA disruption or pknG disruption on wider cell metabolism we grew M . smegmatis and variant strains for untargeted metabolome analysis of about four hundred annotated metabolites by mass spectrometry . An unbiased comparison of the metabolomes of ΔgarAMs with the parent and complemented strains identified a set of 15 metabolites with lower concentration in ΔgarAMs ( log2 ( fold change ) >0 . 5 , and q-value<0 . 05 ) ( Fig 7A and Table 1 ) . Eight of the fifteen significantly changed metabolites were amino acids or intermediates in amino acid biosynthesis . Striking reductions were seen in the intracellular concentrations of glutamate and two direct products of glutamate: GABA and oxoproline/pyroglutamate ( Fig 7A ) , and these changes were reversed by plasmid-borne garA ( Fig 7B ) . Extracellular metabolites were also analysed but significant differences were not found ( S15 Fig ) . To examine the impact of disrupting GarA phosphorylation , we next analysed the intracellular metabolites of ΔgarAMs carrying non-phosphorylatable GarA and ΔpknGMs ( Tables 1 and S1 , Figs 7C and S16 ) . We predicted elevation in intracellular glutamate when GarA cannot be phosphorylated , a reversal of the glutamate deficiency when garA is deleted . Intracellular glutamate was indeed significantly higher ( 1 . 3-fold change , q-value<0 . 0001 , Fig 7D , S1 Table ) but below our chosen threshold for inclusion in Table 1 ( log2 ( fold change ) >0 . 5 ) . The majority of the metabolites that were significantly changed were amino acids or involved in amino acid metabolism , notably intermediates of arginine biosynthesis ( ornithine , citrulline ) , which were elevated in mutant strains . The wide reaching changes in amino acid metabolism could be consequences of perturbed glutamate metabolism , the central hub of NH3 transfer . Beyond amino acid metabolism , we cannot differentiate whether changes in metabolite concentrations are indirect consequences of altered physiology and altered amino acid metabolism or indicative of other GarA/PknG targets . Previously pknG deletion has been shown to cause raised intracellular glutamate and glutamine in M . tuberculosis [5] and raise glutamate production by C . glutamicum [37] , whereas garA deletion abolished glutamate production by C . glutamicum . Our metabolome analysis of M . smegmatis reinforces the functional link between PknG and GarA and their role in regulating amino acid metabolism in Actinobacteria , which is further supported by perturbations in amino acid metabolism seen in the metabolome of pknG-disrupted M . bovis BCG ( S7 Table ) . We chose a medium in which all strains grew at the same rate and in which the wild type strains of M . smegmatis and M . bovis BCG normally contain a mixture of phosphorylated and unphosphorylated GarA . We observed that perturbation of pknG caused changes in the concentrations of intracellular metabolites despite the absence of an obvious growth defect . Together with Figs 1–4 , this highlights the relevance of phosphorylation for regulation of GarA function , and the importance of proper regulation of phosphorylation .
This work has validated the biological significance of the PknG—GarA signaling pathway that is needed for virulence of pathogenic M . tuberculosis and for balanced nutrient utilization of both M . tuberculosis and non-pathogenic M . smegmatis . GarA exhibits a key characteristic of a signaling protein in a kinase pathway , namely variable levels of phosphorylation as cells respond to different environments . Genetic disruptions that led to a loss of responsiveness ( either permanent activation or permanent inactivation ) caused metabolic changes and loss of virulence . This requirement for responsiveness may highlight the changing environmental conditions that are encountered by the pathogen during infection and also the importance of proper regulation of this central node of metabolism and how vulnerable it is to any kind of disruption . This adds to the growing body of evidence linking nutritional adaptation to virulence of M . tuberculosis and other pathogens . The role of PknG in metabolic control via GarA has previously been supported mainly by experiments on purified proteins but here it was demonstrated in cells . It is interesting to note that although PknG appeared to be the main kinase responsible for phosphorylating GarA , we found indications that other kinase ( s ) could be involved in both M . tuberculosis and M . smegmatis , similar to the observation that multiple kinases are involved in C . glutamicum [32] . This level of experimental validation of kinase function has rarely been performed for other bacterial serine/threonine protein kinases . The prevailing view of serine/threonine phosphorylation as a transient , reversible signaling event that occurs upon cellular stimulation derives largely from comparison with eukaryotic kinases , whereas little is known of the stimuli or kinetics of kinase activation in bacteria . Not only are the stimuli of bacterial kinases largely unidentified , but the macronutrients sensed by M . tuberculosis are also unknown [30] . The identification of externally supplied glutamate , aspartate , asparagine and glutamine as stimuli of PknG activity ( Fig 5 ) begins to address these important questions , although the molecular mechanisms remain to be elucidated . Our data also indicate that there are other additional sensory input ( s ) both in the form of other kinases acting on GarA and other stimuli activating PknG ( Fig 8 ) . Given the large multi-domain structure of PknG it is a plausible candidate for integrating multiple sensory inputs to regulate metabolism precisely . Amino acids had not previously been proposed as activators of PknG , but alternative proposals include cellular redox status , since mutation of the rubredoxin domain changes the activity of recombinant PknG [38–40]; nitric oxide exposure , since fatty acid nitroalkenes react with the rubredoxin domain of recombinant PknG [41]; NADH since extracellular NADH induces PknG expression in M . smegmatis [11] . The influence of carbon sources on GarA phosphorylation ( Fig 5 ) could potentially support the suggestion that PknG responds to redox balance or NADH , but these same carbon sources have also been found to influence the differential utilization of KDH or the alternative enzyme α-ketoglutarate ferredoxin oxidoreductase [42] . Several studies have highlighted the link between metabolic adaptation and pathogenicity , for example underlining the essentiality for intact central carbon metabolism and amino acid biosynthetic pathways for virulence [43–45] . The three enzymes controlled by PknG and GarA ( KDH , GDH , GltS ) have been mutated individually in independent studies [24–26] . Although comparison between different strains is complicated by potential differences in metabolism , disruption of garA might be expected to lead to glutamate auxotrophy like disruption of gltS , since glutamate synthase is activated by GarA [31] , while disruption of pknG might be expected to lead to defects in glutamate catabolism , like disruption of gdh and kdh , since these enzymes are inhibited by GarA . The defects we observed in axenic culture match these predictions ( Figs 1–3 ) . However , unlike the gltS mutant , ΔgarAMt had a severe defect in macrophages , probably reflecting the additional roles of GarA in inhibiting KDH and GDH . Mutants lacking KDH and GDH were attenuated in mice or macrophages , like pknG mutant or non-phosphorylatable garA . This could be due to their inability to utilise glutamate from host cells , but could also be due to increased susceptibility to stress as strains lacking KDH and GDH were reportedly more susceptible to killing by nitrosative stress [24 , 25] . Apart from the role of PknG and GarA in regulating metabolism , other roles have been proposed , including regulating the cell envelope , antimicrobial resistance [12] , stress resistance , biofilm formation [11] , redox homeostasis [40] , rhamnose biosynthesis [10] and glycogen metabolism [46] . While our focus has been on metabolism , our investigation has the potential to shed light on these alternative roles for PknG or GarA or to expose new roles . We saw evidence for important changes in amino acid metabolism when either pknG or garA were perturbed . Furthermore , in macrophages and in axenic culture there was phenotypic mimicry between disrupting pknG and disrupting the PknG phosphorylation site of GarA . Our data strongly suggest that the loss of virulence stems from loss of amino acid regulation . However , since unphosphorylated GarA can bind stably to STPKs , we can’t exclude non-physiological effects of GarA variants on other functions of PknG and other kinases . Indeed , since truncated GarA has been observed in cultured M . tuberculosis [47] , GarA itself could influence kinase activity . Prominent amongst the alternative functions of PknG are effects on the cell envelope and biofilm formation [[11 , 12] . The clumping we observed in ΔpknGMs and M . smegmatis carrying non-phosphorylatable GarA could be linked to changes in the cell envelope . That would further suggest that the previously characterized changes in cell envelope or biofilm formation of pknG deficient strains could potentially be linked to the function of GarA in regulating metabolism . Regarding the other proposed functions of PknG and GarA in redox homeostasis and rhamnose biosynthesis , our metabolome data did not reveal significant changes in intracellular TDP-rhamnose , NAD+ , NADH or FAD concentrations , but the changes in maltopentaose could potentially indicate changes in carbohydrate storage . It is possible that changes in the TCA cycle and carbon-nitrogen balance impact on other metabolic pathways , and also possible that disrupted kinase signaling could alter glycogen synthesis/breakdown directly since several of the enzymes are known to be regulated by phosphorylation [48] . Amongst all the proposed roles for PknG , only the function in regulating the TCA cycle has been investigated in other Actinobacteria , indeed this function was originally discovered in C . glutamicum [7] . Apart from the high level of interest afforded to a kinase linked to virulence , there are other differences between the PknG and GarA homologues between M . tuberculosis and C . glutamicum . While environmental/nutrient changes have been reported to influence phosphorylation of the GarA homologue in C . glutamicum , regulation of expression level is thought to play a major role [49] . By contrast , we did not observe increases in GarA expression in M . tuberculosis or M . smegmatis under the studied conditions where GarA was dephosphorylated ( Figs 4 & 5 ) . Thus , we conclude that the changing ratio of phosphorylated GarA to unphosphorylated GarA involves changes in kinase activity and/or phosphatase activity . One of the paradigms of signaling by S/T phosphorylation in eukaryotes is that phosphorylation is reversible by serine/threonine protein phosphatases . Bacterial genomes encode S/T protein phosphatases , but kinetics of dephosphorylation have mainly been studied using recombinant proteins . Our observations of GarA phosphorylation in M . smegmatis showed that phosphorylation was a rapid response , occurring within minutes of exposure to amino acids , which is most likely due to stimulation of kinase activity . By contrast , removal of phosphorylated GarA during starvation occurred slowly over the course of several hours . This result suggests that dephosphorylation of GarA was very slow or may not have occurred at all , as dilution of GarA through cell division and protein turnover could account for the disappearance of the phosphorylated protein , while protein translation could account for the appearance of unphosphorylated GarA and this would remain unphosphorylated if PknG activity were low . The genome of M . tuberculosis encodes one S/T phosphatase , PstP ( Rv0018c ) compared to 11 STPKs , while M . smegmatis genome encodes two S/T phosphatases compared to 13 STPKs . PstP has been found to dephosphorylate recombinant GarA [50] , which questions the value of in vitro methods for identification of phosphatase ( and kinase ) substrate specificity . Compared to kinases , even less is known of the physiological substrate specificity of phosphatases and their role ( s ) . It remains to be seen whether the structure of GarA with self-binding of phosphothreonine by its own FHA domain [31 , 50] makes it uniquely inaccessible to protein phosphatases or whether there are other bacterial phosphoproteins for which S/T phosphorylation in cells is effectively irreversible . The implication for the kinetics that we observed would be slow adaptation to starvation , requiring new GarA synthesis to inhibit GDH and KDH , but rapid adaptation to re-start the TCA cycle when starved/dormant cells were exposed to amino acids ( Fig 8 ) . This process of adaptation to non-growth and re-growth may be critical for survival of M . tuberculosis in vivo and may open new avenues for targeting non-growing bacteria that are notoriously tolerant to antimicrobials .
M . tuberculosis H37Rv and M . smegmatis mc2155 were routinely cultured on Middlebrook 7H10 agar ( Oxoid ) with 10% ADN ( 0 . 5% bovine serum albumin , 0 . 2% dextrose , 0 . 085% NaCl ) and Middlebrook 7H9 medium ( Oxoid ) with 10% ADN and 0 . 05% Tween 80 . A list of strains and plasmids used in this study is provided ( S8 & S9 Tables ) . To analyse nutrient utilization a minimal version of Sauton’s medium was made ( 3 . 7 mM KH2PO4 , 2 mM MgSO4 , 9 . 5 mM sodium citrate , 0 . 17 μM ferric ammonium citrate , pH 7 . 0 [51] ) , to which nutrients were added . Standard Sauton’s consisted of the recipe above with additional 1% glycerol , 30 mM asparagine and 0 . 05% Tween 80 . To disperse the culture surfactants were added at 0 . 05% w/v: either Tween 80 , which can be utilized as a carbon source , or tyloxapol , which cannot . When required antibiotics were used at the following concentrations: kanamycin ( 50 μg/mL ) , hygromycin ( 100 μg/ml ) . We have previously characterized an M . tuberculosis garA conditional knockdown mutant ( cΔgarAMtb ) in which the only functional copy of garA was inserted at the L5 att site under the transcriptional control of the repressible Pptr promoter using an L5-based integrative plasmid [9] . To obtain a garA null mutant , we decided to replace this plasmid with another one not containing garA . This is possible since introduction of an L5-integrative plasmid to a mycobacterial strain in which the L5 att site is already occupied by a similar plasmid leads to an efficient switching between the two plasmids [52] . To this end conditional mutant cΔgarAMtb was electroporated with pMV306 , encoding hygromycin resistance , and switched mutants were selected on hygromycin ( 50 μg/ml ) and asparagine ( 10 mM ) to allow the growth of the garA null mutants . Hygromycin-resistant colonies were analysed to confirm the loss of kanamycin resistance ( encoded in the garA containing integrative plasmid ) and inability to grow in the absence of asparagine of the deletion strain ΔgarAMt . M . smegmatis garA with its promoter region was cloned in the plasmid pRBexint [53] to create pRBexint-garA as described [9] . M . tuberculosis garA was cloned with a hexahistidine tag into pRBexint to create pRBexint-His6garA and with a HA tag into pTTP1B [54] to create pTTP1B-garAHA . Variants of each gene were created by site directed mutagenesis to disrupt the phosphorylation motif ETTS . Also a truncated version of M . smegmatis garA was constructed , garA39-143 , with the first 38 residues missing . THP-1 human cell line was grown at 37°C in a 5% CO2 atmosphere and maintained in RPMI medium ( Gibco ) supplemented with 10% fetal bovine serum ( Gibco ) . After expansion , THP-1 cells were differentiated into macrophages and infected with M . tuberculosis in 96-well plates with a multiplicity of infection of 1:20 CFU per macrophage as previously described [55] . After 90 minutes of incubation at 37°C , the medium was removed , and cells were washed twice with 100 μl of warm phosphate buffered saline to remove extracellular bacteria . Finally , 100 μl of warm RPMI ( with added 20 mM asparagine for S2 Fig ) , was added to each well and the plate was incubated at 37°C . RPMI with or without additional asparagine was replaced every 48 hours . To enumerate intracellular bacteria the medium was removed from three wells , and 100 μl of 0 . 05% sodium dodecyl sulfate was used to lyse macrophages . The suspensions obtained were immediately diluted in 7H9 and plated to determine viable counts . About 95% of macrophages remained viable during the entire experiment , as determined by Trypan blue exclusion . All investigations involving animals were carried out according to the requirements of the Animals ( Scientific Procedures ) Act 1986 with the consent of the University of Leicester Animal Welfare & Ethics Board . The Home Office Licence number is 60/4327 . BALB/c mice ( female , 6–8 weeks old ) were purchased from Charles River , UK and acclimatised for 7 days prior to M . tuberculosis challenge . Frozen aliquots of bacterial strains were thawed and passed through a blunt needle 10 times to disperse clumps and adjusted to 2x 106 CFU ml-1 prior to infection . Mice were inoculated via the intranasal route by the drop-wise administration of 50 μl bacterial suspension onto the nostril of a lightly anaesthetised mouse ( 2 . 5% ( v/v ) flurothane over oxygen ) held in a vertical position . Mice were monitored for full recovery from anaesthetic prior to return to their cages . Mice were housed in cages of 5 animals within a negative pressure rigid isolator ( air change rate 25 changes/hr; pressure -100Pa ) . Mice had free access to water and diet ( 5LF2 , LabDiet ) and monitored daily for welfare and signs of disease over the 28-day experimental period . Experimental groups ( n = 20 ) were inoculated with either M . tuberculosis H37Rv wild type , ΔgarAMt deletion or complementation strain at 105 CFU per animal . A cage of 5 mice from each group was euthanised by cervical dislocation at day 1 , 7 , 21 and 28 and the lungs and spleen were aseptically removed post-mortem ( conformation of death by rigor mortis ) . Lung and spleen tissue ( lung only at day 1 ) was homogenised using a FastPrep-24 ( MP Biomedicals ) in 15ml tubes containing 10 matrix S beads ( MP Biomedicals ) and 9 ml PBS . Three bursts of 20 seconds at 4 m/s with a five minute cool-down in-between was used to homogenate the organs for enumeration of bacteria on 7H10 agar; kanamycin ( 50 μg/ml ) and/or hygromycin ( 100 μg/ml ) was added as required . M . tuberculosis H37Rv strains were grown in 7H9/ADN/Tween 80 with 30 mM asparagine until OD reached 0 . 6–0 . 9 . The cultures were then diluted in Middlebrook 7H9 medium and plated on Middlebrook 7H10 ADN supplemented with and without 30 mM asparagine . Plates were incubated at 37°C and images taken after two weeks . The gene encoding PknG was amplified by Pfu Ultra Hf DNA polymerase ( Agilent ) using an upper primer ( AC 145 ) designed to contain an NheI site immediately before the start codon , and a lower primer ( AC146 ) designed to contain the HA-coding sequence in frame with the coding sequence of pknG , followed by a stop codon and a XbaI site ( S9 Table ) . For expression in M . tuberculosis , fragments containing the Phsp60 promoter sequence from HindIII/NheI-digested pAL36 [56] and the PknG-HA encoding gene digested with NheI/XbaI were transferred to HindIII/XbaI-digested pMV306 vector [57] giving the mycobacterial plasmid pAL299 . The plasmid was electroporated into strain ΔpknGMt and kanamycin-resistant recombinants that had integrated the vector with the PknG insert at the attB site were selected on Middlebrook 7H11-OADC ( BD ) plates and subjected to PCR screening and Western blotting . This approach allowed one colony to be selected that showed the expected PCR amplification products as well as the expected band in PknG-specific Western blotting ( S7 Fig ) ( anti-PknG serum was a generous gift from E Houben and J Pieters , VU Medical Center , Amsterdam and University of Basel ) . This clone , “ΔpknGMt + pknG” was used for further experiments to evaluate the growth under selected amino-acid deprivation conditions . In these experiments , the growth of M . tuberculosis wild-type , deletion and complemented strains was measured by monitoring OD600 of cultures grown in glass tubes at 37°C in standing conditions . Bacteria were grown until late-exponential phase ( OD600 0 . 6–0 . 9 ) in 7H9 ADC medium , washed twice and diluted in the Sauton’s minimum medium supplemented by a specific carbon source to an initial OD600 of 0 . 04–0 . 05 . Sauton’s minimum medium was supplemented with ammonium chloride 10 mM plus one of the following carbon sources: L-Asparagine 10 mM , L-Glutamate 10 mM , Glycerol 0 . 2% , Glucose 1% , Acetate 0 . 2% . Data plotted represent the mean and standard deviation of at least three independent experiments . The growth of M . smegmatis was measured by monitoring OD of cultures grown in microplates at 37°C with shaking . The inoculum used was a late-exponential phase culture ( OD600 0 . 6–0 . 9 ) in 7H9 ADN medium , which was dispersed by passing through a needle and then diluted in the required medium to an initial OD of 0 . 01 . Growth curves used at least five wells per strain and were performed in triplicate . Figures show the mean and standard deviation for a representative experiment . In order to analyse the phosphorylation status of GarA in M . smegmatis mc2155 , strains were grown in 7H9 medium to OD600 0 . 6–0 . 9 then diluted into 10 ml of 7H9 without ADN ( or variant Sauton’s medium where specified ) to OD600 0 . 01 in a 50 ml falcon tube . Cultures were grown with shaking at 37°C and cultures were harvested at OD600 0 . 6–0 . 9 by centrifugation at 4°C . Cells were either lysed by sonication either directly in SDS sample buffer or by sonication in cold Tris-buffered saline pH 8 . 0 containing PhosSTOP Phosphatase Inhibitors ( Roche ) and cOmplete Protease Inhibitors ( Roche ) followed by centrifugation and addition of SDS sample buffer . Protein concentration was measured by using BCA Protein Assay Reagent ( Pierce ) . M . tuberculosis H37Rv strains were grown in standard Sauton’s medium containing 0 . 05% Tween 80 to OD600 0 . 6–0 . 9 and then diluted into 30 ml of the specified Sauton’s medium to OD600 0 . 01 . Cultures were grown with shaking at 37°C to OD600 0 . 6–1 . 0 and harvested by centrifugation . Cell pellets were resuspended in 1 ml SDS sample buffer ( for mass spectrometry ) or in 1 ml 1 M Tris HCl pH 8 . 0 containing PhosSTOP ( Roche ) and cOmplete ( Roche ) ( for Phos-Tag analysis ) . Cells were lysed using a FastPrep ( MP Biomedicals ) with addition of glass beads 150–210 μm ( Sigma ) and insoluble material was removed by centrifugation . Cells were either killed by heating to 100°C for 30 minutes ( for mass spectrometry ) or were rendered non-infectious by filtration ( for Phos-Tag ) . Protein concentration was measured by using BCA Protein Assay Reagent ( Pierce ) . 7 . 5 μg protein was separated by SDS PAGE ( 10% acrylamide ) containing 50 mM acrylamide-pendant Phos-tag ligand ( Wako Pure Chemical ) and 100 mM of Zn ( NO3 ) 2 . The running buffer ( pH 7 . 8 ) contained 0 . 1 M Tris HCl , 0 . 1 M 3- ( N-morpholino ) propanesulfonic acid ( MOPS ) , 0 . 1% w/v SDS and 5 mM sodium bisulfite . The gel was washed as described previously [58] before transfer onto nitrocellulose membrane ( Hybond-C extra , Amersham Biotech ) . Rabbit anti-GarA serum was kindly provided by Dr I Rosenkrands ( Statens Serum Institute ) and GarA was detected using goat anti-rabbit alkaline phosphatase ( Sigma ) with SIGMAFAST BCIP/NBT ( Sigma ) . Blots were photographed and images analysed using ImageJ 1 . 45s software ( U . S . National Institutes of Health , Bethesda , MD , USA ) [59] . For SDS PAGE and blotting without Phos-tag ligand , the same serum , antibodies and method of analysis were used , as previously [9] , and representative images are supplied ( S17 Fig ) . Synthetic peptides corresponding to the tryptic peptides of GarA were purchased from CBio and analysed by LC-MS/MS using an RSLCnano HPLC system ( Dionex ) and 4000 Q-trap mass spectrometer ( Applied Biosystems , Warrington , UK ) . Samples were loaded at high flow rate onto a reverse-phase trap column ( 0 . 3 mm i . d . x 1 mm ) , containing 5 μm C18 300 Å Acclaim PepMap media ( Dionex ) maintained at a temperature of 37°C . The loading buffer was 0 . 1% formic acid / 0 . 05% trifluoroacetic acid / 2% acetonitrile . After a 4 minute wash step , peptides were eluted from the trap column at the flow rate of 0 . 3 μl/min using an increasing proportion of mobile phase B ( 80% acetonitrile/0 . 1% formic acid ) ; 4–45% B in 26 minutes , 45–90% B in 1 minute , held at 90% for 8 minutes , 90–4% in 1 minute , re-equilibration at 4% for 10 minutes . Eluted peptides were separated through a reverse-phase capillary column ( 75 μm i . d . x 250 mm ) containing Symmetry C18 100 Å media ( Waters ) that was manufactured in-house using a high-pressure packing device ( Proxeon Biosystems ) . The output from the column was sprayed directly into the nanospray ion source of the 4000 Q-Trap mass spectrometer . The three forms of the peptide containing the phosphorylation sites ( ETTS ) and a control peptide ( corresponding to a different tryptic peptide from the GarA protein ) were found to separate by retention time and be distinguishable by their fragmentation spectra ( Table 2 ) . Multiple reaction monitoring ( MRM ) precursor/product ion transitions were chosen based on the fragmentation data and were used to produce standard curves for each peptide by injecting known amounts: 50 , 100 , 200 , 400 , 600 and 2000 fmol ( S9 Fig ) . Soluble protein extracts of M . tuberculosis were run on 1D-gels , the region of interest excised , and in-gel trypsin digestion carried out upon each . Gel slices was destained using 200 mM ammonium bicarbonate/20% acetonitrile , followed by reduction ( 10 mM dithiothreitol , Melford Laboratories Ltd . , Suffolk , UK ) , alkylation ( 100 mM iodoacetamide , Sigma , Dorset , UK ) and enzymatic digestion with trypsin ( sequencing grade modified porcine trypsin , Promega , Southampton , UK ) in 50 mM triethylammonium bicarbonate ( Sigma ) using an automated digest robot ( Multiprobe II Plus EX , Perkin Elmer , UK ) . After overnight digestion , samples were acidified using formic acid ( final concentration 0 . 1% ) and analysed by LC-MS/MS using the gradient and MRM transitions outlined above . At the start and end of each analytical run , the synthetic peptides were analysed to allow comparison with the standard curves for the purpose of assessing technical reproducibility . For the PBS starvation experiment using M . smegmatis , strains were cultured and harvested as described above . Pellets were washed twice in PBS and resuspended in 30 ml PBS with 0 . 05% tyloxapol and incubated at 37°C without shaking for up to 5 days . Samples were taken at different time points for analysis of GarA phosphorylation . For analysis of GarA during stationary phase , cells were cultured as described above and incubated for 5 days at 37°C with shaking . For the extended stationary phase experiment strains were cultured from single colonies in 5 ml 7H9/ADN/Tween 80 in a closed 30 ml Universal tube for 3 months at 37°C with shaking . The first sample was taken after the culture reached late exponential phase ( OD600 0 . 6–1 . 0 ) and bacterial viability was estimated by measuring CFU ml-1 by plating aliquots of bacterial suspension on 7H10/ADN plates . Further samples were taken every 4 weeks and CFU determined . Samples for metabolic analysis were collected during early exponential growth phase ( OD600 0 . 3–0 . 5 ) by fast filtration as described previously for Mycobacteria [60] . Briefly , a sample volume equivalent to a biomass of 4 ml at OD600 of 1 . 0 was filtered ( MF-Millipore Membrane , 0 . 45 μm ) , briefly washed with ammonium carbonate buffer ( 75 mM , pH 6 . 6 ) and transferred to 3 ml ethanol 60% ( v/v ) at 78°C for 2 min . Samples were dried at 30°C in a SpeedVac equipped with a cooling trap at -85°C . The dried extracts were dissolved in 100 μl water for metabolite analysis . Sample collection and processing of extracellular samples for metabolomics analysis was performed as previously described [61] . Quantification by targeted mass spectrometry was performed by ion pairing–reverse phase liquid chromatography tandem mass spectrometry on a Waters Acquity UHPLC coupled to a Thermo TSQ Quantum Ultra triple quadrupole instrument using fully U-13C-labled yeast extract as internal standard [62] . Non-targeted mass spectrometry was performed on an Agilent 6550 QTOF instrument [63] . Annotation was performed based on accurate mass determination of ions and the KEGG reference list ( tolerance 0 . 001 Da ) . Removal of unknown ions and annotated ion adducts resulted in 397 putatively annotated ions with unique m/z . Two technical replicate measurements were performed for each sample and merged using their mean . Differential analysis was performed applying an unequal t-test using MatLab ( The Mathwork , Natick ) . For each metabolite , pairwise comparisons were made between test strain and wild type using an unequal t-test , resulting in fold-change and associated q-value ( corrected for multiple hypotheses using the Benjamini Hochberg procedure ) . The results of pairwise comparisons are graphically represented as volcano plots . We chose criteria for significant changes of q<0 . 05 and absolute log2 ( fold change ) >0 . 5 . The borders are shown in the volcano plots and metabolites passing these thresholds are coloured red if higher than wild type or blue if lower . The metabolites scored as significant were compiled for each strain and these lists used for comparison between multiple strains . Metabolites that were significantly changed in >1 strain are highlighted by asterisks in the tables and fold-changes in concentration compared to wild type are summarized in Fig 7D . Some metabolites , including cAMP and mycobactin , were significantly altered in most strains , including complemented strains and other mutant strains unrelated to this project . It is possible that there were technical reasons making the extraction or quantification of these metabolites more variable , or alternatively these metabolite pools may be more susceptible to changes caused by cell stress . These variable metabolites were excluded from Table 1 and are listed in S1 Table . | A key feature of the pathogen Mycobacterium tuberculosis is its ability to survive and replicate within human macrophages . Protein kinase G ( PknG ) is known to be required for virulence of M . tuberculosis and is the only bacterial serine/threonine protein kinase to be known as a virulence factor . However , the molecular mechanisms underlying its function in virulence are unknown and the role ( s ) of PknG are controversial . Here , we disrupted the genes encoding PknG and its putative substrate GarA in M . tuberculosis and related non-pathogenic Mycobacterium smegmatis . We observed changes in protein phosphorylation that suggest GarA is the substrate of PknG , and changes in growth and metabolome that establish this pair of proteins as a bone fide system for metabolic regulation . We also observed a dramatic impact on the ability of GarA-deficient M . tuberculosis to grow and survive in macrophages and mice . This highlights the link between metabolism and virulence and suggests that M . tuberculosis inside macrophages may have restricted access to amino acids . Our study also provides a first indication of the nutrients that may be sensed by M . tuberculosis inside macrophages and provides new insights into the rate and reversibility of serine/threonine phosphorylation in bacteria . | [
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"amin... | 2017 | PknG senses amino acid availability to control metabolism and virulence of Mycobacterium tuberculosis |
Meiotic recombination , an essential aspect of sexual reproduction , is initiated by programmed DNA double-strand breaks ( DSBs ) . DSBs are catalyzed by the widely-conserved Spo11 enzyme; however , the activity of Spo11 is regulated by additional factors that are poorly conserved through evolution . To expand our understanding of meiotic regulation , we have characterized a novel gene , dsb-1 , that is specifically required for meiotic DSB formation in the nematode Caenorhabditis elegans . DSB-1 localizes to chromosomes during early meiotic prophase , coincident with the timing of DSB formation . DSB-1 also promotes normal protein levels and chromosome localization of DSB-2 , a paralogous protein that plays a related role in initiating recombination . Mutations that disrupt crossover formation result in prolonged DSB-1 association with chromosomes , suggesting that nuclei may remain in a DSB-permissive state . Extended DSB-1 localization is seen even in mutants with defects in early recombination steps , including spo-11 , suggesting that the absence of crossover precursors triggers the extension . Strikingly , failure to form a crossover precursor on a single chromosome pair is sufficient to extend the localization of DSB-1 on all chromosomes in the same nucleus . Based on these observations we propose a model for crossover assurance that acts through DSB-1 to maintain a DSB-permissive state until all chromosome pairs acquire crossover precursors . This work identifies a novel component of the DSB machinery in C . elegans , and sheds light on an important pathway that regulates DSB formation for crossover assurance .
Formation of crossovers between homologous chromosomes is essential for successful execution of the meiotic program in most sexually reproducing organisms . In addition to shuffling genetic information between parental chromosomes , crossovers , together with cohesion between sister chromatids , create physical links between homologous chromosomes that enable their segregation to daughter cells during the first meiotic division [1] , [2] . Disruption of crossover formation leads to chromosome nondisjunction and the formation of aneuploid gametes , and thereby greatly reduces fertility . Meiotic recombination is initiated by programmed DNA double-strand breaks ( DSBs ) , a subset of which is repaired to form crossovers between homologous chromosomes ( for a review , see [3] ) . While a minimum number of DSBs is needed to promote the necessary crossovers on all chromosome pairs , excessive DSBs could threaten genome integrity . The number of meiotic DSBs in various organisms appears to be highly regulated , presumably to balance the crossover requirement with the risk of mutation . The timing of programmed DSBs during early meiotic prophase is also tightly controlled to maximize the likelihood of productive repair [4] , [5] . For example , in Saccharomyces cerevisiae , the activities of cell cycle-regulated kinases involved in DNA replication ensure that that DSBs occur only after DNA synthesis is complete [6]–[10] . DSB formation must also be inactivated during meiotic prophase to allow for repair prior to the meiotic divisions . Mechanisms that terminate DSB formation are not well understood , although recent studies have shown that the ATM/ATR family of DNA damage response kinases is involved in down-regulating the number of DSBs in mice , S . cerevisiae , and Drosophila melanogaster [11]–[14] . Further investigations are needed to better understand the mechanisms underlying these various aspects of DSB regulation . Meiotic DSBs are catalyzed by the widely conserved , topoisomerase-related enzyme Spo11 [15] , [16] . Although Spo11 is essential for DSB formation , it does not function alone . In various organisms – including fungi , plants , and animals – additional proteins required for meiotic DSBs have been identified ( for a review , see [17] ) . Unlike Spo11 , other known factors involved in DSB formation are poorly conserved . For example , of five meiosis-specific DSB proteins found in S . cerevisiae , only two ( Rec114 and Mei4 ) have known orthologs in other phyla; and even these two proteins are absent in several species , including Caenorhabditis elegans , D . melanogaster , and Neurospora crassa [18] . Additional DSB proteins have also been identified in other organisms , but none are ubiquitous among eukaryotes [5] , . The nematode C . elegans has emerged as a valuable model system for molecular analysis of meiosis . As in other eukaryotes , SPO-11 catalyzes the formation of meiotic DSBs [23] . MRE-11 and RAD-50 are also required for DSB formation [24] , [25] as in S . cerevisiae [17] , but these proteins have other essential roles in DNA metabolism , including in the resection of meiotic DSBs [3] , [26] . In C . elegans , as in other species , meiosis-specific chromosome architecture contributes to DSB proficiency . In particular , in the absence of HTP-3 , an integral component of chromosome axes , DSBs are abolished or sharply reduced [27] . The related protein HTP-1 , which is also associated with the axial elements , may also contribute to DSB formation , while other axial components appear to be dispensable for DSBs [28]–[30] . Roles for axis components homologous to HTP-3 and HTP-1 in promoting DSBs have also been demonstrated in other organisms [3] , [31] , [32] . Additionally , the meiotic kinase CHK-2 , which regulates many key events during early meiotic prophase , is required for programmed DSBs in C . elegans [33] . Several other factors are known to influence meiotic DSB formation , but their effects may be indirect . These include the chromatin-associated proteins HIM-5 , HIM-17 , and XND-1 , which promote normal levels of meiotic DSBs , but whose functions are pleiotropic and not well understood 34–36 . Apart from SPO-11 , no protein that specifically functions in initiating recombination has previously been reported . Some aspects of C . elegans meiosis are unusual among model organisms , including the fact that synapsis between homologous chromosomes is independent of recombination [23] . Thus , analysis of DSB regulation in C . elegans will likely reveal both conserved aspects of meiosis and how regulatory circuits are remodeled during evolution . Here we identify a novel gene , dsb-1 ( double-strand break factor 1 ) , that is required for meiotic DSB formation in C . elegans . dsb-1 mutants lack meiotic DSBs , and show meiotic defects similar to spo-11 mutants . DSB-1 localizes to meiotic chromosomes coincident with the time of DSB formation , in a manner dependent on the CHK-2 kinase . We also find that a variety of mutations that disrupt crossover formation on one or more chromosomes extend the chromosomal localization of DSB-1 , suggesting that the DSB-permissive state may be prolonged . Based on these observations , we infer the existence of a regulatory circuit in which meiotic nuclei monitor the recombination status of each chromosome pair and act through DSB-1 to maintain a DSB-permissive state until all chromosome pairs have attained crossover-competent recombination intermediates .
In C . elegans , mutations that impair meiotic chromosome segregation result in embryonic lethality and a high incidence of males ( XO ) among the surviving progeny [37] . The dsb-1 ( we11 ) mutant was isolated in a genetic screen for maternal-effect embryonic lethality , and was found to produce a high fraction of males among its few surviving self-progeny . A targeted deletion allele of the affected gene , dsb-1 ( tm5034 ) , was generated independently ( see below ) , and results in defects identical to dsb-1 ( we11 ) based on all assays described here . Whereas self-fertilizing wild-type hermaphrodites produce nearly 100% viable progeny and 0 . 2% males ( Figure 1A , [37] ) , only 3% of progeny from self-fertilizing dsb-1 mutant hermaphrodites survived to adulthood ( n>2000; 12 broods ) , ( Figure 1A , Table 1 ) . Among these survivors , 36–38% were male ( Figure 1A , Table 1 ) . The brood size ( number of fertilized eggs ) of self-fertilizing dsb-1 hermaphrodites was also reduced relative to wild-type animals ( Table 1 ) . Chromosome segregation errors in meiosis often reflect defects in crossover formation between homologs . The levels of embryonic lethality and male progeny observed in dsb-1 mutants are quantitatively similar to several previously characterized mutants that fail to make any crossovers during meiotic prophase , such as spo-11 ( Figure 1A , Table 1 ) , msh-5 , and cosa-1 [23] , [38] , [39] , suggesting that dsb-1 mutants might also lack crossovers . Visualization of DAPI-stained oocytes at diakinesis provides a simple assay for crossover formation in C . elegans . In wild-type hermaphrodites , 6 DAPI-staining bodies are observed in each oocyte nucleus ( average = 5 . 8 , Figure 1B and 1C ) , corresponding to 6 pairs of homologous chromosomes , each held together by a chiasma [40] . In mutants that fail to make crossovers , oocytes typically display 12 DAPI-staining bodies . The number and morphology of DAPI-staining bodies observed in dsb-1 mutant oocytes was similar to spo-11 mutants ( average = 11 . 6 , Figure 1B and 1C ) , indicating an absence of chiasmata in dsb-1 animals . We investigated whether the disruption of crossover formation in dsb-1 mutants might reflect a defect in homologous chromosome pairing or synapsis . Pairing was assessed using fluorescence in situ hybridization ( Figure 1D ) . Early pachytene nuclei of both wild-type and dsb-1 animals contained a single focus or closely apposed pair of foci , indicating that homologous chromosomes were paired ( Figure 1D ) . Further , co-staining of the axial element protein HTP-3 and the synaptonemal complex central region protein SYP-1 indicated that chromosomes were fully synapsed by early pachytene in dsb-1 animals ( Figure 1E ) , as in wild-type animals . These results indicate that dsb-1 mutants are proficient for homologous chromosome pairing and synapsis . To assess whether dsb-1 mutants initiate meiotic recombination , we used antibodies against the DNA strand-exchange protein RAD-51 , which binds to single-stranded regions adjacent to resected DSBs [26] , [41] , as a cytological marker of recombination intermediates [42] , [43] . Whereas wild-type oocytes in early pachytene showed abundant RAD-51 foci , dsb-1 gonads lacked RAD-51 staining ( Figure 2A ) , indicating either failure to form DSBs or failure to load RAD-51 . However , the lack of fragmented chromosomes at diakinesis seemed more consistent with an absence of DSBs . To verify that dsb-1 mutants are defective in DSB formation , and to rule out the possibility of defects in the loading of RAD-51 or downstream steps of the recombination pathway , we tested whether exogenous DSBs could rescue the recombination defects observed in dsb-1 mutants . The same approach established a role for Spo11/SPO-11 in DSB formation [23] , [44] . Young adult dsb-1 mutant hermaphrodites were exposed to 10 Gy of gamma rays , a dose that has previously been shown to efficiently rescue crossovers in spo-11 mutants with minimal associated lethality [25] . Wild-type and spo-11 controls were performed in parallel . At appropriate times after irradiation the animals were assessed for RAD-51 foci , chiasmata , and progeny viability . At 2 hours post irradiation , dsb-1 animals displayed abundant RAD-51 foci ( Figure 2B ) , indicating that the mutants are proficient for resection and RAD-51 loading . At 18 hours post irradiation , both spo-11 and dsb-1 oocytes showed ∼6 DAPI-staining bodies ( Figure 2C and 2D ) . Additionally , the viability of embryos laid 20–30 hours post irradiation increased significantly for both spo-11 and dsb-1 animals , but decreased slightly for wild-type , compared to unirradiated controls ( Figure 2E ) . The ability of exogenous DSBs to rescue the recombination defects of dsb-1 animals indicates that these mutants are specifically defective in meiotic DSB formation . The defects observed in dsb-1 mutant hermaphrodites are virtually indistinguishable from spo-11 ( me44 ) mutants , except that mutations in dsb-1 were associated with reduced brood size ( Table 1 ) . Although dsb-1 ( we11 ) showed linkage to the middle of Chromosome IV , close to the spo-11 locus , complementation tests revealed that we11 is not an allele of spo-11 . Quantitative RT-PCR also indicated that spo-11 mRNA levels were unaffected in dsb-1 ( we11 ) mutants ( Figure S1 ) . Whole-genome sequencing of backcrossed dsb-1 ( we11 ) animals identified several mutations in annotated coding sequences , including a nonsense mutation in the previously uncharacterized gene F08G5 . 1 ( Figure 3A ) , which encodes a predicted protein of 385 amino acids and seemed a plausible candidate based on its meiosis-enriched expression pattern [45] . We found that knockdown of F08G5 . 1 expression via transgene-mediated cosuppression [46] caused embryonic lethality and male progeny , as well as strong reduction of chiasmata , in the oocytes of treated animals ( data not shown ) , supporting the hypothesis that the we11 mutation affects this gene . we11 introduces a premature stop ( tac = >taa ) after lysine 96 ( Figure 3A ) . A targeted deletion allele ( tm5034 ) removes 290 bp from predicted exons 3 and 4 and the intervening intron ( Figure 3A ) , resulting in a frameshift mutation that introduces a glutamine immediately followed by a stop codon after lysine 96 . The phenotype of dsb-1 ( tm5034 ) mutants is indistinguishable from dsb-1 ( we11 ) ( Figure 1 and 2 , Table 1 ) . Both are predicted to lack functional protein based on the early stop codons , and this conclusion is supported by immunofluorescence and immunoblotting experiments ( below ) . Based on the evidence described above that mutations disrupting F08G5 . 1 specifically interfere with meiotic double-strand break formation , we designated F08G5 . 1 as dsb-1 , for double-strand break factor 1 . The DSB-1 protein has no apparent homologs outside of the genus Caenorhabditis , including other nematode genera . Interestingly , the genomes of C . elegans and several other Caenorhabditids each contain 2 predicted paralogs . In an accompanying paper , Rosu et al . show that dsb-1 paralog F26H11 . 6/dsb-2 is also involved in meiotic DSB formation in C . elegans [47] . DSB-1 , DSB-2 , and their homologs cluster into two paralogous groups ( Figure 3B ) . Even within Caenorhabditis , members of this protein family are not well conserved ( Figure S2 ) . DSB-1 lacks identifiable domains that might give clues about its function in DSB formation . One notable feature is its high serine content: 60 of 385 amino acids ( 16% ) are serine residues , compared to an average serine content of 8% encoded by all C . elegans ORFs [48] . Protein structure prediction algorithms indicate that each end of DSB-1 may form alpha-helix secondary structures , but the central portion of the protein , which is especially serine-rich , is predicted to be largely unstructured . This central region is also the least conserved portion of the protein ( Figure S2 ) . Five serine residues within the central region are followed by glutamine ( Q ) , making them candidate phosphorylation targets for ATM or ATR DNA damage kinases . These clustered ATM/ATR consensus motifs are shared by other DSB-1 homologs , including DSB-2 . To further probe the role of DSB-1 in the formation of meiotic DSBs , we generated an antibody against the full-length protein expressed in E . coli . Immunofluorescence staining revealed that DSB-1 is absent from somatic nuclei , and specifically localizes to chromosomes during early meiotic prophase ( Figure 4A and 4B ) , while dsb-1 mutants showed only background staining ( Figure S3 ) . Accumulation of DSB-1 on chromosomes was first observed in nuclei marked by crescent-shaped DAPI-staining morphology , corresponding to the “transition zone” ( leptotene/zygotene ) , and disappeared at mid-pachytene ( Figure 4A ) . Chromosomal localization of DSB-1 preceded the appearance of RAD-51 foci , consistent with an early role for DSB-1 in meiotic recombination ( Figure 4A ) . Thus , the localization of DSB-1 to chromosomes corresponds to the period during which DSBs are likely to be generated . While most nuclei in the late pachytene region of the germline lacked DSB-1 staining , we consistently observed dispersed nuclei in this region that retained bright fluorescence ( Figure 4A and S4 ) . These nuclei also contained abundant RAD-51 foci and frequently displayed compact chromosome morphology resembling that seen in the transition zone , along with evidence of asynapsed chromosomes ( Figure S4A and S4B ) . We tested whether these late DSB-1 positive nuclei might be apoptotic by examining ced-4 mutants , which lack germline apoptosis 49 , 50 , and found that they were still present in similar numbers ( data not shown ) . The persistence of RAD-51 foci and asynapsed chromosomes suggest that these nuclei may be delayed in completing synapsis or other prerequisites for crossover formation , a conclusion reinforced by further analysis of DSB-1 regulation , described below . DSB-1 was distributed as a network of foci and stretches of staining on meiotic chromosomes ( Figure 4B ) . One chromosome consistently showed weaker DSB-1 staining . This seemed likely to be the X chromosome , which has many unique features in the germline , including distinct chromatin marks 51 , 52 and genetic requirements for meiotic DSBs 35 , 36 . Co-staining with antibodies against HIM-8 , which specifically mark the X chromosome 53 , confirmed that the chromosome pair with weaker DSB-1 staining was the X ( Figure 4C ) . DSB-1 and RAD-51 both localized to chromosomes during early pachytene ( Figure 4A ) . However , we found that RAD-51 did not colocalize with DSB-1 ( Figure 4D ) . Similar findings have been reported for DSB proteins in mice and Schizosaccharomyces pombe 18 , 54 . This could indicate that DSB-1 does not act directly at DSB sites , or that it is removed from DSB sites prior to RAD-51 loading . Meiotic chromosomes are believed to be organized as chromatin loops tethered at their bases to the proteinaceous chromosome axis 55 , 56 . Based on work from S . cerevisiae , it has been proposed that DSBs occur at sites within chromatin loops that are recruited to the chromosome axis 57–59 . Most DSB-1 staining was associated with the periphery of DAPI-stained chromosomes rather than axes ( Figure 4E ) , suggesting that DSB-1 is primarily associated with chromatin loops . This localization pattern is similar to what has been observed for several DSB proteins in S . cerevisiae 6 , 60–63 . We tested whether DSB-1 localization depends on other factors required for DSB formation . DSB-1 localized to meiotic chromosomes in the catalytically dead spo-11 ( me44 ) mutant 25 , as well as in spo-11 ( ok79 ) mutants , which lack functional protein 23 , indicating that DSB-1 localizes to chromosomes independently of DSBs and SPO-11 ( Figure 5 ) . DSB-1 localization was also independent of MRE-11 and RAD-50 ( Figure 5 , data not shown ) , which are required for DSB formation in C . elegans 24 , 25 . In htp-3 mutants , which lack an essential axial element component that is important for DSB formation 27 , DSB-1 was detected on meiotic chromosomes ( Figure 5 ) , but the staining appeared somewhat reduced compared to wild-type nuclei . The CHK-2 kinase is essential for several key events during early meiotic prophase in C . elegans , including DSB formation and homolog pairing 33 . We found that nuclear staining of DSB-1 was strongly reduced , albeit still detectable , in chk-2 ( me64 ) mutants ( Figure 5 ) . Although the intensity of DSB-1 staining was sharply reduced in chk-2 mutants , it appears that faint fluorescence observed upon prolonged exposure reflects DSB-1 , because the nuclear staining pattern resembles that seen in wild-type animals , and because chromosomal staining is not detected prior to meiotic entry . Western blot analysis revealed that DSB-1 protein is expressed in chk-2 mutants , although the protein levels appear somewhat reduced compared to wild-type ( data not shown ) . However , the reduction in DSB-1 protein levels in chk-2 mutants does not appear to fully account for the sharply diminished chromosomal localization of DSB-1 . These data indicate that DSB-1 localization to chromosomes is largely dependent on the CHK-2 kinase , and suggest that DSB-1 may act downstream of CHK-2 to promote DSBs . In testing the genetic requirements for DSB-1 localization , we noticed that the zone of DSB-1 staining in the gonad was extended in mutants that disrupt crossover formation . Previous studies have reported a persistence of RAD-51 foci in numerous mutants that are proficient for DSBs but not for crossovers 43 , 64 , 65 . In wild-type animals , and also in most mutants with extended RAD-51 staining , DSB-1 staining disappeared concomitant with , or slightly before , the disappearance of RAD-51 foci ( Figure 4A and S5 ) . Two exceptions were rec-8 and rad-54 mutants , in which DSB-1 staining disappeared by late pachytene , but RAD-51 staining persisted into diplotene ( Figure 6 , data not shown ) . Since DSB-1 is required for DSBs and its localization correlates with the timing of DSB formation , its presence on chromosomes may be indicative of proficiency for DSB formation . Although the presence of DSB-1 on chromosomes may not be sufficient for prolonged DSB formation , we interpret extension of the region of DSB-1 staining as evidence of a prolonged DSB-permissive state ( see Discussion ) . We quantified the extension of DSB-1 localization by comparing the length of the zone of DSB-1-positive nuclei to the total length of the region spanned by the transition zone through late pachytene nuclei , just before oocyte nuclei begin to form a single row near the bend region of the gonad , which coincides with diplotene ( Figure 6A ) . We designated this entire zone as the LZP region ( leptotene-zygotene-pachytene ) , although it also includes a few diplotene nuclei . We found that this metric – the length ratio of the DSB-1-positive region to the LZP region – was consistent across age-matched animals of the same genotype . In wild-type adult hermaphrodites , DSB-1 positive nuclei comprised about 50% of the length the LZP region ( Figure 6A and 6B ) . However , in most mutants that disrupt crossover formation on one or more chromosomes , this zone of DSB-1 staining was significantly extended ( Figure 6A and 6B ) . We saw some variability in this extension , which tended to correlate with the nature of the mutation: Mutations affecting late steps in the crossover pathway , including msh-5 ( me23 ) 38 , cosa-1 ( me13 ) 39 , and zhp-3 ( jf61 ) 66 , extended the DSB-1 zone to ∼75% , of the LZP region ( Figure 6A and 6B ) . Mutations that block earlier steps in homologous recombination , including com-1 ( t1626 ) 67 , rad-51 ( Ig8701 ) 42 , and rad-54 ( tm1268 ) 65 , extended the DSB-1 zone even further , to ∼90% of the LZP region . Mutations that block crossover formation by disrupting synapsis , including syp-1 ( me17 ) 68 and syp-2 ( ok307 ) 43 , also showed an extension of DSB-1 staining to ∼90% ( Figure 6B ) . Significantly , mutants that lack meiotic DSBs , including spo-11 ( ok79 or me44 ) 23 , 25 , mre-11 ( ok179 ) 24 , and rad-50 ( ok197 ) 25 , also showed significant extension of the zone of DSB-1 staining to 69–78% of the LZP region ( Figure 6A and 6B ) . Together these findings indicate that the absence of crossovers or crossover precursors , rather than the presence or persistence of earlier recombination intermediates , triggers extension of the DSB-1 zone . Also of note , in htp-1 and htp-3 mutants 27–29 , in which the axial element is disrupted , the region of DSB-1 staining was shorter than in other crossover-deficient mutants ( Figure 6B ) , despite the fact that no crossovers form in these animals and DSBs are either eliminated or reduced 27–29 . This suggests that axis structure may play a role in detecting or signaling the absence of crossover precursors to prolong DSB-1 localization , consistent with proposed roles for the axis in other species 32 , 69–71 . We tested whether irradiation could suppress the extension of the DSB-1 zone seen in spo-11 mutants . Young adult hermaphrodites were irradiated , then fixed and stained 8 hours later . As controls , we included mutants ( mre-11 and msh-5 ) in which crossover defects are not rescued by exogenous DSBs 24 , 38 . Irradiation reduced the zone of DSB-1 staining in spo-11 ( me44 ) animals to 56% , compared to 70% for unirradiated controls ( Figure 6C ) . In contrast , the length of the DSB-1 zone in wild-type , mre-11 , and msh-5 hermaphrodites was unaffected by irradiation ( Figure 6C ) . These data reinforce the idea that the absence of crossovers or crossover precursors induces prolonged DSB-1 association with chromosomes . Many mutations that result in extension of the DSB-1 zone also cause elevated oocyte apoptosis , which can be triggered in response to persistent DNA damage or asynapsis 43 , 50 , 68 , 72 . We considered the possibility that apoptosis might mediate the observed extension of DSB-1 staining , since this process primarily culls nuclei from the late pachytene , DSB-1 negative region of the gonad ( reviewed in 73 ) . To test this idea , a representative subset of meiotic mutations , including spo-11 ( ok79 ) , msh-5 , syp-2 , him-8 , and zim-2 ( see below ) were combined with ced-4 ( n1162 ) , which abrogates germline apoptosis 49 . These double mutants displayed extended DSB-1 localization similar to that observed in the corresponding single mutants ( Figure S6 ) . We conclude that apoptosis does not account for the extension of DSB-1 staining observed in crossover-defective mutants , nor can it explain the quantitative differences observed among different mutants . To further characterize the extension of DSB-1 localization that occurs in response to defects in crossover formation , we examined mutant situations in which crossover formation was disrupted on only one chromosome . him-8 ( tm611 ) and zim-2 ( tm574 ) specifically disrupt homolog pairing and thus crossover formation on chromosomes X and V , respectively 53 , 74 . him-5 ( ok1896 ) does not impair pairing or synapsis , but abrogates DSBs on the X chromosome 36 . All three of these mutations extended the DSB-1 zone to 83–86% of the LZP region ( Figure 6A and 6B ) . Furthermore , irradiation of him-5 animals , in which the crossover defect can be rescued by exogenous DSBs 36 , but not irradiation of him-8 , suppressed the extension of DSB-1 localization ( Figure 6C ) . These results indicate that the absence of a crossover precursor on a single chromosome pair is sufficient to prolong DSB-1 association with meiotic chromosomes . Analysis of mutants with chromosome-specific defects in interhomolog recombination also allowed us to test whether DSB-1 staining is specifically prolonged on crossover-deficient chromosomes . In him-5 and him-8 mutants , the autosomes , but not the X chromosomes , are proficient for crossover formation . X chromosomes can be specifically marked in these mutants using HIM-8 antibodies ( in him-5 mutants ) or by staining for synaptonemal complex components ( in him-8 mutants ) . In both of these genotypes , we observed persistent DSB-1 staining on all chromosomes throughout the region of extended DSB-1 localization ( Figure 7A , 7B , and 7C ) . As in wild-type nuclei , the X chromosome showed weaker DSB-1 staining than the autosomes ( Figure 4C and 7A ) . These findings indicate that the extension of DSB-1 localization is a genome-wide response affecting all chromosomes within the nucleus . To test whether the extension of DSB-1 localization is regulated by nuclear-intrinsic or extrinsic signals , we examined animals heterozygous for meDf2 , a deficiency of the X chromosome pairing center 40 . In meDf2/+ hermaphrodites , X chromosome pairing and synapsis is partially compromised , such that approximately half the nuclei achieve full pairing and synapsis by the end of the pachytene region 75 . Nuclei with asynapsed X chromosomes can be recognized by their more condensed , transition zone-like chromosome morphology , or by co-staining for axial element and central region proteins of the synaptonemal complex 75 . In the late pachytene region of these animals , we found that DSB-1 staining correlated with the status of individual nuclei: those with asynapsed chromosomes were positive for DSB-1 staining , while fully synapsed nuclei lacked DSB-1 staining ( Figure 7D ) . These results indicate that the extension of DSB-1 localization is a response to a signal intrinsic to individual nuclei , and does not extend to neighboring nuclei within the same region of the gonad . However , as in all mutants examined , DSB-1 disappeared by the end of the pachytene region of the gonad , indicative of an extrinsic , spatially regulated “override” signal that triggers progression to late pachytene and loss of the presumptive DSB-permissive state , even when crossover precursors have not been attained on all chromosomes ( see Discussion ) . The DSB-1 paralog DSB-2 is also involved in meiotic DSB formation 47 . As reported in the accompanying paper by Rosu et al . , the two proteins show very similar localization patterns ( Figure 8A and 8B , 47 ) . Both localize to nuclei from leptotene/zygotene through mid pachytene , although DSB-1 staining appears slightly earlier than DSB-2 staining ( Figure 8A ) . They also disappear simultaneously from meiotic chromosomes , both in wild-type animals and various mutants that disrupt crossover formation ( Figure 8A , data not shown ) . Additionally , both proteins show similar distributions along meiotic chromosomes ( Figure 8B ) . Intriguingly , however , the two proteins do not extensively colocalize , but instead rarely coincide ( Figure 8B ) . To probe the functional interactions between DSB-1 and DSB-2 , we localized each protein in the absence of the other . We found that DSB-1 localized to chromosomes in dsb-2 ( me96 ) mutants , although the fluorescence intensity was reduced relative to wild-type gonads ( Figure 9A and 9B; see also 47 ) . The DSB-1 positive region of the gonad was also somewhat shorter ( Figure 9A ) , despite the reduction of crossovers in dsb-2 mutants 47 . This suggests that localization of DSB-1 to meiotic chromosomes does not require , but may be reinforced or stabilized by , DSB-2 . By contrast , DSB-2 was not detected on meiotic chromosomes in dsb-1 mutants ( Figure 9B ) . Immunoblotting of whole-worm lysates revealed that DSB-1 protein levels are moderately reduced in dsb-2 mutants , while DSB-2 protein levels are severely reduced in dsb-1 mutants ( Figure 9C ) . This parallels our conclusions from in situ localization of these proteins , and suggests that the reduction of staining observed on chromosomes is a consequence of lower protein levels . We also tested the effect of eliminating both DSB-1 and DSB-2 by constructing a double mutant strain . The phenotypes observed in dsb-1; dsb-2 mutant animals were indistinguishable from dsb-1 mutants ( Figure 10A and 10B ) . This result is consistent with the idea that these proteins collaborate in some way to promote DSB formation , and argues against more complex epistasis scenarios .
We have discovered a novel protein , DSB-1 , required for meiotic DSB formation in C . elegans . Our data indicate that DSB-1 acts specifically to promote DSBs , and does not play a major role in DNA repair or other meiotic processes . DSB-1 localizes to chromosomes during meiotic prophase , concomitant with the period of DSB formation . It appears more abundant on the autosomes than the X chromosome . The significance of this finding is unclear , since DSB-1 is clearly required for DSBs on all chromosomes , but it may be related to observations that the X chromosome has distinct chromatin structure and differential genetic requirements for DSB formation 35 , 36 , 51 , 52 . Both DSB-1 and its paralog DSB-2 are required for normal levels of meiotic DSBs . These proteins show a similar temporal and spatial pattern of localization to meiotic chromosomes . The localization of both proteins is also extended to a similar extent in mutants that disrupt crossover formation . In mutants where the localization of both DSB-1 and DSB-2 was assayed simultaneously , as well as in wild-type animals , the proteins localize to the same subset of meiotic nuclei , except that DSB-1 appears slightly earlier , suggesting that they are co-regulated . However , these proteins seem unlikely to act as a complex , since they show little if any colocalization . Although DSB-1 and DSB-2 appear to play similar roles in meiotic DSB formation , the severity of their mutant phenotypes are not equivalent . As shown by Rosu et al . , DSBs are reduced but not eliminated in young dsb-2 mutant hermaphrodites 47 , while dsb-1 mutants lack DSBs regardless of age . The less severe defects observed in young dsb-2 mutants likely reflect the presence of substantial residual DSB-1 protein on meiotic chromosomes in dsb-2 mutants , whereas DSB-2 is not detected on chromosomes in dsb-1 mutants , and protein levels are severely reduced . DSB-1 appears to stabilize DSB-2 , perhaps by promoting its association with chromosomes , and to a lesser extent is reciprocally stabilized/reinforced by DSB-2 . The CHK-2 kinase promotes the chromosomal association of DSB-1 . CHK-2 is also required for DSB-2 localization on meiotic chromosomes 47 , although it is not clear whether CHK-2 promotes DSB-2 loading directly , or indirectly through its role in the loading of DSB-1 . Our findings suggest a model in which DSB-1 and DSB-2 mutually promote each other's expression , stability , and/or localization , with DSB-2 depending more strongly on DSB-1 , to promote DSB formation ( Figure 10C ) . The number of sites of DSB-1 and DSB-2 localization per nucleus – too many to quantify in diffraction-limited images – appears to greatly exceed the number of DSBs , estimates of which have ranged from 12 to 75 per nucleus 65 , 76 , 77 . DSB-1 and DSB-2 may each bind to sites of potential DSBs , with only a subset of these sites undergoing DSB formation , perhaps where they happen to coincide . They could also be serving as scaffolds to recruit other factors required for DSB formation to meiotic chromosomes and/or to promote their functional interaction . This idea is currently difficult to test , since we have not yet been able to detect chromosomal association of SPO-11 in C . elegans , and no other proteins specifically required for DSBs have been identified . Alternatively , these proteins may influence DSB formation by modifying chromosome structure . We did not observe overt changes in chromosome morphology in dsb-1 mutants , but further analysis – e . g . , mapping of histone modifications – may be necessary to uncover more subtle changes . DSBs normally occur within a discrete time window during early meiotic prophase . In C . elegans this corresponds to the transition zone and early pachytene , based on RAD-51 localization . As DSB-1 is necessary for DSB formation , and its appearance on meiotic chromosomes coincides with the timing of DSBs , we infer that the chromosomal localization of DSB-1 is indicative of a regulatory state permissive for DSB formation . We observed that when crossover formation is disrupted , this DSB-1-positive region is extended . Rosu et al . report a similar extension of DSB-2 in crossover-defective mutants 47 . Previous work has shown that RAD-51 foci persist longer and accumulate to greater numbers in various mutants that make breaks but not crossovers 43 , 64 , 65 , 67 , 75 . Extended or elevated RAD-51 staining could reflect an extension of the time that DSBs are made , a greater number of DSBs , or a slower turnover of the RAD-51-bound state . However , persistence of DSB-1 and DSB-2 on meiotic chromosomes in these mutants suggests that the period in which nuclei are competent for DSB formation is extended . In support of this idea , in many crossover-defective mutants defective , the number of RAD-51 foci per nucleus not only reaches a far higher level but also peaks later than in wild-type animals and continues to rise even after RAD-51 is normally cleared from meiotic chromosomes 53 , 75 , 77 , indicating that breaks continue to be generated after their formation would normally cease . Several mutations that impair crossover formation on a limited number of chromosome pairs result in altered crossover distributions on the crossover-competent chromosomes 64 , 78 , which , particularly in light of the current findings , seems likely to reflect changes in DSB activity . In addition , RAD-51 chromatin immunoprecipitation data from our laboratory ( C . V . Kotwaliwale and AFD , unpublished ) have indicated that the DSB distribution is altered in him-8 mutants , one of the genotypes that show extended DSB-1 staining . Taken together , these findings strongly suggest that both the temporal and genomic distribution of DSBs is altered in many situations that perturb crossover formation . A similar phenomenon may also account for the “interchromosomal effect” first observed in Drosophila female meiosis 79 . We note that this raises caveats about previously published estimates of DSB numbers in C . elegans that have been based on quantification of RAD-51 foci in genotypes that do not complete crossovers , such as rad-54 or syp-1 mutants 65 , 76 , 77 . Extension of the DSB-1 zone occurs even when nuclei are unable to initiate meiotic recombination due to an absence of DSBs . This suggests that the extension is not due to the persistence of unresolved recombination intermediates , but is instead a response to the absence of a particular crossover-competent recombination intermediate , or crossover precursor . We found that disruption of crossover formation on a single pair of chromosomes is sufficient to prolong DSB-1 localization on all chromosomes . Based on this result we believe that chromosomes lacking a crossover precursor may emanate a signal that sustains a DSB-permissive state within the affected nucleus . Thus , a single chromosome pair lacking a crossover precursor elicits a genome-wide response that results in extension of DSB-1 localization , which may reflect a modulation of the timing , and perhaps the extent , of DSB formation . Such a mechanism would help to ensure formation of an “obligate” crossover on every chromosome pair . All mutants that we found to extend the localization of DSB-1 cause a disruption in crossover formation , although they have various primary molecular defects . It is possible that extension of DSB-1 localization occurs in response to distinct molecular triggers in different mutant situations . For example , spo-11 mutants may be responding to an absence of DSBs , rad-54 mutants to unrepaired DSBs , and syp-1 mutants to asynapsed chromosomes . A similar model in which different unfinished meiotic tasks can elicit delays in meiotic progression was proposed in a recent study 80 . However , we feel that a parsimonious interpretation of our data is that the absence of a crossover precursor on one or more chromosomes is sufficient to prolong DSB-1/2 localization . The varying degree of extension seen in different mutants could reflect the engagement of additional regulatory mechanisms , such as the synapsis checkpoint and/or DNA damage checkpoint , which might converge with a crossover assurance mechanism to modulate regulators of DSB-1 . We propose that an “obligate crossover” checkpoint mediates the extension of DSB-1 localization ( Figure 11 ) . Our data suggest that DSB formation is activated during early meiosis and normally persists long enough for most nuclei to attain crossover precursors on all chromosomes ( Figure 11 ) . If interhomolog recombination is impaired on one or more chromosome pairs , individual nuclei can prolong the DSB-permissive state in an attempt to generate a crossover on every chromosome . Our observation that a block to crossover formation on a single pair of chromosomes results in persistent DSB-1 throughout the affected nuclei is reminiscent of the spindle assembly checkpoint ( SAC ) , in which failure of a single pair of sister kinetochores to biorient on the mitotic spindle triggers a cell-autonomous delay in anaphase onset that affects cohesion on all chromosomes 81 . Interestingly , a key mediator of the SAC , Mad2 , is homologous to the meiotic axis proteins HTP-3 and HTP-1 28 , 82 , which appear be important for the regulatory circuit that mediates prolonged DSB-1 localization in response to crossover defects . An alternative model would be a negative feedback circuit in which the acquisition of all necessary crossover-intermediates triggers inactivation of DSB formation . According to this view , the presence of crossover precursors generates a signal to exit the DSB permissive state , rather than the absence of precursors extending this period . Such a model would require a ‘counting’ mechanism that enables exit from the DSB permissive state in response to a threshold number of crossover precursors . This seems less likely based on first principles , and also less consistent with our data . Our observations also suggest that there is a minimum duration of proficiency for DSB formation that does not depend on how rapidly chromosome pairs attain crossover precursors . We would expect meiotic nuclei to achieve crossover precursors on every chromosome in a stochastic manner . If DSB-1 were removed from chromosomes upon reaching this state , we would likely see a patchwork of DSB-1 positive and negative nuclei in the early pachytene region , but instead we observe homogenous staining in this region , and abrupt disappearance of DSB-1 within a narrow zone of the gonad . Additionally , in mutants that appear to be defective in triggering the obligate crossover checkpoint , such as htp-3 and htp-1 , a zone of DSB-1-positive nuclei similar in length to that in wild-type animals is observed . Together these observations suggest that there is a preset temporal window for DSB formation that can be extended in individual nuclei but not shortened . The duration of the DSB-permissive state might be specified by an activity or signal that decays with time and/or distance after meiotic entry . We speculate that the disappearance of DSB-1 may reflect a drop below a threshold level of CHK-2 activity , decay of CHK-2-mediated phosphorylation of DSB-1 or other targets , and/or a rise in an opposing activity – e . g . , a phosphatase . Any of these could be inhibited by the putative checkpoint mechanism that prolongs DSB-1 localization in response to impaired crossover formation . The nature of the recombination intermediate that satisfies the requirement for a crossover precursor on all chromosomes remains unknown . We distinguish “crossover precursors” from “interhomolog recombination intermediates” because components that are specifically required for crossovers , including MSH-5 , ZHP-3 , and COSA-1 38 , 39 , 66 , 83 , are all required for timely disappearance of DSB-1 from chromosomes . However , cytological markers for crossovers , including foci of ZHP-3 and COSA-1 , do not appear until the late pachytene region of the gonad 39 , 66 , after DSB-1 and DSB-2 disappear from meiotic chromosomes 47 . Thus , it seems likely that crossover precursors , rather than mature crossovers , are sufficient to allow exit from the DSB-permissive state . Genetic and cytological evidence indicate that nuclei eventually cease to make DSBs , even when crossovers fail to be made on one or more chromosomes . As nuclei approach the bend region of the gonad at the end of pachytene , an “override” signal appears to shut off DSB formation ( Figure 11 ) . Unlike in mammals , where crossover failures result in extensive apoptosis 84 , C . elegans hermaphrodites produce both sperm and oocytes in roughly normal numbers even when homolog pairing , synapsis , and/or recombination are severely impaired . Numerous studies have documented a phenomenon known as the “extended transition zone” in mutants with defects in homolog pairing and/or synapsis 30 , 43 , 53 , 68 . An extended transition zone has been defined as a longer region of the gonad containing nuclei with crescent-shaped DAPI-staining morphology , multiple patches of the nuclear envelope proteins SUN-1 and ZYG-12 , and strong foci of the ZIM proteins 68 , 74 , 85 . An extended transition zone appears to be a response to asynapsed chromosomes 43 , 68 . Previous work from our lab showed that the extension of the transition zone in synapsis-defective animals such as him-8 hermaphrodites was suppressed by mutations in recombination factors , including spo-11 and msh-5 , and we therefore proposed that it might reflect a response to unresolved recombination intermediates 64 . However , subsequent work has revealed that these double mutant situations actually resulted in precocious fold-back synapsis of unpaired chromosomes , thereby silencing the asynapsed chromosome response ( SER and AFD , unpublished ) . Since mutations that abrogate pairing or synapsis also impair interhomolog recombination , it is not surprising that most genotypes with extended transition zones also show persistent DSB-1 localization . However , not all mutants that disrupt crossover formation extend the transition zone . spo-11 and him-5 mutants , for example , are deficient for DSB formation on one or more chromosomes and show extended DSB-1 staining , but do not show typical extended transition zones . Instead , these mutants appear to have extended regions of early pachytene nuclei . Based on these observations , we believe that the obligate crossover checkpoint mechanism is distinct from the response to asynapsed chromosomes . However , these two regulatory circuits serve similar purposes – to enable meiotic nuclei more time to complete synapsis or achieve crossovers on all chromosomes – and they may also involve common molecular components . Proteins with apparent homology to DSB-1 are restricted to the Caenorhabditis lineage . Even within Caenorhabditids , DSB-1 , DSB-2 and their homologs are only weakly conserved . This reinforces abundant evidence from other organisms that apart from Spo11 itself and the Rad50-Mre11 complex , proteins that promote DSB formation diverge rapidly during evolution 17 , 18 , 86 . This might seem surprising given that meiotic DSB formation is an essential aspect of sexual reproduction in most eukaryotes . However , potent and acute evolutionary pressures act on meiosis . For example , the germline is the site of intense warfare between the host genome and selfish genetic elements , which may contribute to the rapid evolution of meiotic proteins . In addition , the genome-wide distribution of DSBs appears to underlie the strongly biased distribution of crossovers observed in many species 87 , 88 , including C . elegans ( C . V . Kotwaliwale and AFD , unpublished ) . The nature of this biased distribution shows interesting variation among species 89 , 90 . Since crossover number and position have a direct impact on the fidelity of meiotic chromosome segregation , mechanisms governing DSB distribution have likely evolved in concert with changes in chromosome structure and the spindle apparatus to maintain reproductive fitness . Several features of meiosis in C . elegans distinguish it from other organisms in which DSB-promoting factors have been identified . In particular , DSBs and early recombination steps contribute directly to homolog pairing and synapsis in many species , while in C . elegans homolog pairing and synapsis occur independently of DSBs . Additionally , C . elegans lacks Dmc1 , Hop2 , and Mnd1 , which are thought to function together as an essential meiotic recombination module in most eukaryotes 91 . C . elegans also lacks the DSB proteins Mei4 and Rec114 , which are conserved between budding yeast and mice 18 . A correlation between the absence of DMC1/Hop2/Mnd1 and Mei4/Rec114 has been noted in several other lineages , and has been suggested to reflect a functional link between the formation of DSBs and their subsequent repair 18 . Interestingly , Rec114 , like DSB-1/2 , has several potential target sites for ATM/ATR phosphorylation , and these are important for regulation of DSBs in budding yeast meiosis 14 . Thus , the DSB-1/2 family of proteins may play analogous roles to known mediators of DSB formation in other species , despite their lack of apparent sequence similarity .
All C . elegans strains were cultured under standard conditions at 20°C . The wild-type strain was N2 Bristol . The nonsense dsb-1 ( we11 ) allele was generated by EMS mutagenesis . The dsb-1 deletion allele ( tm5034 ) was generated by the Japanese National BioResource for the Nematode . Both dsb-1 alleles were extensively outcrossed to wild-type ( 5-6x ) , and additionally outcrossed in a directed three-point cross to dpy-20 unc-30 to eliminate most linked mutations . Additional mutants analyzed in this study were: spo-11 ( me44 , ok79 ) , mre-11 ( ok179 ) , rad-50 ( ok197 ) , chk-2 ( me64 ) , com-1 ( t1626 ) , rad-51 ( Ig8701 ) , rad-54 ( tm1268 ) , msh-5 ( me23 ) , cosa-1 ( me13 ) , zhp-3 ( jf61 ) , htp-3 ( tm3655 ) , htp-1 ( gk174 ) , syp-1 ( me17 ) , syp-2 ( ok307 ) , him-8 ( tm611 ) , zim-2 ( tm574 ) , dsb-2 ( me96 ) , and ced-4 ( n1162 ) . Strains used in this study were: L4 hermaphrodites were picked onto individual plates and transferred to new plates every 12 hours , for a total of 6–8 12-hour laying periods , until newly-laid fertilized eggs were no longer observed . Eggs were counted immediately after each 12-hour laying period . Surviving hermaphrodite and male progeny were counted 3 days later . Polyclonal antibodies against recombinant full-length DSB-1 protein were produced at Pocono Rabbit Farm & Laboratory . 6xHis-DSB-1 was purified from E . coli using Ni beads under denaturing conditions . The protein was resolved on an SDS-PAGE gel and the excised DSB-1 band was used to immunize guinea pigs . Rabbit anti-HTP-3 antibodies were raised against a synthetic peptide ( PTEPASPVESPVKEQPQKAPK ) by Strategic Diagnostics Inc . , SDIX . Additional antibodies used in this study were: guinea pig anti-HTP-3 75 , rat anti-HIM-8 53 , rabbit anti-RAD-51 92 , goat anti-SYP-1 92 , and rabbit anti-DSB-2 47 . Immunofluorescence was performed as previously described 93 . Briefly , hermaphrodites 24–28 hours post L4 were dissected in egg buffer containing sodium azide and 0 . 1% Tween 20 , fixed for 3 min in 1% formaldehyde in the same buffer between a Histobond slide and coverslip , and frozen on dry ice . The coverslip was removed , and slides were transferred to methanol chilled to −20°C . After 1 min , slides were transferred to PBST ( PBS containing 0 . 1% Tween 20 ) , washed in two further changes of PBST , blocked with Roche blocking agent , and stained with primary antibodies in block for 2 hours at room temperature or overnight at 4°C . Slides were then washed with 3 changes of PBST and stained with secondary antibodies . Secondary antibodies labeled with Alexa 488 , Cy3 , or Cy5 were purchased from Invitrogen or Jackson ImmunoResearch . Following immunostaining , slides were washed , stained in 0 . 5 mg/ml DAPI , destained in PBST , and mounted in buffered glycerol-based mounting medium containing 4% n-propyl gallate as an antifading agent . For quantification of DAPI-staining bodies in oocytes , animals were dissected , fixed , and DAPI-stained as described above , omitting the steps involving immunostaining . FISH procedures have also been previously described in detail 93 . Probes used in this study included the 5S rDNA repeat 23 and a short repeat associated with the right end of the X chromosome 53 . All images were acquired using a DeltaVision RT microscope ( Applied Precision ) equipped with a 100× 1 . 40 oil-immersion objective ( Olympus ) or ( for whole gonad images ) a 60× 1 . 40 oil-immersion objective ( Olympus ) . Image deconvolution and projections were performed with the softWoRx software package ( Applied Precision ) . Image scaling , false coloring , and composite image assembly were performed with Adobe Photoshop . All micrographs presented in the figures are maximum-intensity projections of 3D data stacks . Lysate from 50 young adult hermaphrodites , picked at 24 hours post L4 , was used for each lane . Gel electrophoresis was performed using 4–12% Novex NuPage gels ( Invitrogen ) . Proteins were transferred to PVDF membrane . Guinea pig DSB-1 antibodies and rabbit DSB-2 antibodies ( see above ) were used for immunoblotting , followed by detection with HRP-conjugated secondary antibodies and ECL Western Blotting Substrate ( Pierce ) . Young adult worms were irradiated with approximately 10 Gy ( 1000 rad ) from a Cs-137 source . For each experiment , unirradiated controls were treated identically to irradiated animals , other than exposure to radiation . For quantification of DAPI-staining bodies at diakinesis , hermaphrodites were irradiated 4–5 hours post L4 and dissected 18 hours post irradiation . To assess progeny survival , animals were irradiated 4–5 hours post L4 , eggs laid 20–30 hours post irradiation were quantified , and surviving progeny were quantified 3 days later . For quantification of DSB-1 localization , animals were irradiated 16 hours post L4 and dissected 8 hours post irradiation . For RAD-51 immunofluorescence , animals were irradiated 24 hours post L4 and dissected 1 hour post irradiation . 1000 homozygous we11 animals were picked from an outcrossed , balanced strain . A genomic DNA library was prepared as described in the genomic DNA library protocol from Illumina . Libraries were sequenced using 76-bp single-end Illumina sequencing . MAQGene 94 was used to identify mutations present in the we11 mutant strain . A 2 . 1-kb region of genomic DNA including the dsb-1 coding sequence and promoter was amplified by PCR using the following primers: 5′-CCGCTTCCGAATACCGCC-3′ and 5′-GGTGCCGCTGTGTAGAAGAAGC-3′ . 100 ng/µl of dsb-1 PCR product was combined with 50 ng/µl of unc-119 rescuing plasmid pMM051 95 and injected into unc-119 animals . Rescued non-Unc F1 animals were picked to individual plates and assayed for embryonic lethality and male progeny . F2 animals were dissected , stained , and observed to quantify the number of DAPI-staining bodies in oocytes at diakinesis . 12 young adult animals , 24 hours post L4 , were used for each genotype . RNA was purified from animals and reverse transcribed into cDNA with the SuperScript kit from Invitrogen using poly-A primers . spo-11 mRNA levels were compared by real-time PCR analysis with SYBR Green ( Kapa Biosystems ) . act-1 and htp-3 mRNA levels were used as normalization controls . Primers used were as follows: spo-11 ( 5′-TGAGCCCGGATCTGTAGAAT-3′ , 5′-TAGCTTGTTCCTTCGGTGGT-3′ ) , act-1 ( 5′-CCCCATCAACCATGAAGATC-3′ , 5′-TCTGTTGGAAGGTGGAGAGG-3′ ) , and htp-3 ( 5′-CGAGTGATGACAGGGCTATATTC-3′ , 5′-TGCAAGATAAACGCAGTTGG-3′ ) . | For most eukaryotes , recombination between homologous chromosomes during meiosis is an essential aspect of sexual reproduction . Meiotic recombination is initiated by programmed double-strand breaks in DNA , which have the potential to induce mutations if not efficiently repaired . To better understand the mechanisms that govern the initiation of recombination and regulate the formation of double-strand breaks , we use the nematode Caenorhabditis elegans as a model system . Here we describe a new gene , dsb-1 , that is required for double-strand break formation in C . elegans . Through analysis of the encoded DSB-1 protein we illuminate an important regulatory pathway that promotes crossover recombination events on all chromosome pairs to ensure successful meiosis . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
"And",
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] | 2013 | Identification of DSB-1, a Protein Required for Initiation of Meiotic Recombination in Caenorhabditis elegans, Illuminates a Crossover Assurance Checkpoint |
Bubonic is the most prevalent plague form in Madagascar . Indoor ground application of insecticide dust is the conventional method used to control potentially infected rodent fleas that transmit the plague bacterium from rodents to humans . The use of bait stations is an alternative approach for vector control during plague epidemics , as well as a preventive control method during non-epidemic seasons . Bait stations have many advantages , principally by reducing the amount of insecticide used , lowering the cost of the treatment and minimizing insecticide exposure in the environment . A previous study reported promising results on controlling simultaneously the reservoir and vectors , when slow-acting rodenticide was incorporated in bait stations called “Boîtes de Kartman” . However , little evidence of an effective control of the fleas prior to the elimination of rodents was found . In this study , we evaluated bait stations containing insecticide powder and non-toxic attractive rodent bait for their potential to control rat fleas . Its efficacy was compared to the standard method . The impact of both methods on indoor and outdoor rodent fleas , as well as the human household flea Pulex irritans were analyzed at different time points after treatments . Bait stations did not cause any significant immediate or delayed reduction of rat fleas and increasing the number of operational bait stations per household did not significantly improve their efficacy . Insecticide ground dusting appeared to be the most efficient method to control indoor rat fleas . Both methods appeared to have little impact on the density of outdoor rat fleas and human fleas . These results demonstrate limited effectiveness for bait stations and encourage the maintenance of insecticide dusting as a first-line control strategy in case of epidemic emergence of plague , when immediate effect on rodent fleas is needed . Recommendations are given to improve the efficacy of the bait station method .
The Yersinia pestis bacterium , the causative agent of plague , is transmitted between rodents by infected flea bites [1] . The plague cycle involves wild and commensal rodents and their fleas; humans are accidental hosts . This zoonotic disease can lead to significant mortality amongst susceptible rodents , thus releasing host-seeking potentially infected fleas . Humans can develop the bubonic form of plague , when the bacterium is deposited in the dermis by fleas [2] . The bubonic form is characterized by fever , headache , chills , and painful inflammation of lymph nodes draining the site of the flea bite . Yersinia pestis can disseminate from the lymph nodes into the bloodstream causing septicemic plague . Secondary pneumonia occurs when the infection reaches the lungs [2] . Pneumonic plague is responsible for inter-human transmission by inhalation of contaminated droplets and can be associated with high mortality rate if inadequately treated . The disease can be cured , if diagnosed early and treated with the adequate antibiotic . Poor housing and hygiene , proximity to rodents and fleas in everyday life are major and unchanged risk factors in both historical and modern episodes of plague [3] . Currently , most infections of human plague have been reported in low-income countries in the African region . Madagascar has become the country with the highest plague burden [4] . Between 1998 and 2016 , 250 to 1500 suspected cases were reported annually by the Malagasy Central Laboratory for Plague , with 18% case-fatality rates for confirmed and presumptive case-patients ( 1 , 057/5 , 819 ) [5] . The bubonic plague is the most encountered form ( about 80% ) , however , secondary pneumonic plague can lead to overwhelming urban epidemic , such as reported in the capital in 2017 [6 , 7] . In the Central Highlands of Madagascar , plague epidemics mainly occur in remote rural areas , with small household epidemic patterns , during the rainy and warm months from October to April [8] . In rural settlements , housing is usually constructed of mud with thatched roof and the floor is covered with a braided vegetal fiber mat . This type of housing is easily accessible by Rattus rattus , the main reservoir . Traditional outdoor granaries are infrequently used due to thief problems . Crops and food are stored in the bedroom , which promotes contact between humans , rodents and their fleas [5 , 8] . Two flea species have been reported as confirmed vectors . Xenopsylla cheopis , the oriental rat flea is considered the main vector , parasitizing R . rattus , R . norvegicus , and Suncus murinus in rural and urban areas [8] . X . cheopis is associated with indoor rats , which makes this species the target of vector control during plague episodes . Synopsyllus fonquerniei is an endemic flea vector , found mainly in rural areas , above 800 meters of elevation . This species is associated with outdoor R . rattus but can also be found in forest areas harbored by small endemic mammals [8] . Recently , X . brasiliensis was described for the first time in the northern plague focus [9] . The human flea , Pulex irritans , a species that is not associated with rodents , is the most abundant species caught inside human houses . It has also been found naturally infected with plague bacterium during an epidemic [10] . Further studies are needed to determine its involvement in the plague cycle and its competence as a vector . In the context of public health , flea control can play an important role in preventing the expansion of plague epidemic . Reducing the density of rodent fleas as fast as possible is the main purpose of all techniques developed for flea control [11] . Insecticide dusting is the main method recommended by WHO to control rat fleas during plague outbreaks [12–14] . It is now recognized that flea control must be performed before rodent control , in order to minimize the spread of infected fleas and the risk of transmission of the bacterium to humans [11 , 13] . Insecticide dust may be blown into the entrance of rodent burrows but more often a layer of insecticide is deposited on rodent runways , usually indoors at the bottom of the walls [13] . This insecticide treatment is not always accepted , because of toxicity and concerns for health and environmental side effects [15] . The use of host-targeted methods such as a “bait station” or “bait box” appeared to reduce the risks of insecticide exposure , mainly for non-target species and children [12] . In addition , this method can reduce the cost of treatment , because less insecticide is required [16] . Insecticide powder is placed inside a container with a bait to attract the rodents hosting targeted fleas . Rodents entering the bait station come in contact with the insecticide . The bait station technique was found to be highly effective to target ectoparasites of wild rodents [16–21] . However , its use to prevent human plague expansion has been less investigated . Yet , a bait station approach may be a good alternative to direct insecticide dusting inside households . In Madagascar , insecticide dusting inside homes is one of the measures recommended by the National Plague Program , to prevent the spread of plague epidemics [22] . Since X . cheopis , the main vector , is mainly harbored by indoor rodents and plague epidemics occur mostly during the rainy season , insecticide treatments are usually performed indoors . Bait stations have become popular and used as a complementary tool against rat fleas [23 , 24] . In 2003 , a study conducted in the Malagasy capital , Antananarivo , aimed to evaluate the use of wooden tunnel bait stations ( “Boîtes de Kartman” ) containing bait incorporating anticoagulant rodenticide , to simultaneously control the reservoir and vectors of plague [23] . The results appeared promising , with significant reduction of rodent density , but it was difficult to prove the effectiveness of the method for flea control , especially before the control of targeted rodents . Furthermore , elimination of the rodent population during an epidemic may favor the immigration of non-treated and potentially infected rodents from the surrounding area [25] . The objective of this project was to evaluate the efficacy of bait station method on reducing rat flea density while maintaining the rodent population . The results at each time point were compared to the efficacy of direct ground dusting , which remains the standard method . Rat movement between indoor and outdoor environments is well documented in Madagascar [26 , 27] . Insecticide treatments are focused indoors , while exposure to potentially infected small mammal harboring S . fonquerniei is possible outdoors . In this study the impact of both indoor insecticide treatments on outdoor rat flea prevalence was also evaluated . Despite the conflicting role attributed to P . irritans during human plague epidemics [10] , house fleas are not the principal target of vector control in Madagascar . A study conducted in Tanzania showed a correlation between P . irritans density and the risk of bubonic plague [28] . Yet the potential of indoor insecticide treatments on reducing P . irritans prevalence is investigated here for the first time . To summarize the objectives of this project , the efficacy of bait station method and direct insecticide dusting treatments on reducing ( 1 ) indoor and ( 2 ) outdoor rat fleas , as well as ( 3 ) house flea burden will be evaluated . For each condition , the evaluation was performed over two days , six days , and one month after treatment . The study was conducted in two stages in 2016 and 2017 . Field experiments conducted in 2016 compared the effectiveness of direct insecticide dusting and bait station approach on reducing flea burden . Based on the results obtained in 2016 , a subsequent study was implemented in 2017 and focused on examining if increasing the number of operational bait stations or using them for a longer period in households could improve the efficacy .
The study protocol was approved by a local “Ad-Hoc” committee including the University , the Veterinarian School of Antananarivo , and the Ministry of livestock of the Malagasy Government ( Institut Pasteur de Madagascar institutional committee for animal ethic ) and rodent sampling was conducted in accordance with the directive 2010/63/EU of the European Parliament ( http://eur-lex . europa . eu/Lex UriServ/LexUriServ . do ? uri=OJ:L:2010:276:0033:0079:EN:PDF ) . The study was carried out in the commune of Ivato Centre , district of Ambositra , Madagascar ( Fig 1 ) . This district , located in the southern part of the Central Highlands , lies within the plague endemic region of Madagascar [29] . Field trials were conducted during the dry and cool season of low plague transmission . This timeframe corresponded to the end of the rice harvest season in the Central Highlands , which is linked to high rodent numbers in the household environment [27] . Twelve hamlets of about 20 houses each were selected for the study and assigned randomly to treatment groups ( Fig 1 ) . The closest hamlets were separated by approximately one kilometer to prevent rat immigration between hamlets [27] . Study sites were included depending on their accessibility , verbal consent and acceptance of the local leader and household head . Hamlets included in the study were generally organized as described previously [27] , with houses constructed on hill tops and rice fields located in the valleys below ( S1A and S1B Fig ) . Housing was usually a three-level home ( ground floor , first floor and the attic ) with at least two rooms per level , constructed of brick or mud walls and a thatched roof ( S1B and S1C Fig ) . Dirt floors covered with plant fiber mat , or wooden planks were the most common . Homes with concrete floors were rare . The main rooms were located on the first floor and the attic . The ground floor was generally intended for domestic animals ( livestock , rabbits and poultry ) . The efficacy of each treatment on rodent fleas was evaluated by measuring the following parameters during pretreatment and post-treatment rodent sampling occasions: flea index ( FI ) and infestation rate ( IR ) . For each group and at each sampling occasion , the flea index was defined as the mean number of fleas per animal and infestation rate as the percentage of small mammals harboring at least one flea . Rodents were live-trapped using Sherman ( HB Sherman Traps , Inc . , Tallahassee , FL ) and wire-mesh BTS ( Besançon Technique Service , FR ) baited with dried fish and onions . Trapping was conducted for one night in each hamlet and each sampling occasion . Captured animals were euthanized and brushed with fine hair brush over a pan to remove fleas . Body weight and morphological measurements such as head to body , tail , ear , and hind foot length were recorded to identify the small mammal species . Escaped and devoured ( partially consumed by predator ) rodents were removed from the data analysis . Fleas collected from rodents were stored in microcentrifuge tubes containing 95% ethanol for later species identification . The study involved three arms called “groups” , each involving four hamlets ( Fig 1 ) . Fenitrothion powder ( 2% , organophosphate . Prochimad , Antananarivo , Madagascar ) was used in bait stations and dusting . For indoor trapping , a total of 20 Sherman traps and 20 wire-mesh traps were used per hamlet ( one of each per house ) . For outdoor trapping 20 wire-mesh BTS traps per hamlet were deployed and distributed within 5 to 20 meters around the homes . Indoor and outdoor rodent sampling was performed over three sampling periods for each group . The first rodent sampling was conducted , one day before treatment ( day 0 ) to have initial values of parameters , and to assess the uniformity of pretreatment data collected in each group . A second rodent sampling was done in all groups two nights after insecticide dusting and the placement of bait stations , to evaluate the immediate effect ( day 2 ) of each treatment . The third rodent trapping was completed one month after treatments ( day 30 ) to measure the residual effect of each insecticide treatment . Four hamlets did not receive insecticide treatment and considered as “control group” . Four hamlets , assigned as “bait station group” , had one bait-station per house , and bait stations remained for two consecutive nights within each house . Bait stations called “Boîtes de Kartman” were constructed as described in previous research [23 , 27] . The bait stations were locally made four-sided wooden tunnels , measuring 40 cm long x 10 cm wide x 10 cm high . The box was divided into three compartments , with non-poisoned rodent bait in the middle and insecticide dust layer in about 0 . 8 cm deep at both ends . Each bait station contained approximately 20 grams of manually rolled rat pellets ( mixture of laboratory animal diet powder , wheat flour , peanut oil , and water ) . The upper side of the bait station is removable to facilitate baiting and deposition of insecticide ( S2 Fig ) . Bait stations were placed indoors , on the floor , against the wall or on the top wall plate beneath the roof , according to the signs of rodent presence ( droppings , holes , tracks ) . Insecticide dusting was performed in four hamlets called “dusting group” with a hand shaker [12] . Insecticide dust was deposited on the indoor floor surface located 20 to 30 centimeters from the base of the wall , in a layer about three millimeters thick ( 50 grams per m² ) . Entries of rat and mouse burrows were also dusted . The rooms and kitchen were treated after the owner's consent ( certain rooms were not allowed to be treated because of presence of newborn babies or domestic animals ) . Food , cooking utensils and other personal items were removed from the surface to be treated and homeowners were encouraged not to sweep away the insecticide during the study . The impact of treatments on house fleas was monitored by collecting them in all groups , at the same time as rodent sampling . House fleas were trapped using a candle trap ( candle placed in the middle of a plate containing soapy water ) set in the middle of bedroom and lit by the homeowner when they turned off their own light . Fleas were attracted to the light and fell in the soapy water [30] . Trapped fleas were collected manually the next morning using fine forceps and preserved in 95% ethanol . All fleas were later identified at the species level under binocular microscope ( 40 x magnification ) . Fleas collected from small mammals during day 30 sampling were transported alive to the laboratory to conduct insecticide susceptibility tests . Adults fleas were identified at species level and X . cheopis were reared under laboratory conditions until the next generation reached enough numbers to perform insecticidal bioassays as described previously [31] . Each batch of fleas was exposed to 1% fenitrothion impregnated filter paper ( 1 . 5 x 6 cm , Vector Control Research Unit , Penang , Malaysia ) during the diagnostic period . Mortality was recorded 24 hours after exposure . The same hamlets ( twenty houses each ) as in the 2016 study design were included in 2017 , but a new random treatment assignment at hamlet level was organized , independently of hamlet status in previous study ( Fig 1 ) . BTS and Sherman traps were deployed indoors only . The same bait stations as in the 2016 study were used . Instead of rolled pellet , ripe tomato slices were used as attractant . Baits consumption was checked daily and were replenished when necessary by locally recruited health care workers . Rodent sampling was performed before the treatment to collect baseline data ( day 0 ) , day two post-treatment ( day 2 ) to assess the immediate effect , and six-day post-treatment ( day 6 ) to evaluate the effect of longer usage of bait stations ( day 2 versus day 6 ) . No sampling was done at day 30 post-treatment . Three hamlets were set as the “control group” and did not receive any insecticide treatment . Three hamlets received insecticide dusting following the similar protocol as described for 2016 study and were designated as the “dusting group” . Three hamlets , assigned as the “one bait station group” , received one bait station per household over six consecutive nights . The remaining three hamlets received three bait stations per household ( one for each story ) and were assigned as the “three bait station group” . Bait stations were left in house for two consecutive nights . Post-treatment sampling was done at day 2 only , to evaluate the immediate effect of increasing the number of deployed bait stations . The relative abundance of rats was calculated based on trapping success: number of animals captured divided by number of trap nights . Flea index , flea range , and infestation rate were calculated per sampling date for all groups [32] . Uniformity of FI and IR obtained from each group before treatment ( day 0 ) was tested using Kruskal-Wallis and Chi-square test , respectively . Differences were considered significant at p value < 0 . 05 . Separate analyses were done for indoor and outdoor-collected rat fleas . To demonstrate which treatment ( insecticide dusting or bait station ) was the most effective , post-treatment FIs recorded in each group were compared to control group using the Kruskal-Wallis test [32] . The infestation rate between groups was compared two by two , using a Chi-square proportion test [32] . The most effective method was assumed to be the one resulting in significant decrease of FI and IR post-treatment , compared to the control . To compare the efficacy of each method over time , intra-group evaluations were conducted by comparing FIs between day 0 , day 2 and day 6 or day 30 , using the Kruskal-Wallis test followed by a Dunn test , a post-hoc analysis test for multiple comparisons . The same comparison was performed between IR values , using Chi-square proportion test . The efficacy over time for each insecticide treatment was proved by a significant reduction of FI and IR when compared to the initial values ( day 0 ) . The percent control achieved by each treatment method at each time point was calculated using Henderson and Tilton‘s method [33 , 34] . The percent control of FI by a given treatment was determined by comparing the FI of treated group with the FI of the control group as follows: percent control = 100- ( T/U x 100 ) , where T = the post-treatment FI divided by the pre-treatment FI in the treated group and U = the post-treatment FI divided by the pre-treatment mean in the control group . The treatment was considered inefficient when the percent control value was below or equal to zero . For house fleas , the mean flea number per house ( total flea number per hamlet divided by the number of sampled houses ) and range were recorded for groups at each sampling point . On day 0 the mean flea numbers ( decimal logarithm transformed data ) per hamlet were compared , using ANOVA test [35] . To evaluate the effectiveness of the method on house fleas , changes in flea number per house before and after treatment were analyzed separately for each hamlet . A two-way ANOVA test followed by Tukey test was used to compare the two sampling periods after treatment [35] .
Fenitrothion powder ( 2% ) , used as ground dusting treatment was effective in reducing flea load on rodents 48 hours after the completion of the treatment . This result is in accordance with the emergency context of plague outbreak , as exposure for 24 h should be sufficient to kill adult X . cheopis on rats [13] . In addition , insecticide susceptibility tests showed great susceptibility to fenitrothion and is consistent with previous studies [31 , 36] . Our study demonstrated that this conventional treatment remained effective during the first week after treatment , adding supplementary protection to the community . However , the residual insecticide effect was lacking one month later , with a percent control below zero . The lack of residual effect may be explained by the reinvasion of untreated rats , while insecticide powder was scattered progressively , being taken of on feet/shoes and by house cleaning . In addition , if pyrethroid and carbamate insecticides remain effective for 2–4 months , organophosphates have a shorter period of effectiveness , especially in humid conditions [13] . A single insecticide dusting is adequate to lower rat flea burden during an epidemic , but unfortunately , may not be suitable for long term prevention of plague during the transmission season . Results of this study demonstrated that bait station with non-poisonous rodent bait was insufficient to significantly reduce the rat flea burden at least during the first week after their utilization . Caution should be taken when implementing this method , when targeting rat and flea elimination simultaneously [23] , otherwise raising the concern of releasing potentially infected host-seeking fleas . Notwithstanding their multiple benefits when compared to direct dusting , the bait box or bait station technique used during this study produced less satisfactory results on reducing rodent flea density . However , some factors may be considered , which may explain the failure of bait stations to attain an effective flea control . One reason may be a lack of coverage , more likely at the room level than the house level . A study demonstrated that the success of the insecticide delivery tube technique targeting fleas harbored by commensal rat depended on the placement of the device directly on rat runway , in a single room household [37] . In addition , monitoring rat movement in a single-room household scale probably facilitated the implementation of the technique , conversely to multiple-story house . Although , in our study scheme , increasing the bait station number to three per household ( one per story ) was expected to induce more controlling effect on fleas . An immediate effect was lacking in the three-bait station group , even though the density of bait station per hamlet was three-fold higher . Rats could have easily avoided passing through the wooden tunnel bait station , by modifying their trajectory . It was reported that rats can modify their usual movement pattern to avoid new objects in their environment [38] . With ground dusting method , where almost all sides of room were treated , rodents were constantly in contact with the insecticide dust , explaining the better success of this method when compared to single bait station per room . Increasing the number of bait station per room can be proposed as solution . However , one challenge limiting the deployment of higher number of bait station was its transportation . The wooden bait station used in this study was bulky and difficult to transport . A new bait station design , made with lighter material and collapsible ( like Sherman traps ) can resolve the transportation problem , knowing that plague cases in Madagascar often occur in isolated countryside , and are difficult to reach by ground transportation . The width of the bait station can also be reduced , lowering the amount of required insecticide per box , while the number per household can be increased . When used over six consecutive nights rather than only two , better percent control was obtained with bait station ( Table 1 , 2017 ) . However , bait stations were constantly visited by rodents at the same rate , from the first night of implementation to the last night . ( S1 Table ) . This observation was consistent with a previous study where the treatment devices were visited equally over the study [37] . If the bait stations were constantly visited , the observation of tracks or bait consumption could not generate important information related to the percentage of resident rodent actually visiting the bait station per night . The efficacy of the bait stations may be jeopardized if only a low proportion of resident rodents were using the bait stations . The abundance of stored crops during the study could have influenced the attractiveness of the bait . If the bait ( tomato slice ) was entirely consumed by a single rodent , the bait station will be less attractive to the others . This may explain the cumulative efficacy observed through time , when greater percent control was observed after six consecutive nights of utilization rather than after two nights . A solution can be the use of new bait station design , where the bait will be enclosed in compartment inaccessible to rodent but still attractive ( wire mesh for example ) . A durable bait such as peanut butter pellets can be used [27] , without the need of replenishment . This improvement will also reduce the cost of the method directly linked to frequent bait replacement and the recruitment of local workers performing daily replacement of the baits . The potential usage of bait station as a long-term preventive flea control tool should be explored . Lowering the rat flea burden before the onset of plague transmission season can be achieved , provided that fully operational bait stations are maintained in households . In that perspective , the improvement of the bait station design proposed here will improve long term maintenance of the bait station in the community . In addition , the attractiveness of other available bait sources should be considered along with better understanding of rodent species movement and behavior at room level , in order to implement more adapted vector control strategies . The present study was the first that monitored the impact of indoor insecticide treatment on outdoor-caught rodent fleas . It was reported that rodents passing across bait stations , as well as treated surfaces , can spread insecticide dust in their nest [18] . Unfortunately , this indirect treatment effect was not observed ( Fig 3 ) even though a clear rat movement between outdoor and indoor environments has been described in Madagascar [27] . It is now assumed that indoor interventions have no effect on outdoor rodents , whereas rodent to human flea-borne transmission of plague may occur in both environments [22 , 39] . A vector control approach targeting outdoor rodent-associated fleas , at least within immediate peri-domestic area , should be implemented in Madagascar . Targeting outdoor rodents and their fleas is more challenging mainly due to difficulties in locating burrows [1 , 20] . Thanks to the technique initially developed by Leo Kartman [20] , the bait station has been the more suitable technique for wild rodent ectoparasite control . Since then , different approaches using bait stations have been developed , according to the targeted species [16 , 19 , 21 , 37 , 40] . The effectiveness of the bait station type used during study as an outdoor vector control strategy should be addressed , as well as other approaches using systemic insecticides [41–44] . Although insecticide treatments during plague outbreaks are not intended for house fleas , the abundance of P . irritans has been found associated with high plague risk areas [28] . This study is the first to address the impact of insecticide treatment deployed during plague control on house fleas in Madagascar . High P . irritans infestation was observed in hamlets across this study , independently of insecticide treatments ( Fig 4 ) . Besides , the record of off-host rat fleas ( X . cheopis and S . fonquerniei ) captured amongst house flea is striking , highlighting the risk of plague transmission . However , neither insecticide dusting nor bait stations appeared to be suitable to treat house flea infestation . Significant reduction of house fleas per household was only found 30 days post-treatment , in the insecticide dusted group , but only in two of four hamlets ( Fig 4 ) . This delayed effect is not compatible with timeliness required for plague intervention [39] . Although it was not demonstrated during this study , individual household conditions related to general domestic hygiene , insecticide use , presence of domestic animals , insolation , and floor and wall type may play a role in the very heterogenous pattern of house flea abundance in some hamlets ( Fig 4 ) [45] . This may impact the success of insecticide interventions . The WHO recommended the use of a residual spray formulation ( instead of dust ) to control P . irritans , targeting more surfaces , by applying it directly onto clothing and bedding in the sleeping area as well as in soil and wall crevices [12–14] . Besides , effective treatment should be supplemented with adequate household hygiene [46] , which was insufficient or lacking in a large portion of relatively poor households visited during the study ( S1D Fig ) . Sanitation improvement appeared essential to control of house fleas . Moreover , better knowledge of P . irritans behavior and ecology can be a great help on its management and the better understanding of plague transmission . Even if P . irritans was reported to be naturally less susceptible to insecticide than X . cheopis [47] , the development of insecticide resistance in P . irritans populations can be a serious issue for the control of the species . P . irritans developing resistance to organochlorines insecticides were already described in frequently treated plague focus [47 , 48] , as well as the mutation conferring the resistance to pyrethroid insecticides [49] . Unfortunately , data regarding the susceptibility of this species to insecticide are still lacking in Madagascar . To summarize , this study provided information on the relative effectiveness of vector control tools used for plague containment in Madagascar . It was therefore established that insecticide ground dusting is the most efficient tool to control rodent fleas during epidemics . However , more adapted strategies should be implemented regarding house flea infestation and outdoor rat fleas . Regarding the advantages offered by using the bait station technique , studies should be continued in order to address the challenges encountered during this study . Recommendations were given for possible improvement regarding technical and operational aspects of bait station technique . In addition , its potential as a preventive vector control tool should be assessed further . Considering the unsatisfactory results obtained during this study , it is not recommended to use the bait stations as described , as principal vector control strategy during plague outbreaks . | Insecticide ground dusting inside houses is the recommended measure to control rat fleas responsible for bubonic plague transmission . The main inconvenience of this method is the direct contact of houseowners to the toxic insecticide dust and spillage in environment . A bait station approach , where the insecticide is confined in a box or tunnel containing rodent attractant , seems to be a valuable complementary or alternative vector control tool . However currently , little is known about its real efficacy on reducing or eliminating fleas harbored by rats . Guidelines regarding its implementation ( density and duration of use ) as vector control tool are lacking . Those questions were addressed during a field trial study , where bait stations were deployed at different densities per household and followed up at different time points . The efficacy of bait station was compared to the standard method . The present study allowed to demonstrate that bait station approach requires more improvements to be efficient . Meanwhile , insecticide ground dusting is still recommended for to control rat fleas during epidemics . | [
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"disease... | 2019 | Field assessment of insecticide dusting and bait station treatment impact against rodent flea and house flea species in the Madagascar plague context |
Cohesin is a well-known mediator of sister chromatid cohesion , but it also influences gene expression and development . These non-canonical roles of cohesin are not well understood , but are vital: gene expression and development are altered by modest changes in cohesin function that do not disrupt chromatid cohesion . To clarify cohesin's roles in transcription , we measured how cohesin controls RNA polymerase II ( Pol II ) activity by genome-wide chromatin immunoprecipitation and precision global run-on sequencing . On average , cohesin-binding genes have more transcriptionally active Pol II and promoter-proximal Pol II pausing than non-binding genes , and are more efficient , producing higher steady state levels of mRNA per transcribing Pol II complex . Cohesin depletion frequently decreases gene body transcription but increases pausing at cohesin-binding genes , indicating that cohesin often facilitates transition of paused Pol II to elongation . In many cases , this likely reflects a role for cohesin in transcriptional enhancer function . Strikingly , more than 95% of predicted extragenic enhancers bind cohesin , and cohesin depletion can reduce their association with Pol II , indicating that cohesin facilitates enhancer-promoter contact . Cohesin depletion decreases the levels of transcriptionally engaged Pol II at the promoters of most genes that don't bind cohesin , suggesting that cohesin controls expression of one or more broadly acting general transcription factors . The multiple transcriptional roles of cohesin revealed by these studies likely underlie the growth and developmental deficits caused by minor changes in cohesin activity .
Cohesin is a large protein ring that topologically encircles DNA and participates in several chromosome functions , including sister chromatid cohesion , chromosome segregation , DNA repair , and gene expression ( reviewed in [1]–[3] ) . It is loaded onto chromosomes by the kollerin complex , and removed by the releasin complex . Modest changes in cohesin , kollerin or releasin activity alter gene expression , growth , and animal development without measurable defects in chromatid cohesion or chromosome segregation . For instance , minor alterations of kollerin or cohesin activity in humans cause Cornelia de Lange syndrome ( CdLS , OMIM #122470 , #300590 , #610759 , #614701 ) which is associated with significant physical and intellectual deficits ( reviewed in [4] ) . Cohesin also influences gene expression in non-dividing cells [5] , [6] . Thus , cohesin's role in gene expression appears largely independent of its role in cell division , and considerably more sensitive than its other cellular functions to changes in cohesin dosage . Current evidence argues that cohesin directly influences gene transcription . In animal cells , cohesin and kollerin preferentially bind genes important for growth and development near the transcription start site and in the transcribed region [7]–[11] . In Drosophila , cohesin is largely absent from silent genes , and selectively binds active genes in which transcriptionally-engaged RNA polymerase II ( Pol II ) pauses just downstream of the start site [9] , [12] . Upon depletion of cohesin or kollerin , mRNAs from cohesin-binding genes are more likely to be affected than those from non-binding genes , and can change within a few hours [5] , [13] . Current evidence argues that cohesin regulates transcription by multiple mechanisms , including facilitating enhancer-promoter and insulator looping , and by controlling the transition of promoter-proximal paused Pol II to efficient elongation [1] , [2] . The prior studies of how cohesin regulates gene expression measured steady state mRNA levels , and thus do not clearly differentiate the roles of cohesin in transcription from other processes such as RNA splicing , transport , and stability . To gain more direct insights into the mechanisms by which cohesin influences transcription , we measured the effects of cohesin depletion on the genome-wide distribution of Pol II , Pol II phosphorylated at the serine 2 residue in the heptad repeats in the C terminal domain of the Rpb1 subunit ( Ser2P Pol II ) , P-TEFb , and Cdk12 in Drosophila cells derived from central nervous system . Ser2P Pol II is actively elongating and formed by the action of the P-TEFb and Cdk12 kinases . We also measured the effects of cohesin and kollerin depletion on transcriptionally-engaged Pol II by precision global run-on sequencing ( PRO-seq ) . We deduce that cohesin directly promotes the transition of promoter-proximal paused Pol II to elongation at many genes that it binds from comparing the changes in Pol II occupancy and activity in control and cohesin-depleted cells . The evidence indicates , that in many cases , cohesin likely facilitates this transition by supporting long-range enhancer-promoter interactions , but also has other roles directly at the promoter . Surprisingly , we also find that cohesin influences Pol II activity at most genes that don't bind cohesin , possibly through control of broadly-acting transcription factors .
To directly assess the influence of cohesin on gene transcription , we compared the genome-wide occupancy of Pol II and Pol II kinases relative to cohesin binding , and measured the effects of cohesin depletion on Pol II and kinase occupancy . We used genome-wide chromatin immunoprecipitation with tiling microarrays ( ChIP-chip ) to measure the genome-wide binding of Pol II , the Cyclin T ( CycT ) subunit of the P-TEFb complex , and the Cdk12 Pol II kinase in ML-DmBG3 ( BG3 ) Drosophila cells derived from larval central nervous system . We used antibodies against the Rpb3 subunit of Pol II [14] to measure the total Pol II occupancy , and antibodies specific for Ser2P Pol II to measure elongating Pol II . All ChIP-chip experiments were performed with two independent biological replicates and averaged . Genome-wide , Rpb3 correlates well with Ser2P Pol II ( r = 0 . 87 ) , especially on gene bodies ( Table 1; Figure 1 ) . Pol II positively correlates with the CycT subunit of the P-TEFb Pol II kinase ( 0 . 64–0 . 67 ) , and somewhat less , although significantly , with the Cdk12 kinase ( 0 . 39–0 . 45 ) ( Table 1 ) . The Rad21 cohesin subunit strongly overlaps Pol II ( r = 0 . 67–0 . 68 ) , consistent with prior findings [9] , and has a similar correlation with CycT ( 0 . 73 ) , but less with Cdk12 ( 0 . 49 ) ( Table 1 ) . We often detect CycT and Cdk12 at promoters , and enrichment in the gene bodies is frequently similar in strength , as in diminutive , the Drosophila myc gene ( dm , FlyBase FBgn0262656; Figure 1 ) . ChIP does not determine if Pol II is transcriptionally engaged , or the direction it is transcribing . We thus used precision global run-on sequencing ( PRO-seq; [15] ) , a variation of GRO-seq [16] that gives improved resolution to measure the levels and orientation of transcription-competent Pol II genome-wide . PRO-seq varies from GRO-seq in that biotin-labeled ribonucleotides are used to allow run-on for a nucleotide or two , instead of the longer run-on with BrUTP used in GRO-seq . PRO-seq , like GRO-seq [17] , is highly sensitive , and unlike ChIP , does not depend on crosslinking efficiency or antibody specificity , and detects elongation-competent Pol II regardless of the phosphorylation status . Nuclei were isolated under conditions of ribonucleotide depletion to halt transcription , but leave Pol II transcriptionally engaged . The nascent RNA transcripts produced upon restart of transcription were used to generate a cDNA library for high-throughput sequencing . Inclusion of sarkosyl in the run-on transcription reaction prevents new transcription initiation , so that only Pol II that is already transcriptionally engaged is detected , and gene body and promoter paused Pol II are detected with equal efficiency [17] . Two independent biological replicates were used for each PRO-seq measurement ( control , Rad21 RNAi , Nipped-B RNAi ) . The number of PRO-seq reads was quantified for nearly 17 , 000 annotated transcription units , and after normalization for the total number of reads , the genome-wide correlations between the two biological replicates were 0 . 98 for all three groups ( Table S1 ) . We selected approximately 7 , 000 “PRO-seq active” transcription units for detailed analysis by using only those transcription units that had at least 1 read per million in the 200 bp region surrounding the annotated transcription start site , and in the gene body in the control cells ( Table S2 ) . Because genes only bind cohesin when they are active [9] , restricting the analysis to active genes is essential for valid comparisons of cohesin-binding to non-binding genes . Many genes have more than one active transcription start site , and thus the 7 , 000 active transcription units represent approximately 6 , 000 genes . Cohesin-binding genes have more Pol II on average than non-binding active genes as measured by both PRO-seq and ChIP-chip . When active genes are subdivided into four groups ( Figure S1A ) from low to high cohesin binding levels based on the mean Rad21 ChIP signal in the 400 bp region surrounding the transcription start site , the average PRO-seq read density and Rpb3 , Ser2P Pol II , Cdk12 , and CycT ChIP signals at the promoter all increase with cohesin ( Figure 1A–1E ) . Similar results are obtained for both promoters and gene bodies when PRO-seq active genes are split into cohesin-binding and non-binding genes , and Pol II occupancy is measured by ChIP-chip ( Figure S1F ) . A prior report indicated that cohesin preferentially binds genes with promoter-proximal paused polymerase , based in part on genome-wide overlap of cohesin with the Negative Elongation Factor ( NELF ) pausing factor , and the higher levels of short promoter-proximal transcripts produced by cohesin-binding genes [12] . The PRO-seq data , which directly measures pausing , confirms these findings . The pause index is defined as the ratio of the PRO-seq signal density ( normalized reads per base pair ) in the 200 bp promoter region to the density in the rest of the gene body . The average pause index increases with cohesin occupancy , and the genes with the highest cohesin levels have substantially higher pausing ( Figure 1F ) . Conversely , when active genes are divided into four groups ranging from low to high pausing ( Figure S1B ) , the average cohesin occupancy at the promoter increases with the pause index ( Figure S1C ) . Pausing can also be measured by the ratio of the Rpb3 ChIP signal at the promoter to the signal in the gene body [18] , and this analysis also confirms that cohesin-binding genes have higher levels of pausing ( Figure S1D ) . Although Rpb3 ChIP is not as sensitive as PRO-seq , and is not specific for transcriptionally-engaged Pol II , the concordance between the PRO-seq and Rpb3 measures of pausing agrees with the finding that most Pol II at the promoter is transcriptionally-engaged [17] . The Drosophila Nipped-B kollerin subunit was discovered in a genetic screen for factors that control long-range activation of the cut ( FlyBase FBgn0004198 ) and Ultrabithorax ( FlyBase FBgn0003944 ) genes by remote tissue-specific enhancers [19] , and cohesin binds and facilitates the activity of transcriptional enhancers for pluripotency , β-globin , and T cell receptor genes in mammalian cells [6] , [7] , [20] . We thus examined the cohesin and Pol II occupancy of predicted extragenic cis-regulatory modules ( CRMs ) in BG3 cells . Active CRM/enhancer features include DNAseI hypersensitive sites ( DHS ) , and the H3K4me1 and H3K27ac histone modifications ( reviewed in [21] ) . The modENCODE project generated these data for BG3 cells [22] , and by these criteria , there are 2 , 353 potential CRMs , 557 of which are not within annotated transcription units and are at least 500 bp from a transcription start site ( Table S3 ) . Forty-two of the predicted CRMs overlap 21 tissue-specific CRMs curated by the REDfly database that are functional in transgenic reporter constructs [23] . Strikingly , we find that virtually all predicted extragenic CRMs ( 96% ) bind cohesin and Nipped-B ( Figure 2A ) . A similar fraction ( 94% ) of all 2 , 353 CRMs , which includes those located within transcribed regions , bind cohesin . Cohesin levels at the extragenic CRMs correlate positively with both the H3K27ac ( r = 0 . 65 ) and H3K4me1 histone modification levels ( Figure S2 ) . Somewhat less than half of the extragenic CRMs associate with Pol II , and a similar fraction bind Pol II kinases ( Figure 2A ) . Association of Pol II and Pol II kinases with a large fraction of these extragenic sequences supports the idea that they are functional CRMs , and the finding that virtually all bind cohesin is consistent with the idea that cohesin facilitates their function . The average cohesin occupancy of the extragenic CRMs is higher than that for all active promoters , while the Pol II occupancy of active promoters is higher than that of the CRMs ( Figure 2B ) . PRO-seq density indicates that much of the Pol II detected by ChIP at the CRMs is not transcriptionally engaged ( Figure 2B ) . While the median Pol II occupancy of the predicted CRMs by ChIP is only some 3-fold lower than for promoters , the median PRO-seq density at the CRMs is indistinguishable from zero , given that less than 50% of CRMs have PRO-seq signals ( Figure 2B ) . As seen in S2 cells [17] , the mean signals at CRMs are substantially lower than those at promoters , such that the ratio of the mean PRO-seq to mean Pol II ChIP ratio is approximately 50-fold lower at CRMs than at promoters . We theorize , therefore , that most of the Pol II detected by ChIP at CRMs is promoter-bound Pol II that associates with the CRMs through DNA looping , although we cannot rule out the possibility that Pol II is directly recruited by CRM-bound proteins , but cannot initiate transcription . Figure 2C shows clustered CRMs some 68 kb upstream of cut , in a region without genes , and which produces no mRNA . The surrounding region contains several enhancers that regulate the cut gene throughout development . The wing margin enhancer whose function is sensitive to Nipped-B dosage in vivo is 12 kb upstream of these putative CRMs , and several other tissue-specific enhancers are downstream [19] , [24] , [25] . The region with the CRMs contains enhancers critical for differentiation of multiple sensory cells . Gypsy transposon insulator insertions just upstream of the predicted CRMs cause primarily cut wing phenotypes , while insertions just downstream also cause head capsule defects , including deformed antenna [26] . Cohesin ( Rad21 ) depletion substantially reduces the level of elongating Pol II on the cut gene as measured by Ser2P Pol II ChIP ( Figure 2C ) , and the PRO-seq signals decrease some 40% in the gene body ( Table S2 ) . Cohesin depletion also modestly reduces the Ser2P Pol II ChIP signal in the region containing the predicted cut CRMs , lending support to the idea that this is a functional remote enhancer . By ChIP-chip , cohesin also influences association of Pol II with many of the other predicted extragenic CRMs around the genome . Figure 2D shows that Rpb3 and Ser2P Pol II occupancy decrease significantly on 15 to 25% of the predicted CRMs upon Rad21 depletion , consistent with the idea cohesin facilitates interactions of many CRMs with promoters . Stable topological binding of cohesin to chromosomes requires loading by kollerin . Thus , depletion of cohesin and kollerin would be expected to have comparable genome-wide effects on Pol II if topologically-bound cohesin is the form that influences transcription . We compared PRO-seq measurements in mock-treated control BG3 cells to cells in which the Rad21 cohesin subunit or the Nipped-B kollerin subunit were depleted by approximately 80% using RNAi . Under these depletion conditions , there are no measurable defects in sister chromatid cohesion or chromosome segregation , and a modest decrease in the rate of cell division , which may reflect decreased expression of the Drosophila myc ( dm ) gene that promotes cell growth [13] , [27] . These RNAi conditions reduce cohesin chromosome binding by at least 3 to 4-fold at all genes examined by ChIP , including genes that start with very high cohesin and show some of the largest changes in mRNA levels [12] . The effects of Rad21 and Nipped-B depletion on the PRO-seq signals in the promoter regions and gene bodies of the PRO-seq active genes are remarkably similar . The maximal changes included increases and decreases approaching 15-fold at promoters ( Figure 3A ) , and some greater than 16-fold in gene bodies for both cohesin-binding and non-binding genes ( Figure 3B ) . These results indicate that topologically-bound cohesin is the form that influences transcription . Cohesin and kollerin depletion also had very similar effects on the pause index , which measures the efficiency with which paused Pol II enters into elongation . Upon Rad21 or Nipped-B depletion , genes with high cohesin levels showed increased and decreased pausing at similar frequencies ( Figure 3C , 3D ) . Thus , depletion of cohesin or the loading factor have remarkably similar effects on regulatory steps of transcription . Overall , cohesin depletion did not substantially change the median pausing index at cohesin-binding genes , with similar numbers of genes showing increases and decreases ( Figure 3D , Figure S1E ) . This is consistent with the prior findings that cohesin increases expression of some genes and decreases expression of others [13] . One possibility is that in addition to facilitating enhancer-promoter interactions , cohesin might also facilitate interactions of silencers that inhibit transition of Pol II to elongation . Prior studies also show that cohesin blocks transition of paused Pol II to elongation at some genes [12] . Some of these , such as invected and engrailed , are simultaneously targeted by Polycomb silencing proteins , and increase dramatically in expression upon cohesin depletion . PRO-seq confirms that such genes are among those that show the largest pausing decreases ( Table S2 ) . The presence of repressor proteins may be one factor , therefore , that determines when cohesin inhibits transition to elongation . Unexpectedly , cohesin depletion indirectly reduced pausing at most genes that lack cohesin , with a median decrease of 25% ( genes in lowest cohesin group in Figure 3D ) . In control cells , the median pause index at the genes with the highest cohesin levels is 3 . 7-fold higher than at the genes without cohesin ( Figure 1F ) . However , cohesin depletion increases this ratio to 8 . 7 , primarily because of the broad decrease in pausing at genes that lack cohesin . The overall reduction in pausing might suggest that pausing factors are diminished , but the mRNA levels for all NELF and DSIF subunits are virtually unaffected by cohesin or Nipped-B depletion [13] . Both cohesin-binding and non-binding genes show frequent decreases in promoter PRO-seq density , but these decreases are substantially more frequent at genes that lack cohesin , which likely explains why they also show more frequent decreases in the pause index ( Figure 4A ) . If this indirect general pausing decrease caused by cohesin depletion also occurs at cohesin-binding genes , then it will counteract and obscure many direct increases in pausing caused by cohesin depletion . If so , it can be inferred that cohesin directly facilitates transition to elongation even more frequently than the raw data indicates . By facilitating enhancer-promoter contact , cohesin could increase the rates of distinct steps of transcription: Pol II recruitment , transcription initiation , or the transition of paused Pol II to elongation . In addition , cohesin bound at the promoters of cohesin-binding genes could directly influence all three steps . The finding that cohesin depletion reduces promoter PRO-seq density less frequently at cohesin-binding genes than at genes that lack cohesin ( Figure 4A ) argues that recruitment or initiation are less often directly influenced by cohesin . Strikingly , although PRO-seq density is more frequently decreased at the promoters of genes that lack cohesin , there is little difference in the overall effect of cohesin depletion on total Pol II occupancy at cohesin-binding and non-binding promoters as measured by Rpb3 ChIP , further supporting the idea that Pol II recruitment is not usually directly affected by cohesin ( Figure 4A ) . This predicts that genes that lack cohesin would not show as dramatic pausing decrease upon cohesin depletion if pausing was calculated using Rpb3 ChIP instead of PRO-seq data , which was confirmed ( Figure S1E ) . Because the average decrease in PRO-seq at the promoters that lack cohesin is greater than that at cohesin-binding promoters , but the average change in total Pol II occupancy is similar , we deduce that transcription initiation is frequently reduced at genes that lack cohesin . The more frequent increase in transcriptional pausing at cohesin-binding genes relative to genes that lack cohesin in response to cohesin depletion predicts that cohesin more often directly facilitates transition of paused polymerase to elongation at many genes . To confirm this idea , we compared the frequency of absolute changes in Pol II occupancy of promoters and gene bodies caused by cohesin depletion using the genomic ChIP data for Rpb3 and Ser2P Pol II . We set a statistical threshold for increases or decreases ( see Materials and Methods ) to determine how many promoters and gene bodies show significant changes upon cohesin depletion . This revealed that total or Ser2P Pol II occupancy rarely increased at the promoters or in the bodies of either cohesin-binding or non-binding genes upon cohesin depletion ( Figure 4B ) . Decreases in Pol II at the promoters were also rare , but more frequent than increases . Cohesin depletion caused significant absolute decreases in Rpb3 and Ser2P Pol II in the bodies of more than half of the cohesin-binding genes , almost twice as often as in genes that lack cohesin ( Figure 4B ) . We conclude , therefore , that cohesin often directly increases transition of paused Pol II to elongation , and less frequently directly influences Pol II recruitment or transcriptional initiation . Although infrequent , absolute reductions in total Pol II promoter occupancy after cohesin depletion that met the statistical threshold were detected twice as often at cohesin-binding genes than at genes that lack cohesin ( Figure 4B ) . This is still consistent with the finding that the average fold-changes in total Pol II promoter occupancy at cohesin-binding and non-binding genes are similar ( Figure 4A ) , because cohesin-binding genes have higher levels of Pol II at the promoter ( Figure 1B ) . The same absolute change in Pol II occupancy would therefore be a smaller fold-change at most cohesin-binding genes than at most genes that lack cohesin . We suspect that the reduced pausing that reflects reduced transcription initiation at most genes that lack cohesin is caused by altered expression of factors that act broadly at many or all genes , such as basal transcription factors . Cohesin depletion , however , does not significantly reduce expression of known basal factors such as TFIIB [13] . Prior work has shown that cohesin directly promotes dm/myc expression , and the global pattern of decreases in mRNA upon depletion of cohesin in BG3 cells strongly overlaps those seen in dm/myc mutants [13] , [27] , [28] . Thus another possibility , consistent with the recent reports that Myc directly amplifies transcription of most if not all active genes in a variety of mammalian cell types [29] , [30] , is that reduced dm/myc expression could contribute to the broad indirect effect of cohesin depletion on most genes that lack cohesin . The P-TEFb Pol II kinase , which can be recruited by transcriptional activator proteins bound to enhancers or promoters , stimulates transition of paused Pol II to elongation by phosphorylating NELF , DSIF , and the C-terminal domain of the large subunit of Pol II ( reviewed in [31] ) . Cdk12 is also responsible for a large fraction of Ser2P Pol II phosphorylation [32] . We tested the idea that cohesin promotes transition of paused Pol II to elongation by facilitating recruitment of P-TEFb or Cdk12 by comparing the CycT and Cdk12 ChIP signals in control cells and cells in which cohesin was depleted . We restricted the analysis to those genes in which CycT or Cdk12 was detected in the control cells , to make it possible to detect both decreases and increases . Surprisingly , after cohesin depletion , decreases in CycT or Cdk12 in any transcription units are very rare ( Figure 4C ) . Indeed , CycT and Cdk12 both increase more frequently at promoters and gene bodies than they decrease upon cohesin depletion , and more than twice as often in the bodies of cohesin-binding than in non-binding genes ( Figure 4C ) . Similar frequencies of CycT and Cdk12 increases are seen when all active genes are scored , indicating that increases also occur when the kinases are not detected prior to cohesin depletion . These increases are generally modest , but usually occur in genes with Ser2P Pol II decreases , and are strong enough to give up to a 1 . 5-fold increase in ratios of the kinases to total Pol II in the bodies of cohesin-binding genes ( Figure S3 ) . Because there are several heptapeptide repeats in Pol II , a decrease in the fraction of heptapeptide repeats that are phosphorylated within each Rpb1 molecule could increase the net number of unmodified sites available for kinase binding , even with a decrease in the level of Pol II in the gene body . Based on these findings we conclude that the frequent reduction in phosphorylated Pol II in gene bodies upon cohesin depletion is not caused by reduced presence of the Pol II kinases , and theorize instead that cohesin may facilitate efficient modification of Pol II . The higher Pol II occupancy of cohesin-binding genes predicts that they should produce more mRNA on average , assuming that RNA processing , transport and stability do not differ substantially between cohesin-binding and non-binding genes . To test this idea , we used existing mRNA measurements [13] to calculate the ratio of steady-state mRNA to PRO-seq density in the gene body , which we define as “efficiency” . This surprisingly revealed that the average efficiency increases significantly with the cohesin level , and that the genes with the highest cohesin levels produce some 2-fold more steady-state mRNA per transcribing Pol II complex than genes that lack cohesin ( Figure 5A ) . Cohesin is not responsible for the higher efficiency . Upon Nipped-B or Rad21 depletion , the average efficiency of the genes with the highest cohesin levels actually increases modestly ( Figure 5B , 5C ) . We currently do not know why cohesin-binding genes are more efficient , but note that they are highly transcribed , lack histone H3 lysine 36 trimethylation ( H3K36me3 ) , and are highly enriched for UG repeats in the nascent transcripts [12] . H3K36me3 and UG repeats regulate RNA processing , and binding of the TDP-43 protein to UG repeats stabilizes long nascent transcripts and reduces missplicing in mammalian neural tissue [33]–[36] .
These studies provide compelling evidence that cohesin directly influences transcription . Comparing the effects of cohesin depletion on Pol II occupancy and activity shows that on average , cohesin-binding genes respond differently to cohesin depletion than non-binding genes , allowing us to infer that cohesin directly influences Pol II occupancy and activity at genes that it binds . This direct influence is likely mediated by facilitating looping interactions with enhancers , and also direct effects on the transition of paused Pol II to elongation at the promoter ( Figure 6 ) . Beyond the generally higher levels of Pol II , the most remarkable differences between cohesin-binding and non-binding genes are in promoter-proximal transcriptional pausing . Cohesin-binding genes have a substantially higher average pausing index , and are much more likely than non-binding genes to show increased pausing upon cohesin depletion . Coupled with the decreases in Ser2P Pol II in the bodies of most cohesin-binding genes , the increased pausing upon cohesin depletion argues that cohesin facilitates the transition of paused polymerase to elongation at many genes that it binds . Cohesin can increase the rate of Pol II transition to elongation by facilitating enhancer-promoter looping , which would bring transcriptional activators and the P-TEFb they recruit into contact with the paused Pol II to stimulate transition to elongation ( Figure 6 ) . Indeed , genetic evidence from Drosophila and chromosome conformation capture ( 3C ) data from mammalian cells supports the idea that cohesin facilitates communication and looping between enhancers and promoters [6] , [7] , [19] , [20] . In mammals , cohesin is present at the extragenic enhancers for several mammalian pluripotency genes , the β-globin gene and the T cell receptor locus , and at many CRMs defined by the binding of multiple tissue-specific transcription factors [6] , [7] , [20] , [37] . In Drosophila BG3 cells , cohesin occupies essentially all CRMs , and the reduced Pol II occupancy at many upon cohesin depletion further expands the idea that enhancer-promoter communication is one of cohesin's key roles at several genes . Several studies indicate that cohesin also facilitates looping between sites binding the CTCF protein in mammalian cells to regulate gene expression , but this function is not conserved in Drosophila ( reviewed in [1] , [2] ) . Many studies support a role for enhancers in the assembly of pre-initiation complexes at promoters , but also indicate that they can control other steps , including the transition of Pol II at the promoter to elongation [21] . The steps in activation controlled by a particular enhancer likely depend on the constellation of enhancer-bound transcription factors . If an enhancer's main function is pre-initiation complex formation , then we would expect to see frequent Pol II decreases at promoters upon cohesin depletion . Such decreases , however , are actually infrequent compared to gene body decreases in our experiments . Our data suggest , therefore , that once a gene is active , the primary function of most enhancers is to stimulate paused Pol II to enter elongation . The analysis presented here cannot definitively address to what extent reduced enhancer-promoter communication explains Pol II decreases in the bodies of cohesin-binding genes caused by cohesin depletion . A critical limitation is that we do not yet know all the contacts between enhancers and promoters , and whether such contacts are cohesin-dependent . We note , however , that the high levels of cohesin at promoters , including at many genes that likely lack enhancers , raises the possibility that cohesin directly interacts with the paused Pol II complex and influences the transition to elongation . These interactions may involve increasing the efficiency with which P-TEFb and Cdk12 modify Pol II or the NELF and DSIF pausing complexes ( Figure 6 ) . We suggest that cohesin is more critical for kinase efficiency than for kinase recruitment because at most genes where cohesin depletion reduces Pol II phosphorylation , the kinase level in the gene body actually increases . Also consistent with the idea that promoter-bound cohesin directly influences transition to elongation is the finding that cohesin interacts with the Mediator complex [7] . In addition to facilitating assembly of the pre-initiation complex , Mediator is implicated in recruitment of elongation factors and efficient transcriptional elongation post-initiation [38]–[40] . The idea that promoter-bound cohesin directly influences transition of Pol II to elongation is also supported by prior work showing that cohesin inhibits transition to elongation at several cohesin-repressed genes [12] . In those studies , cohesin and pausing factor depletion experiments revealed that cohesin inhibits transition of Pol II to elongation at a step distinct and likely downstream from those controlled by the NELF and DSIF pausing factors . This inhibition is unlikely to be physical obstruction of Pol II movement because cohesin depletion did not increase the rate of elongation along the induced EcR gene . Moreover , many of the cohesin-repressed genes are among the rare cohesin-binding genes targeted by the PRC2 Polycomb group silencing complex . Thus the presence of repressor proteins may be one factor that determines whether promoter-bound cohesin facilitates or inhibits transition to elongation . Many cohesin-repressed genes are those that show the largest increases in mRNA upon cohesin depletion [13] , and more Pol II in the gene bodies in this study . In general , these cohesin-repressed genes show little or no change in Pol II occupancy at the promoter upon cohesin depletion , further supporting the idea that repression largely reflects inhibition of entry into elongation and not Pol II recruitment [12 , this study] . We unexpectedly observed that cohesin depletion reduces promoter-proximal Pol II pausing at most genes that don't bind cohesin . Cohesin depletion does not alter expression of genes encoding subunits of the NELF and DSIF pausing factors or the Pol II kinases , and very modestly increases expression of some Mediator subunit genes [13] . The reduction in transcriptionally-engaged Pol II at the promoter measured by PRO-seq is also more significant than the effect on total Pol II occupancy at genes that lack cohesin . We theorize , therefore , that cohesin controls expression of factors that operate broadly to facilitate transcription initiation . The key suspects for general factors controlled by cohesin are general basal transcription factors , or possibly Diminutive ( Dm ) , the Drosophila Myc protein ( Figure 6 ) . Cohesin depletion does not significantly decrease the mRNAs that encode the known basal transcription factors , but does substantially reduce dm/myc transcription . Recent studies in mammalian cells show that Myc directly amplifies transcription of most active genes [29] , [30] and therefore reduction of dm/myc expression upon cohesin depletion is expected to alter transcription of many genes , including those that do not bind cohesin . The mammalian studies also indicate , however , that chemical ablation of Myc function increases pausing at Myc target genes [29] , [30] , [41] , while our PRO-seq measurements argue that pausing generally decreases upon cohesin depletion . The mammalian experiments measured pausing by Pol II ChIP , which does not distinguish between promoter-bound Pol II that is transcriptionally-engaged from Pol II that has not initiated transcription , or is somehow otherwise blocked from elongation . In our experiments , Pol II ChIP did not show the same pausing decrease as PRO-seq upon cohesin depletion . Thus , although Myc appears to function as an anti-pausing factor , we cannot rule out the possibility that reduced dm/myc expression is responsible for many of the indirect effects of cohesin depletion on transcription initiation . Direct positive regulation of myc by cohesin occurs in Drosophila , zebrafish , mice and humans [8] , [13] , [27] , [42] . As a key regulator of growth and protein synthesis , it is likely that reduced myc expression contributes to the poor growth of individuals with Cornelia de Lange syndrome and Nipbl ( +/− ) mice [42] , [43] . Based on their higher Pol II occupancy , we expected that cohesin-binding genes would produce more mRNA on average , in proportion to the Pol Il levels . We observed , however , that they produced disproportionately more steady-state mRNA per transcriptionally-engaged Pol II complex , with the genes that have high cohesin levels being twice as efficient as the genes that lack cohesin . Cohesin depletion did not reduce the efficiency , indicating that these genes have other features that make them more efficient . Prior studies show that cohesin-binding genes lack the H3K36me3 histone modification , which is found on other active genes , and is mediated by the Set2 protein that travels with the phosphorylated C terminal domain of the Rpb1 Pol II subunit [44] . H3K36me3 influences RNA processing and vice versa [34] , [35] . We currently favor the idea , therefore , that co-transcriptional RNA processing , which also affects RNA transport and stability , is more efficient at cohesin-binding genes . Alternatively , elongation rates , which can be influenced by the higher Pol II density at these genes , may be higher . Cohesin-binding genes are also highly enriched for TG repeats in transcribed plus-strand non-coding sequences 50 to 800 bp downstream of the promoter , and thus the nascent RNAs contain UG repeats [12] . One factor that binds UG repeats is TDP-43 ( TBPH in Drosophila ) , which influences RNA processing , and increases the stability of many long nascent RNAs and splicing fidelity in mouse brain [33] , [36] . It is possible that these repeats also participate in cohesin recruitment , which could explain the correlation between cohesin-binding and high efficiency .
Culture of ML-DmBG3-c2 ( BG3 ) cells and RNAi depletion of Nipped-B and Rad21 were conducted as previously described [13] . Genomic chromatin immunoprecipitation of RNAi-treated and mock-treated BG3 cells was performed using Affymetrix Drosophila 2 . 0R genome tiling arrays as previously described [9] except chromatin sonication was performed under standardized conditions with a Diagenode Bioruptor , and precipitated DNA was amplified using commercial Whole Genome Amplification reagents ( Sigma-Aldrich ) . Reverse-crosslinked chromatin was used to prepare probes for input control arrays . All ChIP-chip data generated for this study is the average of two independent biological replicates . Karen Adelman ( NIEHS ) provided Rpb3 antibodies , Akira Nakamura ( Riken , Japan ) provided CycT antibodies , and Bart Bartkowiak and Arno Greenleaf ( Duke ) provided Cdk12 antibodies . Ser2P Pol II antibodies were purchased from Abcam ( ab5095 ) . The Drosophila Rpb3 antibody has been previously been validated for ChIP-chip [18] . The Ser2P Pol II antibody was previously validated for specificity in Drosophila by in vivo inactivation of P-TEFb by the Pgc protein followed by immunostaining and western blots [45] . We retested the Ser2P Pol II antibody by treating BG3 cells with flavopiridol to inhibit P-TEFb followed by western blotting and observed that the major band decreases in intensity over time , although there is an unaffected minor band that co-migrates with the unmodified Rpb1 detected by the 8WG16 antibody ( Figure S4 ) . The Cdk12 antibody has previously been validated for ChIP [32] . The Drosophila CycT antibody was previously validated [45] and in our tests , it recognizes a single major protein of the expected size in western blots of whole cell extracts that is reduced by CycT RNAi treatment ( Figure S5 ) . MAT software [46] was used to calculate ChIP enrichment across the Drosophila genome . MAT performs within-array normalization using individual probe DNA sequences , and MAT scores measure enrichment relative to an input control . MAT scores scale linearly with log2 IP/control enrichment values as determined by processing the same data with TiMAT ( http://bdtnp . lbl . gov/TiMAT/ ) . MAT is the optimal algorithm for analysis of Affymetrix array ChIP-chip , and provides peak detection sensitivity equivalent to ChIP-seq performed at a density of one read per genome base pair [47] , [48] . ChIP-chip data has been deposited in the GEO database ( accession no . GSE42399 ) . Precision global run-on sequencing for control cells , and cells depleted for Rad21 and Nipped-B , was conducted as described elsewhere [15] , except that a simplified cell permeabilization nuclear isolation protocol was used . All steps were conducted at 4° unless indicated otherwise . 2 . 5 to 7 . 5×108 control or RNAi-treated BG3 cells were collected by centrifugation ( 1000 g for 5 min ) , suspended in 5 to 10 mL Phosphate Buffered Saline ( PBS ) pH 7 . 0 , collected by centrifugation , suspended in 5 mL Buffer W [10 mM Tris-HCl pH 7 . 5 , 10 mM KCl , 150 mM sucrose 5 mM MgCl2 , 0 . 5 mM CaCl2 , 0 . 5 mM dithiothreitol ( DTT ) ] , and collected by centrifugation . The cells were suspended in 5 mL Buffer P ( 10 mM Tris-HCl , pH 7 . 5 , 10 mM KCl , 250 mM sucrose , 5 mM MgCl2 , 1 mM EGTA , 0 . 05% Tween-20 , 0 . 5 mM DTT ) , the suspension was adjusted to 0 . 14% NP-40 , and then incubated on ice for 3 min . The nuclei were washed twice in 5 mL Buffer W , suspended in 1 mL Buffer W , and transferred to a siliconized 1 . 5 mL microcentrifuge tube . The nuclei were collected by centrifugation at 1000 g for 5 min , suspended in 0 . 5 mL Buffer F ( 50 mM Tris-HCL pH 8 . 3 , 40% glycerol , 5 mM MgCl2 , 0 . 1 mM EDTA , 0 . 5 mM DTT ) , and counted using a hemacytometer . The nuclei were then suspended in Buffer F to concentration of 40 to 50×105 per microliter , distributed into 100 microliter aliquots in siliconized 1 . 5 mL tubes , snap frozen in liquid nitrogen , and stored at −80° . The PRO-seq data has been deposited in the GEO database ( accession no . GSE42399 ) . PRO-seq reads for each duplicate sample were summed over the promoter regions and gene bodies of nearly 17 , 000 annotated transcription units and normalized to the total reads for each sample . Mathematical and statistical analysis of the samples was conducted using Microsoft Excel , R ( [49] , http://www . R-project . org ) , and custom programs . After confirming high correlations between the duplicate samples ( Table S1 ) , the values for the two duplicates for each condition ( Mock , Rad21 depleted , Nipped-B depleted ) were averaged ( Table S2 ) . PRO-seq active genes were defined as those in which there were an average of at least 1 read per million in both the 200 bp promoter region and the gene body in control samples . PRO-seq changes in the promoter regions , gene bodies , and pausing index upon cohesin depletion were calculated and plotted using R . To rank genes according to cohesin-binding levels , the Rad21 ChIP-chip MAT scores over the promoter regions of PRO-seq active genes were integrated , and the genes broken into four categories ranging from low to high mean cohesin levels , using a geometric distribution ( Figure 1A , lower right panel ) . The lowest group had mean ChIP MAT scores between 0 to 1 in the 400 bp region surrounding the transcription start site , the next highest group had mean scores between 1 to 2 , then 2 to 4 and the highest group was greater than 4 . This method allowed finer distinction between cohesin-binding levels than quartiles . To measure the fraction of PRO-seq active genes or putative CRMs that bind or do not bind cohesin ( Rad21 , Smc1 , Nipped-B ) , bed files showing binding of Rpb3 , Ser2P Pol II and CycT at p≤10−3 were generated using MAT software . Binding of Rpb3 , Ser2P Pol II and CycT to PRO-seq active genes was determined using BEDTools software [50] to detect overlaps of the bed files with 200 bp promoter regions and gene bodies of PRO-seq active transcription units , and putative active enhancers , with a 1 bp minimum overlap . Existing Smc1 and Nipped-B ChIP-chip data for BG3 cells ( [9] , GEO accession no . GSE9248 ) was used to determine which genes and putative enhancers bind cohesin . For some analyses , the differences in the ChIP enrichment ( MAT scores ) for Pol II or Pol II kinases were calculated at each of the nearly 2 . 8 million points measured across the genome . The distributions of the differences , means , medians and standard deviations of the differences were determined using R . In all cases , there was minimal skew in the distribution of differences , and both the mean and median differences were nearly identical and close to zero . The thresholding tool of the Affymetrix Integrated Genome Browser ( IGB; http://www . affymetrix . com/partners_programs/programs/developer/tools/download_igb . affx ) was used to generate bed files indicating where the Rad21 RNAi sample enrichment differs from the enrichment in control cells by at least two standard deviations from the median genome-wide difference over at least 105 bp ( three microarray features , example in Figure 2 ) . BEDTools was used to detect overlaps between these intervals and the 200 bp promoter regions or gene bodies of the PRO-seq active genes , or predicted extragenic CRMs to identify those with significant changes . The rare instances in which a feature scored positive for both a decrease and an increase in ChIP signal were resolved by visual inspection . In most cases these reflect both a small increase and a small decrease , and the genes were rescored as having no significant change . This method agrees with changes in Pol II occupancy after Rad21 depletion previously measured at multiple genes by quantitative real-time PCR ChIP in independent experiments [12] . To measure mRNA production efficiency we used expression data for 13 , 132 genes in BG3 cells previously measured by Affymetrix Drosophila GeneChip 2 . 0 for mRNA levels in control and Rad21 depleted BG3 cells ( [13] , GEO accession no . GSE16152 ) . For those genes represented by multiple probes , we summed the total signals for all probes , and used the total to compare to the gene body PRO-seq signals . | The cohesin protein complex binds to chromosomes and helps ensure that chromosomes are divided equally into the daughter cells when a cell divides . Cohesin also affects how genes are expressed . Small changes in cohesin alter gene expression and development , causing Cornelia de Lange syndrome , a genetic disease . Cohesin influences the amount of RNA produced by many genes , but the reasons are poorly understood . We investigated this question by measuring how changes in cohesin levels affect the level of RNA polymerase , the enzyme that transcribes genes to make RNA , at all genes in Drosophila cells . We find that genes that bind cohesin have higher average levels of RNA polymerase and produce more final processed RNA per RNA polymerase than genes that don't bind cohesin . We also find that cohesin binds nearly all DNA sequences located outside of genes that are predicted to regulate gene expression . Reducing cohesin affects RNA polymerase levels at many genes and the predicted regulatory sequences , indicating that cohesin facilitates communication between regulatory sequences and genes . Our data also show that cohesin affects transcription of most genes that don't bind cohesin , likely by controlling transcription of broadly acting transcription factors that regulate many genes . | [
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] | 2013 | Genome-Wide Control of RNA Polymerase II Activity by Cohesin |
Hereditary spastic paraplegia ( HSP ) is characterized by a dying back degeneration of corticospinal axons which leads to progressive weakness and spasticity of the legs . SPG11 is the most common autosomal-recessive form of HSPs and is caused by mutations in SPG11 . A recent in vitro study suggested that Spatacsin , the respective gene product , is needed for the recycling of lysosomes from autolysosomes , a process known as autophagic lysosome reformation . The relevance of this observation for hereditary spastic paraplegia , however , has remained unclear . Here , we report that disruption of Spatacsin in mice indeed causes hereditary spastic paraplegia-like phenotypes with loss of cortical neurons and Purkinje cells . Degenerating neurons accumulate autofluorescent material , which stains for the lysosomal protein Lamp1 and for p62 , a marker of substrate destined to be degraded by autophagy , and hence appears to be related to autolysosomes . Supporting a more generalized defect of autophagy , levels of lipidated LC3 are increased in Spatacsin knockout mouse embryonic fibrobasts ( MEFs ) . Though distinct parameters of lysosomal function like processing of cathepsin D and lysosomal pH are preserved , lysosome numbers are reduced in knockout MEFs and the recovery of lysosomes during sustained starvation impaired consistent with a defect of autophagic lysosome reformation . Because lysosomes are reduced in cortical neurons and Purkinje cells in vivo , we propose that the decreased number of lysosomes available for fusion with autophagosomes impairs autolysosomal clearance , results in the accumulation of undegraded material and finally causes death of particularly sensitive neurons like cortical motoneurons and Purkinje cells in knockout mice .
Hereditary spastic paraplegias ( HSPs ) are a group of movement disorders characterized by length-dependent degeneration of upper motoneuron axons resulting in leg weakness and spasticity [1] . More than 70 genetically distinct forms ( SPG1-SPG72 ) are currently recognized [2] . SPG11 represents a complicated form of HSP with cognitive decline , thinning of the corpus callosum , white matter lesions and cerebellar signs among other symptoms very similar to SPG15 [3] . While SPG11 is caused by SPG11 mutations [4] , mutations in SPG15/ZFYVE26 underlie SPG15 [5] . Suggesting that SPG11 and SPG15 are pathophysiologically linked , the protein products of both SPG11 and SPG15 , Spatacsin and Spastizin respectively , associate with the adaptor protein complex 5 ( AP-5 ) , which belongs to a group of tetrameric protein complexes involved in vesicular transport [6–8] . Interestingly , mutations in AP5Z1 encoding the ζ-subunit of AP-5 underlie SPG48 [6] , which shares several clinical features with SPG11 and SPG15 [3] . The subcellular localization of the proteins and their suggested respective functions are quite controversial . DNA repair [6] , cell division [9] , autophagy [10] , axon outgrowth [11] , and endolysosomal trafficking have been proposed [12 , 13] . The latter was suggested because knockdown of individual AP-5 subunits in HeLa cells caused the cation-independent mannose 6-phosphate receptor to become trapped in clusters of early endosomes [12] . Also pointing to this direction degenerating neurons in a recent Spastizin knockout mouse model accumulated autofluorescent material in Lamp1-positive vesicular structures [13] and fibroblasts from both SPG11 and SPG15 patients displayed an enlarged Lamp1-positive compartment [14] . Because autophagosome numbers were increased in fibroblasts of SPG15 patients and in knockdown studies with primary mouse neurons , it was further proposed that the fusion of autophagosomes with lysosomes is impaired [10] . This concept has recently been challenged by in vitro studies on HeLa cells showing that Spatacsin and Spastizin are essential for the regeneration of lysosomes from autolysosomes , a process known as autophagic lysosome reformation ( ALR ) [15] , which has so far only been observed in vitro [16] . According to this model impaired ALR is expected to lead to exhaustion of lysosomes available for fusion of autophagosomes and accumulation of autolysosomes . We here provide data that neurons in Spatacsin knockout mice accumulate abnormal autolysosomes and autolysosome-related autofluorescent material . Autolysosomes also accumulate in knockout mouse embryonic fibroblasts ( MEFs ) , while their lysosome numbers are decreased . Upon starvation lysosomes are depleted in MEFs of both genotypes , but only recover in wild-type during prolonged starvation in accordance with a defect of the regeneration of lysosomes from autolysosomes . Consistently , lysosomes are reduced in knockout Purkinje cells and cortical motoneurons , even before accumulation of autofluorescent material and overt neurodegeneration . The loss of particularly susceptible neurons like cortical motoneurons and Purkinje cells finally causes the complex neurological phenotype of Spatacsin knockout mice .
To model SPG11 in vivo , we injected cells of the ES cell clone EUCE0085_F05 from the European conditional mouse mutagenesis program ( EUCOMM ) into donor blastocysts . The resulting chimeric mice were mated with C57Bl6 wild-type ( WT ) mice to obtain mice with a heterozygous trapped locus . Because the gene-trap cassette is integrated into intron 1 of Spg11 ( Fig 1A ) , the targeted locus is predicted to encode a cytoplasmic fusion protein of the 82 N-terminal amino acids of Spatacsin with βgeo under the control of the endogenous Spg11 promoter , while the following part of Spatacsin is lost . The β-galactosidase activity of βgeo allowed us to assess the expression of Spg11 by LacZ staining of tissue sections of heterozygous trapped mice , which supports a broad expression pattern including cortex and hippocampus ( Fig 1B–1E ) , cerebellum ( Fig 1F ) , neurons of the brain stem ( Fig 1G ) , and the spinal cord ( Fig 1H ) . To get more information on the expression of Spg11 in different types of neurons we co-stained tissue sections for β-galactosidase and various marker proteins including NeuN , a broad neuronal marker , Ctip2 , which preferentially labels layer V neurons [17] , and parvalbumin , which is expressed in a subset of interneurons . From these co-stainings it appears that Spg11 is broadly expressed in different types of neurons including principal cells and inhibitory neurons ( S1 Fig ) . From matings of heterozygous trapped mice we obtained homozygous targeted offspring in the expected Mendelian ratio . While Northern analysis with a probe corresponding to part of exon 30 of Spg11 detected transcripts of the expected size in RNA isolated from WT brains , the transcript was absent in RNA isolated from homozygous trapped mice ( Fig 1I ) . To detect the Spatacsin protein we generated monoclonal antibodies directed against an epitope within the α-solenoid domain of Spatacsin ( Fig 1A ) and affinity-purified the resulting antiserum . Confirming its specificity , the antibody detected a polypeptide of the predicted size of Spatacsin of 273 kDa in brain lysates from WT mice , which was absent from protein lysates isolated from brains of homozygous trapped mice ( Fig 1J ) . Though the antibody was suited for Western blot analysis , it did not detect endogenous Spatacsin in immunostainings . Because it was shown that Spatacsin co-precipitates with Zfyve26 and subunits of the AP-5 complex [6] , we assessed whether Zfyve26 levels are changed in brain lysates of homozygous trapped mice . Indeed , Zfyve26 levels were reduced . In contrast levels of the β-subunit of the AP-5 complex ( Ap5b1 ) were not changed ( Fig 1K , though it has been reported that siRNA mediated knockdown of Spatacsin in HeLa cells caused a decrease of levels for the μ5-subunit of the AP-5 complex [12] . Decreased levels AP5B1 were also reported for fibroblasts isolated from SPG11 patients [15] . Our results are consistent with the assumption that the trapped Spg11 locus corresponds to a null allele and that mice homozygous for the trapped allele represent Spatacsin knockout ( KO ) mice . Further on , homozygous trapped mice are therefore also referred to as Spatacsin KO mice . Spatacsin KO mice younger than 12 months of age did not show any obvious motor phenotype compared to WT littermates . Subsequently , KO mice developed a progressive gait disorder . For quantification of the motor phenotype we measured the foot-base-angle ( FBA ) at toe-off positions of the hind-paws , which decreased with age in KO mice ( Fig 2A–2C ) . Moreover , the latency of KO mice to fall off an accelerating rotating rod decreased in aged mice ( Fig 2D ) . Further suggesting a motor coordination defect the number of falls in the beam walking test was significantly increased at around 13 months of age ( Fig 2E ) . Around this age the body weight of KO mice decreased , consistent with a deterioration of the overall health status of KO mice ( Fig 2F ) . Thus , Spatacsin KO mice show a progressive worsening of motor performance compatible with complex HSP . Suggesting a systemic neurodegenerative disorder the brain size ( Fig 3A and 3B ) and weight ( Fig 3C ) did not differ around 2 months of age but was reduced in 16-month-old KO mice . Quantification of NeuN-positive neurons of the motor cortex revealed a loss of large projection neurons in cortical layers V to VI at 16 but not at 8 months of age ( Fig 3D–3F ) . This was further confirmed by staining with Ctip2 ( Fig 3D and 3E ) . Neuron numbers in layers I-III , where most of the commissural neurons reside [18] , were unchanged , which is consistent with the intact corpus callosum in aged Spatacsin KO mice ( Fig 3G and 3H ) . Large diameter axon fibers were reduced by roughly 50% at 8 and by roughly 75% at 16 months of age in the lumbar corticospinal tract ( L4 , Fig 3I–3K ) . In the cervical corticospinal tract it was reduced by 56% at 16 months of age ( n = 3; Student’s t-test: p<0 . 001 ) . In contrast to previous results from zebra fish [19] the overall structure of motor-endplates was not altered in KO mice ( S2 Fig ) . Purkinje cells were drastically reduced ( Fig 3L–3N ) , while numbers of pyramidal cells in the hippocampus and spinal cord motoneurons were not changed in 16-month-old KO mice ( S3 Fig ) . Neuron loss in the cortex ( Fig 4A and 4B ) and the cerebellum ( Fig 4E and 4F ) was preceded by intraneuronal accumulation of autofluorescent material ( emission wavelength 460–630 nm ) and paralleled by the activation of astrocytes as evident from GFAP stainings ( Fig 4C , 4D , 4G and 4H ) . Cells accumulating autofluorescence co-labeled with β-galactosidase , Ctip2 , SatB2 , parvalbumin or calbindin suggesting that principal cells as well as inhibitory interneurons are affected ( S4 Fig ) . Though autofluorescent material also accumulated in other regions of the central nervous system like hippocampus ( Fig 4I and 4J ) , different nuclei in the brain stem including the vestibular nuclei and the inferior olivary nucleus ( Fig 4M–4P ) , and spinal cord neurons ( Fig 4U and 4V ) , there was no evidence for astrocyte activation in these regions ( Fig 4K , 4L , 4Q–4T , 4W and 4X ) . To characterize the intraneuronal autofluorescent material in more detail , we stained brain sections for different subcellular marker proteins . Different from WT ( Fig 5A–5A” ) , autofluorescent spots in Purkinje cells of KO mice were large , often clustered and were surrounded by membranes positive for the lysosomal marker protein Lamp1 ( Fig 5B–5B” ) . As the contents of these vesicular structures stained for p62 ( Fig 5D–5D” ) , a receptor for cargo destined to be degraded by autophagy , the autofluorescent deposits likely represent undegraded autolysosomal material . Consistent with a defect of autolysosomal clearance , the number of autolysosomes , defined as vesicles positive for both Lamp1 and p62 ( Fig 6A , 6G and 6K ) , and LC3-II levels ( Fig 6B and 6B’ ) were increased in KO MEFs compared to WT . LC3-II levels further increased upon treatment with bafilomycin A1 , which inhibits autolysosome acidification and hence autolysosomal degradation ( Fig 6B and 6B’ ) . Western analysis of Lamp1 levels did not reveal an alteration of overall Lamp1 levels in KO MEFs ( Fig 6C and 6C’ ) . Lysosomal pH as an important determinant for the activity of lysosomal proteases also did not differ between genotypes ( Fig 6D ) and the ratio between the mature and the precursor forms of the lysosomal protease cathepsin D was unchanged ( Fig 6E and 6E’ ) . The number of lysosomes , however , as defined as Lamp1-positive vesicular structures that did not co-stain for p62 was reduced in KO MEFs ( Fig 6F ) . Though lysosomes were depleted upon induction of autophagy by starvation for 6 h in both WT and KO MEFs , only in WT lysosome numbers recovered to baseline after 14 h of ongoing starvation while they remained diminished in KO lysosomes ( Fig 6G–6N and S5 Fig ) . We asked whether the results obtained in MEFs also apply to neurons . As in cultured MEFs , overall Lamp1 levels were unchanged ( Fig 7A and 7A’ ) and the processing of cathepsin D was intact in KO samples as judged from Western blot analysis of brain lysates ( Fig 7B and 7B’ ) . Similar results were obtained for region specific lysates of cortex , hippocampus , cerebellum , and spinal cord ( S6 Fig ) . As a correlate of impaired autolysosomal degradation the ultrastructural analysis of Purkinje cells revealed membrane-bound vesicles filled with heterogeneous material including organelle-like structures in Spatacsin KO but not in WT mice ( Fig 7C and 7D ) . In KO samples we further observed an accumulation of electron-dense deposits of irregular shape reminiscent of lipofuscin interspersed between abnormal autolysosomes , while only some typical lipofuscin particles were found in controls of the same age ( Fig 7C and 7D ) . Similar membranous bodies and lipofuscin-like material were also found in cortical , hippocampal , and spinal cord neurons of aged KO mice ( S7 Fig ) . Levels of p62 were strongly elevated in the Triton X-100 insoluble fractions of KO whole brain lysates as well as in lysates of selected brain regions ( S6 Fig ) , whereas levels of Beclin-1 , one of the key proteins for the initial steps of autophagosome formation [20] , were not changed ( Fig 7E and 7E’ ) . We next quantified Lamp1-positive and p62-negative vesicles in Purkinje cell somata of brain sections from 2-month-old and 11-month-old mice , respectively ( Fig 7F–7H ) . Strikingly , the number of lysosomes was already decreased in Purkinje cells of Spatacsin KO mice at 2 months of age before we observed autofluorescent deposits or any signs of Purkinje cell degeneration . This alteration was preserved at 11 months of age . Similar results were also obtained for cortical motoneurons ( S8 Fig ) .
To study the pathophysiology of SPG11 we generated a Spatacsin KO mouse model . Consistent with HSP and proving the assumption that SPG11 is caused by Spatacsin loss-of-function , axons of cortical motoneurons degenerate in Spatacsin KO mice . Similar to human patients affected by SPG11 , KO mice developed a progressive spastic gait disorder with cerebellar ataxia during the course of the disease [21] , while we did not observe a thinning of the corpus callosum , which is one of the main features of SPG11 patients [21] . This discrepancy may be explained by the fact that corpus callosum phenotypes strongly depend on the respective mouse strain [22 , 23] . Spatacsin KO mice progressively loose large diameter axons of the corticospinal tract similar to other mouse models for HSP . In contrast to mouse models for pure HSP [24–26] but similar to our findings for Zfyve26 [13] cortical motoneurons and Purkinje cells finally die . This neuron loss is paralleled by activation of astroglia as reported for other mouse models with neurodegeneration [27] , while this is not the case in regions without overt neuron loss like hippocampus or spinal cord . Since we did not observe structural changes or motor defects in KO mice younger than 12 months of age , disruption of Spatacsin does not entail obvious neurodevelopmental defects , as might have been expected from compromised axon outgrowth in neurons derived from induced pluripotent stem cells from SPG11 patients and upon siRNA-mediated Spatacsin knockdown in mouse cortical neurons [11] . Neuron loss in Spatacsin knockout mice was preceded by accumulation of intracellular autofluorescent material , which was associated with Lamp1-positive membranes . This is reminiscent of neuronal ceroid lipofuscinosis ( NCL ) , lysosomal storage disorders characterized by lysosomal accumulation of autofluorescent ceroid lipopigments and neurodegeneration [28] . Because the autofluorescent deposits in Spatacsin KO mice also stained for p62 , a receptor for material delivered into autophagosomes , these structures rather represent abnormal autolysosomes instead of lysosomes . Moreover , the ultrastructural analysis revealed membrane-bound vesicles filled with heterogeneous material including organelle-like structures at different stages of degradation consistent with autolysosomes in neurons from different regions . Along this line , membranous structures reported in sural nerve biopsies [29] and iPSC-derived neurons from SPG11 patients [11] may represent abnormal autolysosomes . Their accumulation indicates that autophagic clearance is impaired in Spatacsin KO , while the fusion of autophagosomes with lysosomes still occurs . In agreement with our findings in Purkinje cells , the number of autolysosomes , characterized as vesicles labeled for both Lamp1 and p62 , was increased in KO MEFs , which fits with previous results from siRNA-mediated knockdown of Spatacsin in HeLa cells [15] . Moreover , levels of LC3-II , the lipidated form of LC3 recruited to autophagosomal membranes , were increased as well . Since LC3-II levels in MEFs further increased upon treatment with bafilomycin A1 , which inhibits lysosomal acidification and hence autolysosome clearance [30] , autophagy does not appear to be completely blocked in Spatacsin deficient cells . Because fibroblast proliferation was unchanged in SPG11 patients [14] , compromised autophagy upon disruption of Spatacsin may be less critical for fibroblasts than for postmitotic cells like neurons . Notably , it was reported that disruption of either Atg5 or Atg7 in neurons , which nearly abolishes autophagy completely , caused a loss of cortical neurons and Purkinje cells within the first 6 postnatal weeks while other types of neurons were less sensitive [31 , 32] . Thus the milder phenotype in Spatacsin KO mice , in which motoneurons and Purkinje cells are preserved at 2 months of age , is compatible with a partial impairment of autophagy . It appears that cortical motoneurons and Purkinje cells are particularly sensitive to autophagy defects . The long axonal projections of cortical motoneurons and the complex dendritic arbors of Purkinje cells may render these cells particularly sensitive for secondary transport defects because of accumulation of autophagy substrates . Indeed , axonal transport was compromised in neurons derived from induced pluripotent stem cells obtained from SPG11 patients and upon siRNA mediated Spatacsin knockdown in cortical mouse neurons [11] . The defect of autophagic clearance observed upon disruption of Spatacsin could arise from a primary lysosomal defect , as Spatacsin has been shown to interact with the adaptor protein complex AP-5 , which was suggested to play a role for endosomal sorting [7 , 12] . Accordingly mistargeting of proteins normally destined for lysosomes or missorted cargo proteins may accumulate within lysosomes . Both situations may result in lysosomal dysfunction and hence a diminished turnover of autolysosomes as suggested for different lysosomal disorders [33–35] . Consistent with data obtained upon knockdown of Spatacsin in HeLa cells [15] , a major lysosomal defect in Spatacsin KO cells is rather unlikely , because the processing of the lysosomal protease cathepsin D and the lysosomal pH were unchanged . Instead of a lysosomal defect we observed a depletion of lysosomes in both MEFs and Purkinje cells of KO mice . Lysosomes can either be generated through the endosomal pathway via the trans-Golgi network [36] or can be regenerated from autolysosomes via a process called autophagic lysosome reformation ( ALR ) . The latter process is characterized by budding of “protolysosomal tubules” from autolysosomes , which finally separate and mature into functional lysosomes [16 , 37] . In HeLa cells depleted for either Spatacsin or Spastizin these tubules did not evolve upon serum starvation and hence it was proposed that impaired ALR may underlie SPG11 and SPG15 [15] . Our finding that in Spatacsin KO MEFs lysosomes are not recovered after prolonged starvation together with diminished lysosome numbers in cortical motoneurons and Purkinje cells support this conclusion for SPG11 . Taken together , our observations provide first evidence that ALR , which has so far only been observed in vitro [16 , 37] , is also relevant in vivo . Along this line we propose that a reduction of lysosomes available for fusion with autophagosomes upon disruption of Spatacsin causes a defect in autolysosomal degradation , a consecutive accumulation of undegraded material and finally neuronal death .
To disrupt Spatacsin in mice , we used the EUCE0085_F05 embryonic stem cell clone E14 ( EUCOMM ) harbouring of a genetrap cassette in the first intron of the Spg11 gene . This clone was injected into C57BL/6 donor blastocysts and transferred into foster mice . The resulting chimeric mice were mated with C57BL/6 mice to obtain heterozygous gene-trapped mice , which were subsequently mated to obtain homozygous trapped mice . For genotyping genomic DNA was isolated from tail biopsies . The primers “for” ( cggctgcgggcagtctccaagtgc ) , “rev” ( gggatgggaaaggttccgagaggc ) , and “cas_rev” ( cgactcagtcaatcggaggactgg ) were used in a single PCR reaction . The primer pair for/rev amplified a 256 bp fragment for the wild-type allele and the primer pair for/cas_rev a 167 bp fragment for the trapped allele . Experiments were performed on a mixed 129SvJ/C57BL/6 background in the 4th to 6th generation . Mice were housed in a 12 h light/dark cycle and fed on a regular diet ad libitum . All animal experiments were approved by the Thüringer Landesamt für Lebensmittelsicherheit und Verbraucherschutz ( TLLV ) ( application number 02-016/13 ) and were conducted under strict accordance with the ARRIVE guidelines . Beam-walking and coordination test were performed on a horizontal plastic beam ( 1 , 000 mm long , 40 mm broad , 20 cm elevated from the ground ) leading to the home cage as previously described [13] . For Rotarod analysis mice were placed on the rotating rod of the apparatus ( Ugo basile ) . After constant speed ( 4 rpm ) for a maximum of 2 min the speed was continuously accelerated ( 4–40 rpm in 5 min ) , and the latency until mice fell off the beam was recorded . The mean from two independent trials per day was used for statistical analysis . For the probe we amplified a 602 bp cDNA fragment ( part of exon 30 of Spg11 ) from mouse brain cDNA with the forward primer 5’-gcaaacactaacacacactccgcagtgg-3’ and the reverse primer 5’-gcaacaccagcactagatcctggc-3’ . Northern blot analysis was performed as described previously [38] . Monoclonal antibodies were raised against the epitope EKLSSGSISRDD ( amino acids 1400–1411 ) of the Spatacsin protein in BALB/C mice ( c346 , Abmart ) . The affinity-purified antibody was used in a dilution of 1:50 . Our polyclonal rabbit anti-Zfyve26 antibody described previously [13] was used at a dilution of 1:50 . The following commercially available antibodies were used: mouse anti-Calnexin ( 1:1 , 000 , BD Biosciences ) ; goat anti-Ap5b1 ( 1:500 , Santa Cruz ) ; rabbit anti-β-Galactosidase ( 1:250 , Chemicon ) ; rabbit anti-Calbindin D-28K ( 1:1 , 000 , Millipore ) ; mouse anti-parvalbumin ( 1:5 , 000 , Swant ) ; rat anti-Ctip2 ( 1:200 , Abcam ) , mouse anti-SatB2 ( 1:100 , Santa Cruz ) ; α-bungarotoxin conjugated with Alexa Fluor555 ( 1:500 , Life Technologies ) ; mouse anti-NeuN ( 1:1 , 000 , Millipore ) ; mouse anti-GFAP ( 1:1 , 000 , Millipore ) ; rat anti-Lamp1 ( 1:500 for immunofluorescence studies; 1:1 , 000 for immunoblots , BD Pharmigen ) ; rabbit anti-LC3 ( 1:500 for immunoblots , Novus Biologicals ) ; mouse anti-p62 ( 1:250 used for immunofluorescence studies; 1:1 , 000 used for immunoblots , Abcam ) ; rabbit anti-Beclin-1 ( 1:500 , Santa Cruz ) ; goat anti-CtsD ( 1:500 , Santa Cruz ) ; rabbit anti-β-actin ( 1:2000 , Abcam ) ; goat anti-Gapdh ( 1:500 , Santa Cruz ) . Horseradish peroxidase—labelled secondary antibodies for Western blotting: goat anti-rabbit and goat anti-mouse ( both 1:4 , 000 , Amersham Bioscience ) ; goat anti-rat ( 1:2 , 000 , Santa Cruz ) ; rabbit anti-goat ( 1:1 , 000 , Sigma-Aldrich ) . Fluorescently labelled secondary antibodies: goat anti-rabbit , goat anti-mouse , or goat anti-rat coupled with Alexa 488 and Alexa 546 , respectively ( 1:1 , 000 , Life Technologies ) ; goat anti-mouse , goat anti-rabbit or goat anti-rat coupled with Cy5 ( 1:1 , 000 , Jackson ImmunoResearch Laboratories ) . Nuclei were counterstained with Hoechst-33258 ( 1:10 , 000; Molecular Probes ) . For immunoblotting brain tissue lysates were prepared as described [13] . Triton-X 100 insoluble fractions from total brain and spinal cord as well as from brain specific regions like cortex , hippocampus , and cerebellum were prepared from three mice per genotype as described previously [39] . For the hippocampus 6 hippocampi per genotype were pooled to prepare protein lysates . All samples were denatured for 5 minutes at 95°C in Laemmli buffer and separated by SDS PAGE and blotted onto PVDF membranes ( Roche ) , which were blocked with 2 . 5% ( w/v ) milk powder and 2 . 5% ( w/v ) BSA in TBS-T ( 137 mM NaCl , 2 . 7 mM KCl , 19 mM Tris base , 1% ( w/v ) Tween ) . Proteins were either detected with the ECL Plus Western Blotting Detection System ( GE Healthcare ) on a LAS 4000 system ( GE Healthcare ) or based on fluorescence using a LI-COR Odyssey detection system . Animals were anaesthetized with isoflurane ( Actavis ) and perfused transcardially with 4% PFA in 1xPBS . Brains were removed and post-fixed in 4% PFA overnight at 4°C . LacZ stainings of tissue sections were performed as described [40] . For histological analysis tissues were either embedded in paraffin or in Tissue-Tek ( Sakura ) . Sections from paraffin embedded tissues were 8μm and cryosections 20μm thick . For histological analysis sections were stained either with hematoxylin/eosin or cresyl violet acetate ( Nissl ) according to the manufacturers’ protocols ( Sigma-Aldrich ) . Images were captured with an Olympus DP70 microscope and further analysed by ImageJ . Pyramidal neurons in the Stratum pyramidale of the hippocampus were counted for corresponding regions and normalized to the respective area . The quantification of alpha-motoneurons and large diameter corticospinal axons was performed on semi-thin cervical and lumbar sections stained with Richard’s Blue [41] . Large diameter axons defined by a diameter > 4μm were counted . Alpha-motoneurons were identified because of the location in the ventral horn and their characteristic morphological appearance . Neuromuscular junctions were stained with α-bungarotoxin according to the manufacturer’s protocol ( Life Technologies ) in 20μm cryosections of either the gastrocnemius muscle from the hindlimb or the triceps brachii muscle from the forelimb , respectively . For quantification of cortical neurons 40μm free floating sagittal brain cryosections were stained for NeuN and mounted . Images of the motor cortex were taken with a Leica TCS SP5 confocal scanning fluorescence microscope . Neurons were quantified with the cell counter plug in and the area measurement tool of ImageJ . Free floating 20μm sections of the brain or primary cells were rinsed three times with 1xPBS , then fixed for 15 minutes in 4% PFA in 1xPBS at room temperature and washed three times for 10 min in 1xPBS . 0 . 25% Triton-X in 1xPBS was used to permeabilize the cells . After rinsing the cells once with 1xPBS , blocking solution ( 5% goat serum in 1xPBS ) was added . Primary and secondary antibodies were applied in blocking solution . Images were taken with a Leica TCS SP5 confocal scanning fluorescence microscope with the Z-stack module . To analyze whether the autofluorescent deposits co-localize with subcellular markers in brain tissue sections the fluorescent signal of deposits and the Cy5 secondary antibodies were recorded and further analyzed by linear unmixing as described previously [13] . As lysosomes we defined Lamp1-positive but p62-negative vesicular structures , while autolysosomes are characterized by the presence of Lamp1 and p62 . In order to analyze the number of lysosomes 40 μm thick sagittal brain sections were co-stained for Lamp1 and p62 . Only sections of somata of Purkinje cells not extending beyond the image boundary and hit vertically in respect to the nucleus were selected . The images were recorded with the Leica TCS SP5 confocal scanning fluorescence microscope . The number of free lysosomes were counted and normalized to the area of the cell soma with ImageJ . Co-localization between Lamp1 and p62 as well as between fusion proteins and subcellular markers were performed in BioImageXD as described [42] . For semi- and ultrathin sections , 2 animals per genotype were perfused with 50 ml fixative ( 4% paraformaldehyde , 1% glutaraldehyde ) . Brain and spinal cord were removed and post-fixed overnight at 4°C . 150μm sagittal and coronal sections of brain and spinal cord were cut with a vibratome ( Leica Microsystems ) and processed as described [41] . Semithin sections were stained with Richard’s blue . Ultrathin 80 nm sections ( Ultratome III , LKB Instruments ) were mounted on filmed copper grids ( 100 mesh ) , post-stained with lead citrate , and studied in a transmission electron microscope ( EM 900 , Zeiss ) at 80 kV . Mouse embryonic fibroblasts ( MEFs ) were prepared from E13 . 5 mouse embryos as described [13] . In order to assess the number of free lysosomes MEFs were cultured on 13 mm diameter coverslips ( Marienfeld ) in 24-well plate ( Greiner ) and maintained in DMEM medium ( Life Technologies ) with or without ( starvation condition ) 10% FBS and 2mM L-glutamine as described [37] . Cells at baseline conditions and cells starved for 6 and 14 h were fixed with 4% PFA in 1xPBS , rinsed with 1xPBS , and co-stained with anti-rat-Lamp1 and anti-mouse-p62 as described above . Images were digitally acquired by a Leica TCS SP5 confocal scanning fluorescence microscope and the number of Lamp1-positive and p62-negative lysosomes quantified with ImageJ . To inhibit autophagy cells were incubated with medium containing 100 nM Bafilomycin A1 ( Santa Cruz ) for 16h . Lysosomal pH measurements were carried out as described previously in [43] . More than 1 , 000 lysosomes from 3 independent experiments were analysed per genotype . For repeated experiments two-way ANOVA followed by Bonferroni post-hoc tests were used to compare between genotypes . For morphological and quantitative western blot analysis Student’s two-tailed t-test was used . Data are shown as mean±SEM if not indicated otherwise . | Autophagy is a degradative pathway for the removal and subsequent recycling of dysfunctional intracellular components . The material destined for degradation is initially enclosed by a double membrane , the autophagosome . In autolysosomes , which result from fusion of autophagosomes with lysosomes , the material is finally broken down . Recent in vitro data suggested that the protein Spatacsin plays a pivotal role in the regeneration of lysosomes from autolysosomes . Spatacsin is encoded by SPG11 , the most common gene mutated in autosomal recessive hereditary spastic paraplegia . Here we show that mice devoid of Spatacsin develop symptoms consistent with spastic paraplegia and progressively loose cortical motoneurons and Purkinje cells . In these mice degenerating neurons have a reduced number of lysosomes available for fusion with autophagosomes and consequently accumulate autolysosome-derived material over time . In the long term this causes death of particularly sensitive neurons like cortical motoneurons and Purkinje cells . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | In Vivo Evidence for Lysosome Depletion and Impaired Autophagic Clearance in Hereditary Spastic Paraplegia Type SPG11 |
The most commonly studied prokaryotic sensory signal transduction systems include the one-component systems , phosphosignaling systems , extracytoplasmic function ( ECF ) sigma factor systems , and the various types of second messenger systems . Recently , we described the regulatory role of two separate sensory systems in Streptococcus mutans that jointly control bacteriocin gene expression , natural competence development , as well as a cell death pathway , yet they do not function via any of the currently recognized signal transduction paradigms . These systems , which we refer to as LytTR Regulatory Systems ( LRS ) , minimally consist of two proteins , a transcription regulator from the LytTR Family and a transmembrane protein inhibitor of this transcription regulator . Here , we provide evidence suggesting that LRS are a unique uncharacterized class of prokaryotic sensory system . LRS exist in a basal inactive state . However , when LRS membrane inhibitor proteins are inactivated , an autoregulatory positive feedback loop is triggered due to LRS regulator protein interactions with direct repeat sequences located just upstream of the -35 sequences of LRS operon promoters . Uncharacterized LRS operons are widely encoded by a vast array of Gram positive and Gram negative bacteria as well as some archaea . These operons also contain unique direct repeat sequences immediately upstream of their operon promoters indicating that positive feedback autoregulation is a globally conserved feature of LRS . Despite the surprisingly widespread occurrence of LRS operons , the only characterized examples are those of S . mutans . Therefore , the current study provides a useful roadmap to investigate LRS function in the numerous other LRS-encoding organisms .
The capacity of bacteria to sense and respond to stimuli triggered by the extracellular environment is fundamental for survival , particularly in highly dynamic and/or competitive niches . Prokaryotes currently have several recognized classes of sensory signal transduction systems that are used specifically for this purpose . The most diverse class consists of the one-component systems , which contain single protein fusions of a signal-sensing input domain and a transcription regulatory output domain [1] . The vast majority of one-component systems are soluble proteins that utilize a diverse array of small molecules to modulate their transcription factor activity [1] . Among the best characterized classes of prokaryotic sensory systems are the phosphosignaling systems , exemplified by two-component signal transduction systems ( TCSTS ) and eukaryotic-like serine-threonine kinases/phosphatases ( eSTK/P ) . Phosphosignaling systems respond to environmental stimuli using sensor proteins containing integrated kinase/phosphatase domains , which alter the phosphorylation status of downstream proteins involved in the signaling pathway . For TCSTS , phosphorylation typically controls the sequence-specific DNA binding affinity of one or more cognate transcription regulators [2–5] , whereas eSTK/P usually regulate the phosphorylation status of a broad assortment of proteins [6–8] . The next major class of prokaryotic sensory systems is the extracytoplasmic function ( ECF ) sigma ( σ ) factors . Unlike TCSTS and eSTK/P , ECF systems do not typically encode enzymatic domains within sensor proteins; rather , gene expression is regulated through the production of alternative σ factors that dictate the promoter affinity of RNA polymerase [9 , 10] . ECF σ factors are normally maintained in an inactive state through direct interactions with cotranscribed cognate anti-σ factors that are typically embedded within the cell membrane [11 , 12] . ECF systems can be classified into 50 distinct subgroups [13 , 14] and are activated when the anti-σ factor is inhibited via regulated proteolysis , protein-protein interactions , or through a signal-induced conformational change , thus liberating the σ factor to assemble within the RNA polymerase holoenzyme [15] . Finally , bacteria ( and many other organisms ) also utilize a variety of purine-derived second messenger systems to transduce sensory information via molecules such as cAMP , ( p ) ppGpp , cyclic di-GMP ( c-di-GMP ) , cyclic di-AMP ( c-di-AMP ) , and cyclic GMP-AMP ( c-GAMP ) [16] . With the exception of ( p ) ppGpp , these second messenger systems are generally regulated through the action of two classes of proteins: cyclases that create the second messengers and the phosphodiesterases that degrade them [16–21] . For ( p ) ppGpp , its synthesis is catalyzed by RelA-SpoT family enzymes [22] . Once created , these second messengers can bind directly to their target proteins or RNAs to modulate their functions [20 , 23 , 24] . Recently , we have been examining the regulatory function of two related signal transduction systems in Streptococcus mutans , which we previously named HdrRM and BrsRM . Both systems share a variety of features and appear to be distinct from the aforementioned signal transduction system paradigms . Homologs of these two S . mutans systems , which we broadly refer to as LytTR Regulatory Systems ( LRS ) , can be found in various bacteria , particularly within the Firmicutes phylum [25] . Despite their widespread distribution , all putative LRS in other organisms remain uncharacterized . Thus , our current knowledge of LRS is presently limited to our previous studies of the HdrRM and BrsRM LRS [25–29] . These two LRS are both arranged within 2-gene operons with the first gene encoding a transcription regulator from the LytTR Family [30] and the adjacent downstream gene encoding a transmembrane protein inhibitor of the LRS regulator [25] . Under normal laboratory growth conditions , the HdrRM and BrsRM LRS are both maintained in a basal inactive state , due to the function of their cognate membrane inhibitor proteins [26 , 27 , 29] . Thus , the membrane proteins presumably serve as the proximal switches responsible for LRS activation , much like the analogous role of two-component system sensor kinases or ECF system anti-σ proteins . By mutating either of the membrane inhibitors HdrM or BrsM , it is possible to forcibly activate both LRS and examine their effect upon downstream gene expression . Surprisingly , the HdrRM and BrsRM LRS both contain largely overlapping regulons , which includes natural competence and bacteriocin genes in addition to both LRS operons [26–29] . Thus , these two LRS appear to be both autoregulatory and coregulatory . Furthermore , activation of bacteriocin gene expression by the LRS regulators HdrR and BrsR is critically dependent upon their interaction with direct repeat sequences found upstream of the bacteriocin gene promoters [27 , 29] . These direct repeat sequences conform to a broadly defined consensus recognized by members of the LytTR Family [29 , 30] . While the actual signals responsible for HdrRM and BrsRM activation are currently unknown , both LRS operons are induced by a rapid switch to high cell density growth conditions [26] . Intriguingly , HdrRM and BrsRM also jointly control a potent suicide-like cell death pathway , which underscores their potential ecological significance for S . mutans and perhaps other species [29] . Overall , it is clear that the HdrRM and BrsRM LRS are not cryptic regulators , rather they control distinct regulons that are integrated into a variety of genetic networks . In the current study , we sought to define the key characteristics and global distribution of LRS . We provide evidence that HdrRM , BrsRM , and several other previously unrecognized S . mutans LRS are actually members of a large family of analogous regulatory systems found amongst both bacteria and archaea . The conserved features of these systems indicate that LRS may comprise a previously unrecognized class of prokaryotic signal transduction system .
Our previous investigations of S . mutans LRS have focused upon the HdrRM and BrsRM LRS . However , it was unclear whether additional uncharacterized LRS might also exist in this species . Therefore , we began by searching the S . mutans genome for all of the transcription regulators containing putative LytTR Family DNA binding domains , which identified a total of seven genes . Two of these are obvious TCSTS response regulators ( ComE and LytR ) , two are known LRS regulators ( HdrR and BrsR ) , and the remaining three are uncharacterized hypothetical genes ( SMU_294 , SMU_433 , and SMU_1070c ) . Inspection of the three uncharacterized genes revealed that all are arranged in apparent polycistronic operons and are upstream of open reading frames ( ORFs ) encoding putative transmembrane proteins ( Fig 1A ) . This is highly reminiscent of the hdrRM and brsRM LRS operons , except that each of the uncharacterized operons also includes additional ORFs that are likely cotranscribed , whereas the hdrRM and brsRM operons are simply 2-gene operons . The SMU_294/295 genes are located between a conserved hypothetical gene ( SMU_293 ) and an ORF encoding a putative ketopantoate reductase ( SMU_296 ) , while the SMU_433/434 and SMU_1070c/1069c genes are both likely cotranscribed with ABC transporter genes ( Fig 1A ) . A key feature of the HdrRM and BrsRM LRS is their autoregulatory ability , which can be activated by mutagenesis of their respective membrane inhibitor proteins [26 , 28 , 29] . As shown in Fig 1B , each of the putative membrane proteins from all five operons was required to repress transcription of their respective operons indicating that the membrane proteins all similarly serve as inhibitors of an endogenous autoregulatory ability . The levels of induction triggered by the membrane protein deletions did vary widely however , with the SMU_294/295 , SMU_433/434 , and hdrRM operons all exhibiting ~50 to 60-fold maximum induction , while the SMU_1070c/1069c and brsRM operons exhibited <20-fold and >500-fold induction , respectively ( Fig 1B ) . Overall , the expression characteristics of the operons were quite similar , except for the brsRM LRS , which has only a slightly lower maximum expression but a substantially lower basal expression . Thus , the dynamic range of inducibility for each of these operons seems primarily dependent upon the stringency of operon repression , rather than its maximum expression . In our previous studies , we also observed cross-regulation between the HdrRM and BrsRM LRS [28 , 29] . Thus , we were interested to determine whether this is a unique feature of the HdrRM and BrsRM LRS or if other LRS might also exhibit cross-regulation of other LRS operons . To test this , we mutated each LRS membrane inhibitor protein and examined its resulting impact upon the other four non-cognate LRS luciferase reporter strains . To simplify the analysis , we deleted all but the two LRS of interest for each reporter to test every pairwise combination of LRS . With the exception of the SMU_433/434 LRS , all other LRS were found to trigger ≥2-fold change in reporter activity for one or more non-cognate LRS operons ( Fig 1C ) . Several cross-regulatory interactions were quite strong , such as the opposing roles of the SMU_1070c/1069c LRS as both a potent activator of SMU_294/295 LRS operon expression and as an inhibitor of SMU_433/434 LRS expression ( Fig 1C and 1D ) . The SMU_1070c/1069c LRS was also found to be particularly promiscuous , as it is the lone LRS capable of regulating all other LRS operons ( Fig 1D ) . From these results , we can conclude that the activation of one LRS can influence the production of another , possibly as part of a regulatory network to modulate the kinetics associated with non-cognate LRS activation and/or the control of non-cognate LRS regulons . As mentioned previously , the regulatory function of the HdrRM and BrsRM LRS in S . mutans is strongly indicative that they are not simply cryptic regulators . In further support of this notion , we examined whether the five S . mutans LRS operons are likely to be components of its core genome . 25 randomly selected S . mutans genomes were examined for the presence of all five operons and indeed all were present in every strain examined ( Table 1 ) . It should be noted that there were four strains in which brsM was either not annotated or annotated as a pseudogene due to the presence of apparent frameshift mutations within a poly-A region near the 3’ of the brsM ORF ( Table 1 ) . If such a mutation were truly present in brsM , it should constitutively activate BrsR in these strains . There was also a single instance in which hdrR was simply not annotated , even though the complete ORF is present ( Table 1 ) . Our previous transcriptomic analyses of the HdrRM and BrsRM LRS indicated that both systems are autoregulatory and coregulatory , as we observed potent induction of both LRS operons due to deletions of either of the LRS inhibitor proteins HdrM or BrsM [28 , 29] . The same results could also be recapitulated via ectopic overexpression of either of the LRS regulator genes hdrR or brsR [28 , 29] . As members of the LytTR Family of transcription regulators , both HdrR and BrsR would be predicted to recognize direct repeat sequences conforming to a broadly defined consensus [30] . Accordingly , LytTR Family consensus direct repeats are essential for HdrR and BrsR activation of bacteriocin gene expression [27 , 29 , 31–33] . However , a previous in silico analysis of the S . mutans genome failed to detect LytTR Family direct repeats in any of the LRS operon promoter regions [31] . Thus , we were curious whether the autoregulatory activity of LRS is mediated directly by the LRS regulators or via an indirect mechanism . As a test case , we first scanned the intergenic region upstream of the hdrRM operon to identify potential promoters . A strong candidate containing a putative extended -10 sequence was identified in this region in addition to a pair of direct repeats located 8 nucleotides upstream of the putative -35 sequence ( Fig 2A ) . The spacing and length of the direct repeats are identical to those found in the multiple bacteriocin promoters regulated by HdrR and BrsR , but the operon direct repeat sequence diverges from the reported LytTR Family consensus [30–33] . This likely explains why it had not been previously detected . To further examine the identified operon promoter and direct repeats , we created two separate transcription fusion reporter strains , one in which a luciferase ORF replaced the hdrRM ORFs ( i . e . ΔhdrRM ) and another in which the luciferase ORF was inserted immediately downstream of the hdrRM ORFs ( i . e . wild-type hdrRM ) . Using the ΔhdrRM reporter strain , we mutagenized the putative extended -10 sequence in the operon promoter , which resulted in substantially lower reporter activity compared to the parent strain ( Fig 2B ) . In addition , the -10 deletion created a dominant phenotype that could not be suppressed even via ectopic hdrR overexpression , strongly supporting the role of this sequence as part of the operon promoter . To determine whether the upstream direct repeats might comprise an HdrR binding site , we performed electrophoretic mobility shift assays ( EMSAs ) using full-length recombinant HdrR and a small DNA fragment encompassing the hdrRM direct repeat region upstream of the -35 . Sequence-specific mobility shifts were both detectable and critically dependent upon the identified direct repeats ( Fig 2C ) . Next , we further assayed the same direct repeat mutations shown in Fig 2C using an hdrRM reporter strain containing a luciferase ORF inserted immediately downstream of the operon ORFs . A double mutation of hdrM and the direct repeats in this reporter confirmed that the direct repeat mutations are similarly dominant , as they resulted in reporter activity below that of the parent strain ( Fig 2D ) . This indicated that the operon direct repeats further increase the basal expression of the operon via HdrR . It is worth noting that the basal luciferase activity of the ΔhdrRM reporter strain in Fig 2B is lower than that of the wild-type hdrRM reporter in Fig 2D ( S1 Fig ) . We attributed this difference to modest levels of HdrR autoactivation upon the hdrRM operon promoter in the wild-type reporter strain and the lack of such regulation in the ΔhdrRM reporter . As further support for this notion , we created an ectopic hdrRM overexpression strain and observed an identical dependence upon the operon direct repeats to maintain the parental level of basal expression ( Fig 2E ) . Thus , in addition to its role in bacteriocin production and natural competence development [27 , 28] , we can conclude that HdrR also directly serves as an autoregulatory transcription activator , triggering positive feedback autoregulation upon its own operon via two 9 bp direct repeat sequences located just upstream of the operon promoter . Next , we scanned the brsRM operon as well as the three other putative S . mutans LRS operons for similar promoter elements as those found in hdrRM . Like the hdrRM operon , we found that each of the other four operons indeed contain similar direct repeats located 4–11 bp upstream of their operon -35 sequences ( Table 2 ) . With the exception of the SMU_1070c/1069c LRS , each set of direct repeats is separated by 12 bp of intervening sequence . For the SMU_1070c/1069c LRS , the repeats are separated by 11 bp . For all five LRS , the locations of the direct repeats immediately upstream of the -35 sequences indicate they share similar regulatory mechanisms utilizing positive feedback autoactivation of their respective operons . LRS share some analogous features that are highly reminiscent of TCSTS and ECF σ factor systems . In fact , while searching for novel LRS operons in S . mutans and other species , we noticed a number of instances in which uncharacterized LRS regulators are erroneously annotated as LytTR Family response regulators . This would imply that such genes encode members of TCSTS , perhaps as orphan response regulators . While the LytTR Family does include numerous TCSTS response regulators , most members of this family are not [5 , 30] . We compared the domain architectures of the two S . mutans response regulators containing LytTR Family DNA binding domains ( ComE and LytR ) with each of the five S . mutans LRS regulators . While the sizes of all of the LytTR Family DNA binding domains are comparable , the response regulators ComE and LytR are larger proteins overall due to the additional presence of signal receiver domains ( Fig 3A ) , which are key features found in canonical response regulators [5] and are notably absent from the LRS regulators . Likewise , response regulators encode strictly conserved aspartate residues that are essential for phosphosignaling ( S2A Fig ) , yet these are also absent from LRS regulators ( S2B Fig ) . Obvious differences are similarly apparent when comparing TCSTS sensor kinases with LRS membrane inhibitors . The cognate sensor kinases for ComE and LytR ( ComD and LytS , respectively ) are considerably larger proteins due to the presence of various sensory domains and/or ATPase domains ( Fig 3B ) , which are key features essential for sensor kinase function [34] . No predicted kinase domains or any other putative enzymatic functions are detectable in the five LRS membrane proteins , although four of these proteins do encode either of two Domains of Unknown Function ( DUF3021 or DUF2154 ) ( Fig 3B ) . Like TCSTS , ECF σ factor systems are a major class of prokaryotic multi-protein sensory signal transduction system that share some analogous characteristics of LRS . One of the defining features of ECF systems is their utilization of ECF σ factors , which are distinct from those in the σ70 family , due to their lack of the conserved sigma 3 region ( Fig 3A ) [9 , 35] . Both conserved domain analyses ( Fig 3A ) and DNA binding characteristics ( Fig 2A–2E ) clearly indicate that LRS regulators are bona fide transcription factors rather than σ factors , thus precluding them from being part of true ECF systems . Regardless , LRS membrane proteins do share some basic characteristics with most ECF anti-σ factors , as they are similarly sized membrane proteins , lack obvious enzymatic domains , and serve as inhibitors ( Figs 1B and 3B ) [11 , 12] . Interestingly , after screening the genome sequence data of a phylogenetically diverse group of ECF system-encoding bacteria , we identified at least 10 separate Domains of Unknown Function encoded by ECF anti-σ factors , but we were unable to identify a single instance of anti-σ factors encoding either DUF3021 or DUF2154 . Thus , this could be one major distinction between anti-σ factors and LRS membrane proteins . Given the highly conserved features of S . mutans LRS operons , we expanded our search for putative LRS in other species and were surprised to discover that LRS are encoded by a far broader diversity of organisms than previously recognized ( Fig 4 and S3 Table ) . Using a multi-tiered search strategy modeled on the five S . mutans LRS , we were able to identify >4600 putative LRS operons spread amongst the genomes of numerous Gram positive and Gram negative bacteria as well as some archaea ( S3 Table ) . Overall , the majority of identified LRS are encoded within the Firmicutes phylum , which agrees with previous observations [25] . Of the five S . mutans LRS , the BrsRM-type LRS exhibits the most diverse distribution and is the most commonly encoded ( Fig 4 ) . In all cases , the identified LRS operons are arranged similarly as in S . mutans with the LRS regulator encoded upstream of the membrane inhibitor ( S3 Table ) . We also observed a conservation of ABC transporter genes linked to the SMU_433/434-like and SMU_1070c/1069c-like LRS of other species ( Fig 5 ) . The conserved co-occurrence of LRS and ABC transporter genes suggests that the respective encoded proteins all function together in related genetic pathways . However , this was not the case for the genes surrounding the SMU_294/295-type LRS , as only the very closely related species Streptococcus troglodytae contained a similar 4-gene operon ( Fig 5 ) . Therefore , the 4-gene operon structure of the S . mutans SMU_294/295 LRS ( Fig 1A ) is presumably either incidental or a niche-specific adaptation . Intriguingly , the LRS operons of other organisms all share highly analogous promoter regions to those of S . mutans LRS indicating that they similarly function via positive feedback autoregulation . Table 3 illustrates some of the diversity of LRS operon promoter elements that can be identified in both bacteria and archaea . Similar to S . mutans , most LRS operon direct repeat sequences are separated by 12 bp , but a minority is separated by either 11 bp or 13 bp . It is also evident there is a particularly strong bias for the direct repeats to be oriented 10 bp upstream of -35 sequences . The S . mutans LRS operons are somewhat unusual in this regard , as only the SMU_433/434 LRS operon contains direct repeats located exactly 10 bp upstream of the operon promoter . We also used Protter [36 , 37] to illustrate the predicted topologies of S . mutans LRS membrane proteins to their corresponding weakest homology examples shown in Fig 5 and all yielded highly similar structures despite their limited sequence similarities ( S3A–S3E Fig ) . Overall , the data indicate that most of the identified LRS in S3 Table are highly likely to be orthologs of the S . mutans proteins . While searching for putative LRS in other species , we also encountered a number of potentially novel LRS-types that are not found in S . mutans . The LRS listed in Table 3 for Staphylococcus aureus , Listeria monocytogenes , and Treponema bryantii have characteristics that are all nearly identical to the LRS found in S . mutans , except that their LRS membrane proteins exhibit no obvious homology to those of S . mutans . For the S . aureus membrane protein SACOL_RS12400 , its predicted topology is also obviously distinct from the five S . mutans LRS membrane proteins ( S3F Fig ) . Furthermore , members of the Bacteroides fragilis group , such as B . thetaiotaomicron and B . ovatus , encode “LRS-like” operons ( Btheta7330_RS19920/RS19915 and Bovatus_RS21370/RS21375 ) that exhibit a number of distinctions from S . mutans LRS . These Bacteroides operons encode the membrane proteins upstream of the LytTR Family regulators . Unlike S . mutans LRS , the encoded membrane proteins contain two conserved domains , an NfeD-like domain in addition to DUF2154 , which is the same domain found in the S . mutans LRS membrane protein HdrM ( Figs 3B and S3G ) . The LytTR Family regulators encoded in the Bacteroides operons are also unusual , as they contain multiple transmembrane segments before the DNA binding domains , whereas all of the S . mutans-type LRS encode soluble transcription regulators ( Fig 3A ) . The intergenic regions of the Bacteroides LRS-like operons also contain 11 bp direct repeats separated by 11 bp of intervening sequence with the repeats located 11 bp upstream of the operon promoters [38 , 39] ( Table 3 ) . Presumably , these repeats similarly function in autoregulatory transcription activation of the operons . The presence of these distinct LRS-like operons indicates that additional uncharacterized varieties of LRS are likely to exist . As mentioned previously , little is known about the environmental and/or cellular signals that naturally activate LRS from their basal inactive states . Given the broad distribution and conservation of LRS , it was of interest to gain further insight into LRS activation , as similar mechanisms may exist in other organisms . We created a mariner transposon library of >10 , 000 mutants to screen for mutations that could trigger activity from a transcription fusion brsRM-gusA β-glucuronidase reporter strain . We selected the brsRM LRS for several reasons: 1 ) we have previously studied the BrsRM LRS [29] , 2 ) BrsRM is the most stringently regulated LRS ( Fig 1B ) , and 3 ) BrsRM is the most broadly distributed LRS ( Fig 4 ) . Prior to transposon mutagenesis , we deleted all other LRS from the brsRM-gusA reporter strain to eliminate any potential impact of cross-regulation between LRS ( Fig 1C and 1D ) . After screening the library , we initially identified 49 transposon mutants that exhibited various levels of β-glucuronidase activity . We retransformed these mutations into the parent brsRM-gusA reporter to assess reproducibility and then identified the insertion sites of clones exhibiting β-glucuronidase reporter activity ( S4 Fig ) . The final list of 11 distinct brsRM-inducing mutations is shown in Table 4 . We next introduced these same mutations into a brsRM-gusA transcription fusion reporter strain in which the brsRM ORFs were replaced by gusA ( i . e . ΔbrsRM ) . In the ΔbrsRM background , only the rgpD and SMU_2060–2061 intergenic region ( IGR ) mutants still exhibited obvious reporter activity ( Table 4 ) , suggesting these two mutations increase brsRM operon expression independent of BrsR autoregulation ( i . e . the BrsRM LRS is not required ) . The remaining 9 mutations in Table 4 do require BrsRM to induce reporter activity and are therefore likely to function via the activation of the BrsRM LRS . Of these , we were next interested to determine whether there is any common theme or pathway among them that might yield clues as to the source of their BrsRM activation phenotypes . After testing various hypotheses , ultimately , it was purine metabolism that proved to be a key aspect of BrsRM activation . Since several of the genes listed in Table 4 have either verified or predicted roles in purine metabolic processes ( tilS , mnmE , and SMU_1297 ) , purines were among the numerous reagents tested for brsRM-gusA reporter activity using chemically defined medium agar plates . As shown in Fig 6A , in adenine/guanine drop-out medium , the reporter strain exhibited no obvious response after four days of incubation . In contrast , low concentrations of adenine and guanine both served as potent activators of the reporter . Interestingly , reporter activity increased concomitantly with adenine concentration , whereas the opposite was observed with guanine ( Fig 6A ) . We repeated the purine experiment using the mutant strains listed in Table 4 and all but the SMU_1297 mutant exhibited obvious reporter activity after incubating for only two days in the presence of adenine , and to a lesser extent , guanine as well ( Fig 6B–6E ) . Despite the lack of reporter activity from the SMU_1297 mutant , this strain still exhibited an intriguing response to adenine , as it was the only mutant likely exhibiting adenine auxotrophy ( Fig 6D and 6E ) . Thus , SMU_1297 is presumably an unrecognized key component of purine metabolism . Similarly , both the rpoB and rgpD mutants grew poorly on defined medium in the absence of purine supplementation , whereas both grew normally on complex medium . It is worth noting that the rpoB mutant likely encodes a partially functional RpoB protein , as the transposon insertion occurred near to the 3’ of the rpoB ORF ( S4 Fig ) . This reduced functionality is apparently problematic for growth on chemically defined medium , as only a fraction of the rpoB mutant cells was able to grow in this condition ( Fig 6B–6E ) . Despite this , the rpoB mutant as well as the tilS mutant were the only ones to exhibit obvious brsRM expression in the absence of purines , although purine supplementation could still further augment their reporter activity like most of the other mutants ( Fig 6B–6E ) . Overall , these results support a major role for purines ( especially adenine ) as mediators of BrsRM activation .
The current study provides the first insights into a widely conserved , but almost entirely uncharacterized group of prokaryotic sensory systems . In S . mutans , these systems , termed LytTR Regulatory Systems , are included within its core genome ( Table 1 ) and control diverse regulons as well as a cell death pathway [28 , 29] . The key features of LRS are distinct from the other 2-protein sensory systems ( TCSTS and ECF σ factor systems ) ( Fig 3A and 3B ) suggesting LRS possibly represent a novel class . Despite the large number of putative LRS operons we identified amongst both bacteria and archaea , the true breadth and diversity of LRS is likely to be underestimated , as our analyses were performed using S . mutans LRS as model systems , due to the current lack of relevant studies in other species . For example , in the MRSA strain S . aureus COL , the two-gene operon SACOL_RS12395/RS12400 encodes a putative LytTR Family regulator upstream of a DUF3021-containing membrane protein and the operon contains typical LRS repeats located 9 bp upstream of the operon -35 sequence ( Table 3 ) . However , the LRS membrane protein SACOL_RS12400 lacks significant sequence similarity to those of S . mutans LRS and it exhibits a distinct predicted topology as well ( S3F Fig ) . Despite this , the putative SACOL_RS12395/RS12400 LRS is widely encoded among the staphylococci and many other Gram positive species . A similar result can be observed from the lmo0984 –lmo0987 operon of L . monocytogenes , except this operon also includes an ABC transporter much like those associated with the SMU_433/434 and SMU_1070c/1069c LRS of S . mutans ( Fig 5 ) . Whether these LRS are weak homology orthologs of S . mutans LRS or represent entirely distinct varieties of LRS remains to be determined . However , protein topology predictions suggest the latter scenario is more likely to be the case ( S3A–S3F Fig ) . Furthermore , we have also encountered a number of “LRS-like” operons that are analogous , but clearly distinct from those of S . mutans or the aforementioned unclassified LRS from S . aureus and L . monocytogenes . Such operons can be found among members of the Bacteroides fragilis group , such as B . thetaiotaomicron and B . ovatus , and exhibit a unique operon arrangement encoding transcription regulators and membrane proteins unlike those of S . mutans LRS ( Table 3 and S3G Fig ) . Despite the unique qualities of these operons , the obvious parallels to S . mutans LRS suggest that LRS likely exist in a greater variety than is currently recognized . One of the key features defining LRS control in S . mutans is the autoregulatory positive feedback regulation encoded within the operons . For the HdrRM LRS , this is mediated directly by HdrR and is critically dependent upon its recognition of the direct repeats located upstream of the hdrRM operon promoter ( Fig 2A–2E ) . It is now evident that these direct repeats are not only key to LRS function in S . mutans , but they appear to be a defining feature of most , if not all , LRS encoded by a wide diversity of prokaryotes ( Table 3 ) . Among the putative orthologous LRS found in other species , there is low overall sequence conservation of the individual direct repeats , whereas the direct repeat lengths , their spacing , and their locations immediately upstream of LRS operon promoters are all highly conserved ( Table 3 ) . Another conserved characteristic of S . mutans LRS is the inhibitory function of LRS membrane proteins , which play key roles in dictating the basal expression levels of LRS operons ( Fig 1B ) . Presumably , it is the inhibitory equilibrium maintained between an LRS membrane protein and its cognate regulator , which is the principal determinant of LRS operon basal expression . The inhibitory function of LRS membrane proteins can also yield misleading results when performing genetic studies of unrecognized LRS , since single mutations of LRS regulators or double mutations of cognate LRS regulators and membrane proteins are both likely to result in wild-type phenotypes [26] . To observe LRS-related phenotypes , one must solely mutate the LRS membrane protein to constitutively activate the system . Based upon these conserved features of LRS , several inferences can be made regarding their functionality . Firstly , LRS exist in a basal inactive state . A variable , but limited amount of autoregulation is permitted under normal growth conditions ( Figs 1B , 2B , 2D and 2E ) , which would ensure that the cell maintains a minimal abundance of LRS for the detection of relevant stimuli . Upon signal detection , LRS abundance should quickly increase due to positive feedback autoregulation , thus amplifying both the signal detection apparatus as well as the downstream transcriptional response . Secondly , LRS presumably respond to unusual growth conditions and/or environmental stress . This is supported by several observations: 1 ) LRS exist in a basal inactive state , 2 ) the HdrRM LRS responds to a rapid switch to high cell density growth conditions [26] , 3 ) purines , which mediate activation of the BrsRM LRS ( Fig 6A–6E ) are also central transducers of environmental stress signals [19 , 21 , 22] , and 4 ) DUF2154 , which is found in HdrM , is encoded by proteins responding to cell envelope damage [40–42] . These features are also highly reminiscent of ECF systems . Like LRS , ECF systems are maintained in a basal inactive state , due to the inhibitory function of cognate anti-σ factors . Furthermore , ECF systems are similarly dispensable under normal growth conditions [43 , 44] and their activation is typically dependent upon positive feedback autoregulation , ultimately triggered by environmental stress [11 , 12 , 15 , 45] . The lack of shared domains between ECF anti-σ proteins and LRS membrane proteins ( Fig 3B ) as well as the obvious distinctions between σ factors and transcription regulators suggest that ECF systems and LRS evolved independently , although it is conceivable that both systems could be products of convergent evolution . When examining the distribution of LRS , it is evident that these systems are encoded by a phylogenetically diverse group of Gram positive and Gram negative bacteria and even some archaea ( Fig 4 ) . However , their distribution appears highly biased as well with a subset of Firmicutes encoding the majority of LRS , especially the Lactic Acid Bacteria ( Fig 4 and S3 Table ) . It is currently unclear why such a bias exists . This could be partly due to the utility of some LRS for the regulation of bacteriocin genes . Lactic Acid Bacteria are particularly rich sources of diverse bacteriocins that are regulated by LytTR Family-like repeats upstream of the bacteriocin gene -35 sequences [25 , 29 , 31 , 46–51] . Another possibility that is not mutually exclusive with the former could be that LRS are a fairly recent evolutionary innovation originating within the Firmicutes phylum . In which case , a biased overrepresentation in these species would be expected [52] . Certainly , it is also possible , if not likely , that our current view of LRS distribution is reflective of only a subset of LRS as a consequence of our comparisons to S . mutans . In this case , an apparent skewed overrepresentation among the Lactic Acid Bacteria might be simply due to their close phylogenetic relatedness to S . mutans . As mentioned previously , the presence of LRS-like operons in other distantly related organisms hints at the possibility of a greater diversity of LRS than is currently recognized . Further clarity should arise once additional functional data are available from other LRS-encoding species .
All bacterial strains used in this study are listed in S1 Table and were either grown in an anaerobic chamber containing 85% N2 , 10% CO2 , and 5% H2 at 37°C , a 5% CO2 incubator at 37°C , or cultured with aeration at 37°C . The S . mutans strain UA140 [53] was used as the parent wild-type for all experiments . S . mutans strains were cultured using Todd Hewitt medium supplemented with 0 . 3% wt vol-1 yeast extract ( THYE , Difco ) or in chemically defined medium [54] , while E . coli strains were cultured with Lennox LB ( LB , Difco ) medium . For antibiotic selection , cultures were supplemented with the following antibiotics: S . mutans– ( 10 μg ml-1 erythromycin , 1 mg ml-1 spectinomycin , 0 . 02 M p-chlorophenylalanine [4-CP] , and 800 μg ml-1 kanamycin ) and E . coli– ( 100 μg ml-1 ampicillin , 50 μg ml-1 chloramphenicol , 250 μg ml-1 erythromycin , and 100 μg ml-1 spectinomycin ) . All primers used for strain construction are listed in S2 Table . All PCR reactions employed Phusion DNA Polymerase ( NEB ) . PCR amplicons were purified using the Zymo Research DNA Clean & Concentrator-25 . All constructs were assembled using an overlap extension PCR ( OE-PCR ) strategy . The S . mutans luciferase reporter strains used in Fig 1B were created by inserting the green renilla luciferase ORF immediately downstream of the LRS operons . Briefly , the luciferase open reading frame ( ORF ) containing the S . mutans ldh ( lactate dehydrogenase ) ribosome binding site was amplified from the strain ldhRenGSm [55] using the primer pair RenG-F/RenG-R . The ermAM erythromycin resistance cassette was PCR amplified from the plasmid pJY4164 [56] using the primer pair ( RenG ) erm-F/erm-R . Primers used to amplify the respective upstream and downstream homologous fragments for each reporter construct are as follows: wild-type SMU_294/295 LRS [SMU294-LF/SMU295 ( RenG ) -R and ( erm ) SMU295-RF/SMU295-RR] , SMU_294/Δ295 LRS [SMU294-LF/SMU294 ( RenG ) -R and ( erm ) SMU294-RF/SMU295-RR] , wild-type SMU_1070c/1069c LRS [SMU1070c-LF/SMU1069c ( RenG ) -R and ( erm ) SMU1069c-RF/ SMU1070c-RR] , SMU_1070c/Δ1069c LRS [SMU1070c-LF/SMU1070c ( RenG ) -R and ( erm ) SMU1070c-RF/SMU1070c-RR] , wild-type SMU_1854/1855 ( hdrRM ) LRS [hdrRM159-LF/hdrM ( RenG ) -R and ( erm ) hdrM-RF/hdrRM159-RR-2] , SMU_1854/Δ1855 ( hdrRΔM ) LRS [hdrRM159-LF/hdrR ( RenG ) -R and ( erm ) hdrR-RF/hdrRM159-RR-2] , SMU_2080/2081 ( brsRM ) LRS [brsM-LF/brsM ( RenG ) -R and ( erm ) brsM-RF/brsM-RR] , SMU_2080/Δ2081 ( brsRΔM ) LRS [brsM-LF/brsR ( RenG ) -R and ( erm ) brsR-RF/brsM-RR] . All PCR amplicons were purified and mixed in equal molar concentrations and then subjected to a 4-fragment OE-PCR reaction using the respective upstream forward/downstream reverse primer pairs . The assembled PCR amplicons were transformed into S . mutans strain UA140 and selected on agar plates supplemented with erythromycin to obtain the following strains: 294-295-RenG , 294-RenG , 1070c-1069c-RenG , 1070c-RenG , hdrRM-RenG , hdrR-RenG , brsRM-RenG , and brsR-RenG . The wild-type SMU_433/434 and SMU_433/Δ434 LRS luciferase reporter constructs were PCR amplified from strains 01-luc and 01-luc-434 . The resulting PCR amplicons were then transformed into S . mutans strain UA140 and selected on agar plates supplemented with spectinomycin to obtain the strains 433-434-RenG and 433-RenG . To create markerless in-frame deletions of all 5 LRS in S . mutans UA140 , we first deleted SMU_433/434 using our previously described markerless mutagenesis protocol [57] . Two fragments corresponding to the upstream and downstream regions of the SMU_433/434 operon were amplified with the primer pairs SMU433-LF/ ( IFDC2 ) smu433-LR and ( IFDC2 ) smu434-RF/SMU434-RR , respectively . The IFDC2 cassette was amplified from the plasmid pIFDC2 [57] using the primer pair ldhF/ermR . The three fragments were mixed and used as templates for OE-PCR with the primer pair SMU433-LF/SMU434-RR . The resulting OE-PCR product was transformed into UA140 and selected on medium containing erythromycin to isolate transformants containing the IFDC2 cassette . Next , DNA fragments containing the SMU_433 upstream region and SMU_434 downstream region were amplified with the primer pairs SMU433-LF/smu433-LR2 and smu434-RF2/SMU434-RR . The two fragments were mixed and assembled with OE-PCR using the primer pair SMU433-LF/SMU434-RR . The OE-PCR amplicon was then transformed into the IFDC2-containing strain and selected on the medium containing p-chlorophenylalanine ( 4-CP ) to remove the IFDC2 cassette and obtain the markerless deletion mutant . This strain was then used as a recipient for the sequential deletion of SMU_1070c/1069c , SMU_294/295 , hdrRM , and brsRM using the same approach to obtain the final 5 LRS deletion strain ifdLRS . Genomic DNA from strains 294-295-RenG , 1070c-1069c-RenG , hdrRM-RenG , brsRM-RenG , and 433-434-RenG were transformed into strain ifdLRS and selected on THYE plates contains erythromycin or spectinomycin to obtain the single LRS luciferase reporter strains ifdLRS/294-295-RenG , ifdLRS/1070c-69c-RenG , ifdLRS/hdrRM-RenG , ifdLRS/brsRM-RenG , and ifdLRS/433-434-RenG . To examine potential cross-regulation between different LRS , ORFs encoding LRS membrane proteins were replaced by a kanamycin resistance cassette using the single LRS luciferase reporter strains as recipients . Briefly , upstream and downstream homologous fragments of SMU_295 were amplified using the primer pairs SMU294-LF/ ( kan ) smu295-LR and ( kan ) smu295-RF/SMU295-RR as well as UA140 genomic DNA as a template . The kanamycin resistance gene was amplified using the primer pair kan-F/kan-R and plasmid pWVTKs [58] as the template . Three fragments were mixed and assembled with OE-PCR using the primer pair SMU294-LF/SMU295-RR . The OE-PCR amplicon was transformed into the single luciferase reporter strains ifdLRS/1070c-69c-RenG , ifdLRS/hdrRM-RenG , ifdLRS/brsRM-RenG and ifdLRS/433-434-RenG to obtain d295/1070c-69c-RenG , d295/hdrRM-RenG , d295/brsRM-RenG and d295/433-434-RenG . A similar approach was used to delete hdrM and brsM in each of the single LRS reporter strains . The SMU_434 and SMU_1069c mutations were PCR amplified from d-smu434/UA140 and d-smu1069/UA140 and then transformed into the single LRS reporter strains . The S . mutans firefly luciferase reporter strains used in Fig 2 were created using a markerless mutagenesis approach . To create the markerless replacement of the hdrRM ORFs with that of luciferase , we first created an allelic replacement of the hdrRM ORFs with the counterselectable IFDC2 cassette [57] . Using UA140 genomic DNA as a template , two fragments corresponding to the upstream and downstream regions of the hdrRM operon were amplified with the primer pairs hdrRupF/hdrRupR-ldh and hdrMdnF-erm/hdrMdnR , respectively . The IFDC2 cassette was amplified using the primer pair ldhF/ermR . The three fragments were mixed and used as template for OE-PCR with the primer pair hdrRupF/hdrMdnR . The resulting OE-PCR product was transformed into UA140 and selected on medium containing erythromycin to obtain strain RMIFDC2 . Next , a DNA fragment containing the hdrR upstream region and firefly luciferase ORF was amplified with the primer pair hdrRupF/lucR-1856 and strain LZ89-luc [26] as a template . Using strain UA140 as a template , a fragment corresponding to the hdrM downstream region was amplified with the primer pair 1856F-luc/hdrMDnR . The two fragments were mixed and assembled with OE-PCR using the primer pair hdrRupF/hdrMdnR . The OE-PCR amplicon was transformed into strain RMIFDC2 and selected on medium containing p-chlorophenylalanine ( 4-CP ) to obtain strain RpLuc . To create strains Rp+1luc and Rp-10mluc , the upstream and downstream regions of the hdrRM operon were amplified from strain UA140 with the primer pairs hdrRupF/ ( luc ) hdrRp-R or hdrRupF/ ( luc ) hdrRp-10-R and ( lucR ) hdrMdn-F/hdrMDn-R , respectively . The luciferase ORF was amplified from strain RpLuc with the primer pair lucF/lucR . The three fragments were mixed and used as template for OE-PCR with the primer pair hdrRupF/hdrMdnR . OE-PCR products were transformed into RMIFDC2 and selected on medium containing 4-CP to obtain the strains Rp+1luc and Rp-10mluc . Strains Rp+1luc and Rp-10mluc were both transformed with the plasmid pHdrRoe [27] to create the strains Rp+1lucROE and Rp+1lucROE-10 . Using the genomic DNA from strain RpLuc as a template , two fragments were amplified with the primer pairs hdrRupF/ ( repeat-m ) hdrR-LR and ( repeat-m ) hdrR-RF/hdrMDnR . The two PCR amplicons were mixed with hybridized EMSA-hdrRpm-F/R primers and assembled using OE-PCR with the primer pair hdrRupF/hdrMdnR . The OE-PCR amplicon was transformed into strain RMIFDC2 and selected on medium containing 4-CP to create the strain RpDRmluc . To create the hdrR ectopic overexpression plasmid pJYROE , a fragment containing the hdrR ORF fused to the ldh promoter was first amplified from pHdrRoe using the primer pair ldhF-bamHI/hdrRR-hindIII . The resulting PCR amplicon was digested with BamHI and HindIII and then ligated to pJY4164 to obtain the suicide vector pJYROE . To create the hdrM ectopic overexpression plasmid pMOE , an ldh promoter-hdrM transcription fusion was assembled by first PCR amplifying the ldh promoter and hdrM ORF using the primer pairs ldhF-BamHI/ldhR-SpeI and hdrMF-SpeI/hdrMR-EcoRI as well as UA140 gDNA as a template . The resulting amplicons were then digested with BamHI/SpeI and SpeI/EcoRI and subsequently ligated to the BamHI/EcoRI restriction sites of the E . coli-Streptococcus shuttle vector pDL278 [59] to create the plasmid pMOE . The suicide vector pJYROE was transformed into strain RpLuc or RpDRmluc to create the strains ROE or ROE/DR- , while the shuttle vector pMOE was transformed into strain ROE to obtain the strain RMOE . To insert the luciferase ORF downstream of the hdrRM ORFs , a DNA fragment containing the hdrR upstream region and IFDC2 were PCR amplified from strain RMIFDC2 with the primer pair hdrRupF/ermR-lucf . Using the genomic DNA of RpLuc as a template , the luciferase ORF was amplified with the primer pair lucF-erm/lucmR . The two amplicons were assembled using OE-PCR and the primer pair hdrRupF/lucmR . The resulting overlapping PCR products were transformed into RpLuc strain and selected on medium containing erythromycin to obtain the strain RMlucIFDC2 . Next , two fragments encompassing the hdrRM locus were amplified from strain UA140 with the primer pair hdrRupF/MterR-luc , while the luciferase ORF was amplified from strain RpLuc with the primer pair lucF-Mter/lucmR . The PCR amplicons were mixed and assembled by OE-PCR using the primer pair hdrRupF/lucmR . The resulting OE-PCR amplicon was transformed into strain RMlucIFDC2 and selected on plates supplemented with 4-CP to obtain the strain hdrRMluc . To mutate hdrM in strain hdrRMluc , three fragments were amplified from this strain using the primer pairs hdrRupF/ ( spec ) smu1853R , ( spec ) smu1853-hdrR-LF2/hdrM ( TAA ) R , and hdrM ( TAA ) F/lucmR . The spectinomycin resistance cassette aad9 was amplified from the E . coli-Streptococcus shuttle vector pDL278 [59] using the primer pair specF/specR . The four amplicons were mixed and assembled by OE-PCR using the primer pair hdrRupF/lucmR . The resulting OE-PCR amplicon was transformed into strain hdrRMluc to obtain the strain dhdrMluc . To mutate the direct repeats upstream of the hdrRM promoter in strain dhdrMluc , two fragments were amplified from this strain using the primer pair hdrRupF/ ( repeat-m ) hdrR-LR and ( repeat-m ) hdrR-RF/lucmR . The two PCR amplicons were mixed with hybridized EMSA-hdrRpm F/R primers and assembled using OE-PCR and the primers hdrRupF/lucmR . The resulting OE-PCR amplicon was transformed into strain hdrRMluc to obtain the strain dhdrMdDRluc . To create markerless gusA transcription fusions to the brsRM operon , a brsRM upstream homologous fragment was amplified from strain UA140 or ifdLRS using the primer pair brsRM-LF/ ( gusA ) brsRM-LR , while the brsRM downstream homologous fragment was amplified from strain UA140 using the primer pair ( gusA ) brsRM-RF/brsRM-RR . The gusA ORF was amplified from plasmid pZX7 [60] using the primer pair GusA-F/GusA-R . The three amplicons were assembled via OE-PCR with the primer pair brsRM-LF/brsRM-RR . The two resulting OE-PCR amplicons were then transformed into the strain ifdLRS/brsRM ( IFDC2 ) and selected on the medium containing 4-CP to obtain the strains ifdLRS/brsRM-gusA and ifdLRS/brsRMp-gusA respectively . The ifdLRS/brsRM-gusA reporter strain transposon library was generated by a previously described transposon mutagenesis protocol [61] . Briefly , the primer pair MmeI-MGL-erm-F/MmeI-MGL-erm-R was used to amplify the erythromycin resistance cassette from plasmid pJY4164 . Sequences at the 5’ ends of both primers add repeat sequences recognized by the himar transposon onto both ends of the PCR amplicon . The resulting amplicon was then ligated to the pGEM®-T vector ( Promega ) to obtain pT-MGL-erm . In vitro transposon mutagenesis was performed by combining MarC9 transposase , genomic DNA from strain ifdLRS , and plasmid pT-MGL-erm and then incubating at 30°C for 1 h . Transposon junctions were subsequently repaired and then the transposition reaction was transformed into strain ifdLRS/brsRM-gusA . Transposon mutants were selected on THYE plates containing erythromycin and 5-bromo-4-chloro-3-indolyl-β-D-glucuronic acid ( X-gluc , 200 μg ml-1 ) . After 5 days of incubation , blue colonies were selected . Transposon insertion sites were mapped according to the published protocol [61] , except that PCR amplicons were ligated into the pGEM®-T vector , transformed into E . coli DH5α , and then the resulting plasmid inserts were sequenced . PCR was used to confirm the expected locations of transposon insertions sites in each of the mutant strains . Genomic DNA from confirmed transposon mutants was also transformed into strain ifdLRS/brsRMp-gusA ( ΔbrsRM ) to compare its reporter activity with the corresponding transposon mutants obtained in the ifdLRS/brsRM-gusA ( brsRM+ ) background . The hdrR ORF was amplified from strain UA140 using the primer pair hdrRF-NdeI/HdrRR-Hind . The amplicon was then digested with NdeI/HindIII and ligated to the expression vector pET29b to create the plasmid pEcROE . Recombinant HdrR was purified using pET29b and the E . coli BL21 ( DE3 ) pLysS expression system . Cultures were grown to OD600 0 . 6 at 37°C with aeration before adding 0 . 1 mM IPTG and culturing for an additional 12 hr . at 20°C . Cells were harvested by centrifugation ( 6000 x g , 5 min , 4°C ) , washed twice with binding buffer ( 20 mM Tris , 300 mM NaCl , 5 mM imidazole , 10% glycerol , pH 7 . 9 ) and then resuspended in 20 ml of the same buffer . Next , the cells were chilled on ice , lysed by sonication , centrifuged to recover supernatants ( 20 , 130 x g , 20 min , 4°C ) , and then HdrR-His6 was purified using Ni-NTA agarose chromatography ( Novagen ) . Proteins were eluted with 4 ml elution buffer ( 20 mM Tris , 300 mM NaCl , 500 mM imidazole , 10% glycerol , pH 7 . 9 ) and concentrated by ultrafiltration ( Millipore membrane , 3 kDa cut-off size ) . Purified proteins were stored in 10% glycerol at -80°C . EMSAs were performed similarly as previously described [62] . Briefly , double-stranded probes were obtained by annealing equal molar concentrations of two oligonucleotides ( S2 Table ) in 50 mM Tris-HCl ( pH 8 . 0 ) , 10 mM MgCl2 , 50 mM NaCl and 1 mM EDTA , with the forward primer 5′-end labeled with digoxigenin-11-ddUTP ( Roche ) . The oligonucleotide pair EMSA-hdrRp-F/EMSA-hdrRp-R served as the wild-type probe , while the oligonucleotide pair EMSA-hdrRpm-F/EMSA-hdrRpm-R served as the direct repeat mutant probe . 1 ng of DNA probe was incubated individually with various concentrations of HdrR-His6 at 25°C for 20 min in a 20 μl reaction volume . After incubation , the reaction mixtures were separated by electrophoresis and electro-transferred to nylon membranes . Images were detected using chemiluminescence and X-ray films . For competition experiments , 50- and 200-fold excess of unlabeled probes ( S2 Table ) were added to the binding reactions before performing electrophoresis and imaging as described above . Assays of firefly and green renilla luciferase activity were performed using a previously described methodology [55] with mid-log phase cultures . Reporter data were normalized by dividing luciferase values by their corresponding optical density ( OD600 ) values . Luciferase activity was measured with a GloMax Discover 96-well luminometer ( Promega ) . To identify homologs of LRS membrane proteins , we searched the NCBI non-redundant nucleotide collection ( nr/nt ) and whole-genome shotgun ( wgs ) databases using tBLASTn ( E-value <10 , >25% positives ) . These putative LRS membrane proteins ( except for SMU_295 homologs ) were then refined contingent on containing either DUF3021 or DUF2154 domains , as determined by NCBI RPS-tBLASTn ( E-value <1 ) . Qualifying LRS membrane protein results were further filtered based upon the presence of adjacent upstream LytTR Family transcription regulator homologs identified using tBLASTn ( E-value <0 . 1 ) . To assess the effect of purines on the BrsRM LRS , overnight cultures of ifdLRS/brsRM-gusA and isogenic transposon mutants were harvested by centrifugation , washed thrice with an equal volume of 0 . 9% NaCl , and spotted on adenine/guanine-replete or adenine/guanine drop-out chemically defined medium ( CDM ) agar plates [54] . Different concentrations of adenine ( 0 mM , 0 . 075 mM , 0 . 15 mM , 0 . 3 mM and 0 . 6 mM ) or guanine ( 0 mM , 0 . 066 mM , 0 . 132 mM , 0 . 264 mM and 0 . 53 mM ) were added to the CDM medium and plates were incubated at 37°C with 5% CO2 for 4 days . To assay the impact of purines on the transposon mutants of ifdLRS/brsRM-gusA , adenine and/or guanine was added to the CDM at a final concentration of 0 . 15 mM and/or 0 . 132 mM , respectively . Plates were incubated at 37°C with 5% CO2 for 2 . 5 days . All statistical analyses were performed using GraphPad Prism software to calculate significance via two-tailed Student’s t-tests with Welch’s correction . Statistical significance was assessed using a cutoff value of P < 0 . 05 . | The ability to sense stimuli triggered by the extracellular environment is a fundamental requirement of all cellular life . For prokaryotes , there are a variety of recognized classes of sensory systems that are used to detect and respond to environmental stimuli . In the current study , we provide the first evidence for the existence of a potentially new class of prokaryotic sensory system , which we refer to as LytTR Regulatory Systems ( LRS ) . Here , we show that LRS are broadly distributed among prokaryotes and are distinct from the other commonly studied sensory systems like two-component signal transduction systems and ECF sigma factor systems . Presently , there are only two characterized examples of LRS , both from Streptococcus mutans . We employ these LRS as models to first define the key features of LRS and then demonstrate how some of these characteristics are likely universally conserved among the plethora of uncharacterized LRS in other organisms . Based upon these data , we further describe how these sensory systems are likely to function in diverse species and illustrate how to identify and investigate the function of novel LRS . | [
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"technique... | 2018 | LytTR Regulatory Systems: A potential new class of prokaryotic sensory system |
Understanding sleep and its perturbation by environment , mutation , or medication remains a central problem in biomedical research . Its examination in animal models rests on brain state analysis via classification of electroencephalographic ( EEG ) signatures . Traditionally , these states are classified by trained human experts by visual inspection of raw EEG recordings , which is a laborious task prone to inter-individual variability . Recently , machine learning approaches have been developed to automate this process , but their generalization capabilities are often insufficient , especially across animals from different experimental studies . To address this challenge , we crafted a convolutional neural network-based architecture to produce domain invariant predictions , and furthermore integrated a hidden Markov model to constrain state dynamics based upon known sleep physiology . Our method , which we named SPINDLE ( Sleep Phase Identification with Neural networks for Domain-invariant LEearning ) was validated using data of four animal cohorts from three independent sleep labs , and achieved average agreement rates of 99% , 98% , 93% , and 97% with scorings from five human experts from different labs , essentially duplicating human capability . It generalized across different genetic mutants , surgery procedures , recording setups and even different species , far exceeding state-of-the-art solutions that we tested in parallel on this task . Moreover , we show that these scored data can be processed for downstream analyzes identical to those from human-scored data , in particular by demonstrating the ability to detect mutation-induced sleep alteration . We provide to the scientific community free usage of SPINDLE and benchmarking datasets as an online server at https://sleeplearning . ethz . ch . Our aim is to catalyze high-throughput and well-standardized experimental studies in order to improve our understanding of sleep .
The SPINDLE method presented here ( sketched in Fig 1 ) is designed to achieve high predictive performance preserved across different experimental settings and labs . Its architecture is carefully crafted in an end-to-end fashion around a convolutional neural network ( CNN ) which operates on top of the preprocessed time-frequency channels of EEG/EMG . We exploit the ability of the CNN to learn highly discriminative and translation-invariant features , as this allows us to remain agnostic to changes in sleep patterns , in both time and frequency dimension . On top of the CNN , a hidden Markov model ( HMM ) describes vigilance state transition dynamics and suppresses physiologically infeasible vigilance state transitions when applicable . To account for artifacts , SPINDLE contains an additional CNN with binary output: artifact or non-artifact , which is combined with the predictions of vigilance states to determine the artifact types ( steps ( d ) and ( g ) in Fig 1 respectively ) . For a detailed explanation , please refer to the Materials and Methods section further below . SPINDLE was tested on data produced in three independent sleep labs: BrownLab ( www . sbrownlab . com ) , TidisLab ( http://tidis-lab . org/ ) and BaumannLab ( http://www . sleep . uzh . ch/en/research-groups/group-baumann . html ) . The collected data consists of a number of rodent EEG/EMG recordings acquired during sleep studies performed with varying experimental paradigms . The recordings were clustered into four animal cohorts with similar characteristics as summarized in Table 1 , and evaluated separately .
One of the major issues of visual inspection is the intrinsic subjectivity of human experts in data annotation , especially in ambiguous cases when signal patterns do not clearly adhere to the predefined scoring rules . For example , during the transition between vigilance states , it is not always clear where the actual state change occurs . Taking this into consideration is particularly relevant for the validation of an automated sleep scoring method . To this end , we analyzed the inter-expert scoring agreement in evaluation of identical EEG/EMG data ( see Fig 2 ) . To estimate the coherence between human experts , we first measured their agreement in regions that no expert identified as “artifacts”—EEG/EMG perturbations related to environmental interference rather than changes in brain state . We then computed the accuracy from the corresponding 3 × 3 vigilance state submatrices from Fig 2 . When comparing human experts from the same lab , the estimated agreement rate of sleep scoring in non-artifact data was 95-96% , while the inter-lab agreement was about 90% . On the other hand , the disagreement between human experts in classification of artifacts was notably higher , as the figure indicates . To measure this , we calculated the ratio between the number of epochs marked as corrupted by both experts and the number of epochs marked as corrupted by at least one of the two experts: a r t i f a c t _ s c o r i n g _ a g r e e m e n t = | a r t i f a c t _ i n t e r s e c t i o n ( e x p e r t 1 , e x p e r t 2 ) | | a r t i f a c t _ u n i o n ( e x p e r t 1 , e x p e r t 2 ) | ( 1 ) These numbers provide rough estimates of the expected accuracy bounds of a hypothetical sleep scoring method comparable to human experts in terms of the predictive performance . To identify the key obstacles towards robust cross-subject sleep classification , we analyzed the fluctuations of EEG in epochs classified as belonging to the same vigilance state . In the context of our problem , ideally , for each vigilance state we would have signal patterns which are consistent ( i ) across epochs within the same subject; ( ii ) across subjects within the same animal cohort; ( iii ) across subjects from different animal cohorts analyzed under different experimental conditions . To explore the variability across these categories , for each animal and for each vigilance state we separately averaged EEG frequency spectra over all epochs , and then compared these measures within and across cohorts ( Fig 3 ) . Whether we applied coarse-grained histogram binning according to the commonly used frequency bands ( Fig 3 , middle column ) or finer binning ( Fig 3 , right column ) , spectral energy was differently distributed among frequency bands for different animal cohorts . For example , even though the figure indicates the existence of certain patterns in sleep state signatures i . e . a prominent peak at around 7Hz characteristic of REM sleep , this cannot be simply interpreted as a rule due to high cross-epoch , cross-animal and cross-cohort variabilities [25 , 27] . This is arguably the main reason why the classical methods which base their features on energies of different frequency bands of power spectrum do not generalize well . The feature vectors of equal vigilance states are highly inconsistent across subjects , especially if animals have significantly different backgrounds . To overcome these variations , SPINDLE employs a preprocessing procedure to increase the consistency of spectral patterns within the samples of the same vigilance class . The effects of preprocessing are illustrated in Fig 4 . On one hand , the log transformation attenuates the discrepancies in magnitudes , and on the other the typical zero mean/unit variance standardization emphasizes the differences between vigilance states . The core characteristic of SPINDLE , however , is its ability to adapt to the variations of sleep state pattern variations in the frequency axis . This flexibility is achieved through translational invariance , an intrinsic property of CNNs . Whenever the frequency spectrum of evaluated data sample deviates from a hypothetically expected spectral pattern in terms of small shifts of relevant peaks , the CNN absorbs these shifts through the convolutional and max-pooling layers ( see Materials and methods ) . Before providing a rigorous statistical performance evaluation of SPINDLE , we illustrate its general applicability in Fig 5 , where we visually compare the scorings of two human experts from different sleep labs with the predictions of our method on identical portion of an EEG/EMG recording from the cohort C . The figure shows that the agreement between the predictions of SPINDLE and the corresponding experts is visually appealing and also sheds light on some common sources of disagreements in the sleep scoring procedure . When vigilance state is constant , the predictions are mainly in agreement , but during the transitions between vigilance states , disagreements are frequent both between human expert scorers and between human and automatically generated scorings . The figure also shows that artifacts are another common source of disagreements . Finally , it is illustrated why time-frequency representation is useful for understanding sleep dynamics i . e . it is easy to notice the correlation between the spectral patterns in the spectrogram and the corresponding vigilance states . In a comprehensive quantitative study we evaluated different performance aspects of SPINDLE . For this purpose , the data set ( previously summarized in Table 1 ) was separated into training and testing subsets . There was no overlap between training and testing sets in any of our experiments . The training set consisted of 2 wildtype mice taken from the cohort A , while the validation was performed on the rest of the data i . e . 20 remaining rodents from different labs , strains , and species . Splitting the data set in this way enabled us to test the main premise of this paper: the robustness of our method holds for different experimental settings and labs without any additional model adaptation . By training SPINDLE only on wildtypes we were able to investigate how well it generalizes across the subjects of the same kind ( two other wildtypes from the same cohort A ) , genetically mutated animals ( 4 mice from the cohort B ) , different animal species ( the rats from the cohort C ) , and different sleep labs ( comparing across cohorts A/B , C , and D ) . First , to diminish the effect of subjectivity in manual sleep scoring , we evaluated the predictions against human expert scoring intersection—epochs in which two human experts agree on the label , since all animal recordings were double scored by two individuals . Here , the epochs in which two human experts disagreed did not have any influence upon the performance evaluation . Secondly , to avoid discarding hard-to-score signal regions , we additionally analyzed the performance with respect to the human experts individually . Finally , as we mention above , artifacts represent a major source of difficulty for human expert scorers ( recall Fig 2 ) . To evaluate SPINDLE more accurately in this respect , we then considered “clean” regions and artifacts separately . More concretely , we first measured the agreement of vigilance state predictions ( given by the output of step ( f ) in Fig 1 ) with the corresponding human expert scorings only for the epochs not marked as artifacts ( by any of the two human experts ) . To evaluate the quality of artifact detection , the 4-category scorings were re-labeled into non-artifacts and artifacts , and then compared to the corresponding predictions ( given by the output of step ( d ) in Fig 1 ) . In addition , we investigated the benefits of applying hidden Markov model ( HMM ) based post-processing , comparing the predictions of HMM ( the output of step ( f ) in Fig 1 ) against the predictions of the convolutional neural network ( CNN ) ( the output of step ( e ) in Fig 1 ) . Evaluations were conducted according to the usual classification metrics , first of overall Accuracy AC , and then for each vigilance state in terms of Precision PR ( C ) , Recall RC ( C ) and F1-score F1 ( C ) for each vigilance state C , in percentages: A C = T P + T N # s a m p l e s · 100 P R ( C ) = T P ( C ) T P ( C ) + F P ( C ) · 100 R C ( C ) = T P ( C ) T P ( C ) + F N ( C ) · 100 F 1 ( C ) = 2 · P R ( C ) · R C ( C ) P R ( C ) + R C ( C ) · 100 ( 2 ) where TP ( C ) , FP ( C ) and FN ( C ) are for vigilance class C the numbers of true positives , false positives and false negatives respectively . TP and TN represent the total number of true positives and true negatives . Table 2 demonstrates the predictive performance of SPINDLE with respect to these metrics ( excluding artifacts as described above ) . We compared the predictions of SPINDLE against the scoring intersection of corresponding human experts . The evaluation was performed with and without HMM-based postprocessing which additionally enforced physiological constraints on vigilance state transitions . Although CNN generates impressive performance alone ( top rows ) , HMM leads to additional improvements ( bottom rows ) , most notably in identification of the REM phase which is usually more challenging to score [10] . SPINDLE hence showed that injecting sleep domain knowledge into the model may induce very positive effects on the predictive performance . Across all wildtype and mutant mouse species and across labs , overall accuracies exceeded 97% . Across species , overall accuracy remained at 93% , demonstrating the generalization capabilities of SPINDLE . The corresponding confusion matrices computed by comparing the label intersection of two human experts to our predictions are given in Fig 6 and show how relatively few mislabeled epochs are confused across different vigilance states . For completeness sake , the figure also shows the confusion with respect to artifact identification . The final agreement calculated when artifacts are taken into account is presented in addition . Next , we compared the predictions of SPINDLE against the scorings of individual human experts . Doing so enabled us to ( i ) include into our analysis the epochs in which two experts disagreed; ( ii ) investigate the potential of SPINDLE to generate predictions which are indistinguishable from the scorings produced by human experts: ideally , the agreement between a human expert and SPINDLE would be close to the agreement between two human experts . The results of the analysis given in Table 3 are more than encouraging and show that in terms of the global scoring agreement i . e . the accuracy , SPINDLE is perfectly comparable to human experts . The agreement rate between the predictions of SPINDLE and each expert is close to the agreement rate between two corresponding experts . Finally , in a separate set of experiments we evaluated the predictive performance of SPINDLE in identifying artifacts . The subjectivity in distinguishing artifacts from clean epochs is generally known to be overwhelming , and similar conclusions may be derived from Fig 2 . To ensure a fair performance estimation we computed the agreement rate with each human expert separately , and then compared it to the agreement rate between the two human experts . Table 4 suggests that SPINDLE’s predictions are , in terms of global agreement as defined by Eq 1 , de facto equal to that of human experts . We compared SPINDLE to three previously reported approaches , using the same data identically split into training and testing sets as already described . Analysis was performed only on the epochs where human experts agreed on the label . Since other algorithms lack dedicated artifact detection and analysis subroutines , corrupted epochs were not taken into account . The following three methods were used as our baselines: After epoch-by-epoch classification , sleep data is typically pooled across each vigilance state , and then quantitatively evaluated with respect to various parameters of sleep architecture , sleep timing and EEG spectral power . Therefore , we next compared such quantitative outputs when calculated using classifications from SPINDLE or from human scorers . To that end , we analyzed the performance of the downstream analysis applied to the predictions of SPINDLE to investigate its capability to ( i ) predict major parameters of the sleep architecture; ( ii ) detect sleep alteration induced by a genetic mutation performed on the cohort B , with respect to the cohort A . Entire framework described in our study was integrated into a web service which can be found at https://sleeplearning . ethz . ch . This service ensures an easy access to the sleep researchers around the world and offers a possibility of accurate evaluation of EEG/EMG recordings in no time . Furthermore , we are continuously improving our framework aiming to create a self-sufficient environment for large scale animal analysis e . g . which would include output analysis in addition . Lastly , we would like to emphasize the two following aspects ( i ) the adaptable artifact threshold functionality; ( ii ) further technical considerations .
We described a new data processing architecture for fast , accurate and physiologically plausible automated sleep scoring of animal EEG/EMG recordings . SPINDLE is based on end-to-end learning and is capable of learning robust features which generalize across domains . From the robust benchmarking procedures that we employed we concluded that SPINDLE ( i ) showed de facto human level performance in the quality of sleep classification; ( ii ) significantly outperformed current state-of-the-art solutions; ( iii ) was able to preserve predictive performance across animals from different experimental settings , labs and species , without additional parameter calibration; ( iv ) was able to detect mutation-induced sleep alteration . Our extensive statistical evaluation suggests that the proposed method is in every way the equal of human experts , and holds great promise to improve cross-lab standardization of sleep analysis . A part of SPINDLE’s success lies in the use of convolutional neural networks ( CNNs ) . CNNs , a variant of deep neural networks , have enjoyed great success , particularly in the domain of natural signal processing [12 , 15 , 16] . They revolutionized the field of computer vision with their unprecedented success on image recognition tasks [12] . Here , we use them to combat a fundamental problem that has plagued automated sleep scoring: feature variance . Classical approaches based on manually crafted features are likely to fail when generalized across experiments simply because class-specific features or feature combinations may differ across data samples , or mutants , or species . Thus , because these models form their feature vectors from the energies of certain frequency bands , even small shifts of the spectral peaks may cause a different distribution of spectral energy with respect to the histogram bins , ruining predictive performance . All three methods that we tested alongside our own arguably suffer from this spectral pattern inconsistency issue related to manual feature extraction . By contrast , a CNN extrapolates well across spectral profile variations much as a human expert would do . Furthermore , SPINDLE employs a hidden Markov model layer to describe probabilistic transitions between vigilance states . Thus , impossible transitions—for example wake to REM , known not to occur naturally—can be artificially suppressed by the user . Provided the assumptions about these physiological constraints hold , HMM generates physiologically plausible prediction sequences leading to improved performance . Unlike other existing solutions which lack a principled way for distinguishing epochs corrupted by artifacts , SPINDLE employs an additional CNN to offer a dedicated computational tool for that task . The identification of artifacts from data is known to be quite challenging due to very large discrepancies in their annotation when different human experts are compared . Nevertheless , we showed that SPINDLE is performing well on this task as well , again reaching human expert level . Furthermore , to still provide some dose of flexibility in this highly biased and human expert-dependent procedure , our framework offers additional functionality which allows users to adapt the degree of artifact rejection . Our framework remains parameter-free and this optional functionality exist to ensure smoother convergence towards full standardization of the sleep scoring procedure . There are some relevant design decisions worth of noting . Firstly , in our work we used out-of-the box CNN , and not any of the commonly used architectures such as AlexNet [12] or ResNet [28] . We empirically found that increasing the depth and width of our network did not led to any improvements , and hence found no reason to e . g . add additional layers to the main architecture . Our hypothesis is that the structural complexity of “spectrograms” is not comparable to the structural complexity of natural images , thus there were additional benefits in having an increased complexity in our CNN architecture . Secondly , in contrast to some related work in the domain of human sleep scoring [8 , 19–21 , 24] , our final architecture does not include memory models i . e . recurrent neural networks ( RNNs ) . We found that their inclusion did not lead to any performance improvements and moreover was harmful to across-domain generalization . Our tests indicated that RNN tends to overfit to subject-specific dynamics and hence makes it difficult to extrapolate knowledge across environments . On the other hand , even though not mutually exclusive , HMMs offer more fine-grained control over the transition dynamics . Secondly , we utilized separate CNNs for artifacts and vigilance states for the following reasons: ( i ) semantically , the patterns in data are different—vigilance states are recognized by somewhat predefined rules while the artifacts are data outliers; ( ii ) the described setup allows us to identify three different types of artifacts , thus more faithfully emulating visual scoring procedure; ( iii ) we found that the proposed framework leads to better predictive performance . Finally , as a part of our future work , we intend to investigate the capabilities of and re-fine our method further in subsequent studies possibly involving other experimental paradigms such as sleep deprivation for example , which might possibly cause other types of distortions of spectral profiles . Another interesting aspect would involve including more human scorers and consequently applying more principled ways of combining their knowledge ( e . g . see [29] ) .
We detail the steps in collecting the data previously summarized in Table 1 . In total , 4 animal cohorts containing 22 animals were acquired from 3 independent sleep labs . Animal studies were performed by authorized researchers according to all applicable laws and regulations of the cantons of Zürich ( BrownLab , BaumannLab ) and Bern ( TidisLab ) , and were each approved by the relevant cantonal authorities . Each sleep recording consists of a pair of EEG signals and an EMG signal simultaneously recorded . Manual labeling of wake-sleep states based on EEG/EMG signals was performed by trained experts from the corresponding sleep labs on all consecutive 4 second epochs . Raw EEG traces were visually inspected offline and scored in three vigilance states , wakefulness , NREM sleep and REM sleep . Wakefulness was defined based on increased EMG activity for more than 50% of the epoch duration . NREM sleep was defined by reduced EMG activity and increased EEG power in < 4Hz frequency ranges . REM sleep was characterized based on low EEG power in > 4Hz oscillations and high EEG power in 6-9Hz frequency bands and intermediate muscle tone . Unclear stages or technical artifacts were excluded and subsequently labeled as artifacts . The data preprocessing module ( step ( c ) in Fig 1 ) serves primarily to form the input for the convolutional neural networks ( CNNs ) . We subject EEG/EMG signals to a sequence of transformations which enhance the learning process and consequently improve classification performance . The transformations are performed per animal and are meant to diminish non-informative differences in subject specific spectral patterns . The preprocessing procedure is illustrated in Fig 12 . Both EEG signals are first resampled to the frequency of 128Hz to neutralize the differences in sampling rates coming from different recording devices . Resampled EEGs are then transformed into the time-frequency domain by applying fast Fourier transform on overlapping frames of size 256 ( corresponds to 2 seconds ) with steps of size 16 . Hamming windows were applied to reduce edge effects . Power spectral density ( PSD ) in time-frequency representation is estimated as squared magnitude of the Fourier transform . Each of the two dimensional spectrograms constructed from EEG signals is treated as a separate feature map on top of which the CNNs convolve . This is analogous to the well-known CNN image classification architectures where the input consists of 3 RGB channels [12] . EEG spectrograms are additionally band-pass filtered ( 0 . 5-24Hz ) , as we experimentally determined that classification performance remains unaffected . Both time-frequency channels are then transformed to log scale and finally each channel is per frequency component standardized ( zero mean / unit variance ) . The EMG signal on the other hand carries the information about muscle activity of evaluated subjects . The total energy of EMG indicates the activity of the corresponding muscle . To decrease the noise we compute signal energy by integrating PSD over a limited frequency band ( 0 . 5-30Hz ) . In other words , we sum up the rows in our time-frequency representation within the given frequency range ( see Fig 12 ) . This leaves us with one-dimensional signal which measures the change in muscle activity over time . However , in order to form a consistent input for CNN with respect to two-dimensional representations of EEG , we introduce an additional dimension by repeating the signal as illustrated in the figure . This way of forming input is beneficial because in each time instance the CNN filters can relate the total EMG signal power with spectral patterns in different regions of the frequency axis . A Convolutional Neural Networks ( CNN ) [12 , 31] is an artificial neural network most commonly composed of a sequence of ( i ) convolutional layers which learn high level signal representations; ( ii ) pooling layers which increase the translational feature invariance; ( iii ) dense layers which learn high-level feature combinations in a discriminative manner; and ( iv ) a softmax layer which generates class probabilities . Details of these layers follow below . For a more thorough introduction to CNN we refer the reader to [32] . To obtain a finer-grained modeling control over the dynamics of vigilance state transitions we utilize a hidden Markov model ( HMM ) . Broadly speaking , HMMs are tools for representing probability distributions over sequences of indirectly observable states . A first-order HMMs is fully specified by the probability distribution of the initial state P ( S1 ) , the matrix of transition probabilities between neighboring states P ( St|St−1 ) and the output model defined by the emission likelihoods P ( Yt|St ) where Yt is the indirect observation of variable St . In the context of our problem , the hidden state in some moment t is the vigilance state of the brain St ∈ {WAKE , REM , NREM} , while the observation Yt is the corresponding region of EEG/EMG signal ( in Fig 13 that would be epocht−2 to epocht+2 ) from which hypothetically the state St can be inferred . Our goal in the training time is to find the optimal parameters λ = [P ( Yt|St ) , P ( St|St−1 ) , P ( S1 ) ] of the HMM , and then to use learned parameters later in the test time to obtain the most probable sequence of vigilance states given input signal: argmax S 1 . . N P ( S 1 . . N | λ , Y 1 . . N = EEG/EMG ) where N is the number of epochs . Firstly , to find the indirect observation emission likelihoods P ( Yt|St ) we use the probabilities generated by the discriminatively trained CNN and we treat them as the model output . Namely , CNN produces posterior probabilities over states P ( St|Yt ) which can be used for computing HMM emission likelihoods P ( Yt|St ) . They are connected by the following equation which is derived directly from the Bayes rule: P ( Y t | S t ) P ( Y t ) = P ( S t | Y t ) P ( S t ) ( 8 ) Secondly , without loss of generality and any substantial effect to the predictive performance we may assume that all initial states P ( S1 ) are equal . Finally , the last component of the HMM is the probability transition matrix P ( St|St−1 ) which is of size 3 × 3 in our case . In order to generate physiologically feasible prediction sequences , when applicable , using the probability transition matrix we can embed the domain knowledge on infeasible vigilance state transitions . An illustration is given in Fig 14 . Whenever it is known in advance that certain transitions are not valid such as REM → NREM and WAKE → REM [36 , 37] which is the case for all the recordings in our data set , we can zero out the corresponding entries in the probability transition matrix . Since there is an additional constraint that the rows of the transition matrix must sum up to 1 , this leaves us with effectively only four remaining free parameters . These can be set to be of equal value , or tuned additionally to improve the smoothing of the vigilance state sequence estimates . Having specified the model , new posterior probabilities over vigilance states are generated as follows: P ( S t | Y t , S t - 1 ) = P ( Y t | S t ) P ( S t | S t - 1 ) P ( Y t ) = P ( S t | Y t ) P ( S t | S t - 1 ) P ( S t ) ( 9 ) where we used Eq 8 to obtain the last equality . Since the CNN is trained in a class-balanced way which compensates for an unequal class ratio in the training set , the prior probabilities of states P ( St ) may be assumed to be equal . Finally , using the specified HMM model we may now simply apply Viterbi decoding to find the most probable sequence of vigilance states ( the output of step ( f ) in Fig 1 ) . In summary , the HMM allows us to combine the CNN estimates used for computing emission likelihoods P ( Yt|St ) ( observation modeling ) with the knowledge encoding capability of the HMM probability transition matrix P ( St|St−1 ) ( dynamics modeling ) to produce more plausible and consequently more accurate prediction sequences . It is also worth noting that the commonly advocated problem of HMMs that the observations are assumed to be independent ( given state ) is treated through the inclusion of neighboring epochs into the convolving field of the CNN ( recall Fig 13 ) . | Machine learning-based approaches hold great promise to pave the way for high-throughput animal sleep monitoring . With the novel developments of gene-engineering techniques and the proliferation of experimental sleep studies , the need for the automation and cross-lab standardization of sleep scoring becomes more imminent . Traditionally , the classification of electroencephalographic ( EEG ) signatures is done by trained human experts via visual inspection . Here we present a novel algorithm based upon neural networks to automatically generate accurate and physiologically plausible predictions . Performed experiments demonstrate that the proposed solution offers de facto human level performance , is more accurate than any other approach to date ( 93-99% accurate compared to multiple trained human scorers ) , and functions across different genetic mutants , surgery procedures , recording setups and even different species . Moreover , our method was capable of detecting mutation-induced changes in sleeping patterns . To allow for its widespread adaptation , we make our framework freely available through the provision of an online server and an easy to use interface . This community tool will both contribute to the standardization of experimental studies and enhance scientific understanding of sleep . | [
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... | 2019 | SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species |
Central nervous system ( CNS ) infection continues to be an important cause of mortality and morbidity , necessitating new approaches for investigating its pathogenesis , prevention and therapy . Escherichia coli is the most common Gram-negative bacillary organism causing meningitis , which develops following penetration of the blood–brain barrier ( BBB ) . By chemical library screening , we identified epidermal growth factor receptor ( EGFR ) as a contributor to E . coli invasion of the BBB in vitro . Here , we obtained the direct evidence that CNS-infecting E . coli exploited sphingosine 1-phosphate ( S1P ) for EGFR activation in penetration of the BBB in vitro and in vivo . We found that S1P was upstream of EGFR and participated in EGFR activation through S1P receptor as well as through S1P-mediated up-regulation of EGFR-related ligand HB-EGF , and blockade of S1P function through targeting sphingosine kinase and S1P receptor inhibited EGFR activation , and also E . coli invasion of the BBB . We further found that both S1P and EGFR activations occurred in response to the same E . coli proteins ( OmpA , FimH , NlpI ) , and that S1P and EGFR promoted E . coli invasion of the BBB by activating the downstream c-Src . These findings indicate that S1P and EGFR represent the novel host targets for meningitic E . coli penetration of the BBB , and counteracting such targets provide a novel approach for controlling E . coli meningitis in the era of increasing resistance to conventional antibiotics .
Bacterial meningitis is currently recognized as one of the top ten leading causes of global deaths from infectious diseases . Case fatality rates range from 5–25% , and approximately 25–50% of survivors sustain neurologic sequelae [1–4] . The morbidity and mortality rates of bacterial meningitis vary , depending on age , immune state , patient location , and causative organism . Patient groups at risk of high rates of mortality and morbidity include newborns , the elderly , and those living in developing countries , while the infections with higher rates of mortality and morbidity are those caused by Gram-negative bacilli [2 , 3] . Escherichia coli is the most common Gram-negative bacillary organism causing meningitis [1–4] . Most cases of E . coli meningitis develop from hematogenous spread [5 , 6] , and occur as a result of the bacterial penetration of the blood–brain barrier ( BBB ) , which is a prerequisite for the development of central nervous system ( CNS ) infection [1–4] . The BBB consists of brain microvascular endothelial cells , astrocytes and pericytes , and is a structural and functional barrier that maintains the neural microenvironment by regulating the passage of molecules into and out of brain , and prevents circulating microbes from penetrating into the brain [1 , 2] . Meningitis isolates of E . coli , however , have been shown to invade the BBB [1–4] , but it remains incompletely understood how circulating E . coli strains penetrate the BBB . Several lines of evidence from human cases and experimental animal models of E . coli meningitis indicate that E . coli penetration into the brain follows a high level of bacteremia , and that cerebral capillaries are the portal of entry into the brain [1–6] . Since E . coli penetration into the brain occurred in the cerebral microvasculature [5] , we developed the in vitro BBB model with human brain microvascular endothelial cells ( HBMEC ) to investigate E . coli invasion of the BBB [7 , 8] . We also developed the in vivo animal model of experimental hematogenous meningitis to mimic E . coli penetration into the brain that occurs in neonatal meningitis [5] . We have shown with both in vitro and in vivo models that E . coli invasion of HBMEC is directly correlated with its penetration into the brain [9–15] , suggesting that elucidation of the mechanisms involved in E . coli invasion of HBMEC is likely to enhance our knowledge on the pathogenesis of E . coli meningitis . We took advantage of genome sequencing information available from meningitis isolates of E . coli ( e . g . , strains IHE3034 , S88 , RS218 ) to study E . coli penetration of the BBB . Using functional genomics studies ( e . g . , transposon and signature-tagged mutagenesis , DNA microarray and comparative genome hybridization ) , we have identified several microbial factors contributing to meningitic E . coli invasion of HBMEC , which include OmpA , FimH , NlpI , IbeA , IbeB , IbeC and CNF1 [9–12 , 15–22] . We have also shown that these microbial factors exploit specific host receptors and host cell signaling molecules for bacterial invasion of HBMEC [2 , 3] . For example , OmpA interacts with gp96 on HBMEC , resulting in activation of focal adhesion kinase ( FAK ) , while FimH interaction with CD48 and CNF1 interaction with 37 kDa laminin receptor precursor ( 37LRP ) lead to activation of RhoGTPases [23–27] . Biological relevance of these microbial-host interactions in the pathogenesis of E . coli meningitis is shown by the demonstrations that ( a ) exogenous OmpA and gp96 and anti-gp96 antibodies block E . coli invasion of HBMEC , but do not exhibit any blocking effect on the OmpA mutant [23 , 24] , ( b ) addition of exogenous FimH- or CD48- antibodies inhibits E . coli invasion of the HBMEC [25] , and ( c ) expression levels of 37LRP dictates the ability of E . coli to invade HBMEC , but exhibited no effect on the CNF1 mutant [26 , 27] . Despite the extensive information available on microbial and host factors as well as host cell signaling molecules contributing to E . coli invasion of HBMEC [2–4] , the mechanisms involved in E . coli penetration of the BBB remain incompletely understood . Since meningitic E . coli invasion of HBMEC is correlated with its penetration into the brain , we used E . coli invasion of HBMEC as a biologically relevant model in the present study for screening of a chemical library to discover novel targets affecting E . coli penetration of the BBB . In our screen , we identified that gefitinib , a selective inhibitor of epidermal growth factor receptor ( EGFR ) tyrosine kinase [28] , significantly inhibited E . coli invasion into HBMEC monolayers . EGFR belongs to the ErbB family of receptor tyrosine kinases ( RTKs ) , consisting of four closely-related members ( ErbB1/EGFR , ErbB2 , ErbB3 , ErbB4 ) [29–31] . EGFR is initially expressed in the plasma membrane in an inactive form , and becomes activated through certain kinases and/or after binding to its specific ligands , which are produced as transmembrane precursors and released by proteolytic cleavage [29–32] . To date , several bacterial pathogens have been reported to target EGFR through different mechanisms to facilitate their infection of host cells , including Neisseria gonorrhoeae , Neisseria meningitidis , Helicobacter pylori , Haemophilus influenzae , and Klebsiella pneumoniae [33–38] . However , to our knowledge , it is unknown whether EGFR is involved in meningitic E . coli invasion of the BBB . In the present study , we reported for the first time that sphingosine 1-phosphate ( S1P ) -mediated activation of EGFR represents a novel mechanism exploited by meningitic E . coli for penetration of the BBB , the essential step in the development of E . coli meningitis . EGFR as well as S1P are , therefore , likely to represent the novel targets for investigating the pathogenesis , prevention and therapy of E . coli meningitis .
Our chemical screen identified gefitinib as an inhibitor of meningitic E . coli invasion of HBMEC monolayer . Gefitinib is a low-molecular-weight anilinoquinazoline that selectively inhibits EGFR [28] . EGFR has been shown to play essential roles in cell proliferation , survival , and migration as well as in carcinogenesis and cancer progression , and is considered an attractive target for anticancer therapies [29–31] . However , the role of EGFR in E . coli meningitis is unknown . To address this issue , we first determined the effect of gefitinib on meningitic E . coli strain RS218 binding to and invasion of HBMEC . We found that gefitinib inhibited RS218 invasion of HBMEC in a dose-dependent manner without affecting E . coli adhesion to HBMEC ( Fig 1A ) , suggesting that its target , EGFR , is likely to be involved in E . coli invasion of the BBB . Gefitinib did not affect bacterial growth , as assessed by determination of colony-forming units ( CFUs ) in the presence and absence of gefitinib ( Fig 1B ) . The cytotoxicity and proliferation of the HBMEC , as assessed by live/dead stain ( Molecular Probes ) and MTT assays , were not affected by gefitinib ( Fig 1C ) . EGFR was subsequently knocked out from HBMEC via CRISPR-Cas9 editing approach , and bacterial invasion of the EGFR knock-out cells ( KO#35 ) were compared with that of the control cells . The EGFR was not detectable in the KO#35 HBMEC by the Western blotting ( Fig 1D ) , and RS218 invasion of the KO#35 cells was significantly decreased compared with that of the control cells ( Fig 1D ) . We next examined the contribution of EGFR tyrosine kinase activity to E . coli invasion of HBMEC . We showed a time-dependent tyrosine phosphorylation of EGFR in response to meningitic E . coli RS218 infection ( Fig 1E ) but no change in EGFR transcription or expression ( Fig 1F and 1G ) . E . coli invasion of HBMEC transfected with dominant-negative EGFR , pcDNA-EGFR-GGS , encoding EGFR without tyrosine kinase activity [39] , was significantly decreased compared with that of control vector-transfected cells ( Fig 1H ) . Moreover , we examined the role of EGFR in meningitic E . coli penetration into the brain in a neonatal animal model , involving intraperitoneal administration of gefitinib to 1-week-old mice . The results , as determined by bacterial counts ( CFUs ) recovered from the blood and brain specimens of mice receiving gefitinib or vehicle control , showed that gefitinib did not affect the level of bacteremia , but was efficacious in preventing E . coli penetration into the brain ( Fig 1I ) . In addition , we showed a co-localization of EGFR with meningitic E . coli strain RS218 in HBMEC monolayer ( Fig 1J ) . Together , these data support our novel concept that EGFR contributes to meningitis-causing E . coli penetration of the BBB . As indicated above , we demonstrated both in vitro and in vivo that EGFR is involved in meningitic E . coli RS218 penetration of the BBB , but there was no information on how EGFR is activated in response to E . coli invasion . Since E . coli penetration of the BBB requires specific microbial factors that contribute to HBMEC invasion [1–4] , we examined whether EGFR activation occurred in response to those E . coli factors contributing to HBMEC invasion . The wild-type strain RS218 and its mutants with deletion of ompA , cnf1 , fimH , ibeA , ibeB , ibeC or nlpI were examined for their involvement in EGFR tyrosine phosphorylation in HBMEC . We found that the mutants deleted of ompA , fimH or nlpI exhibited a significantly lower level of EGFR activation compared with wild-type strain RS218 ( Fig 2A ) , suggesting that E . coli OmpA , FimH , and NlpI proteins are likely to contribute to EGFR activation in HBMEC . In contrast , the mutants deleted of ibeA , ibeB , ibeC or cnf1 did not show the decrease in EGFR activation ( Fig 2A ) . The involvement of OmpA , FimH and NlpI in EGFR activation was further supported by the demonstration that antibodies directed against OmpA , FimH and NlpI inhibited EGFR activation in response to strain RS218 in HBMEC ( Fig 2B ) . As expected , the triple mutant deleted of ompA , fimH and nlpI could not induce a discernible EGFR activation , similar to that of uninfected HBMEC ( Fig 2C ) . The exploitation of EGFR by OmpA , FimH and NlpI in E . coli invasion of the cells was further examined by using another selective EGFR inhibitor , erlotinib [40] , as well as the EGFR KO#35 cells . The results showed that erlotinib inhibited invasion of the wild-type strain RS218 in a dose-dependent manner , while it did not affect the HBMEC invasion by the triple mutant deleted of ompA , fimH , and nlpI ( Fig 2D ) . Similarly , the triple mutant’s invasion of the KO#35 cells did not differ from that of the control cells ( Fig 2E ) . Taken together , these findings indicate that E . coli virulence factors OmpA , FimH , and NlpI are likely to be involved in exploitation of EGFR in meningitic E . coli invasion of HBMEC . Recent studies have shown that S1P acts as a multifunctional bioactive sphingolipid metabolite implicated in a wide range of biological effects , such as cell proliferation , immune and allergic reactions , and regulation of the vascular cell function [41–44] . The role of S1P in E . coli meningitis , however , has not been previously appreciated . In our study , we found that the same microbial factors involved in EGFR activation also contributed to S1P generation in response to E . coli infection in HBMEC . S1P levels were significantly higher in HBMEC infected with meningitic E . coli wild-type strain RS218 compared to those infected with the triple mutant deleted of ompA , fimH and nlpI , as measured by LC-MS/MS after lipid extraction of HBMEC [45] . The S1P content in HBMEC was normalized to lipid phosphate in the extracted samples and expressed as pmol/nmol lipid phosphate ( mean ± SD of three samples ) , being 2 . 45 ± 0 . 35 after 30 min at 37°C in HBMEC incubated with wild-type RS218 vs . 1 . 29 ± 0 . 36 in cells incubated with the triple deletion mutant ( Fig 3A , p<0 . 05 ) . Accordingly , a trend for the decreased amount of sphingosine was observed in HBMEC incubated with wild-type RS218 compared to that incubated with the triple mutant ( Fig 3A ) . Since the same microbial factors contributing to HBMEC invasion are involved in EGFR activation and S1P generation , we hypothesized that the contribution of EGFR to E . coli penetration of the BBB is likely to be related to that of S1P . S1P synthesis is catalyzed by sphingosine kinases 1 and 2 ( SphK1 and SphK2 ) [41–44] . We , therefore , examined the role of S1P in E . coli invasion of the BBB by determining the involvement of SphK1 and SphK2 using specific inhibitors against SphK1 and/or SphK2 , and an inactive analogue ( Fig 3B ) [46–50] . Both ( S ) -FTY720-vinylphosphonate ( inhibitor of SphK1 and SphK2 , abbreviated as ( S ) -FTY-Pn ) and ( R ) -FTY720-methyl ether ( selective inhibitor of SphK2 , abbreviated as ROME ) significantly inhibited E . coli RS218 invasion of HBMEC , while SphK1 inhibitors ( RB-032 and RB-033 ) and the inactive analogue ( RB-034 ) did not exhibit any inhibition ( Fig 3C ) , suggesting the involvement of SphK2 , not SphK1 , in meningitic E . coli invasion of HBMEC . This finding was further supported by the dose-dependent inhibition of E . coli RS218 invasion by the SphK2 inhibitor ( Fig 3D ) , as well as the time-dependent activation of SphK2 ( analyzed as the ratio of p-SphK2/SphK2 ) in response to strain RS218 in HBMEC , which was abolished in HBMEC pretreated with the SphK2 inhibitor ( Fig 3E ) . Next , the role of SphK2 in E . coli penetration into the brain was examined in SphK2−/− mice compared with wild-type C57BL/6j mice [42] . We found that the magnitudes of bacteremia did not differ between the two groups of mice , as shown by the similar bacterial counts in the blood of wild-type and SphK2−/− mice ( Fig 3F ) . However , the bacterial counts in the brains of SphK2−/− mice were significantly lower than those in the brains of wild-type mice ( Fig 3F ) , indicating that deletion of SphK2 resulted in decreased E . coli penetration into the brain without affecting the magnitude of bacteremia . These in vitro and in vivo findings demonstrate the novel concept that SphK2 contributes to meningitic E . coli penetration of the BBB . S1P is known to exhibit diverse activities by binding to and signaling through its specific cell-surface receptors , which are members of the G protein-coupled receptors ( GPCR ) family [41 , 43 , 44] . We next determined the role of S1P receptors in meningitic E . coli invasion of HBMEC using selective receptor antagonists ( VPC23019 for S1P1 and S1P3 , and JTE-013 for S1P2 ) [41 , 44] . We found that pretreatment of HBMEC with VPC23019 at indicated concentrations had no effect on E . coli invasion ( Fig 3G ) , suggesting that receptors S1P1 and S1P3 are not likely to be involved in E . coli invasion of the BBB . In contrast , HBMEC pretreated with S1P2-specific antagonist JTE-013 displayed a dose-dependent decrease in E . coli invasion ( Fig 3G ) , suggesting that S1P2 plays a role in meningitic E . coli invasion of HBMEC . Taken together , our findings thus far support the concept that meningitic E . coli infection of HBMEC increases the generation of S1P through SphK2 activation , and that the interaction of S1P with S1P2 is involved in E . coli invasion of the BBB . As indicated above , we showed that S1P levels were significantly less in HBMEC incubated with the triple mutant deleted of ompA , fimH and nlpI , compared to wild type strain . To further support our hypothesis that the contribution of EGFR to E . coli penetration of the BBB is related to that of S1P , the E . coli factors involved in EGFR activation ( OmpA , FimH , NlpI ) were examined for their contributions to SphK2 activation in HBMEC . The results revealed that phosphorylation of SphK2 was decreased in HBMEC infected with the mutants with deletion of ompA , fimH and nlpI , individually and in combination , compared with HBMEC infected with wild-type RS218 ( Fig 3H ) . Therefore , these findings support that E . coli factors OmpA , FimH and NlpI , the key contributors to bacterial adhesion and invasion , participate in both EGFR activation and SphK2-S1P-S1P2 signaling cascade , and suggest a linkage between EGFR and SphK2-S1P-S1P2 in meningitic E . coli penetration of the BBB . Since the same microbial factors ( OmpA , FimH , NlpI ) are involved in SphK2 activation , S1P generation , and EGFR activation , we examined the potential relationship between the SphK2-S1P-S1P2 cascade and EGFR activation in meningitic E . coli invasion of the BBB . We found that pharmacological inhibition of EGFR with gefitinib did not affect E . coli-induced phosphorylation of SphK2 , as shown by the time-dependent activation of SphK2 in response to meningitic E . coli in HBMEC with and without gefitinib treatment ( Fig 4A ) . We next compared E . coli-induced SphK2 phosphorylation in HBMEC transfected with EGFR dominant-negative construct pcDNA-EGFR-GGS with that in cells transfected with the vehicle control pcDNA3 . 1 . The results showed that the pattern of increased SphK2 phosphorylation upon infection with meningitic E . coli was similar between HBMEC expressing dominant-negative EGFR and HBMEC expressing the control vector ( Fig 4B ) . These findings indicate that pharmacological inhibition and dominant-negative expression of EGFR did not interfere with SphK2 activation by meningitic E . coli . Next , we examined and compared the EGFR activation upon E . coli infection in HBMEC , with and without inhibition of S1P function . As shown in Fig 4C , blockade of the S1P signaling cascade by the S1P2 antagonist JTE-013 was effective in inhibiting EGFR activation in response to meningitic E . coli . These findings demonstrate the novel concept that both SphK2-S1P-S1P2 and EGFR contribute to meningitic E . coli invasion , and that the SphK2-S1P-S1P2 signaling cascade is likely to act upstream of EGFR in meningitic E . coli penetration of the BBB . EGFR consists of an extracellular ligand-binding domain , a single membrane-spanning region , and a cytoplasmic kinase domain [31 , 51 , 52] . It is known that EGFR activation occurs via kinases and/or transactivation through binding to specific ligands . At present , several EGFR-related ligands are known , including EGF , transforming growth factor α ( TGFα ) , heparin-binding EGF-like ligand ( HB-EGF ) , amphirugulin ( AREG ) , betacellulin ( BTC ) , epiregulin ( EREG ) , epigen and neuregulin family members [31 , 51 , 52] . We next performed quantitative real-time PCR to investigate whether expressions of the above-mentioned ligands are affected in response to meningitic E . coli . We selected six ligands , EGF , AREG , BTC , EREG , HB-EGF , and TGFα , representing the EGFR-related ligands previously examined in a study with N . gonorrhoeae , and determined their transcriptional levels in response to meningitic E . coli infection . Our quantitative PCR data showed that three ligands , HB-EGF , AREG and EREG , displayed significant up-regulation at 60 min after infection with strain RS218 , while the transcriptional levels of EGF , BTC and TGFα remained unchanged during the infection ( Fig 4D ) . Since the E . coli factors OmpA , FimH , and NlpI were shown to be important in EGFR activation , we examined whether the transcriptional levels of those ligands were changed in response to the triple deletion mutant ( ΔompAΔfimHΔnlpI ) , compared with the wild-type strain . The results showed that the triple deletion mutant was able to induce significant up-regulation of AREG and EREG after 60 min , while there was no up-regulation of HB-EGF , as well as EGF , BTC , and TGFα , after infection with the triple deletion mutant ( Fig 4D ) . These findings demonstrate that HB-EGF is the ligand up-regulated in response to meningitic E . coli strain RS218 , compared with the triple mutant with deletion of ompA , fimH , and nlpI , suggesting that increased expression of HB-EGF might be involved in the increased activation of EGFR by meningitic E . coli strain . As shown above , the SphK2-S1P-S1P2 signaling cascade is shown to be upstream of EGFR , and we determined the effect of SphK2-S1P-S1P2 blockade on up-regulation of HB-EGF in response to E . coli . As expected , we observed that HB-EGF up-regulation in response to strain RS218 was abolished in HBMEC pretreated with SphK2 inhibitor ROME and S1P2 antagonist JTE-013 ( Fig 4E ) . Taken together , these findings support that the SphK2-S1P-S1P2 cascade exploits EGFR activation via up-regulation of HB-EGF in response to meningitic E . coli , and that SphK2-S1P-S1P2 is upstream of EGFR in meningitic E . coli invasion of HBMEC . HB-EGF is synthesized as a membrane-spanning precursor molecule and proteolytically processed by metalloproteinases of the ADAM ( a disintegrin and metalloproteinase ) family to be involved in binding to and activation of EGFR [53 , 54] . To further examine the contribution of HB-EGF to EGFR activation and E . coli invasion of the BBB , we investigated the effect of HB-EGF shedding on EGFR activation and E . coli invasion of HBMEC using the diphtheria toxin mutant Cross-Reacting Material 197 ( CRM197 ) , a nontoxic mutant of the diphtheria toxin that retains the ability to bind pro-HB-EGF and prevent its shedding from EGFR stimulation [55] . The results showed that CRM197 was effective in preventing EGFR activation in response to E . coli strain RS218 ( Fig 4F ) , and also significantly inhibited RS218 invasion of HBMEC in a dose-dependent manner . The triple deletion mutant , as expected , exhibited significantly decreased HBMEC invasion and CRM197 did not decrease the triple mutant’s invasion , and significant inhibition was demonstrated at the highest concentration tested ( Fig 4G ) . As shown previously [1–4] , HBMEC invasion frequency of less than 5% of wild type strain’s invasion , however , is less likely to be biologically relevant . We next examined and compared the release of secretory HB-EGF in the supernatants of HBMEC in response to infection with wild-type strain RS218 or its triple deletion mutant by ELISA . The HB-EGF levels in HBMEC infected with the triple deletion mutant for up to 4 h were below the detection limit ( 16 pg/mL ) , similar to those of the uninfected control . In contrast , the HB-EGF levels in HBMEC infected with wild-type RS218 were increased by approximately 3-fold at 4 h of infection ( p<0 . 01 ) ( Fig 4H ) . Taken together , the above findings demonstrate that meningitic E . coli RS218 , with the help of specific microbial factors OmpA , FimH and NlpI , up-regulates the expression and release of HB-EGF , resulting in the transactivation of EGFR , and that this transactivation is dependent on the SphK2-S1P-S1P2 signaling cascade . Meningitic E . coli strains exploit specific host cell signaling molecules to promote their invasion of the BBB [1–4] . The phosphotyrosine residues in the cytoplasmic domain of EGFR can serve as bait for recruitment of proteins containing SH2 domains or certain phosphotyrosine-binding domains , depending on stimuli , and can act as a switch to assist and extend the signal transduction of RTK pathways [29] . The c-Src tyrosine kinase was shown to be recruited by the phosphotyrosine residues of EGFR upon activation and to function as a mediator of EGFR signaling , e . g . , ligand-independent activation of EGFR [56] . Moreover , c-Src tyrosine kinase was reported to regulate host cell actin cytoskeleton rearrangement and contribute to E . coli invasion of HBMEC [57] . To examine whether c-Src tyrosine kinase is involved in EGFR signaling in response to meningitic E . coli invasion , we performed co-immunoprecipitation and Western blotting to assess the possible recruitment and activation of c-Src by EGFR . Our co-immunoprecipitation experiments with an anti-EGFR antibody showed that c-Src tyrosine kinase ( 60 kD ) was associated with EGFR and that this association was maintained in HBMEC during 60 min incubation with meningitic E . coli RS218 ( Fig 5A ) . After stripping , the membrane was re-probed for EGFR , showing an unchanged level of EGFR upon E . coli invasion ( Fig 5A ) , consistent with our earlier demonstration of no change in EGFR expression in response to meningitic E . coli infection . We subsequently examined the activation of c-Src tyrosine kinase in response to E . coli RS218 infection , and showed that c-Src activation occurred in a time-dependent manner , but was abolished in HBMEC pretreated with the EGFR kinase inhibitor gefitinib ( Fig 5B ) as well as in HBMEC expressing the EGFR dominant-negative construct ( Fig 4B ) . These findings suggest that c-Src tyrosine kinase acts downstream of EGFR in meningitic E . coli invasion of HBMEC . We next examined the contribution of c-Src to E . coli invasion of HBMEC . As expected from the c-Src association with EGFR , E . coli invasion of HBMEC was significantly decreased by pretreatment of HBMEC with the c-Src tyrosine kinase inhibitor PP2 compared with the vehicle control , as well as by transfection of HBMEC with c-Src dominant-negative construct compared with the control vector ( Fig 5C and 5D ) . In support of our demonstration that c-Src is downstream of EGFR activation , which is in turn a downstream event of SphK2-S1P-S1P2 signaling in E . coli invasion of HBMEC , we found that pretreatment of HBMEC with the S1P2 antagonist JTE-013 inhibited c-Src activation in response to E . coli RS218 ( Fig 5E ) , while HBMEC pretreated with the c-Src kinase inhibitor PP2 or transfected with the c-Src dominant-negative construct did not affect SphK2 and EGFR activation in response to RS218 infection in HBMEC ( Fig 5F and 5G ) . Taken together , these findings demonstrate that c-Src tyrosine kinase is downstream of the SphK2-S1P-S1P2-EGFR signaling cascade and that SphK2-S1P-S1P2-EGFR-c-Src contribute to meningitic E . coli invasion of the BBB .
Bacterial pathogens , including meningitis-causing pathogens , exploit host cell signaling molecules to promote their infections , but the underlying mechanisms vary depending upon the types of pathogens and host tissues . Recent studies have shown that EGFR plays a role for several pathogenic organisms in the pathogenesis of their infections . For example , Pseudomonas aeruginosa and H . pylori were shown to induce transactivation of EGFR to prevent epithelial cell apoptosis during the early stage of infection , thereby facilitating their survival in the host cells [36 , 58] . EGFR activation by nontypeable H . influenzae attenuates host defensive and immune responses by negatively regulating the expression of Toll-like receptor 2 [37] . K . pneumoniae induces EGFR and its downstream signaling cascades to attenuate the activation of NF-κB , thereby modulating host immune responses [38] . Pasteurella multocida toxin exploits the transactivation of EGFR and subsequent mitogen-activated protein kinase activation to induce fibroblast proliferation [59] . In addition , N . gonorrhoeae modulates the activity and cellular distribution of host EGFR to facilitate its invasion and transmigration across the epithelium [33 , 34] . Meningitic E . coli strains have been shown to penetrate the BBB via a transcellular mechanism [2] , but the underlying mechanisms remain incompletely understood . In this report , we demonstrate a novel role of EGFR in E . coli penetration of the BBB , a prerequisite for the development of E . coli meningitis . Noticeably , our chemical library screen using a model of E . coli invasion of HBMEC identified gefitinib , a selective inhibitor of EGFR tyrosine kinase , as an effective inhibitor of E . coli invasion of HBMEC . We , then , showed that EGFR contributed to E . coli penetration of the BBB , indicating that our approach of targeting E . coli invasion of HBMEC is likely to identify targets involved in the pathogenesis of E . coli meningitis . Here , our demonstration of EGFR contribution to meningitic E . coli penetration of the BBB is provided by several lines of evidence . These include ( a ) pharmacological inhibition and knock-out of EGFR inhibited meningitic E . coli strain RS218 invasion of HBMEC in vitro , ( b ) administration of gefitinib effectively inhibited the penetration of circulating E . coli into the brain of neonatal mice , and ( c ) the co-localization of EGFR with the E . coli strain in HBMEC . These findings support the novel concept that EGFR is a biologically relevant host factor affecting E . coli penetration of the BBB , but the underlying mechanisms remain unclear . We have previously shown that meningitic E . coli exploits specific host factors for invasion of the BBB , and that host factors contribute to invasion of the BBB by functioning as receptors for specific microbial factors and/or exploiting specific host cell signaling molecules [1–4] . If EGFR functions as a cell surface receptor for specific microbial factors , then blockade of EGFR would have affected E . coli binding to and invasion of the BBB . However , we found that pharmacological inhibition of EGFR blocked E . coli invasion of HBMEC in a dose-dependent manner and there was no effect on bacterial adhesion , indicating that EGFR is likely to contribute to E . coli invasion of the BBB by affecting host cell signaling molecules . This concept was also supported by our demonstration that three bacterial determinants contributing to E . coli invasion of the BBB ( OmpA , FimH , NlpI ) were involved in EGFR activation . Since these E . coli proteins were shown to interact with different host receptors [2 , 23–25] , it is unlikely that EGFR functions as a co-receptor for different microbial-host factors . Studies are needed to elucidate how different microbial-host interactions exploit EGFR activation for E . coli penetration of the BBB . S1P is recognized as a novel bioactive lipid mediator involved in physiological and pathogenic vascular functions [60] . For example , S1P regulates angiogenesis by activating the S1P1 and S1P3 receptors ( GPCRs ) on endothelial cells , which is required for endothelial cell morphogenesis into capillary-like networks [61] . S1P was also shown to induce adherens junction assembly through the Gi/MAPK pathway and the small GTPase Rho and Rac pathways [61] . The S1P receptor S1P1 was shown to be essential for vascular maturation , and S1P1-mediated migration was defective in S1P1 mutant cells through their inability to activate the small GTPase Rac [62] . In addition , the S1P receptor S1P2 regulates vascular inflammation and atherosclerosis by inducing the release of inflammatory cytokines IL-1β and IL-18 and retaining macrophages in plaques [63] . Since various lines of evidence have shown that S1P functions in the vasculature and vascular-related cells [41 , 60] , we investigated whether S1P is involved in microbial penetration of the BBB . Our data demonstrate for the first time a novel role of S1P and a potential crosstalk between EGFR and SphK2-S1P-S1P2 signaling in HBMEC upon meningitic E . coli infection . This novel concept was shown by our demonstrations ( a ) that S1P levels were significantly increased in HBMEC in response to meningitic E . coli infection , ( b ) that SphK2 inhibitors , but not SphK1 inhibitors , and S1P2 antagonist , but not S1P1/3 antagonist , inhibited E . coli invasion of HBMEC and ( c ) that the E . coli factors contributing to EGFR activation ( OmpA , FimH , NlpI ) were also shown to be involved in the activation of SphK2 , and subsequent S1P generation in response to E . coli RS218 . Moreover , S1P was shown to be an upstream signaling molecule of EGFR , by demonstrations that blockade of S1P function inhibited EGFR activation in response to meningitic E . coli , while blockade of EGFR function did not affect SphK2 activation . These findings prompted us to investigate how EGFR activation occurs , and to determine the role of S1P signaling in EGFR transactivation in meningitic E . coli invasion of the BBB . To address these questions , we investigated the mechanisms of EGFR activation in response to meningitic E . coli in HBMEC . Activation of EGFR can occur upon binding of a specific ligand to its extracellular ligand-binding domain , leading to EGFR homo- and hetero-dimerization , and tyrosine phosphorylation of the cytoplasmic tyrosine kinase domain [31 , 51 , 52] , and is classified as a ligand-dependent transactivation . A previous study on N . gonorrhoeae invasion of genital epithelial cells showed that EGFR transactivation occurred through up-regulation of several ligands including HB-EGF , AREG , and TGFα [33] . Here , we examined whether EGFR activation occurred in response to meningitic E . coli in a ligand-dependent manner by analyzing the expression levels of EGFR-related ligands . Our quantitative real-time PCR analysis revealed that upregulation of HB-EGF differed between HBMEC infected with wild-type meningitic E . coli and those infected with its triple mutant deleted of ompA , fimH and nlpI . Additionally , we were able to detect significant levels of HB-EGF in the supernatants of HBMEC infected with meningitic E . coli strain RS218 , but not with the triple deletion mutant , suggesting that HB-EGF is likely to be involved in the activation of EGFR . In addition , HB-EGF is shown to bind to EGFR and ErbB4 [64] , and our experiments here with EGFR knock-out and dominant-negative construct supported the involvement of EGFR in E . coli invasion of HBMEC . The contribution of HB-EGF was also shown by our demonstration that blockade of HB-EGF with CRM197 [55] , which prevents ectodomain shedding of proHB-EGF , significantly inhibited EGFR activation as well as E . coli invasion of HBMEC . These findings support that the ligand-dependent action involving OmpA/FimH/NlpI exploitation of HB-EGF is likely to contribute to transactivation of EGFR in meningitic E . coli invasion of HBMEC . Our time-dependent mRNA up-regulation of HB-EGF , however , did not follow the same time-dependent kinetics as EGFR activation . We showed that EGFR activation occurred in HBMEC as early as 15 min after infection with meningitic E . coli , while our real-time PCR analysis revealed that the mRNA level of HB-EGF was up-regulated at 60 min after infection , implying that EGFR activation in response to meningitic E . coli may occur via two mechanisms , comprising an early activation ( perhaps representing a ligand-independent activation ) and a late ligand-dependent activation involving HB-EGF . Previous studies have reported the crosstalk between GPCRs and EGFR , in which both ligand-independent and ligand-dependent activation of EGFR by GPCRs was demonstrated [65] . Here , our data showed that SphK2-S1P-S1P2 was upstream of EGFR in meningitic E . coli invasion , and more importantly that the EGFR activation at 30 min after infection was abolished by treatment with the S1P2 antagonist JTE-013 , implying that the early EGFR activation might arise from the S1P-S1P2 pathway , which involves GPCR-related signaling . While S1P signaling occurred , it also trigged the late ligand-dependent activation of EGFR by regulating the expression of the EGFR-associated ligand HB-EGF . This is supported by the demonstration that blockade of S1P function with the SphK2 inhibitor and the S1P2 antagonist effectively prevented HB-EGF mRNA up-regulation at 60 min . The ligand-dependent transactivation of EGFR by GPCRs is shown to occur by proteolytic processing of the transmembrane pro-EGFR-ligand precursor followed by paracrine activation of EGFR [53–55 , 65] . This process requires the participation of metalloproteinase activity , the so-called “Triple Membrane Passing Signal” ( TMPS ) mechanism . Therein , GPCRs stimulation induces metalloproteinase activity that cleaves the EGF-like ligand precursor and allows shedding of the ligand to bind to the extracellular ligand-binding domain of the receptor , thus transactivating EGFR signaling [65] . Therefore , the metalloproteinase-mediated shedding of EGF-like ligands might serve as a key step in GPCR-induced EGFR transactivation . In the present study , our finding for SphK2-S1P-S1P2 signaling to EGFR via HB-EGF was compatible with this TMPS mechanism . Noticeably , our results supported the exclusive involvement of HB-EGF , rather than other ligands , in EGFR activation in response to meningitic E . coli , and that is the reason for using CRM197 to specifically block HB-EGF function , rather than using a broad inhibitor of metalloproteinases . Nevertheless , several questions concerning the TMPS mechanism remain to be clarified for complete elucidation of the mechanisms underlying EGFR activation in response to meningitic E . coli invasion , such as identification of the specific member in the metalloproteinase family , the functional domain of metalloproteinases required for ligand precursor cleavage , and the manner in which activation occurs through GPCR signaling in HBMEC upon infection . At the present time , our findings support the involvement of S1P signaling in both early ( ligand-independent ) and late ( ligand-dependent ) activation of EGFR in response to meningitic E . coli . Further studies are needed to elucidate the mechanisms involved in the S1P-mediated early activation of EGFR in response to meningitic E . coli invasion of the BBB . Meningitic E . coli triggers the activation of multiple host cell signal transduction pathways for invasion of the BBB [1–4] . The signaling molecules such as FAK and its associated cytoskeletal protein paxillin , phosphatidylinosital 3-kinase ( PI3K ) , RhoGTPases , cytosolic phospholipase A2 ( cPLA2 ) and ERM ( ezrin , radixin , and moesin ) protein family have all been identified to be involved in this process , mostly likely through their promoting actin cytoskeleton rearrangements in HBMEC [11 , 13 , 14 , 23 , 25–27 , 66 , 67] . Tyrosine-phosphorylated Src is found to be associated with EGFR upon stimulation of certain GPCRs . It has been shown to function as a mediator of the EGFR signaling pathway and can also be recruited by the activated EGFR phosphotyrosine domain [65] . The implication of host cell Src family tyrosine kinase in bacterial internalization has been reported in several pathogens . For example , ErbB2 phosphorylation results in recruitment and activation of the tyrosine kinase Src , which is dependent on the intrinsic kinase activity of ErbB2 , and selective inhibitors of both ErbB2 and Src inhibited N . meningitidis internalization [35] . In Opa52-mediated phagocytosis of N . gonorrhoeae by human neutrophils , the activity of the Rho-family member Rac was controlled by a Src-like tyrosine kinase for efficient uptake [68] . Likewise , Src , together with Rho family GTPases , are involved in the internalization of Shigella into epithelial cells [69 , 70] , and in triggering the formation of actin polymerization foci induced by Shigella [71] . Previously , c-Src tyrosine kinase was shown to regulate host cell actin cytoskeleton rearrangement and PI3K activation in HBMEC by E . coli , and contributed to E . coli invasion of HBMEC [57] . Here , we showed an association of c-Src with EGFR by co-immunoprecipitation analysis , and demonstrated that c-Src activation followed the SphK2-S1P-S1P2-EGFR signaling cascade and played an important role in meningitic E . coli invasion of the BBB . Additional studies are needed to elucidate the mechanisms involved with EGFR-c-Src signaling in E . coli invasion of the BBB . In summary , our findings report a novel mechanism exploited by meningitic E . coli for penetration of the BBB . Via its OmpA , FimH , and NlpI proteins , meningitic E . coli induces activation of SphK2 , leading to increased generation of S1P that interacts with its receptor S1P2 . This SphK2-S1P-S1P2 signaling is involved in early activation of EGFR , and also induces up-regulation and release of the EGFR-related ligand HB-EGF , which is responsible for transactivation of EGFR . Activated EGFR subsequently recruits c-Src and induces tyrosine phosphorylation of c-Src kinase , which promotes reorganization of the actin cytoskeleton in HBMEC and E . coli entry of the BBB ( Fig 6 ) . Hijacking of this SphK2-S1P-S1P2-EGFR-c-Src signaling cascade will facilitate meningitic E . coli to penetrate the BBB , the essential step in the development of E . coli meningitis . Recent reports have indicated that antimicrobial resistance is an emerging problem in E . coli causing meningitis [3 , 4] , necessitating the development of novel targets for effective therapy . To our knowledge , this is the first demonstration that meningitic E . coli exploits S1P activation of EGFR for penetration of the BBB in vivo and in vitro , suggesting that S1P-EGFR represents a novel target for therapeutic development of E . coli meningitis .
E . coli strain RS218 ( O18:K1:H7 ) was obtained from cerebrospinal fluid of a neonate with meningitis [15] . All the mutants used in this study were derived from strain RS218 as previously described [9–11 , 15 , 20–22] . E . coli strains were cultured at 37°C overnight in brain heart infusion broth with appropriate antibiotics unless otherwise specified . HBMEC were isolated and characterized as described previously [7] . Cells were routinely grown in RPMI 1640 containing 10% heat-inactivated fetal bovine serum , 10% Nu-Serum , 2 mM L-glutamine , 1 mM Sodium pyruvate , nonessential amino acids , vitamins , and penicillin and streptomycin ( 100 U/ml ) in 37°C incubator under 5% CO2 until they reached confluence . In some experiments , confluent HBMEC were washed thrice with Hanks’ Balanced Salt Solution ( Corning Cellgro , Manassas , VA , USA ) and starved in serum-free medium ( 1:1 mixture of Ham’s F-12 and M-199 ) for 16–18 h before further treatment . The EGFR tyrosine kinase inhibitor gefitinib , S1P receptors antagonists VPC23019 and JTE-013 , and c-Src kinase inhibitor PP2 were purchased from Cayman Chemical Company ( Ann Arbor , MI , USA ) . Other inhibitors of EGFR and ErbB were purchased from Selleck ( Houston , TX , USA ) . The inhibitors of SphK1 and SphK2 and inactive analogue , including ( S ) -FTY720-vinylphosphonae , ( R ) -FTY720-methyl ether , RB-032 , RB-033 , and RB-034 , were described previously [46–50] and used at 10 μM . The diphtheria toxin mutant CRM197 was obtained from BioAcademia Inc . ( Osaka , Japan ) . Protein G Agarose Fast Flow beads and anti-phosphotyrosine ( 4G10 ) horseradish peroxidase ( HRP ) -conjugate antibody ( used at 1:1000 for EGFR and c-Src phosphorylatyion detection ) were purchased from EMD Millipore Corporation ( Temecula , CA , USA ) . Anti-EGFR , anti-c-Src , and anti-c-Src-HRP conjugated antibodies were from Santa Cruz Biotechnology ( 1:1000 ) ( Santa Cruz , CA , USA ) . A sphingosine kinase activation antibody kit was obtained from ECM Biosciences ( Versailles , KY , USA ) . Cy3-conjugated antibody were purchased from Abcam ( Cambridge , MA , USA ) . Anti-rabbit IgG HRP-conjugate antibody and Alexa Fluor 488-conjugated antibody were purchased from Cell Signaling Technology ( Danvers , MA , USA ) . Anti-actin antibody was from Sigma-Aldrich ( 1:3000 ) ( St . Louis , MO , USA ) . Preparations of OmpA- , FimH- , and NlpI-specific antibodies were previously described [20–22] . The TurboFect transfection reagent was purchased from Thermo Scientific ( Suwanee , GA , USA ) and used according to the instructions . G418 sulfate solution was from Corning Cellgro . The EGFR and c-Src dominant-negative constructs , pcDNA-EGFR-GGS and pEGFP-N1-Src-DN , along with their vector controls pcDNA3 . 1 and pEGFP-N1 were obtained from Drs . Hristova and Taylor , respectively [39 , 72] . We used the Johns Hopkins Drug Library ( JHDL ) , which is comprised of 3 , 400 chemicals that are approved by the US Food and Drug Administration and entered phase 2 clinical trials or approved for use abroad [73] , for discovery of novel targets affecting E . coli invasion of HBMEC , as follows . Drugs were arrayed in 96-well plates and screened at a final concentration of 10 μM in DMSO ( solvent ) . HBMEC grown in 96-well tissue culture plates were incubated with the JHDL for 60 min at room temperature , and then , examined for E . coli invasion , by a modification of the HBMEC invasion assay [9 , 10 , 15] . Briefly , 10 μl containing approximately 1×106 CFUs of E . coli strain RS218 were inoculated into each well of HBMEC in the plate . The plates were incubated at 37°C for 90 min for bacterial invasion to occur , and then intracellular CFUs were determined . This screening assay always included E . coli strain RS218 in vehicle ( DMSO ) -treated HBMEC as a positive control for invasion , while bacteria without HBMEC were used as a control for assessing any inhibitory effect of the chemicals on the growth of E . coli . Since this JHDL contains antibiotics , those wells exposed to antibiotics were used as a positive control for identification of chemicals that inhibit E . coli growth . The assay was highly reproducible , and the coefficient of correlation from at least two separate experiments was r = 0 . 98 ( p<0 . 0001 ) . From this assay , we identified gefitinib , which inhibited meningitic E . coli invasion of HBMEC greater than 90% . It is important to note that gefitinib did not affect bacterial growth , as assessed by comparing CFUs in experimental medium with or without the drug and also did not affect HBMEC viability , as assessed by live/dead stain ( Molecular Probes ) . It is also important to note that EGFR has not been previously appreciated to affect E . coli penetration of the BBB . The ability of E . coli to bind to and invade HBMEC was determined as previously described [9–12 , 15 , 20–22] . Briefly , E . coli strains were grown overnight in brain heart infusion broth with streptomycin ( 50 μg/ml ) . Bacteria were resuspended in experimental medium ( M199-Ham F12 [1:1] medium containing 5% heat-inactivated FBS ) and added into the confluent HBMEC monolayer grown in 24-well plate at MOI of 100 . The plate was incubated at 37°C incubator with 5% CO2 for 90 min to allow binding . HBMECs were then washed three times to remove unbound bacteria , and lysed in 0 . 025% Triton X-100 buffer . Bacterial counts of adhesion were determined by plating with appropriate dilutions . For invasion assay , bacteria were added into HBMEC as described above for the adhesion assay . Subsequently , cells were washed three times to remove the unbound bacteria and incubated in experimental medium containing 100 μg/ml gentamicin for another 1 h to kill extracellular bacteria . HBMEC were washed and lysed as above mentioned . The released intracellular bacteria were quantified by appropriate dilutions and plating . As specified in some experiments , HBMEC were pretreated with various inhibitors for 1 h prior to addition of bacteria and then processed for bacterial invasion . The results were calculated as percentages of the initial inoculums , and presented as percent relative adhesion/invasion compared with that in the presence of the vehicle control ( DMSO ) . Each assay was performed in triplicate . MTT Cell Proliferation Assay Kit was purchased from BioVision ( Milpitas , CA , USA ) and used according to the instructions . HBMEC were seeded in 96 well plates at 5×103 per well in 100 μL culture medium and incubated for 24 h . Gefitinib was added as indicated in HBMEC binding and invasion assays . Supernatant of each well was removed and MTT dissolved in serum-free medium was added and further incubated for another 4 h . After incubation , 100 μL of MTT solvent was added into each well , and the plate was wrapped in a foil and shaked on an orbital shaker for 15 min . Absorbance of all wells at 570 nm were determined . HBMEC monolayers grown in 100-mm dish were serum-starved overnight and incubated with E . coli strain RS218 or the triple deletion mutant at a MOI of 100 for 30 min at 37°C . Sphingolipids were extracted using acidified organic solvents and quantitated by HPLC electrospray ionization triple quadrupole mass spectrometry and quantitated using mass labeled internal standards [74] . Briefly , sphingolipids were extracted from cell lysates as previously described [74 , 75] . Prior to extraction , a mixture of C17 sphingolipids ( 125 pmol/sample ) was added to each sample as the internal standards . Sphingolipids were quantitated by HPLC electrospray ionization tandem mass spectrometry using selected ion monitoring on an ABSciex 4000 Q-Trap instrument as described previously [76–78] . Total phospholipids for each sample were measured using modified Ames and Dubin assay as previously described [75 , 79] . HBMEC were transfected with empty vectors control or expression vectors encoding EGFR or c-Src dominant-negative constructs using the TurboFect transfection reagent as described previously [66 , 80] . pcDNA3 . 1 cloned with green fluorescence protein ( GFP ) and pEGFP-N1 vectors were used to determine the transfection efficiency by fluorescence microscopy . A human codon-optimized Cas9 expression vector was obtained from Addgene , plasmid #41815 [81] , and Cas9 was cloned into the pEF6 expression vector ( Invitrogen , Carlsbad , CA , USA ) , downstream and in-frame with a nuclear-localized YFP , linked by a piconaviral 2A bicistronic peptide [82] , such that nuclear localization signal ( NLS ) -YFP and Cas9 are expressed in approximate equimolar quantities . A hEGFR guide RNA ( gRNA ) construct , including the U6 promoter , was synthesized as a double stranded DNA fragment and cloned into the pEF6-nls-YFP-2A-Cas9 vector by InFusion Cloning ( Clontech , Mountain View , CA , USA ) . This vector was used for transfection of HBMEC as mentioned above and clones resistant to blasticidin were identified and used for isolation of a single clone . Single clones were used for expression of EGFR by Western blot and bacterial invasion assay . HBMEC were seeded at 1×106 cells/100-mm dish and cultured until confluence . Cells were then serum-starved overnight , stimulated with E . coli strains at MOI of 100 for specified periods of time , and processed for immunoprecipitation and Western blotting analysis as previously described [66 , 80] . Confluent HBMEC grown in 100-mm dishes were serum-starved overnight and then infected with E . coli at a MOI of 100 for indicated periods of time . At each time point , the medium was removed and the cells were lyzed for total RNA preparation using the TRIzol reagent ( Invitrogen ) . Contaminating DNA was removed by DNase I treatment ( New England Biolabs , Ipswich , MA , USA ) . Aliquots ( 1 μg ) of the total RNA in each sample were subjected to cDNA synthesis using ProtoScript Taq RT-PCR kit ( New England Biolabs ) . Real-time PCR was performed with a QuantStudio 12K Flex Real-Time PCR System ( Applied BioSystems , Foster City , CA , USA ) using Power SYBR Green PCR master mix ( Applied BioSystems ) , according to the manufacturers’ instructions . The primer sequences for human EGFR and its ligands were as follows: EGFR , 5'-CAAGTGCTGGATGATAGA-3' ( forward ) and 5'-GAAGTTGGAGTCTGTAGG-3' ( reverse ) ; EGF , 5'-GTTGGCAGGTGGTGAAGTTG-3' ( forward ) and 5'-CCACAGGAGCACAGTCATCT-3' ( reverse ) ; AREG , 5'-ATTATGCTGCTGGATTGG-3' ( forward ) and 5'-GAGGACGGTTCACTACTA-3' ( reverse ) ; BTC , 5'-CCAAGCAATACAAGCATTAC-3' ( forward ) and 5'-GTCCTCTGTCTCCTCTTAG-3' ( reverse ) ; EREG , 5'-AGTTCAGACAGAAGACAATC-3' ( forward ) and 5'-ACATCGGACACCAGTATA-3' ( reverse ) ; HB-EGF , 5'-TATACCTATGACCACACAAC-3' ( forward ) and 5'-CACATCATAACCTCCTCTC-3' ( reverse ) ; TGFα , 5'-GGCTGTCCTTATCATCAC-3' ( forward ) and 5'-AGACCACTGTTTCTGAGT-3' ( reverse ) . Primers for human glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) were provided in the RT-PCR kit . The amplification conditions were: 50°C for 2 min and 95°C for 10 min , followed by 40 cycles of 95°C for 15 s and 60°C for 1 min . The products were then applied to a melt curve stage with denaturation at 95°C for 15 s , anneal at 60°C for 1 min , and slow dissociation by ramping from 60°C to 95°C at 0 . 05°C/s to ensure the specificity of the PCR products . To determine the release of secretory HB-EGF from HBMEC , cells were infected with meningitic E . coli strain RS218 or its triple deletion mutant for varying time points , and the supernatants were discarded and cells were washed with 1 . 5 M NaCl/1×PBS/1% BSA to dissolve heparin-bound HB-EGF . Cleaved and secretory HB-EGF levels were then quantified from wash buffer using HB-EGF Human ELISA Kit , purchased from Abcam ( Cambridge , MA , USA ) , according to the manufacturer’s instructions . C57BL/6j mice were purchased from Jackson Laboratory ( Bar Harbor , Maine ) . SphK2−/− mice in the background of C57BL/6 were described previously [42] . Male or female mice at 1 week of age were used for induction of hematogenous E . coli meningitis . All procedures and handling techniques were approved by the Animal Care and Use Committee of the Johns Hopkins University . Each mouse received approximately 3×105 CFU of E . coli strain RS218 in 50 μl sterile normal saline via intracardiac injection . At 1 h post-inoculation , the mice were euthanized and blood from the right ventricle was collected for quantitative bacterial cultures . Subsequently , the mice were perfused as previously described [14] , and their brains were removed , weighed , homogenized , and plated to determine the bacterial counts , which were expressed as CFUs per gram . In some experiments , gefitinib , applied at therapeutic dosage ( 10 mg/kg ) [83] , was intraperitoneally administrated 2 h before bacterial challenge . HBMEC was grown on collagen-coated glass slide to confluency . Cells were washed thrice with serum-free medium and then pre-incubated for 30 minutes in experimental medium . Cells were then incubated with E . coli containing a red fluorescence protein ( RFP ) -expressing plasmid ( RS218-RFP ) , at an MOI of 1:100 for a period of 90 minutes at 37°C with 5% CO2 . Cells were washed with PBS to remove the free , unbound bacteria , and then fixed with 4% paraformaldehyde , permeabilized with Triton X-100 solution , and blocked with 5% BSA in PBS . Cells were then incubated with EGFR antibody overnight at 4°C , washed , and incubated with Alexa Fluor 488-labeled secondary antibody ( Life Technologies A11034 ) , followed by nucleus staining with DAPI ( Vector Laboratories H-1200 ) . The glass slide was mounted and visualized using fluorescence microscopy . Data were expressed as mean ± standard errors of the mean ( SEM ) unless otherwise noted . Differences of the bacterial counts in adhesion and invasion assays were determined by Student’s t-test . Differences of the bacterial counts between different treatments or groups of mice were determined by the Wilcoxon rank-sum test . P-values of < 0 . 05 were considered significant . This study was carried out in strict accordance with the current recommendations in the Guide for the Care and Use of Handling Animals , NIH publication DHHS/USPHS . The animal protocol was approved by The Johns Hopkins Animal Care and Use Committee ( Animal Welfare Assurance Number: A3272-01 ) . All efforts were made to provide the ethical treatment and minimize suffering of animals ( mice ) employed in this study . | Escherichia coli is the most common Gram-negative bacillary organism causing meningitis , and E . coli meningitis continues to be an important cause of mortality and morbidity . E . coli penetration of the blood–brain barrier ( BBB ) is essential for the development of E . coli meningitis , but the underlying mechanisms remain incompletely understood . Recent reports of E . coli strains producing CTX-M-type or TEM-type extended-spectrum β-lactamases , including antimicrobial-resistant E . coli sequence type 131 ( ST131 ) are of particular concern . These findings necessitate searches for new targets for investigating the pathogenesis and therapeutic development of E . coli meningitis . Our work demonstrated for the first time that sphingosine 1-phosphate ( S1P ) activation of epidermal growth factor receptor ( EGFR ) represents a novel mechanism by which CNS-infecting E . coli strains penetrate the BBB , and that blockade of S1P and EGFR prevented E . coli penetration of the BBB . We also determined that the specific E . coli factors contributing to penetration of the BBB exploit S1P-EGFR signaling , and that c-Src is downstream of S1P-EGFR . Our findings reveal a novel mechanism by which meningitic E . coli penetrates the BBB , and also demonstrate the novel targets for investigating the pathogenesis , prevention , and therapy of E . coli meningitis . | [
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"mutagen... | 2016 | Sphingosine 1-Phosphate Activation of EGFR As a Novel Target for Meningitic Escherichia coli Penetration of the Blood-Brain Barrier |
Many candidate genes have been studied for asthma , but replication has varied . Novel candidate genes have been identified for various complex diseases using genome-wide association studies ( GWASs ) . We conducted a GWAS in 492 Mexican children with asthma , predominantly atopic by skin prick test , and their parents using the Illumina HumanHap 550 K BeadChip to identify novel genetic variation for childhood asthma . The 520 , 767 autosomal single nucleotide polymorphisms ( SNPs ) passing quality control were tested for association with childhood asthma using log-linear regression with a log-additive risk model . Eleven of the most significantly associated GWAS SNPs were tested for replication in an independent study of 177 Mexican case–parent trios with childhood-onset asthma and atopy using log-linear analysis . The chromosome 9q21 . 31 SNP rs2378383 ( p = 7 . 10×10−6 in the GWAS ) , located upstream of transducin-like enhancer of split 4 ( TLE4 ) , gave a p-value of 0 . 03 and the same direction and magnitude of association in the replication study ( combined p = 6 . 79×10−7 ) . Ancestry analysis on chromosome 9q supported an inverse association between the rs2378383 minor allele ( G ) and childhood asthma . This work identifies chromosome 9q21 . 31 as a novel susceptibility locus for childhood asthma in Mexicans . Further , analysis of genome-wide expression data in 51 human tissues from the Novartis Research Foundation showed that median GWAS significance levels for SNPs in genes expressed in the lung differed most significantly from genes not expressed in the lung when compared to 50 other tissues , supporting the biological plausibility of our overall GWAS findings and the multigenic etiology of childhood asthma .
Asthma ( OMIM 600807 ) is a leading chronic childhood disease with prevalence rates reaching a historically high level ( 8 . 9% ) in the United States and continuing to increase in many countries worldwide [1] , [2] . Asthma is characterized by airway inflammation and bronchoconstriction leading to airflow obstruction , but the mechanisms leading to asthma development remain unknown . Genetic risk factors likely play a central role in asthma development . Twin studies support a strong genetic component to asthma ( especially childhood asthma ) with heritability estimates suggesting that 48–79% of asthma risk is attributable to genetic risk factors [3] . In an effort to localize disease susceptibility genes , genome-wide linkage studies have identified at least 20 linkage regions potentially harboring disease genes [4] , and over 100 positional and biological candidate genes have been tested for association with asthma [3] . However , no genes have been definitely shown to influence this complex disease . Genome-wide association studies ( GWASs ) have emerged as a powerful approach for identifying novel candidate genes for common , complex diseases . In the first asthma GWAS , using 307 , 328 single nucleotide polymorphisms ( SNPs ) , Moffatt et al . found highly statistically significant associations of SNPs in adjacent genes ORM1-like ( S . cerevisiae ) ( ORMDL3; OMIM 610075 ) and gasdermin B ( GSDMB or GSDML; OMIM 611221 ) with risk of childhood asthma in German and British populations [5] . Meta-analysis of the Moffatt et al . study and five subsequent replication studies , including our own study , supports the association of ORMDL3 and GSDML SNPs with risk for childhood asthma across various populations [6] . More recently , using 518 , 230 SNPs , Himes et al . implicated SNPs in phosphodiesterase 4D , cAMP-specific ( phosphodiesterase E3 dunce homolog , Drosophila ) ( PDE4D; OMIM 600129 ) with risk of asthma in whites from the United States and replicated this finding in two other white populations [7] . Using only 97 , 112 SNPs , Choudhry et al . implicated chromosome 5q23 SNPs for association with asthma in Puerto Ricans [8] , but no other Puerto Rican cohorts are available for replication . Few genetic studies of asthma have included Hispanic populations , and replication of positive genetic findings is scarce across Hispanic groups . Hispanics have differing proportions of Native American , European , and African ancestries . This admixture introduces special considerations ( such as controlling for population stratification in association studies ) , but admixture in Hispanic populations also provides a unique opportunity to use ancestry analysis to evaluate our genetic association findings [9] , [10] . Mexico City is one of the most polluted cities in the world , and its inhabitants experience chronic ozone exposure , which has been linked to asthma development in children and adults and asthma exacerbations [11]–[13] . We conducted a GWAS to identify novel candidate susceptibility genes associated with childhood asthma in case-parent trios from Mexico City and tested the most significantly associated SNPs in an independent study of trios of Mexican ethnicity . GWAS findings were then examined in the context of ancestry analysis and genome-wide expression data to provide supportive evidence for associations with childhood asthma .
The 520 , 767 autosomal SNPs passing quality control were tested for association with childhood asthma using additive modeling with the log-linear method in 492 children with asthma and their biological parents from Mexico City . Not surprisingly given the study size , no SNP met genome-wide significance using a conservative Bonferroni adjustment . Nonetheless , the comparison of observed and expected p-values in the quantile-quantile plot ( Figure 1 ) shows several top SNPs with some deviation from expectation . These deviations may occur by chance or may represent a true excess of small p-values . Figure 2 shows the observed p-values plotted against chromosomal location . An intergenic SNP on chromosome 16 had the most significant association with childhood asthma [rs1867612 ( p = 1 . 55×10−6 ) ] , followed by an intronic SNP in potassium voltage-gated channel , Shab-related subfamily , member 2 ( KCNB2; OMIM 607738 ) on chromosome 8 [rs2247572 ( p = 1 . 94×10−6 ) ] and two intergenic SNPs on chromosome 20 [rs6063725 ( p = 3 . 52×10−6 ) ] and rs720810 ( p = 5 . 13×10−6 ) ] with only moderate linkage disequilibrium ( LD ) ( r2 = 0 . 59 ) . The next most significant SNP ( rs2378383 ) highlights a cluster of SNPs on chromosome 9q21 . 31 ranking among the top GWAS SNPs . This cluster of SNPs spans transducin-like enhancer of split 4 ( E ( sp1 ) homolog , Drosophila ) ( TLE4; OMIM 605132 ) and its upstream region . LD analysis of SNPs with p≤0 . 001 in this cluster shows two large LD blocks in this region with one block encompassing TLE4 and the other block encompassing the upstream region ( Figure S1 ) . Eleven of the 18 most significantly associated SNPs met our criteria to be selected for replication in 177 case-parent trios of Mexican ethnicity from the Genetics of Asthma in Latino Americans ( GALA ) study [14] . The GWAS p-values for the 11 SNPs selected for replication testing ranged from 3 . 30×10−5 to 1 . 55×10−6 ( Figure 2 ) . There were no significant deviations in Hardy-Weinberg equilibrium ( HWE ) for the replication SNPs in either the GWAS or replication study ( p>0 . 12 ) , and minor allele frequencies ( MAFs ) were similar between the two studies ( Table 2 ) . The replication study had at least 70% power to detect an association with four SNPs ( rs2378377 , rs4674039 , rs1830206 , and rs3814593 ) and at least 80% power to detect an association with the remaining seven SNPs ( rs1867612 , rs2247572 , rs6063725 , rs720810 , rs2378383 , rs6951506 , and rs3734083 ) for similar MAFs and relative risk ( RR ) estimates observed in the GWAS . Association results in the GWAS and replication studies are compared in Table 2 . No SNPs were significant with conservative Bonferroni correction for multiple testing , but two SNPs were associated with childhood asthma in the replication study with a p-value close to 0 . 05 . The chromosome 9q21 . 31 SNP rs2378383 , which is located 147 kb upstream of TLE4 in an intergenic region between coiled-coil-helix-coiled-coil-helix domain containing 9 ( CHCHD9 ) and TLE4 , was associated with childhood asthma in the replication study with p = 0 . 03 . Meta-analysis of rs2378383 in the two studies gave a combined p-value of 6 . 79×10−7 , and the RR estimate for carrying one copy of the rs2378383 minor allele ( G ) compared to carrying no copies in the GWAS [RR , 0 . 61; 95% confidence interval ( CI ) , 0 . 49–0 . 76] was quite similar to the RR estimate in the replication study ( RR , 0 . 63; 95% CI , 0 . 41–0 . 96 ) . The SNP rs2378377 , a neighboring intergenic SNP in moderate LD with rs2378383 ( r2 = 0 . 73 ) , had a marginal association with childhood asthma in the replication study with p = 0 . 06 ( combined p = 2 . 68×10−6 ) . RR estimates for the rs2378377 minor allele ( G ) were also similar between the GWAS ( RR , 0 . 64; 95% CI , 0 . 53–0 . 79 ) and the replication study ( RR , 0 . 71; 95%CI , 0 . 50–1 . 02 ) . None of the other nine SNPs were associated in the replication study ( Table 2 ) . Association results from additive modeling for SNPs in the region spanning TLE4 and its upstream region ( chromosome 9 nucleotide positions from 81 , 114 , 500 to 81 , 531 , 500 , NCBI build 36 . 3 ) were obtained from the previous GWASs of asthma [5] , [7] , [8] . Our top two SNPs from this region were genotyped only in the GWAS in whites from the United States [7] , where they were not associated with asthma ( p = 0 . 59 for rs2378383 and p = 0 . 65 for rs2378377 ) . Eighty-nine other SNPs were available in this region , and the smallest p-value was observed for rs1328406 ( p = 0 . 056 ) . There were 54 SNPs available in this region from the GWAS in German and British populations [5] , with the smallest p-values observed for rs2807312 ( p = 0 . 0041 ) , rs7849719 ( p = 0 . 018 ) , rs7862187 ( p = 0 . 033 ) , rs10491790 ( p = 0 . 043 ) , and rs946808 ( p = 0 . 049 ) . From the 26 SNPs available from the GWAS in Puerto Ricans [8] , the smallest p-value was 0 . 19 . In our GWAS in Mexicans , there were several SNPs in TLE4 and its upstream region with small p-values , and the SNPs listed above are located in close proximity to many of our associated SNPs . Similar to our GWAS and replication study , only cases with childhood-onset asthma were included in both GWASs in white populations [5] , [7] , and the cases from Himes et al . were predominantly atopic ( 91 . 2% ) as defined by at least one positive skin prick test [7] . In contrast , the GWAS in Puerto Ricans included both childhood-onset and adulthood-onset asthma cases , and 83% of the cases were considered atopic as defined by total IgE>100 IU/mL [8] . Associations of rs2378383 and rs2378377 were examined in data from the GWAS population stratified by residential ambient ozone exposure ( 199 trios with ≤67 ppb and 214 trios with>67 ppb annual average of the maximum 8 hour averages ) and by current parental smoking ( 253 trios with and 233 trios without current parental smoking ) . The minor alleles of both SNPs were inversely associated with asthma at p<0 . 05 in all strata ( results not shown ) , thus giving no evidence for effect modification in the presence of these environmental exposures . Among the 445 cases with skin test data available , 408 can be classified as atopic by virtue of having at least one positive skin test . We repeated the GWA scan in this subset of 408 trios . Chromosome 9q21 . 31 SNPs predominated among the top ranked SNPs , with rs2378383 ( p = 7 . 18×10−7 ) and rs2378377 ( p = 1 . 08×10−6 ) being the two top ranking SNPs . In addition to smaller p-values , the magnitudes of association with asthma were slightly stronger for rs2378383 ( RR , 0 . 54; 95% CI , 0 . 42–0 . 69 ) and rs2378377 ( RR , 0 . 54; 95% CI , 0 . 42–0 . 72 ) in the subset of trios where the asthmatic child is also known to be atopic . The chromosome 9q21 . 31 SNPs rs2378383 and rs2378377 were tested for association with the number of positive skin tests as a quantitative measure of the degree of atopy in the trios with skin test data . Both SNPs were associated with degree of atopy ( p = 0 . 0018 for rs2378383 and p = 0 . 0010 for rs2378377 ) . Their RR estimates indicate an inverse association in which carrying one copy of the minor allele is associated with a decreasing number of positive skin tests ( RR , 0 . 92; 95% CI , 0 . 87–0 . 97 for rs2378383 and RR , 0 . 92; 95% CI , 0 . 88–0 . 97 for rs2378377 ) , consistent with the direction of association for asthma . The Mexican subjects in the GWAS had mean ancestral proportions of 69 . 5±15 . 6% for Native American , 27 . 3±14 . 3% for European , and 3 . 2±3 . 0% for African ancestries . Given the predominance of Native American ancestry , we evaluated Native American transmission in the GWAS along the chromosomal arm ( 9q ) containing the replicated SNP ( rs2378383 ) by ancestry analysis . As shown in Figure 3 , there is a significant under-transmission of Native American ancestry at rs2378383 ( z-score = −2 . 21 and two-sided p = 0 . 028 ) and surrounding SNPs . The deficiency in Native American ancestry at this locus suggests that a protective allele occurred at a higher frequency in the Native American ancestral population than in the European and African ancestral populations . An examination of this SNP in the HapMap and Human Genome Diversity Panel ( HGDP ) data reveals that the frequency of the G allele is generally low in European , African , and East Asian populations ( 0 . 125 in HapMap European , 0 . 033 in HapMap African , 0 . 122 in HapMap Chinese , and 0 . 273 in HapMap Japanese ) , while its frequency is much higher in Native American populations ( 0 . 57 in HGDP Pima and 0 . 36 in HGDP Mayan ) . This pattern suggests that the G allele may be tagging a protective allele in the Native American ancestral population . This conclusion is consistent with the finding that the G allele is associated with a decreased risk for childhood asthma in the GWAS and replication analyses ( Table 2 ) . We examined gene expression patterns in 51 diverse human tissues in the context of GWAS findings to determine whether genes expressed in asthma relevant tissues ranked higher than genes not expressed in such tissues and thus to assess the biological plausibility of our overall GWAS findings . These results are presented in Figure 4 . In the 14 , 330 genes with GWAS SNPs in the gene or nearby , median false discovery rate q-values ( derived from the GWAS p-values ) were compared between genes expressed versus genes not expressed in each of 51 human tissues . Among the 51 tissues , the most significant deviation between the median q-values was found between 3 , 618 genes expressed in the lung versus 10 , 712 genes not expressed in the lung ( Figure 4 , p = 0 . 00025 ) . This finding remains significant even after a conservative Bonferroni correction for multiple testing . The q-values for the 3 , 618 lung-expressed genes are presented in Table S1 . TLE4 did not contribute to the observation of significantly lower GWAS q-values in lung-expressed genes , as TLE4 was classified as not expressed . TLE4 displays a nearly ubiquitous expression pattern with similar low intensity levels across many tissues , so even though it is present in the lung , its expression level in the lung did not exceed our 75th percentile threshold to be classified as expressed . Other tissues in the respiratory and immune system also showed deviations in the median GWAS q-values for expressed versus not expressed genes , including thymus and lymph node at the p<0 . 01 level ( uncorrected ) and fetal lung , trachea , tonsil , smooth muscle , and bronchial epithelium at the p<0 . 05 level ( uncorrected ) . Thus , SNPs in genes more highly expressed in tissues related to the pathogenesis of asthma and allergies tend to give more significant GWAS results than genes more highly expressed in other tissues . These results give biological credibility to our overall GWAS findings and are consistent with the multigenic etiology of asthma . A two-dimensional cluster analysis was conducted to identify the implicated tissues with correlated gene expression patterns . As shown in Figure S2 , lung tissue is grouped in a cluster with fetal lung and placenta tissues , thus suggesting that gene expression patterns in lung are most similar to gene expression patterns in fetal lung and placenta and that their signals are correlated . There are 2 , 385 genes classified as expressed in all three tissues – lung , fetal lung , and placenta . The gene expression patterns of other implicated tissues are also highly correlated , including the immune tissues tonsil , lymph node , and thymus ( Figure S2 ) .
Genetic studies of asthma are few in Hispanic populations , and to our knowledge , this work presents the first asthma GWAS in Mexicans and the most extensive coverage of genetic variation for an asthma GWAS in any Hispanic population . The GWAS included 492 Mexican case-parent trios . Given the moderate GWAS sample size , no SNP met genome-wide significance . However , the ranking of GWAS SNPs highlighted a potentially important candidate region for childhood asthma susceptibility , chromosome 9q21 . 31 . Several chromosome 9q21 . 31 SNPs with small GWAS p-values were located in TLE4 and its upstream region , and two of these SNPs ( rs2378383 and rs2378377 ) were tested for replication in an independent study of 177 case-parent trios of Mexican ethnicity . Despite the small sample size for replication , both SNPs gave p-values close to 0 . 05 and the same direction and magnitude of association as the GWAS . Neither rs2378383 nor rs2378377 have a known impact on TLE4 expression , but given their location upstream of TLE4 , it is possible that these SNPs reside in a TLE4 regulatory region . Ancestry analysis in this chromosomal region provided supportive evidence that rs2378383 ( G ) is associated with a decreased risk of childhood asthma in Mexicans . Ancestry and transmission-based association analyses provide complementary but not completely independent lines of evidence . At each SNP , the log-linear method only used parents who were heterozygous in genotype , while ancestry analysis used all parents who are heterozygous in ancestry , including parents who are homozygous in genotype . We did not a priori expect that ancestry analysis results would corroborate log-linear association results . Our ancestry analysis uses the same principles that underlie admixture mapping and relies on the key assumption of different risk allele frequencies between the ancestral populations , primarily Native American and European in this study . Under this assumption , individuals with disease in the admixed population would be expected to share an excess of ancestry from the population with the highest risk allele frequency at the disease locus [9] . In contrast , individuals with disease in the admixed population would be expected to share a shortage of ancestry from the population with the highest protective allele frequency at the disease locus . At chromosome 9q21 . 31 , there was less Native American ancestry than expected , suggesting that the Native American ancestral population had a higher frequency of the protective rs2378383 allele ( G ) . Ancestry analysis implicated chromosome 9q21 . 31 as a chromosomal region that may underlie ethnic differences in childhood asthma . Complex diseases with differing disease prevalence rates in the ancestral populations are most suitable for ancestry analysis [15] . Prevalence rates of childhood asthma in the true ancestral Native American , European , and African populations are unknowable , but it is interesting to note that Mexicans have the highest Native American ancestry and the lowest asthma prevalence rate among Hispanic populations [16] . Differing frequencies of genetic risk factors in the ancestral populations presumably contribute to the differing prevalence rates of childhood asthma in modern populations . Our study found an association between Native American ancestry and a lower disease risk . Similarly , Native American ancestry was associated with milder asthma in a previous study of subjects of Mexican ethnicity from the GALA study [17] . These findings collectively suggest that the Native American ancestral population had higher frequencies of alleles that decrease prevalence and severity of asthma in the modern Mexican population . A comparison of asthma prevalence and severity among modern Native Americans , Europeans , and Africans would further support this interpretation , but such data are scarce [17] . The evidence for locus-specific ancestry around rs2378383 has implications for replication . Because rs2378383 ( G ) occurs at relatively low frequency in European , African and East Asian populations , genetic association studies in these populations are likely to suffer from lack of power at this locus . In contrast , the G allele occurs at moderate frequency in the Native American populations surveyed in HGDP [18] . Such disparate allele frequencies facilitate ancestry analysis in the region and improve the statistical power of transmission-based tests , as there are many more heterozygous parents in the Mexican population than a European , African or East Asian population . In fact , we obtained association results for SNPs in the chromosome 9q21 . 31 region from previous GWASs and found that SNPs in this region had only nominal evidence for association with asthma in the GWASs in white populations [5] , [7] . It is not surprising that substantial evidence for replication was not found given the ethnicity differences ( whites for Moffatt et al . and Himes et al . [5] , [7] and Puerto Ricans for Choudhry et al . [8] ) . Future replication and fine-mapping of the region would be most effective if performed in Native American populations , or admixed populations with high Native American ancestral contributions . The chromosome 9q21 . 31 SNPs associated with childhood asthma in the GWAS map to TLE4 and its upstream region . The TLE family of proteins in humans is homologous to the Drosophila Groucho protein , which participates in cell fate determination for neurogenesis and segmentation . The highly conserved structure among the Drosophila Groucho and human TLE gene products suggest similar functions as transcriptional regulators in cell fate determination and differentiation [19] . Six genes encode the TLE family of proteins in humans ( TLE1 , TLE2 , TLE3 , TLE4 , TLE5 , TLE6 ) , as deposited in the NCBI database . The distinct expression patterns among the TLE genes suggest a complex mechanism in humans involving non-redundant roles for the TLE genes [20] . The TLE4 gene , in particular , shows ubiquitous expression across many tissues [19] , [21] , and TLE4 functions as a transcriptional co-repressor in several key developmental pathways [22] . More specifically , TLE4 has been implicated in early B-cell differentiation . TLE4 interacts with the transcription factor Paired box 5 ( PAX5; OMIM 167414 ) , which activates B-cell specific genes and represses alternative lineage fates [23] . A spliced version of TLE4 acts as a negative regulator for the PAX5/TLE4 function [23] . An alteration of B-cell differentiation involving TLE4 could be relevant to immune development and thus asthma . TLE interacts with Runt-related transcription factor 3 ( RUNX3; OMIM 600210 ) in a manner that may be directly relevant to asthma . In mice , loss of RUNX3 function results in an allergic asthma phenotype due to accelerated dendritic cell maturation and resulting increased efficacy to stimulate T cells [24] . Interaction with TLE is required for RUNX3 to inhibit dendritic cell maturation [25] . A recent paper provides support for the interaction of RUNX3 specifically with TLE4 [26] . Interestingly , the chromosome 9q21 . 31 SNPs rs2378383 and rs2378377 near TLE4 are associated with asthma as well as degree of atopy in our data , and their associations with asthma became more pronounced when considering only the asthmatic children with atopy and their parents . These findings suggest that the influence of TLE4 on asthma may be related to its influence on immune system development . Childhood asthma is a complex disease , and there are likely many susceptibility genes influencing immune system development and asthma in the Mexican population . The examination of GWAS in the context of genome-wide expression illustrated the biological plausibility of our GWAS findings and showed consistency with the involvement of multiple genes . Genes expressed in the lung show association signals that differ most significantly from the association signals from genes not expressed in the lung when compared to 50 other human tissues . The lung represents a major pathogenic site for asthma , and this finding implies that multiple genes expressed in the lung are collectively associated with an increased risk of childhood asthma . Some of the other implicated tissues may represent false positives , but several of the highlighted tissues are biologically plausible for childhood asthma , including trachea , bronchial epithelium , smooth muscle , and immune tissues such as thymus , tonsil , and lymph node . Other GWASs have implicated different susceptibility loci . Several SNPs implicated in the first asthma GWAS by Moffatt et al . in the ORMDL3 region [5] were associated with childhood asthma in our GWAS [including rs9303277 ( p = 0 . 036 ) , rs11557467 ( p = 0 . 014 ) , rs8067378 ( p = 0 . 020 ) , rs2290400 ( p = 0 . 037 ) , and rs7216389 ( p = 0 . 042 ) ] but were not ranked among our top 5 , 000 SNPs . More recent GWASs have implicated loci other than ORMDL3 . The PDE4D SNPs implicated by Himes et al . [7] were not associated with childhood asthma in our GWAS at p<0 . 05 . Two nearby SNPs , not in LD with the implicated SNPs , were associated [rs13158277 ( p = 0 . 030 ) and rs7717864 ( p = 0 . 015 ) ] but were also not ranked among our top 5 , 000 SNPs . Chromosome 5q23 SNPs implicated by Choudhry et al . [8] were not associated with childhood asthma in our GWAS at p<0 . 05 . Initial GWAS findings regarded as replicated may not be ranked among the front runners in a genome-wide scan in the replication populations for statistical [27] as well as other biological reasons ( such as ethnic differences , phenotypic heterogeneity , genetic heterogeneity , differing patterns of interacting environmental exposures , or multigenic etiology ) . This trend in discordant GWAS findings is quite common for various complex diseases [28] , and follow-up studies are crucial in separating true genetic associations from false positives . The major limitation of this study is the sample size for the GWAS and replication study . The Mexican population is largely under-studied given its size , and only moderate sample sizes are currently available for the study of asthma genetics . In our study , no SNPs met genome-wide significance , and no replication SNPs met the significance threshold when using a conservative Bonferroni correction for multiple testing . Despite this limitation , top GWAS findings , replication in an independent population , and ancestry analysis taken together implicate a novel region for association with asthma in Mexican children . This study has several strengths . The case-parent trio design and the log-linear analysis protects against bias due to population stratification [29] , so our GWAS results are not confounded by population stratification in this admixed population . Also , disease misclassification is minimal . Although bronchial hyper-reactivity was not tested , children with asthma were given reliable diagnoses based on clinical grounds by pediatric allergists at a pediatric allergy specialty clinic . The allergy clinic is a tertiary referral clinic , so the children with asthma were previously seen by a generalist and a pediatrician over time for recurrent asthma symptoms . Physician diagnosis of asthma has been shown to have a high level of validity in children after the first few years of life [30] . Further , the asthmatic children were predominantly atopic to aeroallergens based on skin prick testing limiting heterogeneity of the disease phenotype . The GWAS and replication association results and the supporting ancestry analysis implicate chromosome 9q21 . 31 as a novel susceptibility locus for childhood asthma in the Mexican population . This region contains a biologically plausible novel susceptibility gene for childhood asthma , TLE4 , but further work is needed to decipher whether TLE4 or a nearby gene explain the signals from the chromosome 9q21 . 31 region . Further , childhood asthma is a complex disease with a proposed multigenic etiology , but most single studies will not have sufficient power to examine such complex relationships . Identification of important interacting risk factors in childhood asthma and other complex diseases will require very large sample sizes . This work identifies chromosome 9q21 . 31 ( including TLE4 ) as a novel candidate susceptibility locus for childhood asthma , suggests that this region may underlie ethnic differences in childhood asthma , and emphasizes the presence of multiple genetic risk factors in the complex mechanism leading to childhood asthma .
The study protocol was approved by the Institutional Review Boards of the Mexican National Institute of Public Health , Hospital Infantil de Mexico Frederico Gomez , and the US National Institute of Environmental Health Sciences ( NIEHS ) . Parents gave written informed consent for the children's participation , and children gave their assent . Children with asthma ( aged 5–17 ) and their biological parents were recruited between June 1998 and November 2003 from a pediatric allergy specialty clinic at a large public hospital in central Mexico City , Hospital Infantil de Mexico Frederico Gomez . The case-parent trio design protects against bias due to population stratification in this admixed population [29] , [31] . Blood samples were collected from enrolled children and their parents for DNA extraction . Children were diagnosed with asthma by a pediatric allergist at the referral clinic based on clinical symptoms and response to treatment [32] . Asthma severity was rated as mild ( intermittent or persistent ) , moderate , or severe by the pediatric allergist according to symptoms in the Global Initiative on Asthma schema [33] . Questionnaires on the children's asthma symptoms and risk factors , including environmental tobacco smoking , were completed by parents , nearly always the mother . The clinical evaluation also included skin prick testing to measure atopy and pulmonary function testing , as previously described [6] . A battery of 24 aeroallergens common in Mexico City was used for skin prick testing . Histamine was used as a positive control , and the test was considered valid if the histamine reaction was 6 mm or greater [34] . Glycerin was used as a negative control . Children were considered atopic if the diameter of skin reaction to at least one allergen exceeded 4 mm . Pulmonary function testing was performed at a later date using the EasyOne spirometer ( ndd Medical Technologies , Andover , Massachusetts ) according to American Thoracic Society guidelines [35] . Children were asked to withhold asthma medications on the morning of the test . The best test of three technically acceptable tests was selected . Percent predicted forced expiratory volume in 1 second ( FEV1 ) was calculated using spirometric prediction equations from a childhood population in Mexico City [36] . Measurements of ambient ozone were obtained from the Mexican government's air monitoring station closest to each child's residence ( within 5 km ) . The annual average of the daily maximum 8 hour averages of the ozone level was collected for the year prior to study entry and dichotomized at the median for stratified analyses . Further details on the ozone measurement protocol have been described elsewhere [6] . Peripheral blood lymphocytes were isolated from whole blood , and DNA was extracted using Gentra Puregene kits ( Gentra Systems , Minneapolis , Minnesota ) . A total of 498 complete case-parent trios with previously confirmed parentage and sufficient amounts of DNA were genotyped for 561 , 466 SNPs using the Illumina HumanHap 550 K BeadChip , version 3 ( Illumina , San Diego , California ) at the University of Washington , Department of Genome Sciences . Genotypes were determined using the Illumina BeadStudio Genotyping Module , following the recommended conditions . Results for three unrelated study subjects fell below the genotyping call rate threshold of 95% resulting in exclusion of three trios . The remaining study subjects were genotyped with an average call rate of 99 . 7% . Quality control analyses were conducted using PLINK ( http://pngu . mgh . harvard . edu/~purcell/plink/ ) [37] , unless otherwise stated . In preliminary SNP-level quality control , SNPs were excluded due to poor chromosomal mapping ( N = 173 ) , missingness>10% ( N = 988 ) , MAF<0 . 001% ( N = 253 ) , HWE p-value ( in parents only ) <1×10−10 ( N = 557 ) , Mendelian errors in more than two families ( N = 4 , 945 ) , and heterozygous genotype calls for chromosome X SNPs in more than one male ( N = 380 ) . All SNP exclusions were made sequentially in the above order . Subject-level quality control verified that no subjects had unusual autosomal homozygosity or an inconsistent sex between genotype and collected phenotype data . Subject-level quality control next assessed subject relatedness to identify unknown intra- and inter-family relationships . This identified two duplicated trios and one trio with first-degree relative parents requiring exclusion . Trio exclusions were not necessary for other identified relationships , including parents in different trios being first-degree or second-degree relatives and nuclear families with two children with asthma being split into two case-parent trios . There were 492 complete case-parent trios ( 1 , 468 study subjects ) in the final analysis data set . Final SNP-level quality control made exclusions due to one or more discordant genotypes across 14 HapMap replicate samples identified using the Genotyping Library and Utilities application ( http://code . google . com/p/glu-genetics/ ) ( N = 921 ) [38] . Final SNP exclusions were also made due to more stringent missingness and MAF thresholds , missingness>5% ( N = 3 , 137 ) and MAF<1% ( N = 16 , 696 ) . Of the 533 , 416 SNPs passing all quality control criteria ( 95 . 0% ) , the 520 , 767 autosomal SNPs were analyzed for purposes of this study . Subjects of Mexican ethnicity from the GALA study were used for replication testing . The GALA study protocol has been described elsewhere [14] . Subjects of Mexican ethnicity with physician-diagnosed asthma and presence of two or more asthma symptoms in the past two years ( wheezing , coughing , and shortness of breath ) were enrolled along with both biological parents in San Francisco , California , US , and Mexico City , Mexico . Total plasma IgE was measured for all subjects with asthma . The children with asthma in the GWAS ( less than 18 years old ) were enrolled at a pediatric allergy clinic , so nearly all had allergic asthma . To maximize comparability with the GWAS , the replication study included only the 177 complete case-parent trios comprising subjects of Mexican ethnicity having childhood-onset ( age of onset<18 years ) asthma and atopy ( total IgE>100 IU/mL ) and their parents . SNPs were ranked by GWAS p-value . The top SNP and 10 other top ranking GWAS SNPs were tested for replication in the GALA study . The highest ranking SNP along with 10 other top ranking SNPs with no strong LD ( r2<0 . 9 ) with other higher-ranked SNPs and MAF>10% were selected for replication . Statistical power to detect associations in the replication study of 177 case-parent trios was calculated for each selected SNP using QUANTO ( http://hydra . usc . edu/gxe ) [39] . The MAFs and RR estimates observed from the GWAS with a log-additive model were specified in the power calculation for each selected SNP . Genotyping for replication SNPs was performed on the Applied Biosystems ( ABI , Foster City , California ) PRISM 7500 Real-Time PCR System using primers and probes from ABI's Assay-by-Demand . The assay was performed under universal conditions , with each reaction containing 3 . 75 ng DNA , 0 . 125 µL 40X Assay Mix and 2 . 5 µL TaqMan Universal PCR master mix brought to a final volume of 5 µL with sterile water . Thermal cycling conditions began at 95°C for 10 minutes and then proceeded with 60 cycles of 92°C for 15 seconds and 60°C for 2 minutes . After the PCR reaction , plates were scanned by the ABI PRISM 7500 PCR system to determine genotypes by allelic discrimination . The log-linear likelihood approach was used to examine associations of individual SNPs with childhood asthma in the GWAS and replication study [29] , [40] . The log-linear method is a generalization of the classic family-based test for association between genetic variants and disease , the transmission disequilibrium test [41] , which compares the distribution of alleles transmitted from parents to affected offspring with the distribution of alleles not transmitted . An asymmetry of allele distributions implies that the variant under study is associated with disease within families . This inference requires the assumption of Mendelian inheritance , such that the allele under study is not related to the parents' fertility or to the offspring's survival [40] . The log-linear method and other transmission-based methods test the same null hypothesis of no linkage or no association ( i . e . no LD ) between the allele and disease [40] . Unlike other transmission-based methods , the log-linear method provides risk estimates to assess the direction and magnitude of association . Robustness to population stratification is a well-known property of the case-parent trio design and transmission-based methods . The log-linear method achieves robustness to population stratification by stratifying on the six possible parental mating types , which are defined by the number of copies of the allele carried by each of the two parents [40] . The assumption of HWE is not required for the log-linear method , but we tested for HWE in parents as a check for genotyping error . The log-linear method was implemented using the LEM computer program [42] with a one degree-of-freedom log-additive risk model specified . When missing genotypes occur , the log-linear method uses the expectation-maximization algorithm to maximize the likelihood , allowing incomplete trios to contribute information and minimizing loss of statistical power [31] . P-values were generated to assess statistical significance , and the RR of carrying one copy of the risk allele was calculated to assess the direction and magnitude of association . For the most significantly associated GWAS SNPs , pair-wise LD was assessed in the parents using PLINK ( http://pngu . mgh . harvard . edu/~purcell/plink/ ) [37] or HAPLOVIEW ( http://www . broad . mit . edu/mpg/haploview/ ) [43] . A combined p-value from meta-analysis of the GWAS and replication association results was computed using MANTEL [44] . Interesting SNPs from the GWAS and replication study were tested for effect modification with environmental exposures relating to air pollution and environmental tobacco smoking . Data from the GWAS population were stratified by current parental smoking ( yes/no ) and residential ambient ozone exposure ( stratified at the median of 67 ppb annual average of the daily maximum 8 hour averages ) , and the log-linear method was used to test for genetic associations in each stratum . Additional analyses were conducted in the GWAS population using the skin prick testing data . The genome-wide association scan was repeated in the subset of trios with children classified as atopic ( one or more positive skin tests ) in an effort to reduce phenotypic heterogeneity and thus reduce genetic heterogeneity . Then , an extension of the log-linear method for quantitative traits [45] was used to test for associations of interesting SNPs with the number of positive skin tests to a battery of 24 aeroallergens as a measure of atopy . This analysis was performed only on the case-parent trios with skin test data and complete genotype data . The program FRAPPE was used to estimate individual ancestry proportions , assuming three ancestral populations: Native American , European , and African [46] . HapMap ( phase 3 ) genotype data from 109 individuals from the United States with northern and western European descent and 108 individuals from Nigeria were included to represent ancestral European and African individuals , respectively . Genotype data from 35 Mayan and Pima Indians were taken from the HGDP , which have been genotyped using Illumina 650 K arrays [18] , as the best available representation of ancestral Native American individuals . Individual ancestry proportions were averaged across all study subjects to determine the mean Native American , European , and African ancestral contributions . The program SABER was used to infer locus-specific ancestry in each individual [47] . Our goal was to elucidate the pattern of ancestry segregation near the replicated chromosome 9q SNP ( rs2378383 ) . SNPs along chromosome 9q were analyzed to more accurately infer ancestry , but since we were only interested in examining the pattern of ancestry segregation at rs2378383 a priori , a multiple testing correction was not applied . A trio-based ancestry analysis test was implemented similar to that described in Clarke and Whittemore [48] , which parallels the transmission disequilibrium test [41] . Because the African ancestry in this population is quite low ( <5% ) , we tested Native American versus non-Native American ancestry . At each SNP , parents were considered to have no Native American ancestral alleles if their posterior Native American ancestry was less than 0 . 1 , one ancestral allele if between 0 . 4 and 0 . 6 , and two ancestral alleles if greater than 0 . 9 . Conservative thresholds were set to minimize misclassification . Parents with intermediate ancestry estimates falling outside the specified ranges were excluded , resulting in a 10% missing rate . For parents who were heterozygous in ancestry , the null hypothesis that the Native American allele is transmitted to the children with probability equal to ½ was tested at each SNP [48] . The Genomics Institute of the Novartis Research Foundation maintains a freely accessible database ( http://symatlas . gnf . org ) of genome-wide expression profiles of the protein-encoding transcriptome in many diverse human and mouse tissues and cell lines [21] . As reported , tissue samples were predominantly obtained from the normal physiological state [21] . The custom expression array for humans targeted 44 , 775 transcripts corresponding to known , predicted , and poorly characterized protein-encoding genes [21] . We obtained the expression data for the 44 , 775 transcripts in 51 diverse human tissues and mapped these transcripts to 15 , 047 unique genes after accounting for multiple transcripts per gene and mapping to current nomenclature . Gene expression patterns in the Novartis data were examined in the context of GWAS results . Of the 15 , 047 genes with expression data , 12 , 199 genes had at least one genotyped GWAS SNP mapping within the gene , and an additional 2 , 131 genes had at least one genotyped SNP mapping near the gene for a total of 14 , 330 genes . SNPs mapping within the 5′- most extent to the 3′- most extent over all isoforms for a gene or within a larger region expanded by 50 kb in both directions were considered . This broader region was considered in order to capture potential regulatory regions . For each gene , one false discovery rate q-value was calculated using the log-linear p-values of SNPs mapping within or near the gene based on a method for combining p-values by Peng et al . [49] . Genes were then categorized as expressed or not expressed in each of the 51 tissues examined . The expression threshold was the 75th percentile of normalized intensity values for each tissue . The global median q-values across genes expressed versus genes not expressed were calculated for each tissue , and a two-tailed Wilcoxon rank-sum test was conducted to generate a test of significance for this difference in median q-values . Gene expression patterns are correlated across different tissues . We performed a two-dimensional hierarchical clustering to describe the correlation of expression patterns using Spearman's correlation coefficient across genes and across tissues . Genes and tissues with similar gene expression patterns were grouped into clusters using Ward's distance as the linkage function to be optimized . Partek Genomics Suite 6 . 08 . 1010 ( Partek Inc . , St . Louis , Missouri ) software was used to perform this analysis using the continuous expression values in the 15 , 047 genes with expression data . | Asthma is a leading chronic childhood disease with a presumed strong genetic component , but no genes have been definitely shown to influence asthma development . Few genetic studies of asthma have included Hispanic populations . Here , we conducted a genome-wide association study of asthma in 492 Mexican children with asthma , predominantly atopic by skin prick test , and their parents to identify novel genetic variation for childhood asthma . We implicated several polymorphisms in or near TLE4 on chromosome 9q21 . 31 ( a novel candidate region for childhood asthma ) and replicated one polymorphism in an independent study of childhood-onset asthmatics with atopy and their parents of Mexican ethnicity . Hispanics have differing proportions of Native American , European , and African ancestries , and we found less Native American ancestry than expected at chromosome 9q21 . 31 . This suggests that chromosome 9q21 . 31 may underlie ethnic differences in childhood asthma and that future replication would be most effective in populations with Native American ancestry . Analysis of publicly available genome-wide expression data revealed that association signals in genes expressed in the lung differed most significantly from genes not expressed in the lung when compared to 50 other tissues , supporting the biological plausibility of the overall GWAS findings and the multigenic etiology of asthma . | [
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] | 2009 | Genome-Wide Association Study Implicates Chromosome 9q21.31 as a Susceptibility Locus for Asthma in Mexican Children |
Enterovirus A71 ( EV-A71 ) is a non-polio neurotropic enterovirus with pandemic potential . There are no antiviral agents approved to prevent or treat EV-A71 infections . We here report on the molecular mechanism by which a novel class of tryptophan dendrimers inhibits ( at low nanomolar to high picomolar concentration ) EV-A71 replication in vitro . A lead compound in the series ( MADAL385 ) prevents binding and internalization of the virus but does not , unlike classical capsid binders , stabilize the particle . By means of resistance selection , reverse genetics and cryo-EM , we map the binding region of MADAL385 to the 5-fold vertex of the viral capsid and demonstrate that a single molecule binds to each vertex . By interacting with this region , MADAL385 prevents the interaction of the virus with its cellular receptors PSGL1 and heparan sulfate , thereby blocking the attachment of EV-A71 to the host cells .
Since the first large outbreak in 1997 , enterovirus A71 ( EV-A71 ) ( genus Enterovirus , family Picornaviridae ) has been reported to cause 2–3 year cyclic epidemics in the Asia-Pacific region [1 , 2] . In the last two decades , the increasing number of EV-A71 cases and the spread of the virus across Asia have raised major concerns about its pandemic potential . The virus is primarily transmitted by the oral-fecal route [3 , 4] . Most EV-A71 infections are characterized by mild symptoms , with the typical signs of the hand , foot and mouth disease ( HFMD ) : slight fever , red rashes on the palms of hand and soles of feet , and ulcers in the mouth . However , EV-A71 infections are also associated to severe neurological complications ( such as encephalitis , aseptic meningitis and poliomyelitis-like syndrome ) and acute pulmonary edema , which may be highly limiting and fatal particularly in children under the age of 5 years [5 , 6] . In 2010 , a large outbreak of HFMD in China resulted in an estimated 1 . 7 million cases and 905 deaths [7] and an outbreak in Cambodia in 2012 resulted in the death of 54 children [8 , 9] . A sub-genogroup C4 EV-A71-inactivated vaccine has recently been approved in China , but worldwide coverage and long-term protection still need to be addressed [10–12] . There are no antiviral agents approved against EV-A71 nor against any other enteroviruses . EV-A71 has been reported to bind to several cell surface receptors , including scavenger receptor B2 ( SCARB2 ) [13 , 14] , P-selectin glycoprotein ligand-1 ( PSGL1 ) [14 , 15] and heparan sulfate ( HS ) glycosaminoglycan [16] . Other host factors such as cyclophilin A , annexin II , sialylated glycans , vimentin , nucleolin , fibronectin and prohibitin have also been reported to promote infection , although their importance in viral entry is still less noted [17–23] . It has been shown that EV-A71 interaction with PSGL1 on leukocytes requires the presence of sulfated tyrosine ( Tyr ) residues at the N-terminal region of PSGL1 [24] and depends on two highly conserved lysine residues , VP1_244K and VP1_242K , near the 5-fold vertex of the viral capsid [25] . A spatially close residue , VP1_145 , is another determinant for PSGL1 binding [26] . Similarly to PSGL1 , HS has also been proposed to interact near the 5-fold vertex of the viral capsid [15 , 16 , 22] . A well-known class of inhibitors of entero- and rhinovirus entry ( such as pirodavir , pleconaril and vapendavir ) bind into a hydrophobic pocket under the floor of the viral “canyon” formed by VP1 . Drug binding prevents receptor interaction and/or increases particle stability , which in turn blocks the conformational changes required for viral uncoating [27–32] . Despite their notable potency in vitro , none of these compounds reached advanced clinical trials . Attachment of EV-A71 to host cells can also be blocked by suramin and other sulfated and sulfonated analogs , including NF449 , which bind the positively charged residues clustered at the five-fold axis of the viral capsid , in turn preventing PSGL1 and HS attachment [33–35] . According to the proposed mechanism of action by Ren et al . , the VP1_145 residue was found to be critical for the inhibitory profile of suramin [33] . On the other hand , amino acid changes at position VP1_244 and VP1_98 modulated the antiviral effect of NF449 [34] . These findings reveal a pivotal role of the 5-fold vertex of the viral capsid for binding to host receptors and lodging molecules able to inhibit EV-A71 replication . Recently , we discovered a class of inhibitors with dual activity against HIV and EV-A71 [36 , 37] . The lead compound of this family , MADAL385 , is a tetrapodal derivative with a pentaerythritol core , 4 trivalent spacer arms and 12 tryptophan ( Trp ) residues on the periphery [38] . Because the tryptophan dendrimers are linked to the central scaffold through their amino groups , their carboxylates are free and exposed to the solvent . Our earlier biological studies demonstrated that MADAL derivatives inhibit HIV entry into its target cell by interaction with glycoproteins gp120 and gp41 of the viral surface [36] . For EV-A71 , we demonstrated that MADAL derivatives exhibit low micromolar activity against the lab-adapted strain BrCr and low-nanomolar/high-picomolar activity against a large panel of EV-A71 clinical isolates from different genogroups and various geographic origins [38] . Structure-activity relationship ( SAR ) studies on the periphery and central scaffold of MADAL highlighted the importance of free carboxylic groups for optimal antiviral activity , those carried by Trp or Tyr residues . In the present work , we elaborate on the precise molecular mechanism of action of the lead compound MADAL385 by means of in vitro biological assays , cryo-EM analysis and molecular modeling . Our data support a model of activity by which a single MADAL385 molecule binds on each of the 5-fold vertices of the EV-A71 capsid , thereby blocking the engagement of the virus with host receptors PSGL1 and/or HS .
From the series of Trp dendrimers endowed with high in vitro potency against EV-A71 replication , MADAL385 ( Fig 1A ) was selected for further mechanistic studies ( EC50 against the lab-adapted BrCr strain: 0 . 29 ± 0 . 07 μM , CC50: 30 . 0 ± 2 . 5 μM ) [38] . In a cytopathic effect ( CPE ) -based reduction assay , MADAL385 is ~1 , 800 to ~20 , 000-fold more effective against a representative EV-A71 clinical isolate ( EV-A71_11316 , geno-group B2 ) than two earlier reported EV-A71 inhibitors , namely pirodavir [31] and suramin [39] ( S1 Fig ) . MADAL385 inhibits , in a dose-dependent manner , ( i ) the formation of infectious EV-A71 BrCr particles ( Fig 1B ) and ( ii ) the replication of a mCherry-reporter EV-A71 BrCr ( Fig 1C ) . MADAL385 inhibits the early stages of EV-A71 replication [38] and to precisely determine which step of viral entry is targeted , binding and internalization assays were performed with pirodavir and suramin as reference . MADAL385 ( 10 μM ) markedly reduces -akin to suramin- the binding of EV-A71 to the host cells and prevents as well the internalization of the virus ( Fig 1D ) . In contrast , pirodavir does not show any inhibitory activity in either the binding or the internalization assays , which is in line with the reported mechanism of action ( i . e . inhibition of viral uncoating ) . By tightly binding in the hydrophobic pocket , “classical’ capsid binders such as pirodavir and pleconaril increase the rigidity of the viral particle and , as a consequence , its resistance to heat inactivation . MADAL385 , in marked contrast to the capsid-binder pirodavir protects the virus only slightly against heat inactivation ( Fig 1E ) . Taken together , these results suggest that MADAL385 blocks viral attachment by binding to the virus particle in a region outside the hydrophobic pocket . To gain first insights into the putative binding site of MADAL385 , two independent , MADAL385-resistant strains were generated via a stepwise clonal resistance selection procedure ( Fig 2A ) . Full genome sequence analysis revealed two amino acid replacements in the capsid protein VP1 of the BrCr strain: S184T and P246S ( Table 1 ) . To confirm that S184T and P246S are responsible for resistance to MADAL385 , the single or double mutant ( s ) were engineered by site-directed mutagenesis in the EV-A71 BrCr strain infectious clone . All mutant variants show replication kinetics comparable to that of the wild-type virus ( S2A Fig ) . The susceptibility of the single S184T and P246S mutants to MADAL385 is decreased by 7- and 17-fold , respectively; the double mutant ( S184T_P246S ) confers the highest ( 32-fold ) resistance to the compound ( Table 2 ) . Next , to address the role of viral VP1 in antiviral susceptibility , we engineered a recombinant virus by swapping the VP1 of the BrCr strain with that of the EV-A71 11316 clinical isolate , against which MADAL385 is ≥1000-fold more potent ( BrCr strain EC50: 0 . 28 ± 0 . 01μM versus 11316 strain EC50: 0 . 21 ± 0 . 03nM ) ( Fig 2B ) . The susceptibility of the recombinant EV-A71 BrCr_VP1 ( 11316 ) strain to MADAL385 increases dramatically to the level observed with the clinical isolate ( EC50: 0 . 12 ± 0 . 02nM ) ( Fig 2C ) . A comparison of sequences of BrCr and selected circulating EV-A71 clinical isolates ( including EV-A71_11316 ) revealed 8 amino acid differences in the viral VP1 ( S2B Fig ) . These residues were individually engineered into the BrCr infectious clone . Only R148P and L241S mutants show an increased sensitivity to the drug , without however fully restoring the susceptibility of the EV-A71_11316 strain ( Fig 2D ) . Altogether , these data further point towards the VP1 as the molecular target of MADAL385 . Notably , most of the sensitivity and resistance residues are located in the proximity of the 5-fold vertex of the EV-A71 capsid , in a region known to be involved in HS and PSGL1 receptor binding [25 , 40] ( Fig 2E ) . As expected , both the MADAL385-resistant strain and the more susceptible recombinant strain EV-A71 BrCr_VP1 ( 11316 ) were shown to retain wild-type sensitivity to the capsid binders pirodavir and vapendavir . However , to our surprise , no cross-resistance with suramin was observed , despite the fact that this molecule was earlier reported to interact with the positively charged region surrounding the 5-fold axis of the capsid [33] . In addition , when combined , MADAL385 and suramin result in a strong synergistic in vitro antiviral effect with mean volume of 771 μM2% ( S2C Fig ) . According to MacSynergy method [41] , values over 100 μM2% indicate strong and biologically relevant synergy . The lack of cross-resistance and the in vitro synergistic effect thus suggest a different mode of antiviral action of MADAL385 and suramin . To define the binding area of MADAL385 and to understand the possible mechanism underlying resistance and susceptibility to the compound , cryo-EM single particle analysis of MADAL385 bound to the EV-A71_11316 strain was performed . Purified EV-A71_11316 particles were vitrified before and after incubation with MADAL385 ( cryo-EM 2D class average images S3 Fig ) . The corresponding three-dimensional ( 3D ) maps were reconstructed at 3 . 3 and 3 . 6 Å resolutions , respectively , by applying icosahedral-symmetry averaging and using the images from a Falcon-3 direct electron detector ( S1 Table ) . The atomic models of free and drug-bound EV-A71_11316 ( Fig 3A ) superimpose with an overall RMSD value of 0 . 292 Å ( Fig 3B ) , indicating that the incubation with MADAL did not cause any significant conformational change on the viral capsid proteins . In the two atomic models , the pocket factor lipid ( sphingosine ) has slightly different conformations , but the corresponding cryo-EM densities have similar intensities ( S4 Fig ) , suggesting that the drug binding likely induces subtle conformational changes around the pocket region but does not initiate pocket factor release . When the two 3D reconstructions were compared , strong extra densities were identified on the 5-fold vertices of the EV-A71_11316-MADAL385 complex ( Fig 3C ) , indicating that MADAL385 binds on the 5-fold vertex . The drug density fills the pore on the 5-fold symmetry axis and is connected to the capsid density . Also , the density intensity is comparable to that of the capsid shell ( Fig 3D ) . Thus , MADAL385 binds on the 5-fold vertex and full saturation of the binding sites is achieved by the incubation . In particular , the intensity of MADAL385 density was highest at the symmetry axis , suggesting that the bound drug molecule occupies the very centric area of the 5-fold vertex . Due to steric hindrance and electrostatic repulsion , these observations suggest that only one molecule of MADAL385 binds on each vertex ( Fig 3D ) . The location of the two MADAL385-resistance mutations and the eight sensitivity mutations were mapped on the atomic model of EV-A71 ( Figs 2E and 3E ) . Among those , the sensitivity variant VP1_148P , is located on the surface of the channel at the 5-fold axis ( Figs 2E and 3F ) and is found within the electron density envelope connected to MADAL385 density . The presence of a positively charged arginine residue in the BrCr strain ( instead of a proline ) may provide an explanation for the reduced drug sensitivity of this lab strain . Two other residues , VP1_244K and 245Y , with their corresponding side-chains extended towards MADAL385 , are also found within the density envelope at the interface between capsid and drug ( Fig 3G ) . The two resistance mutations , VP1_184T and 246S , are located closely to the drug density but are not found within the density envelope ( Fig 3F and 3G ) , therefore interactions between MADAL385 and those residues cannot be verified or excluded based on the cryo-EM result . The rest of the other sensitivity mutations are distal from the cryo-EM densities corresponding to MADAL385 , suggesting origins for the susceptible phenotypes ( such as adaptive mutations ) other than the physical binding of the drug . Further interpretation was limited because of the icosahedral symmetry averaging applied during the cryo-EM reconstruction on the asymmetrically bound drug molecules . To complement the cryo-EM analysis , molecular dynamics ( MD ) simulations in explicit water were performed on both the free MADAL385 ( for conformational sampling ) and its complex with the VP1 pentamer ( for assessing pose stability and characterization of intermolecular interactions ) . In the case of the Cα-restrained complex , the pentaerythritol core of the tetrapodal MADAL385 was initially lodged in the external pore region corresponding to the highest electron density while three of its ‘legs’ projected into the inter-subunit crevices and the remaining one occupied the outer part of the pore ( S5 Fig ) . In this location at the 5-fold axis , the Trp carboxylates and indole rings of MADAL385 establish interactions with the positively charged residues and the hydrophobic cavities . This binding mode is reminiscent of the binding to sulfated Tyr residues ( S1 Movie ) of PSGL1 and HS . To identify the VP1 residues at the 5-fold axis that contribute predominantly to MADAL385 binding , van der Waals and solvent-screened electrostatic interactions , together with the cost of desolvation upon complex formation , were calculated [42] . In line with the cryo-EM analysis , VP1_244K , 245Y , 145Q , and 148P are the residues with which MADAL385 establish the strongest favorable interactions ( S6 Fig ) . HS and PSGL1 have been proposed to play critical roles in the early steps of EV-A71 infection by interacting with the positive charges at the 5-fold vertex of the EV-A71 capsid . In addition , our structural analysis suggests that the binding site of MADAL385 overlaps with the binding sites of both receptors . We hence hypothesized that MADAL385 exerts its antiviral activity by blocking virus attachment to PSGL1 and/or HS . The experimental setup is depicted in Fig 4A . Binding-inhibition assays were performed with heparin or PSGL1-coupled beads in either the presence of MADAL385 or reference compounds and the extent of inhibition was quantified by RT-qPCR or Western-blot . First , the ability to bind soluble heparin was assessed by neutralization assay for the BrCr , MADAL385-resistant and sensitive EV-A71 strains . Similar sensitivity is noted across the different viruses ( Fig 4B ) . Next , HS-binding inhibition assay revealed that binding affinity between heparin and EV-A71 BrCr is gradually lost with increasing concentration of MADAL385 ( Fig 4C ) . In contrast , MADAL385 does not inhibit heparin binding of the resistant strain ( EV-A71 VP1_S184T_P245S ) , even at the highest concentration tested ( Fig 4D ) . Interestingly , and in line with the high susceptibility to the antiviral action of MADAL385 , binding of the recombinant EV-A71 BrCr_VP1 ( 11316 ) strain to heparin is inhibited by MADAL385 more efficiently as compared to the BrCr strain , with more than 50% binding affinity lost at low micromolar concentrations ( Fig 4E ) . As expected , the pocket binder pirodavir does not significantly affect binding of EV-A71 to heparin , whereas suramin completely blocks heparin binding of all viruses ( Fig 4C–4E ) . Next , the effect of MADAL385 on the interaction of PSGL1 with EV-A71 was studied . For this purpose , PSGL1-Fc was over-expressed in HEK293T cells ( S7 Fig ) and concentrated on protein G beads . MADAL385 , akin to suramin , blocks binding of the EVA71-PB strain to PSGL1 in a concentration-dependent manner . The capsid binder pirodavir has , as expected , no effect on this binding event ( Fig 4F ) . To determine whether MADAL385 affects attachment to PSGL1 and HS during infection , we employed human SCARB2- or PSGL1-overexpressing L929 cells . EV-A71_812 strain , a PSGL1 binding strain sensitive to MADAL385 ( EC50: 1 . 78 ± 0 . 81nM ) was used to study binding to the cell receptors in presence of MADAL385 ( 0 . 1 μM ) . To account for ( and exclude ) the contribution of HS , L929-SCARB2 and PSGL1 cells were treated in parallel with sodium chlorate ( NaClO3 , 50mM ) , an inhibitor of cell-surface sulfation . MADAL385 greatly reduced the binding of EV-A71 to both L929-SCARB2 and PSGL1 cells; however , in NaClO3-treated cells , MADAL385 effectively reduced binding of EV-A71 to L929- PSGL1 but not to L929-SCARB2 cells ( Fig 5A ) . Quantification of the infectious content of EV-A71_812 in L929-SCARB2 and PSGL1 cells treated with MADAL385 in presence of NaClO3 recapitulate the data of the binding assay; i . e . MADAL385 is only effective against EV-A71 in L929- PSGL1 cells ( Fig 5B ) . These data support the hypothesis that MADAL385 blocks EV-A71 infection by preventing viral attachment to either HS , PSGL1 or both .
We report here on the mechanism of action of MADAL385 , the lead compound of a novel class of tryptophan dendrimers with exquisitely potent in vitro antiviral activity against EV-A71 . Cryo-EM studies revealed that the highly conserved lysine residue at position 244 of VP1 ( VP1_244K ) , near the icosahedral 5-fold vertex , is closely connected to the density of MADAL385 . This residue also plays a key role in the interaction of EV-A71 with PSGL1 and HS . Both receptors are sulfated molecules ( i . e . endowed with a negative charge at physiological pH ) whose interaction with the positively charged VP1_244K capsid residue is thought to involve a strong electrostatic interaction . As a result of this high-affinity interaction , we showed that MADAL385 inhibits EV-A71 binding with PSGL1 and HS . Together with biochemical evidence , we also demonstrate that MADAL385 inhibits the binding of EV-A71 to human SCARB2- or PSGL1-expressing L929 cells . We observed that the activity of MADAL385 in L929-SCARB2 cells was exclusively dependent on the inhibition of HS binding since the activity of MADAL385 was lost in cells treated with sodium chlorate ( NaClO3 ) , a molecule that prevents cell-surface sulfation . In addition , this experiment also demonstrates the importance of HS binding for efficient entry and replication of EV-A71 in both L929-SCARB2 and L929-PSGL1 overexpressing cells . Recent SAR studies performed with MADAL derivatives [37] point to the crucial role of the carboxylic acid groups for the antiviral efficacy . The importance of these carboxylates is corroborated by the lack of activity observed with the corresponding tryptamine ( a “decarboxylated” analogue of Trp ) and methyl ester derivatives ( COOCH3 instead of COOH ) [37] . The nature of the amino acid side chains is also very important for activity since the indole ring of Trp is preferred , most likely due to its relative polarity and hydrogen-bonding potential , particularly towards the hydroxyl of Thr141 ( according to our MD simulations ) . These observations suggest that the carboxylic acid ( -COOH ) or carboxylate ( -COO- ) groups of MADAL385 can mimic the sulfate groups ( -SO3H or -SO3- ) of human PSGL1 or HS . By competing with the sulfate groups , MADAL385 may prevent virus attachment to these host receptor ( s ) and thereby the entry into and infection of host cell . In agreement with the cryo-EM results showing that MADAL385 is lodged in the external pore region , the MD simulations revealed the preference of the drug for binding inside the cavity lined by the adjoined 141TPTGQVVP148 and 242QSKYP246 loops of the five VP1 subunits . Three of the MADAL385 ‘legs’ projected into the inter-subunit crevices and the remaining one occupied the outer part of the pore . It seems , therefore , that a certain conformation of these two exposed VP1 loops is necessary for MADAL385 binding , most likely for providing relative accessibility of VP1_244K to interact with the negative charges of MADAL385 . We propose that the location of the MADAL385-sensitive variants VP1_148P and VP1_245Y is critical for stacking and establishing van der Waals interactions with the indole moieties of MADAL385 . Furthermore , our data demonstrate that the MADAL385-resistant variants may only reduce viral sensitivity to MADAL385 in the context of the BrCr strain VP1 . Indeed , the clinical isolate EV-A71_11316 carries the VP1_184S residue , a resistant variant for the BrCr strain . Suramin and its derivative NF449 are known to specifically interact with residues VP1_145Q and VP1_98E_244K at the 5-fold vertex , respectively [33 , 35] . Of interest , we show that the susceptibility to suramin is not affected in the presence of both S184T and P246S amino acid substitutions . In addition , we observed a prominent in vitro antiviral synergistic effect between MADAL385 and suramin . These results indicate that , despite the common binding site for these two classes of drugs , the subtleties of their binding modes are different , which is not entirely surprising given their very distinct chemical structure . However , based on molecular modelling considerations , the simultaneous binding of MADAL385 and suramin on the same 5-fold vertex seems most unlikely due to the large size and negatively charged character of both entities . Cyclophilin A is a newly reported uncoating regulator for EV-A71 entry , and its binding site is very close to that of PSGL1 and HS on EV-A71 virion [22] . The MADAL class of tryptophan dendrimers may thus have the potential to block Cyclophilin A binding . However , the Cyclophilin A inhibitor cyclosporin A ( CsA ) did not affect binding of EV-A71 BrCr to RD cells ( S8 Fig ) nor could we observe any antiviral activity of CsA or Debio-025 ( another specific Cyclophilin A inhibitor ) against BrCr and the clinical isolates 11316 and 812 ( CsA EC50>30μM and Debio-025 EC50>21μM ) , suggesting that this host factor does not play a prominent role in the entry of the strains that we used in our study . Medicinal chemistry efforts are currently ongoing to simplify and reduce the backbone of MADAL385 without affecting the antiviral activity . Half-sized compounds ( ~1500 Da versus 3575 . 84 Da ) have now been identified that are equipotent to MADAL385 against EV-A71 and that have little or no adverse effect on the host cells ( at concentrations up to 100 μM ) . In vivo studies to assess tolerability and antiviral efficacy will be complementary to medicinal chemistry efforts to pursue the development of this class of compounds as novel EV-A71 antiviral agents .
RD cells ( Human rhabdomyosarcoma cells ) and Hela cells , obtained from ATCC , were cultured in DMEM ( Life Technologies ) supplemented with 10% heated-inactivated fetal bovine serum ( FBS ) ; Enterovirus A71 BrCr strain ( EV-A71 BrCr ) , a kind gift from Prof . F . van Kuppeveld ( University of Utrecht ) , was propagated on RD cells with 2% FBS-DMEM . L929-human SCARB2 cells and L929-human PSGL1 cells were kindly provided by Prof . Satoshi Koike ( Tokyo Metropolitan Institute of Medical Science , Japan ) and were grown in DMEM supplemented with 10% FBS , 1% sodium bicarbonate , 1% L-glutamine and 10μg/ml puromycin . To reduce sulfate from the cell surface , 50mM of sodium chlorate ( NaClO3 ) was added to the culture medium one week before the experiment . EV-A71 clinical isolates were obtained from Dr . Shih-Cheng Chang ( Chang Guang University ) or purchased from The National Collection of Pathogenic Viruses ( NCPV ) . The Trp-containing dendrimer MADAL385 was synthesized as described previously [38] . Vapendavir and pirodavir were kindly provided by Aviafen Therapeutics and A . Muigg , respectively . Suramin sodium salt and heparin sodium were purchased from Sigma-Aldrich . All compounds were dissolved in DMSO and stored at 4°C . The plasmid pEV-A71 ( Nagoya-VP231 ) was generously provided by Dr . Arita ( National Institute of Infectious Disease , Japan ) and was modified to carry the viral genome of EV-A71 BrCr ( pEV-A71-BrCr ) by classic cloning . For the construction of EV-A71BrCr-mCherry , the genome of EV-A71 BrCr was inserted to a pShuttle-BAC vector . In the spacer region between the 5'UTR and VP4 , mCherry gene was inserted and flanked by 2Apro cutting sites . The plasmids pLX304-PSGL-1 was purchased from DNASU , and vector pFUSE-hIgG1-Fc1 was from InvivoGen . PSGL-1 extracellular region was cloned into the expression vector pFUSE-hIgG1-Fc1 to create recombinant protein with human IgG Fc-tag at the C-terminal domain ( pPSGL1-hFc ) . The EV-A71 VP1 purified MaxPab rabbit polyclonal antibody was obtained from Abnova . Goat anti-Human IgG Fc cross-absorbed secondary antibody , HRP was purchased from Thermo Scientific . Heparin Sepharose CL-6B and Dynabeads Protein G were supplied by Pharmacia and Thermo Scientific , respectively . CPE reduction assays were performed as described previously [43] . Briefly , RD cells in 96-well cell culture plates were seeded , after which serials of diluted compounds and EV-A71 inoculum were added . CPE was quantified by MTS assay at 3 days post infection and expressed as percentage of untreated controls . The 50% effective concentration ( EC50 ) was calculated by logarithmic interpolation and is defined as the concentration at which the virus-induced CPE is reduced by 50% . EV-A71 viral RNA was isolated using NucleoSpin RNA kit ( Macherey-Nagel ) according to the manufacturer’s protocol . The following primers and probe were used for EV-A71 BrCr qRT-PCR: EV-A71 forward primer 5’ CCGCATTGAACCACTGTAATTT 3’ , reverse primer 5’ GGAGCCAACGTGATAGTGATAG 3’ and probe 56-FAM/ACTATTGGT/ZEN/GGTGCCTATCA/3IABKFQ Recombinant plasmid pPSGL-1-Fc was transfected in HEK-293T cells . After 36-48h , cells were harvested and lysed ( 1% Triton X-100 in NTE buffer supplemented with protease inhibitor cocktail ) for 30 min in ice . Insoluble cell debris was removed by centrifugation 15 , 000 × g for 15 minutes . To confirm pPSGL-1 expression , the supernatant was subjected to Western-blot with goat anti-Human IgG Fc antibody . After confirmation , the soluble recombinant PSGL1-Fc was incubated with protein G Dynal beads ( Sigma ) overnight at 4° C on a rotary mixer . Unbound PSGL1-Fc was removed after 3 washes with cold TBS . The EV-A71 viruses were mixed with indicated concentrations of compounds at 37° C for 1 hour . Next , the beads loaded with PSGL1 were incubated with viruses in presence/absence of compounds at 4° C for 2 hours . After 3 washing with TBS to remove the unbound virus , beads were treated with 2x laemmli buffer ( Sigma-Aldrich ) and subject to 10% SDS-PAGE and further Western-blot with EV-A71_VP1 MaxPab rabbit polyclonal antibody and goat anti-Human IgG Fc antibody . For heparin-Sepharose coated beads , after 3 washing with TBS to remove the unbound virus , lysis buffer was added and viral RNA was extracted and quantified by real time qRT-PCR . EV-A71_11316 was purified as described previously [44] . Briefly , EV-A71_11316 was propagated in HeLa cells for 24 h . The media and cells were collected and processed by freezing and thawing three times . Cell debris was pelleted by centrifugation and the supernatant was precipitated with sodium chloride and polyethylene glycol ( PEG ) 8000 . After ultracentrifugation through a 30% sucrose buffer cushion , the pellets were resuspended and applied to a 10 to 35% K-tartrate step gradient . The virus was collected and dialyzed against 10 mM Tris , 200 mM NaCl , 50 mM MgCl2 , pH 7 . 5 . EM samples were prepared and data sets were recorded at the Pennsylvania State University—Huck Institutes of the Life Sciences in the following way: prior to incubation and vitrification of the sample , the virus buffer was exchanged to phosphate-buffered saline ( PBS ) . MADAL385 was incubated at 2 . 8 μM with about 58 nM of the virus at 37°C for 1 hour , which equates to four copies of molecule per each of vertex on the virus capsid . Three microliters of each sample was pipetted onto a Quantifoil R2/1 grid ( Quantifoil Micro Tools GmbH , Jena , Germany ) , blotted to remove excess , and plunge-frozen in liquid ethane using a Vitrobot ( Thermo Fisher , USA ) . Grids were imaged in a Titan Krios G3 under automated control of the FEI EPU software . An atlas image was assembled from micrographs taken at 165x magnification on a FEI BM-Ceta camera , and suitable areas were selected for imaging on the FEI Falcon 3EC direct electron detector . The microscope was operated at 300 kV with a 70 μm condenser aperture and a 100 μm objective aperture . Magnification was set at 59 , 000x yielding a calibrated pixel size of 1 . 1 Å . Images were recorded in movie mode saving 44 fraction images , each fraction including 56 frames . The total accumulated exposure was 46 e-/Å2 . The cryo-EM density maps for the EV-A71 virus and the two virus-complexes are available at the Electron Microscopy Data Bank via accession codes EMD-7905 ( sharpened and unsharpened virus maps ) and EMD-7913 ( sharpened and unsharpened virus-MADAL385 complex maps ) . The atomic coordinates for the viruses built in the two maps are available at the PDB via accession codes 6DIJ ( virus ) and 6DIZ ( virus-drug complex ) . PyMOL was used for model building of MADAL385 fragments , their assembly into larger fragments and the full dendrimer , as well as for trajectory visualization and 3D figure generation [45] . Geometry optimization and point charge derivation for suitably capped fragments , namely , the pentaerythritol core , the trivalent spacer , and the peripheral N-acetylated Trp residues , were achieved by means of the PM3 hamiltonian available in the sqm program [46] . The standard ff14SB force field parameter set in AMBER 16 was used for both ligand and protein atoms [47 , 48] . A three-dimensional cubic grid consisting of 65x65x65 points with a spacing of 0 . 375 Å centered on the VP1 pore region displaying electron density for MADAL was defined for docking purposes . Electrostatic , desolvation , and affinity maps for the atom types present in MADAL385 were calculated using AutoGrid 4 . 2 . 6 and thereafter the Lamarckian genetic algorithm implemented in AutoDock4 was used to generate 100 docked conformations of a large variety of incrementally larger fragments [49] . Intra- and intermolecular energy evaluation of each configuration allowed the selection of the 10 best scoring solutions for each fragment . Significant clustering of solutions with the best scores was apparent for the smaller fragments and visual inspection confirmed the feasibility of the best binding poses , which were used for model building of the VP1 complex with the full dendrimer . For conformational sampling of the full dendrimer , MADAL385 was immersed in a cubic solvent box ( TIP3P water molecules ) extending 12 Å away from any ligand atom and neutralized by addition of 12 sodium ions to the locations with the most negative molecular electrostatic potential . The same procedure was later used for the VP1:MADAL385 complex; in this case , a weak harmonic restraint of 5 kcal mol-1 Å-2 on the protein Cα atoms was used throughout . Thereafter , all hydrogens and water molecules in both systems were first reoriented in the electric field of the solute and then all atoms were relaxed by performing 25 000 steps of steepest descent followed by 100 000 steps of conjugate gradient energy minimization . The resulting geometry-optimized coordinate sets were used as input for the ensuing molecular dynamics ( MD ) simulations at 300 K and 1 atm using the pmemd . cuda engine , as implemented in AMBER 16 [50] . The application of SHAKE to all bonds allowed an integration time step of 2 fs to be used . A cutoff distance of 9 Å was selected for the nonbonded interactions and the list of nonbonded pairs was updated every 25 steps . Periodic boundary conditions were applied and electrostatic interactions were represented using the smooth particle mesh Ewald method with a grid spacing of 1 Å [51] . The coupling constants for the temperature and pressure baths were 1 . 0 and 0 . 2 ps , respectively . Water molecules and counterions were first equilibrated around the positionally restrained solute for a first run of 0 . 5 ns . For the remaining 150 ns of simulation the whole systems were allowed to relax and coordinates were saved every 0 . 1 ns for further analysis by means of the cpptraj module in AMBER [52] . Subsequently , a simulated annealing procedure was followed to cool down snapshots taken every 5 ns from 300 to 273 K over a 1-ns period [53] . The geometries of these “frozen” systems were then optimized by following an energy minimization protocol until the root-mean-square of the Cartesian elements of the gradient was less than 0 . 01 kcal·mol-1·Å 1 . The final ensemble containing 30 energy-minimized frozen molecules , which can be expected to be closer to the global energy minimum , were taken as representative of the dendrimer and the VP1-MADAL385 complex . | Enterovirus A71 ( EV-A71 ) is the virus responsible for most of the severe forms of hand , foot and mouth disease ( HFMD ) associated with neurological involvement and mortality in young children under the age of 5 . Seasonal outbreaks of HFMD -with a 2–3 years epidemic cycle- are recurring around the world , especially in the Asia-Pacific region . To date , no antiviral agent has been approved for the treatment of EV-A71 infections . Here , we report on a recently uncovered class of tryptophan dendrimers with an extraordinary antiviral activity in vitro against circulating EV-A71 clinical isolates . Mode of action studies revealed that this class of compounds targets the 5-fold vertex of EV-A71 , in turn blocking receptor binding . Our finding may open an entirely novel line of research and largely aid in anti-enterovirus drug development . | [
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"meth... | 2019 | Viral engagement with host receptors blocked by a novel class of tryptophan dendrimers that targets the 5-fold-axis of the enterovirus-A71 capsid |
The recent discovery of functional brown adipocytes in adult humans illuminates the potential of these cells in the treatment of obesity and its associated diseases . In rodents , brown adipocyte-like cells are known to be recruited in white adipose tissue ( WAT ) by cold exposure or β-adrenergic stimulation , but the molecular machinery underlying this phenomenon is not fully understood . Here , we show that inducible brown adipogenesis is mediated by the microRNA miR-196a . We found that miR-196a suppresses the expression of the white-fat gene Hoxc8 post-transcriptionally during the brown adipogenesis of white fat progenitor cells . In mice , miR-196a is induced in the WAT-progenitor cells after cold exposure or β-adrenergic stimulation . The fat-specific forced expression of miR-196a in mice induces the recruitment of brown adipocyte-like cells in WAT . The miR-196a transgenic mice exhibit enhanced energy expenditure and resistance to obesity , indicating the induced brown adipocyte-like cells are metabolically functional . Mechanistically , Hoxc8 targets and represses C/EBPβ , a master switch of brown-fat gene program , in cooperation with histone deacetylase 3 ( HDAC3 ) through the C/EBPβ 3′ regulatory sequence . Thus , miR-196a induces functional brown adipocytes in WAT through the suppression of Hoxc8 , which functions as a gatekeeper of the inducible brown adipogenesis . The miR-196a-Hoxc8-C/EBPβ signaling pathway may be a therapeutic target for inducing brown adipogenesis to combat obesity and type 2 diabetes .
Brown adipose tissue ( BAT ) combusts excess energy through mitochondrial energy uncoupling mediated by Uncoupling protein-1 ( Ucp1 , also known as thermogenin ) in nonshivering thermogenesis [1] . Recent discoveries of metabolically active BAT in adult humans [2]–[6] have highlighted BAT as a new therapeutic target for treating obesity and its associated diseases , such as type 2 diabetes mellitus [7] . The activity of BAT is inversely correlated with body mass index in humans [3]–[4] , implying a significant role for BAT in the development of obesity . Importantly , the brown adipocyte-like cells in white adipose tissue ( WAT ) can be generated by cold exposure or β3-adrenergic stimulation in rodents [8]–[9] , and the activity of BAT can be increased by cold exposure or β3-adrenergic stimulation in humans [2] . The molecular mechanisms underlying this inducible brown adipogenesis have not been fully elucidated . The expression patterns of the Hox family of homeobox genes ( Hox genes ) are characteristically distinct between BAT and WAT [10]–[12] , which implies a significant role of Hox genes in the determination of two fat types . But its significance has not been fully understood . Hox genes are representative of developmental genes and confer an anteroposterior positional identity during embryogenesis . Several Hox genes have roles in differentiation systems , such as hematopoiesis [13] , myogenesis [14] , and cardiogenesis [15] , but relatively less is known about their roles in adipogenesis . Among the differentially expressed Hox genes , Hoxc8 is more highly expressed in WAT than in BAT and is categorized as a white-fat gene [11] , [16] . These observations imply that Hoxc8 may have an unknown role in the determination of the two fat types . microRNAs ( miRNAs ) are important regulators of the gene networks underlying diverse biological phenomena [17] . miRNAs are small , non-coding RNAs that base pair with specific mRNAs and suppress gene expression post-transcriptionally [18] . miRNAs constitute an essential regulatory layer at the level of the transcriptional network [19] . Through their regulatory capacity , miRNAs affect the output of signaling networks by fine-tuning or switching output levels [19] and promote or redirect dynamic flow in genetic circuits and affect differentiation [20] . The roles of miRNAs in the inducible brown adipogenesis in WAT are not well understood . We here show that single miRNA miR-196a is capable of recruiting the metabolically functional brown adipocytes in WAT in mice . The miR-196a expression is induced in the WAT-progenitor cells in mice exposed to cold or β3-adrenergic stimulation . The induction of miR-196a is required for the brown fat gene expression and is sufficient to generate the metabolically functional brown adipocyte-like cells in WAT in mice . The target gene of miR-196a is white-fat gene Hoxc8 , which directly represses the expression of C/EBPβ , a master regulator of brown adipogenesis .
Recent reports have shown that the WAT-derived progenitor cells undergo brown adipogenesis in vitro in both mice [16] , [21] and humans [16] , [22] . Consistently , the human fat progenitor cells derived from flank subcutaneous WAT ( hereafter , WAT-progenitor cells ) exhibited increased brown-fat gene expression after differentiation ( Figure S1A and S1B ) . HOXC8 is categorized as a white-fat gene [16] and RNA-seq analysis revealed that HOXC8 was most highly expressed among the clustered HOX genes in the human WAT-progenitor cells ( Figure 1A and 1B ) . We noticed that HOXC8 was down-regulated in the differentiated adipocytes ( Figure 2A and 2B ) . Contrarily , the expression of HOXC6 did not change after differentiation ( Figure S1C ) and was not particularly high in WAT ( Figure S1D ) , though HOXC6 is located adjacent to HOXC8 in HOXC cluster and was the second most highly expressed gene ( Figure 1A and 1B ) . These results implied the existence of specific regulatory machinery for HOXC8 expression . Down-regulation of HOXC8 was observed at the protein level ( Figure 2C ) but not at the mRNA level ( Figure 2D ) . These results implied that HOXC8 might be regulated post-transcriptionally . Transduction of HOXC8 in the human WAT-progenitor cells suppressed the brown-fat genes including C/EBPβ [23] , UCP1 [24] , and ADIPSIN ( also known as CFD ) ( Figure 2E ) [23] . In contrast , HOXC8 did not suppress the white-fat genes including leptin [11] , CD24 [25] , HMGA2 [26] , and ADIPOQ ( also called adiponectin ) ( Figure 2E ) . These results suggested that HOXC8 might regulate the brown-fat genes and that HOXC8 might be an important regulator for brown adipogenesis of the WAT-progenitor cells . To extend our findings in vitro to in vivo , we proceeded to a mouse model of brown adipogenesis . In mice , the Hoxc8 expression was higher in WAT than BAT and other tissues ( Figure S2 ) . Stromal vascular fraction ( SVF ) of fat depots contains fat progenitor cells ( hereafter , SVF cells ) . The Hoxc8 expression was suppressed after the SVF cells were induced to undergo brown adipogenesis ( Figure 3A and 3B ) and expressed Ucp1 ( Figure 3C ) , Pgc-1α , and C/EBPβ ( Figure 3D ) . In mice , brown adipogenesis can be induced in WAT by administering a β3-adrenergic agonist , CL-316 , 243 , or by exposing mice to cold environment . After administration of CL-316 , 243 , the expression of Hoxc8 was down-regulated prominently in inguinal WAT ( ingWAT ) ( Figure 3E ) . The down-regulation of Hoxc8 was relatively modest in epididymal WAT ( epiWAT ) and interscapular BAT ( iBAT ) than in ingWAT ( Figure 3E ) . To delineate the Hoxc8 expression changes during white and brown adipogenesis , the Hoxc8 expression levels were compared between the progenitor cell fraction ( SVF ) and tissue fraction mainly composed of mature adipocytes . As a result , the Hoxc8 expression is slightly increased in saline-treated WAT than in SVF and is down-regulated in CL-316 , 243-treated fat that underwent brown adipogenesis , indicating that Hoxc8 is down-regulated specifically during brown adipogenesis , but not during white adipogenesis ( Figure 3F ) . Thus , the down-regulation of Hoxc8 is observed during brown adipogenesis both in vitro and in vivo . We next sought to identify the mechanism underlying the down-regulation of Hoxc8 during brown adipogenesis . Post-transcriptional regulation of Hoxc8 was suggested by the in vitro experiments . Characteristically , a number of Hox genes are regulated by miRNAs [14] , [27]–[29] and the Hoxc8 expression can be down-regulated by evolutionally conserved miR-196a via translational inhibition during vertebrate development [28] . There are two genes encoding miR-196a ( miR-196a-1 and miR-196a-2 ) located within the Hox gene clusters [28] . Based on the hypothesis that Hoxc8 might be regulated by miR-196a , we investigated the miR-196a expression during the brown adipogenesis in mice . We found that the miR-196a expression was induced in WAT depots of mice exposed to cold environment or β3-adrenergic stimulations ( Figure 4A ) . More specifically , miR-196a was more highly induced in the SVF cells ( Figure 4B ) than in mature adipocytes ( Figure S3 ) . Thus , miR-196a expression is induced in the SVF cells in mice exposed to β3-adrenergic stimulation or cold exposure . The in situ hybridization analysis of miR-196a showed the induction of miR-196a in WAT after CL-316 , 243 administration ( Figure 4C ) . Based on the finding that the miR-196a expression is induced during the brown adipogenesis in WAT in mice , we next investigated whether the miR-196a induction is required for the induction of brown adipogenesis and Hoxc8 suppression . In vitro , the miR-196a expression is induced during the differentiation of WAT-progenitor cells derived from both mice ( Figure 4D ) and humans ( Figures S4A ) . More detailed analyses showed that miR-196a was induced by forskolin , an adenylyl cyclase activator , implying the significant role of cyclic AMP pathway to regulate miR-196a expression ( Figure S4B ) . To address the necessity of miR-196a in the brown adipogenesis , antisense oligonucleotide ( ASO ) against mR-196a was transfected to the mouse SVF cells . The miR-196a expression was suppressed in the transfected cells ( Figure 4E ) and the Hoxc8 expression was recovered in the transfected adipocytes ( Figure 4F ) , indicating that Hoxc8 suppression was mediated by miR-196a . The ASO against miR-196a suppressed the expression of Ucp1 ( Figure 4G and 4H ) and other brown-fat genes ( Figure 4H ) , but not the leptin expression , indicating that miR-196a is necessary for the brown fat gene expression . Thus , the upregulation of miR-196a is required for the induction of brown fat gene expression during the differentiation of WAT-progenitor cells . We next sought whether the findings above are possible to be generalized to the conventional brown adipogenesis , which occurs in the iBAT . The miR-196a expression level was significantly lower in iBAT than WAT ( Figure S4C ) and was not altered during the differentiation of the iBAT-SVF cells ( Figure S4D ) , suggesting that miR-196a might not be involved in conventional brown adipogenesis in iBAT . Furthermore , endogenous expression of Hoxc8 was not detected in iBAT-SVF cells ( Figure S5 ) . Taken together , miR-196a is upregulated in the WAT-progenitor cells during the inducible brown adipogenesis in mice and is required for the induction of brown fat gene expression . We next asked whether Hoxc8 was an essential target of miR-196a for the induction of brown-fat genes . We cloned the wild-type ( Hoxc8-wt3′UTR ) and miR-196a-binding site-deleted ( Hoxc8-ΔmiR-196-BS ) Hoxc8-3′UTR into a pCX4 retroviral vector and transduced these constructs into human WAT-progenitor cells ( Figure S6A ) . The exogenous expression levels were comparable among the constructs ( Figure S6A ) . After the adipogenic induction , the protein expression of Hoxc8 was suppressed in the Hoxc8-wt3′UTR-transduced cells than in Hoxc8-ΔmiR-196-BS- or Hoxc8-transduced cells ( Figure S6B ) , suggesting that the suppression of Hoxc8 was dependent on the miR-196a-binding site in the Hoxc8 3′UTR . The brown fat gene expression was specifically high in the Hoxc8-wt3′UTR-tranduced cells ( Figure S6C ) , indicating that the induction of brown-fat genes was regulated in a manner dependent on the miR-196a-binding site of Hoxc8 mRNA . These results suggest that miR-196a regulates brown-fat genes through suppression of Hoxc8 . To further corroborate that Hoxc8 suppression is an important step , Hoxc8 was knocked down using Hoxc8 shRNA ( Figure S7 ) . As a result , the brown-fat genes including C/EBPβ and Ucp1 were induced ( Figure S7A and S7B ) , indicating that the suppression of Hoxc8 is a critical step for the induction of brown-fat genes . Based on the finding that miR-196a is required for the inducible brown adipogenesis , we next addressed whether miR-196a is capable of inducing brown adipogenesis in mice . We created transgenic mice in which miR-196a and EGFP were expressed under the control of the aP2 promoter/enhancer , which is exclusively active in adipose tissues [30] . The transgenic mice ( hereafter , the miR-196a mice ) were born in a Mendelian ratio and were viable . The SVF cells isolated from the miR-196a mice were EGFP-negative immediately upon isolation , but they became EGFP-positive while they were kept in culture ( Figure S8A ) and expressed miR-196a ( Figure S8B ) , resulting in Hoxc8 suppression ( Figure S8C and S8D ) . After differentiation induction , the cells expressed more intense EGFP and underwent adipogenesis . The aP2 promoter activity was observed in the fibroblast-like cells in ingWAT depots ( Figure S8E ) , which might represent the fat progenitor cells undergoing adipogenesis . The SVF cells isolated from the miR-196a mice expressed brown-fat genes more highly than the cells from wild-type ( WT ) mice after differentiation in vitro ( Figure S8F ) , indicating that miR-196a promotes brown adipocyte differentiation of the WAT-progenitor cells . To ask whether the miR-196a function is cell-autonomous , the human WAT-progenitor cells were transduced with lentivirus expressing miR-196a . As a result , miR-196a enhanced the brown fat gene expression during differentiation , indicating the cell-autonomous function of miR-196a ( Figure S9 ) . In vivo , the gene-expression analysis revealed an induction of brown-fat genes , including C/EBPβ , Prdm16 , and Ucp1 in ingWAT ( Figure 5A ) , and the histological analysis revealed clusters of multilocular cells with Ucp1 expression ( Figure 5B ) . It is known that different WAT depots respond to brown fat-inducing stimulations to different extents [31] , and we therefore addressed the responses to the miR-196a expression in different fat depots . The miR-196a expression levels were comparable among the different fat depots in the miR-196a mice ( Figures 5C and S10 ) . The induction of C/EBPβ , Ucp1 , and Pgc-1α was more prominent in the ingWAT than in the epiWAT ( Figure 5D and 5E ) and was further augmented after CL-316 , 243 treatment ( Figure 5D and 5E ) . In the iBAT , no appreciable influence of miR-196a was observed ( Figure 5D and 5E ) . Thus , miR-196a induces the brown adipocyte-like cells with characteristic appearance and gene expression profile of brown adipocytes in WAT . Based on the finding that miR-196a is capable of inducing the brown adipocyte-like cells , we next addressed whether they were metabolically functional . The miR-196a mice showed a tendency to be leaner than WT mice ( Figure 6B ) , and even when fed a high-fat diet , they exhibited resistance to obesity ( Figure 6A and 6B ) , despite the fact that their food intake tended to be increased compared with that of the WT littermates ( Figure 6C ) . The weight reduction was attributable to a reduced fat accumulation ( Figure S11 ) . To interrogate the mechanism behind the obesity resistance of the miR-196a mice , indirect calorimetry was performed . We used mice with similar body weight under a normal diet . As a result , the oxygen consumption ( Figure 6D ) and the energy expenditure ( Figure 6E and Table S1 ) were enhanced during both the light and dark phases in the miR-196a mice compared to the WT mice , indicating the accelerated energy metabolism . The difference of the oxygen consumption and the energy expenditure was even enlarged when the mice were fed a high-fat diet ( Figure S12 ) . The core body temperature was higher in the miR-196a mice than in the WT mice ( Figure 6F ) . These findings suggest that miR-196a boosted the cellular energy combustion through the induction of brown adipocyte-like cells . We next analyzed impacts of miR-196a on glucose metabolism in the miR-196a mice . In the glucose tolerance tests , the miR-196a mice showed lower blood glucose ( Figure 6G ) and insulin levels ( Figure 6H ) . After insulin administration , they exhibited more pronounced declines in their blood glucose levels ( Figure 6I ) . These results imply that miR-196a prevented the mice from developing insulin resistance , the premorbid condition of type 2 diabetes . Taken together , these findings suggest that the miR-196a-induced brown adipocyte-like cells are metabolically functional and have favorable impacts on glucose metabolism in mice . The concept that miR-196a induces brown adipogenesis through the suppression of Hoxc8 , which might function as a gatekeeper of brown adipogenesis in WAT , facilitated us to investigate the target gene of Hoxc8 transcription factor . The chromatin immunoprecipitation ( ChIP ) assays among the candidate genes revealed a significant enrichment of Hoxc8 in the C/EBPβ locus in the mouse genome ( Figure 7A ) . C/EBPβ is a crucial regulator of brown adipogenesis , which is highly expressed in BAT compared to WAT [23] . The enrichment was found in the 3′ region , which harbors high interspecies conservation ( Figure 7B , “4” ) . In human WAT-progenitor cells , too , the enrichment of HOXC8 was observed in the C/EBPβ 3′ region ( Figure 7C and 7D ) . The enrichment of HOXC8 was also observed in the promoter of osteopontin ( OPN ) gene used as a positive control ( Figure 7C ) [32] . To ask whether the binding of Hoxc8 in the 3′ of C/EBPβ has a regulatory role , we performed the reporter assay by replacing the C/EBPβ coding region with luciferase gene . Indeed , the C/EBPβ 3′ sequence induced luciferase activity , which was further augmented by adipogenic stimulation ( Figure 7E ) . This luciferase expression was suppressed by concomitant transfection of Hoxc8 but not by that of Hoxc8 with a mutated homeodomain ( HDm ) lacking DNA-binding capacity ( Figure 7F ) [33] . These results implied that Hoxc8 regulates the C/EBPβ expression via the C/EBPβ 3′ regulatory sequence . Furthermore , the suppressive effect of Hoxc8 was abolished by trichostatin A , a histone deacetylase ( HDAC ) inhibitor , indicating that the suppressive effect involves histone deacetylation ( Figure 7G ) . In this regard , Hoxc8 interacted with HDAC3 ( Figure 7H ) [34]–[35] , but not with HDAC1 or HDAC2 . The interaction was independent of the DNA binding capacity of Hoxc8 ( Figure 7I ) . To further corroborate that HDAC3 cooperates with Hoxc8 , HDAC3 was suppressed using siRNA ( Figure 7J ) , resulting in partial elimination of the suppressive effects of Hoxc8 ( Figure 7K ) . To demonstrate that C/EBPβ is an essential target of Hoxc8 , C/EBPβ was transfected into the human WAT-progenitor cells that stably expressed human HOXC8 , resulting in restoration of the brown-fat gene expression that had been suppressed by HOXC8 ( Figure 7L ) . Thus , Hoxc8 targets and represses C/EBPβ in an HDAC3-dependent manner . In summary , during the brown adipogenesis induced by cold exposure or β3-adrenergic stimulations , miR-196a is induced in WAT-progenitor cells and suppresses Hoxc8 , which targets C/EBPβ , an essential regulator of brown adipogenesis . The miR-196a expression is required for the brown-fat gene expression and sufficient to induce metabolically functional brown adipocyte-like cells in WAT in mice . Our findings imply the therapeutic potential of targeting the miR-196a-Hoxc8-C/EBPβ signaling pathway that induces metabolically functional brown adipocytes in WAT to treat obesity and its associated diseases .
Recent discoveries of metabolically active BAT in adult humans have highlighted BAT as a therapeutic target for treating obesity and its associated diseases . The brown adipocyte-like cells in WAT can be generated by cold exposure or β-adrenergic stimulation in rodents , but the molecular mechanisms underlying these phenomena have not been fully elucidated . In this work , we elucidated that miR-196a induces functional brown adipocytes in WAT in mice . miR-196a is upregulated in WAT-progenitor cells during brown adipogenesis induced by cold or β-adrenergic stimulations . miR-196a is required for the brown fat gene expression and is sufficient to induce metabolically functional brown adipocyte-like cells in mice . The target gene of miR-196a is Hoxc8 , which is categorized as a white-fat gene with a previously undermined role in adipogenesis . Hoxc8 directly targets and represses C/EBPβ , a master switch of brown adipogenesis . Thus , the miR-196a-Hoxc8-C/EBPβ pathway underlies the brown adipogenesis in WAT ( Figure 8 ) and might be a therapeutic target for the treatment of obesity and type 2 diabetes . Elucidation of the molecular mechanism regulating the brown adipogenesis in WAT is important from both a biological and clinical viewpoint . Recent studies uncovered the existence of WAT-progenitor cells that harbor a potential to differentiate to brown adipocytes [16] , [21]–[22] , [36] . The molecular mechanism behind the inducible brown adipogenesis in WAT is relatively unknown , but recent studies elucidated the importance of cyclooxygenase-2 [36]–[37] and Prdm16 [38] . C/EBPβ is an essential regulator of brown fat gene program [23] , [39]–[41] , but whether C/EBPβ has a significant role in the inducible brown adipogenesis was not fully understood . We found that miR-196a suppresses Hoxc8 , thereby derepressing C/EBPβ , which leads to the activation of the brown fat gene program . Our findings imply the relevance of C/EBPβ not only in the conventional brown adipogenesis but also in the inducible brown adipogenesis in WAT . The cellular origin of the inducible brown adipocyte-like cells in WAT is an important question . Transdifferentiation is a significant mechanism that has been reported to contribute to brown adipocyte recruitment in WAT [42]–[43] . Because the increase in Ucp1 mRNA is detectable within a few hours after cold stimulation [1] , [31] , and in vitro SVF cell differentiation is a longer process , transdifferentiation might have a significant role in the rapid response to stimulation . The important questions include the relative contribution of transdifferentiation and the progenitor cell-mediated mechanism in brown adipocyte recruitment throughout the different phases upon exposure to a cold environment and physiological energy regulation . miRNAs regulate the gene networks underlying various physiological and pathological phenomena and might be therapeutic targets [18]–[19] , [44]–[46] . miR-196a has been implicated in the in vitro osteoblast differentiation of human fat progenitor cells , where miR-196a suppresses Hoxc8 [47] , but the in vivo relevance remains unknown . We elucidated that miR-196a is induced in the WAT-progenitor cells after the induction of brown adipogenesis , is required for the induction of brown fat gene expression , and is sufficient to induce the metabolically functional brown adipocyte-like cells in WAT . Our observations indicate that miR-196a has only a modest , if any , effect on iBAT . The endogenous expression of Hoxc8 and miR-196a was much lower in iBAT than in ingWAT and epiWAT . The forced expression of miR-196a in mice did not yield appreciable effects in iBAT . Treatment of mice with β3-adrenergic receptor agonists usually leads to a much more moderate induction of Ucp1 expression in iBAT than in WAT depots . Although the primary cultures of brown adipocytes from iBAT are highly sensitive to β3-adrenergic activation [1] , a moderate but significant induction of Ucp1 was reported in iBAT in response to β3-adrenoreceptor agonists in vivo [48] . A relatively modest response from iBAT to the β3-adrenergic receptor agonist compared with subcutaneous and visceral WAT has also been reported in other studies [16] , [43] , [49] . These results imply that distinct machinery regulates brown adipocyte recruitment in iBAT , which was previously suggested by Petrovic et al . [21] . A number of miRNAs function as a molecular switch [46] , [50]–[53] , and further elucidating how the miRNAs influence the physiological output will enable better understanding and clinical use of miRNAs . The significance of the distinct expression patterns of Hox genes between BAT and WAT has been unknown [10]–[12] . We here demonstrate that Hoxc8 functions as an important determinant of white fat lineage and negatively regulates the induction of brown adipogenesis in WAT-progenitor cells by repressing C/EBPβ , which is a master switch of brown adipogenesis [39]–[41] . Mechanistically , Hoxc8 directly represses the C/EBPβ expression through the 3′ regulatory sequence . Similar conserved non-coding regulatory elements have been reported for the Foxp3 gene [54] , and previous studies suggested that the majority of transcription factors bind to sites other than the promoter [20] , [55] . Hoxc8 recruits HDAC3 , which is implicated in the regulation of metabolic genes [34] , [35] . Since the HDAC proteins lack DNA-binding activity , they are recruited to target genes via association with transcriptional factors [56] . Our findings imply the possible therapeutic efficacy of HDAC inhibitors for obesity through inducing brown adipogenesis , but further study is required to address the possibility . The induction of brown adipogenesis in WAT has great therapeutic potential . Our findings suggest that the miR-196a-Hoxc8-C/EBPβ pathway may constitute a promising strategy for addressing the social and health problems caused by obesity and its associated diseases .
Mice were handled in accordance with protocols approved by the Ethics Committee for Animal Experiments of the Osaka University Graduate School of Medicine . The coding sequence of human Hoxc8 ( Gene ID: 3224 ) was cloned into pCX4-puro [57] and pCAGIP vector [58] . The pCX4-Hoxc8 retroviral vector was used to generate human WAT-progenitor cells stably expressing Hoxc8 . Human C/EBPβ was cloned into the pCAGIP vector . The homeodomain mutant ( I195A/Q198A/N199A/M202A ) [33] of Hoxc8 ( HDm ) was created by site-directed mutagenesis . For lentivirus-mediated shRNA expression , pLenti6-miR-196a , -shHoxc8 , and -shLacZ were generated from pcDNA6 . 2 constructs by Gateway reactions . Lentivirus was generated by cotransfection of the pLenti6 construct with packaging plasmids into 293FT cells according to the manufacturer's instruction ( Invitrogen ) . For Hoxc8 3′UTR analysis , human Hoxc8 3′UTR sequence was cloned and inserted to the 3′ end of Hoxc8 cDNA . The miR-196a binding site ( CCCAACAACTGAGACTGCCTA ) was deleted to generate Hoxc8-ΔmiR-196a-BS . Total RNA was isolated using the RNeasy Lipid Tissue Mini Kit ( QIAGEN , CA ) . Reverse transcription and quantitative PCR were performed as previously described [59] . For microRNA quantification , total RNA was isolated using a mirVana miRNA isolation kit ( Applied Biosystems ) . Reverse transcription and quantitative PCR were performed according to the manufacturer's instructions . A list of probes is provided in Text S1 . RNA from human white fat ( WAT ) progenitor cells was extracted with RNeasy ( QIAGEN ) following the manufacturer's instructions . 12 . 5 µg of total RNA were subjected to two rounds of oligo-dT purification using Ambion MicroPoly ( A ) Purist Kit ( Ambion ) . 50 ng of the fragmented poly ( A ) RNA by using RNaseIII were ligated to SOLiD Adaptor Mix and were reverse-transcribed by using SOLiD Total RNA-Seq Kit ( Life Technologies ) . First-strand cDNA from 100 bp to 150 bp was selected by using Agencourt AMPure XP reagent ( Beckman Coulter Genomics ) and was amplified by SOLiD 5′ PCR primer and barcoded SOLiD 3′ PCR primers ( Life Technologies ) . Sequencing libraries were prepared according to Life Technologies' protocol . RNA-seq libraries were sequenced with SOLiD 4 . Mapping of resulting reads was performed by Bioscope ( Life Technologies ) , and analysis of mapped reads ( 31 , 825 , 850 reads in hADSC_1 and 42 , 009 , 231 reads in hADSC_2 ) was performed by Cufflinks [60] . Human WAT-progenitor cells were isolated from human flank subcutaneous fat lipoaspirate ( Lonza , Switzerland ) and maintained in mesenchymal stem cell growth medium ( Lonza ) . For adipogenesis , 2-d post-confluent cells were treated with an induction medium containing 0 . 5 mM IBMX , 10 µg/ml insulin , and 1 µM dexamethasone ( MDI ) . The induction medium was changed every 2 d . Forskolin ( 40 µM , Sigma-Aldrich ) was added to the medium as noted . Antisense oligonucleotide against miR-196a ( Anti-miR miRNA inhibitor , AM10068 , Ambion ) was transfected according to the manufacturer's instruction . The fat progenitor cells were isolated from inguinal white adipose tissue ( WAT ) or interscapular BAT ( iBAT ) of C57Bl/6 mice using a standard method [61] . Adipogenic induction was performed by treating the cells with the induction medium for 2 d . Western blotting was performed with antibodies against Hoxc8 ( 1∶1 , 000 , ab86236 , abcam ) , C/EBPβ ( 1∶200 , sc-150 , Santa Cruz Biotechnology , CA ) , UCP1 ( 1∶1 , 000 , U6382 , Sigma-Aldrich ) , PGC-1α ( 1∶1 , 000 , ab54481 , abcam ) , β-actin ( 1∶5 , 000 , AC-15 , Sigma-Aldrich ) , and GAPDH ( 1∶5 , 000 , ab8245 , abcam ) . The secondary antibodies ( GE Healthcare ) were used at a 1∶1 , 000 dilution ratio . Immunoreactive bands were detected with Chemi-LumiOne L ( Nacalai Tesque ) or ECL plus ( GE Healthcare ) . Densitometry was performed with the ImageJ software ( NIH; http://rsb . info . nig . gov/ij/ ) . Immunocytochemistry was performed using antibodies against Hoxc8 ( 1∶200 , MMS-286R , Covance ) , Hoxc6 ( 1∶200 , ab41587 , Abcam ) , Pgc-1α ( 1∶300 , ab54481 , Abcam ) , or UCP1 ( 1∶500 , ab10983 , Abcam ) as previously described [59] . The primary antibodies were detected using anti-mouse-Alexa Fluor 546 , anti-mouse-Alexa Fluor 488 , or anti-rabbit-Alexa Fluor 546 ( 1∶1 , 000 , Invitrogen ) . Cells were counterstained with CellTracker Green Bodipy ( Invitrogen ) , Bodipy 493/503 ( D3922 , Invitrogen ) , and 4′-6-diamidino-2-phenylindole ( DAPI , Invitrogen ) . These experiments were approved by the Ethics Committee for Animal Experiments of the Osaka University Graduate School of Medicine . Male outbred C57Bl/6 mice were used . For acute cold-exposure studies , 3- to 4-mo-old male mice were housed at 4°C for 5 h . For β3-adrenaline receptor stimulation , CL-316 , 243 ( Sigma ) , at 0 . 5 mg/kg , was injected intraperitoneally once daily for 7 d . Transgenic mice with fat-specific forced expression of miR-196a were generated using a transgene encoding miR-196a driven by the enhancer/promoter of the aP2 gene [30] , and littermates were used as the wild-type controls . Inguinal fat sections were fixed in 10% buffered formalin and stained with hematoxylin-eosin . For immunohistochemistry , paraffin-embedded sections were incubated with antibodies against UCP1 ( 1∶1 , 000 , ab10983 , Abcam ) followed by detection using ABC Vectastain-Elite kit ( Vector Labs ) . Nuclei were counterstained with modified Mayer's hematoxylin ( Diagnostic BioSystems ) . Inguinal WAT depots of mice were dissected after perfusion and fixation with Tissue Fixative ( Genostaff ) , embedded in paraffin , and sectioned at 6 µm . The sections were de-waxed with xylene and rehydrated . The sections were fixed with 4% paraformaldehyde ( PFA ) for 15 min , treated with 8 µg/ml proteinase K for 30 min at 37°C , re-fixed with 4% PFA , and placed in 0 . 2 N HCl for 10 min . The sections were acetylated with 0 . 1 M tri-ethanolamine-HCl , pH 8 . 0 , and 0 . 25% acetic anhydride for 10 min . After being washed with PBS , the sections were treated with PBS at 80°C for 5 min . The sections were hybridized with 3′-digoxygenated probes ( 18 pmol/ml , miR-196a-AS-LNA1: cCcaAcaAcaTgaAacTacCta , Control ( Ctrl ) -LNA1: cGacTacAcaAatCagCgaTtt , capitals denote LNA ) in Probe Diluent-1 ( Genostaff ) at 50°C for 16 h and washed in 5× HybriWash ( Genostaff ) at 50°C for 20 min , 50% formamide in 2× HybriWash at 50°C for 20 min , twice in 2× HybriWash at 50°C for 20 min , and twice in 0 . 2× HybriWash at 50°C for 20 min . The sections were treated with 0 . 5% blocking reagent ( Roche ) in TBST for 30 min and incubated with anti-DIG AP conjugate ( 1∶1 , 000 , Roche ) for 2 h at RT . The sections were washed twice with TBST and incubated in a solution with a composition of 1 , 000 mM NaCl , 50 mM MgCl2 , 0 . 1% Tween-20 , 100 mM Tris-HCl , pH 9 . 5 . Coloring reactions were performed with NBT/BCIP solution ( Sigma ) overnight followed by counterstaining with Kernechtrot stain solution ( Mutoh ) . Mice were given a standard diet or a high-fat diet ( 20 . 4% protein , 33 . 2% fat , 46 . 4% carbohydrates by calories; MF+; Oriental Yeast Co . , Japan ) . Metabolic measurements were performed on 3- to 4-mo-old mice with similar body weight that were given a standard diet . Food intake and body weight were measured daily and weekly , respectively . For glucose tolerance tests , the mice were deprived of food for 16 h and were injected intraperitoneally with glucose ( 2 g/kg ) . For insulin tolerance tests , the mice were allowed ad libitum access to food followed by an intraperitoneal injection of human insulin ( 0 . 75 U/kg , Eli Lilly ) . The plasma concentration of glucose was measured with a Glucometer ( Sanwa Kagaku Kenkyusho , Japan ) , and insulin was measured with an ELISA ( Morinaga Institute of Biological Science , Japan ) . Indirect calorimetry was performed under 12 h light and dark cycles beginning at 8:00 a . m . and 8:00 p . m . , respectively . After 1 d of acclimation , V˙O2 and V˙CO2 were recorded every 3 min over 3 d using the Metabolism Measurement System ( MK-5000 , Muromachi Kikai , Japan ) . Energy expenditure ( EE ) was calculated using the equation of Weir: EE ( kcal/kg/h ) = ( 3 . 815×V˙O2 ) + ( 1 . 232×V˙CO2 ) . For body temperature measurement , mice were housed singly and unrestrained and had free access to food and water . Body temperature was measured using a rectal probe ( Perimed , Sweden ) . Chromatin immunoprecipitation was performed as previously described [62] with 3T3-L1 preadipocytes expressing Flag-tagged human Hoxc8 . Primer sequences are listed in Text S1 . The C/EBPβ3′-luciferase constructs ( C/EBPβ-Luc ) were generated by cloning the 3′ sequence of the human C/EBPβ gene ( +1 , 021 to +1 , 837 ) into the downstream of luciferase gene in pGL3 promoter plasmid ( Promega ) . Dual luciferase assays were performed as previously described [62] with 3T3-L1 preadipocytes . Trichostatin A ( 330 nM , Sigma-Aldrich ) was added 4 h after transfection as indicated . Mission siRNA ( Sigma ) for HDAC3 ( sense: 5′GUAUCCUGGAGCUGCUUAATT , antisense: 5′UUAAGCAGCUCCAGGAUACTT ) was transfected using Neon transfection system ( Invitrogen ) . Nuclear extracts were prepared as previously described [62] from 3T3-L1 preadipocytes transfected with Flag-Hoxc8 , pretreated with Protein G Sepharose beads ( Amersham Bioscience ) , and incubated with anti-Flag M2 Affinity Gel ( A2220 , Sigma-Aldrich ) or control mouse IgG AC ( Santa Cruz ) overnight at 4°C . The beads were washed 3 times with nuclear isolation buffer containing 500 mM NaCl and 0 . 15% NP-40 . Purified proteins were subjected to immunoblotting using antibodies against HDAC1 ( 3∶1 , 000 , Millipore ) , HDAC2 ( 1∶2 , 000 , H3159 , Sigma ) , and HDAC3 ( 1∶500 , ab16047 , Abcam ) . The statistical analysis was performed with StatView 5 . 0 software , JMP8 ( SAS Institute , NC ) and SPSS ( IBM ) . All results are expressed as mean ± SEM . The data were compared using ANOVA , followed by Dunnett's test for pairwise comparisons against controls and by Tukey's test for multiple comparisons . For the analysis of energy expenditure , a one-way analysis of covariance ( ANCOVA ) was conducted . The body weight was used as the covariate . Statistical significance was defined as p<0 . 05 . The RNA-seq data have been submitted to the NCBI Sequence Read Archive ( SRA ) . The accession number is SRA048274 . 1 . | Obesity is caused by the accumulation of surplus energy in a fatty tissue called white adipose tissue ( WAT ) and can lead to important health problems such as diabetes . Mammals additionally possess brown adipose tissue ( BAT ) , which serves to generate body heat to stabilize body temperature under exposure to cold , and is abundant in hibernating animals and human neonates . In performing its function BAT consumes energy , thereby reducing WAT fat accumulation . Recent studies have shown that exposure to a cold environment stimulates the partial conversion of WAT to BAT in mice , and given that human adults have a limited amount of BAT , such a conversion has the potential to afford a novel method of obesity control . Here , we analyze the molecular mechanism of this conversion using genetically manipulated mice and cells isolated from human adipose tissue . We find that the expression levels of a microRNA , miR-196a , positively correlate with the conversion of WAT to BAT under cold exposure conditions . We show that forced expression of miR-196a in mouse adipose tissue increases BAT content and energy expenditure , thereby rendering the animals resistant to obesity and diabetes . Mechanistically , we observe that miR-196a acts by inhibiting the expression of the homeotic gene Hoxc8 , a repressor of brown adipogenesis . These findings introduce the therapeutic possibility of using microRNAs to control obesity and its associated diseases in humans . | [
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] | 2012 | Essential Role for miR-196a in Brown Adipogenesis of White Fat Progenitor Cells |
Burkholderia thailandensis is a Gram-negative soil bacterium used as a model organism for B . pseudomallei , the causative agent of melioidosis and an organism classified category B priority pathogen and a Tier 1 select agent for its potential use as a biological weapon . Burkholderia species are reportedly “highly resistant” to antimicrobial agents , including cyclic peptide antibiotics , due to multiple resistance systems , a hypothesis we decided to test using antimicrobial ( host defense ) peptides . In this study , a number of cationic antimicrobial peptides ( CAMPs ) were tested in vitro against B . thailandensis for both antimicrobial activity and inhibition of biofilm formation . Here , we report that the Chinese cobra ( Naja atra ) cathelicidin NA-CATH was significantly antimicrobial against B . thailandensis . Additional cathelicidins , including the human cathelicidin LL-37 , a sheep cathelicidin SMAP-29 , and some smaller ATRA peptide derivatives of NA-CATH were also effective . The D-enantiomer of one small peptide ( ATRA-1A ) was found to be antimicrobial as well , with EC50 in the range of the L-enantiomer . Our results also demonstrate that human alpha-defensins ( HNP-1 & -2 ) and a short beta-defensin-derived peptide ( Peptide 4 of hBD-3 ) were not bactericidal against B . thailandensis . We also found that the cathelicidin peptides , including LL-37 , NA-CATH , and SMAP-29 , possessed significant ability to prevent biofilm formation of B . thailandensis . Additionally , we show that LL-37 and its D-enantiomer D-LL-37 can disperse pre-formed biofilms . These results demonstrate that although B . thailandensis is highly resistant to many antibiotics , cyclic peptide antibiotics such as polymyxin B , and defensing peptides , some antimicrobial peptides including the elapid snake cathelicidin NA-CATH exert significant antimicrobial and antibiofilm activity towards B . thailandensis .
Burkholderia pseudomallei is a Gram-negative soil bacterium which acts as a facultative intracellular pathogen that can infect both humans and animals , causing melioidosis . Melioidosis is endemic to Southeast Asia and Northern Australia , where the mortality rates are 50% and 19% respectively [1–3] . In addition , B . pseudomallei is of interest because it is considered a class B priority pathogen and a Tier 1 Select Agent and has potential for aerosol delivery . In this study , Burkholderia thailandensis is used as a model for B . pseudomallei [4] . B . thailandensis is a BSL-2 organism closely related to B . pseudomallei with an LD50 in mice 1000-fold higher than that of B . pseudomallei , making it an easier and safer model organism with which to work [5] . B . thailandensis has been successfully demonstrated to be a useful BSL-2 surrogate for B . pseudomallei [4 , 6–8] for both in vitro and in vivo experiments . Thus , B . thailandensis may be a good model in which to study the molecular actions of full length cathelicidins such as LL37 both as antibacterial and antibiofilm peptides against Burkholderia strains . In B . pseudomallei and B . thailandensis the significant resistance towards several categories of antibiotics , including chloramphenicol , quinolones , tetracyclines , and trimethoprim , is mediated by the overexpression of efflux pumps [9 , 10] . B . pseudomallei and B . thailandensis are typically grown in the laboratory in the presence of >100 mg/ml polymyxin B [11]; such ready growth indicates their high level of resistance to cyclic peptide antibiotics . In fact , the genus Burkholderia is said to have “extreme antimicrobial peptide and polymyxin B resistance” [12] . Therefore , the discovery of novel therapeutic alternatives is urgently required . We have previously studied the cathelicidin peptide from the elapid snake Naja atra and designed smaller peptide derivatives called ATRA peptides; we reported that these peptides were highly active against both Gram-positive and Gram-negative bacteria , such as Gram-positive Staphylococcus aureus and Gram-negative Pseudomonas aeruginosa [13–16] . We were very interested to know whether B . thailandensis would be susceptible to other antimicrobial peptides , and particularly to the very effective cathelicidin peptide ( NA-CATH ) and smaller peptide derivatives from elapid snakes that we had been studying . Cationic antimicrobial peptides ( CAMPs ) are produced as part of the innate immune system by higher-order organisms . These peptides are also referred to as host-defense peptides ( HDPs ) . CAMPs are low-molecular-weight , cationic , and often amphipathic peptides , and their overall positive charge enables association with the negatively charged bacterial outer membrane [17] . In this study , we tested two types of CAMPs: the cathelicidin type and the defensin type . It has been previously reported that Burkholderia species , specifically B . cepacia , are very resistant to beta-defensins , a category of defensin CAMPs [18] . Defensins function by replacing Ca2+ and Mg2+ ions in the bacterial membrane , disrupting membrane stability and leading to loss of electric potential and eventual cell lysis [19 , 20] . Defensins are important to consider because they are found in human skin under inflammatory conditions [21] and could potentially play a role during a wound infection by B . pseudomallei . In this study , we tested the antibacterial activity of two alpha-defensins and a small peptide from beta-defensin against B . thailandensis . Cathelicidins are a class of antimicrobial peptide characterized by a highly conserved cathelin domain [22] and a sequence-variable active cathelicidin domain . The majority of cathelicidin peptides form amphipathic alpha helices when in contact with a membrane , and these helices are believed to play a crucial role in their function [23 , 24] . Recently a cathelicidin , designated NA-CATH , has been discovered in Naja atra , the Chinese cobra [25] , an elapid snake found in Southeast Asia [26] . This cathelicidin contains an imperfect repeated 11-amino-acid motif named the ATRA motif ( Table 1 ) [13] . The first repeat is called ATRA-1 and the second repeat ATRA-2 [13] . A derivative , ATRA-1A , was created by replacing the 3rd residue of ATRA-1 with an alanine [13] . In previous work we demonstrated that the full-length cathelicidin ( NA-CATH ) and peptides based on the first repeat ( ATRA-1 and ATRA-1A ) were effective broad-spectrum antimicrobial agents against Francisella novicida , Aggregatibacter actinomycetemcomitans , Pseudomonas aeruginosa , and Staphylococcus aureus [13–16] . Therefore , the Naja atra cathelicidin NA-CATH and its ATRA derivatives were chosen for studies against Burkholderia species [10] . It is of note that we previously demonstrated that the cathelicidins LL-37 , D-LL-37 , NA-CATH , and NA-CATH derivatives cause no hemolysis at the antimicrobial concentrations used in our study [13 , 14] . This leads us to suggest that these peptides may be very useful as a potential new therapeutic approach , perhaps in a topical application , by virtue of their demonstrated antimicrobial action and minimal host-cell cytotoxicity , with the D-peptides having the added advantage of less susceptibility to protease digestion .
B . thailandensis ( E264 ) was obtained from the American Type Culture Collection ( Manassas , VA ) , ATCC 700388 , and grown in nutrient broth overnight in a shaking incubator at 37°C . Cultures of B . thailandensis were grown up and the stocks were aliquotted , frozen in 20% glycerol , and stored at -80°C . Cultures were enumerated by serial dilution on nutrient agar . The antimicrobial activity of various antimicrobial peptides against B . thailandensis was determined as previously described [16] . Briefly , in a sterile 96-well plate , 1x105 CFU per well of bacteria were incubated with serial dilutions of antibiotic ( control ) and peptide in 10 mM phosphate buffer ( 3 h , 37°C ) . Bacterial survival was then determined by serial dilution at each peptide concentration in sterile PBS . Dilutions were plated in triplicate on nutrient agar and incubated at 37°C for 24 h; colonies were then counted to determine survival . Bacterial survival was calculated by the ratio of the number of colonies on each experimental plate to the average number of colonies in the control plates lacking any antimicrobial peptide . The antimicrobial peptide concentration required to kill 50% of B . thailandensis ( EC50 ) was determined by graphing percent survival versus log of peptide concentration ( log μg/ml ) . Data were plotted using GraphPad Prism 5 ( GraphPad Software Inc . , San Diego , CA , USA ) . Survival was determined as the ratio of colonies from experimental plates relative to the average number of colonies from plates lacking peptide . EC50 was determined by fitting the data to a standard sigmoidal dose-response curve . Each experiment was performed three times with three replicates per experiment for n = 9 . Error is reported as 95% confidence intervals ( CI ) for each antimicrobial peptide . Biofilm was grown and measured as previously described [27–29] . Modified Vogel and Bonner’s medium ( MVBM ) [30] was inoculated from an overnight culture of B . thailandensis and allowed to incubate for 18 h in a shaking incubator at 37°C . The optical density at 540 nm ( OD540 ) was adjusted to 0 . 8 OD540 . Bacterial suspension ( 100 μl ) was added to wells of a sterile tissue-culture-treated 96-well plate along with various concentrations of peptide and fresh MVBM ( final volume 200 μl ) . Wells containing only medium or no peptide served as negative and positive controls , respectively . Plates were then incubated aerobically at 37°C for 3 h . Following aerobic adhesion , supernatant fluid was removed from wells ( to remove planktonic bacteria ) , fresh MVBM/peptide was added to each well ( 200 μL final volume ) , and plates were incubated for 21 h at 37°C . After incubation , supernatant was removed and replaced with 200 μL of fresh MVBM/peptide , then incubated at 37°C for an additional 24 h . This is described as a 48 h biofilm . After final incubation , the plate was read at OD600nm to measure bacterial growth , then washed , fixed , and stained with crystal violet as previously described [31] . Each assay was performed in triplicate and the experiment repeated three times for n = 9 . Biofilm dispersion assay was performed using B . thailandensis E264 ( ATCC 700388 ) in 100 μL MVBM and was incubated 24h , 37°C . After allowing biofilm to form for 24h , the biofilm was treated with 10 μg peptide or 0 peptide and then incubated at 37°C for an additional 24h . The optical density was measured prior to staining to measure bacterial growth after 48h incubation . Eight wells were used for each peptide ( n = 8 ) . Production of biofilm was measured using crystal violet staining as described previously [31] . Protein ID numbers were obtained from the UniProt protein database . The protein ID number of LL-37 , the human cathelicidin , is P49913 . The protein number for SMAP-29 , the sheep cathelicidin , is P49928 . The protein number for NA-CATH , the cathelicidin from the Chinese King Cobra , is B6S2X0 .
In this study , we demonstrated the effectiveness of various snake-derived cathelicidin peptides against B . thailandensis , including NA-CATH . Control peptides included SMAP-29 and LL-37 ( Table 2 ) . Ceftazidime is the first-line antibiotic against B . pseudomallei [32] . Therefore , it was used as a positive control for observing a bactericidal effect in the antimicrobial plating assay . We determined the first-line antibiotic ceftazidime to have an EC50 value of 0 . 328 μM ( 95% CI of 0 . 20–0 . 548 μM ) ( Fig 1 ) . We found that the Naja atra peptide NA-CATH had EC50 values of 0 . 877 μM ( 95% CI of 0 . 61–1 . 26 μM ) against B . thailandensis . This compared favorably to the sheep peptide SMAP-29 , which was observed to have an EC50 value of 0 . 628 μM ( 95% CI of 0 . 194–2 . 03 μM ) . The human cathelicidin LL-37 was also found to have a good antimicrobial effect against B . thailandensis , with an EC50 value of 1 . 87 μM ( 95% CI of 1 . 20–2 . 94 μM ) . Data are presented in μM to reflect the number of peptide molecules , thereby compensating for differing molecular weights . These results are consistent with the published values for the effect of LL-37 against Burkholderia [33–35] . These three cathelicidin peptides ( NA-CATH , SMAP-29 , LL-37 ) were not statistically different in their anti-B . thailandensis performance . The activity of LL-37 is similar to that shown for B . pseudomallei [34] . In previous work , these same cathelicidin peptides were tested against P . aeruginosa , S . aureus , and F . novicida [14–16] . We had expected Burkholderia species to have a higher EC50 than those organisms because of their wide range of mechanisms to evade destruction by antibiotics and antimicrobial peptides [10] . Surprisingly , the EC50 results against B . thailandensis were similar to cathelicidin EC50 values against other Gram-negative bacteria [14–16] . Naja atra cathelicidin peptide derivatives were tested for antimicrobial activity against B . thailandensis . Each imperfect repeat from NA-CATH ( ATRA-1 and ATRA-2 ) was tested , as well as the synthetic peptide ATRA-1A , in which amino acid 3 of ATRA-1 was switched from phenylalanine to alanine [13] [26] . The EC50 of ATRA-1 against B . thailandensis was determined to be 6 . 94 μM ( 95% CI of 4 . 26–11 . 3 μM ) ( Fig 2 ) . EC50 plating assays determined that ATRA-2 was not an effective antimicrobial peptide , correlating with our previous ATRA-2 results with other bacteria [14–16] . This leads to the conclusion that the first imperfect repeat of NA-CATH contributes to most of the observed antimicrobial activity of NA-CATH . We then looked at a synthetic peptide , named ATRA-1A , which contains a single amino acid change at position 3 ( F->A ) . This synthetic peptide exhibited an EC50 of 9 . 83 μM ( 95% CI of 6 . 52–14 . 8 μM ) . In previous work we demonstrated that peptides produced with each amino-acid in the D-form ( enantiomer ) can be antimicrobial [14 , 15 , 36] . In addition , peptides in the D-form are resistant to proteases such as trypsin [14 , 15 , 37 , 38] . Dean et al . demonstrated that while the L-form of the LL-37 peptide is digested by trypsin , the D-form shows no degradation after 1 h trypsin digestion [15] . Thus , we synthesized all-D-enantiomers of LL-37 and ATRA-1A to compare the antimicrobial activity of these protease-resistant enantiomers . We found the antimicrobial effect of the D-enantiomer to be comparable to that of the L-enantiomer for both ATRA-1A and LL-37 ( Fig 3 ) . LL-37 had an EC50 value of 1 . 87 μM ( 95% CI of 1 . 20–2 . 94 μM ) , while D-LL-37 had a statistically similar EC50 of 3 . 64 μM ( 95% CI of 2 . 04–6 . 53 μM ) . D-ATRA-1A had an EC50 value of 4 . 82 μM ( 95% CI of 3 . 20–7 . 27 μM , as compared to 9 . 83 μM ( 95% CI of 6 . 52–14 . 8 μM ) for ATRA-1A . For both enantiomeric conversions , the 95% confidence intervals of the D-peptide results overlapped those from the normal L version of the peptide . These data demonstrate that converting each peptide to an all-D-enantiomer did not statistically alter its antimicrobial effect . We examined a second category of CAMPs , the defensins , for anti-Burkholderia activity . Under conditions of B . pseudomallei respiratory infection in mice , neutrophil granules were observed to be the predominant cell type seen in association with B . pseudomallei infection [39] . Neutrophil granules are known to be a significant source of cathelicidins and human neutrophil peptides ( alpha-defensins ) [40] . Therefore , to further explore the effect of defensins upon B . thailandensis , human alpha defensin-1 ( aka human neutrophil peptide 1 , HNP-1 ) and human alpha defensin-2 ( HNP-2 ) were chosen as candidates to test for antimicrobial killing against B . thailandensis ( Fig 4 ) . For HNP-1 , at the highest concentration tested ( 1000 μg/ml peptide ) , only 65% killing could be achieved for B . thailandensis , suggesting that this is a highly ineffective peptide . HNP2 was even less effective than HNP1 in killing B . thailandensis at every concentration tested . Sahly et al . demonstrated that the LD50 of human beta-defensin-3 ( hBD-3 ) against multiple Burkholderia species was >100 μg/ml [18] . However , other reports demonstrated that regions of cationic peptides in the C-terminus of hBD-3 possessed antimicrobial activity against E . coli and P . aeruginosa [41 , 42] . Based on our previous work , we tested a small fragment ( Peptide 4 ) of human beta-defensin 3 ( hBD-3 ) , which was previously shown to have significant activity against another Gram-negative bacterium , E . coli [42] . Incubation of B . thailandensis with Peptide 4 of hBD-3 at the highest concentration tested ( 1000 μg/ml ) resulted in only 65% killing for B . thailandensis , suggesting that this is a highly ineffective peptide . These findings confirmed published reports [18 , 43] that the beta-defensin CAMPs are ineffective against Burkholderia species , and demonstrated that the alpha-defensins are also ineffective against B . thailandensis . As B . pseudomallei has been reported to form biofilm [44 , 45] , we sought to demonstrate the ability of various cathelicidins to inhibit biofilm formation in the model organism B . thailandensis . In addition , we and others have demonstrated that the cathelicidin LL-37 inhibits biofilm formation in P . aeruginosa [15 , 46] , an important gram-negative pathogen . Therefore , a panel of cathelicidins was tested in biofilm inhibition assays . We first had to establish conditions under which the biofilm of B . thailandensis could be reliably formed and measured . To do this , we grew the bacteria in modified Vogel-Bonner medium ( MVBM ) [30] overnight and then adjusted OD540 to 0 . 8 . Following growth and measurement , a biofilm inhibition assay was performed . This assay incubates the test compound with the bacteria and determines if the compound can inhibit biofilm formation . The growth of bacteria is also measured to control for bactericidal effects of the test compound although bactericidal effects are reduced due to the high salt concentration of the media used in these assays . We demonstrated that the antibiotic ceftazidime did not inhibit biofilm production but simply killed the B . thailandensis ( Fig 5 ) , as we would expect . Interestingly , the cathelicidins LL-37 , SMAP-29 , and NA-CATH all showed at least 50% biofilm inhibition at peptide concentrations at or above 3 μg/ml . The negative control peptide , which was a scrambled LL-37 ( same amino acid composition and net charge , different sequence of amino acids ) , did not inhibit biofilm , as we previously reported [14] . In addition , the D-enantiomers of the peptides were tested for their effect on biofilm inhibition . D-LL-37 produced results similar to those of its L-enantiomer . Both inhibited at least 50% of biofilm at concentrations as low as 3 μg/ml . Thus both the L- and D- form of LL-37 exhibit anti-biofilm activity against B . thailandensis . When the ATRA-1A enantiomers were compared , the results differed slightly ( S1 Fig ) . ATRA-1A did not inhibit biofilm formation , whereas D-ATRA-1A did slightly , but only at the highest concentration of peptide tested ( 300 μg/ml ) . At these levels , this is unlikely to be a significant activity of the D-ATRA-1A peptide . ATRA-1 and ATRA-2 , the imperfect repeats from NA-CATH , were also tested for biofilm inhibition and did not inhibit biofilm formation ( S1 Fig ) . Thus , the full-length cathelicidin peptides including the snake cathelicidin NA-CATH were able to inhibit B . thailandensis biofilm formation . We have demonstrated that some of our cathelicidins can inhibit biofilm formation in B thailandensis . We wanted to know if these cathelicidin peptides could also disperse pre-formed biofilms . Therefore , a number of cathelicidins were chosen for the pre-formed biofilm dispersion assay as described in the methods . We demonstrated that LL-37 and D-LL-37 were able to disperse at least 50% of the pre-formed biofilm when 10 μg ( 11μM ) peptide was added to 24h pre-formed biofilms ( Fig 6 ) , showing statistically equivalent activity . Other cathelicidins , SMAP-29 and NA-CATH ( which demonstrated biofilm inhibition ) did not demonstrate the ability to disperse pre-formed biofilms . Also , as expected , small NA-CATH derivative peptides ( ATRA-1A , D-ATRA-1A , ATRA-2 ) showed no biofilm dispersion activity . The ability of D-LL-37 to disperse preformed Burkholeria biofilm has not been previously reported . These results suggest that LL-37 and D-LL-37 have a unique property that enables dispersion of preformed biofilm in this organism .
B . thailandensis and B . pseudomallei have a wide range of mechanisms for evading antibiotics and antimicrobial peptides . These mechanisms include , but are not limited to , having a more impermeable membrane , multi-drug-resistant efflux pumps , inactivation of host proteins , and modification of drug targets [10] . In this study , we demonstrate that certain peptides can evade these mechanisms and exhibit antimicrobial activity against B . thailandensis , despite its reported extreme peptide resistance . We also demonstrate that D-amino acid peptides exhibit comparable antimicrobial activity [15] . Finally , we describe the anti-biofilm activity of some of these peptides against B . thailandensis . The modes of action of CAMPs against bacteria are varied and require further elucidation; however , two potentially co-existing mechanisms exist . The first model is that these CAMPs cause the formation of transmembrane pores , causing dissolution of the membrane potential and eventual destruction of the bacterial cell [47] . A second proposed mechanism includes the internalization of the CAMP which can then bind to internal targets and interfere with cell wall synthesis [48] . In humans , only one cathelicidin has been identified: LL-37 . This cathelicidin is released by proteolysis from the C-terminus of CAP18 protein [49] . The LL-37 cathelicidin , as well as other cathelicidins with similar alpha-helical structures , has been demonstrated to associate with the bacterial membrane and cause bacterial death [50 , 51] . The effective killing concentration of these alpha-helical cathelicidin-type CAMPs has driven the search for new cathelicidins . Recently , a cathelicidin has been discovered in the species Naja atra , the Chinese cobra [25 , 52] which is effective against multiple bacteria [13–16] Previous reports indicated that LL-37 is antimicrobial against B . thailandensis and B . pseudomallei . It has been reported that 15 μM LL-37 in 1 mM potassium phosphate buffer ( PPB ) kills 105 cfu/mL of B . thailandensis [35] . Another study with B . thailandensis demonstrated that 5 strains of B . thailandensis were >90% killed at concentrations of 12 . 5 mg/L or greater in 1 mM PPB [33] . We obtained the same result , but in this study we have reported our results in terms of EC50 rather than lethal concentration . Research has also shown that LL-37 is effective at killing B . pseudomallei . One study reports that LL-37 effectively killed 24 strains of B . pseudomallei at a peptide concentration of 100 μM in 1 mM PPB [34] , while another demonstrated >90% killing of 9 strains at concentrations of 6 . 25 mg/L ( 1 . 39 μM ) or greater [33] . Under conditions of B . pseudomallei respiratory infection in mice , neutrophil granules were observed to be the predominant cell type seen in association with B . pseudomallei infection [39] . Since neutrophil granules are known to be a significant source of cathelicidins [40] , our data and published results suggest a significant potential role of LL-37 expression during the infection of humans by Burkholderia species . We were able to demonstrate that LL-37 , SMAP-29 , NA-CATH , and small NA-CATH-derived ATRA peptides exert strong antimicrobial activity against B . thailandensis . Another group reported that the bovine cathelicidin BMAP-18 was antimicrobial against B . pseudomallei at 20 μM [53] . Together , these studies suggest that B . thailandensis and B . pseudomallei may be quite susceptible to cathelicidins as a class of peptides . A recent study also found that additional peptides , including the 12-aa peptide bactenecin , the hybrid peptide CA-MA , and RTA3 , were antimicrobial against B . pseudomallei [53] , suggesting that there are peptides that appear to be effective against this organism , particularly those with predominantly helical properties . In addition , we addressed the potential issue of proteolytic degradation of AMPs in vivo by bacterial proteases . Sieprawska-Lupa et al . demonstrated that mammalian hosts and numerous bacteria express proteases capable of degrading and inactivating LL-37 [54] . Therefore , we tested D-enantiomers , which we previously showed to be resistant to trypsin digestion [15] , to compare their antimicrobial activity to that of the natural L-enantiomer . Our results show that for both LL-37 and ATRA-1A , the D-enantiomer exhibited antimicrobial activity comparable to that of the L-enantiomer , suggesting that bacterial proteases were not active against this peptide . Our results also demonstrate that defensin peptides have at best a weak antimicrobial effect against Burkholderia . Human beta-defensins had previously been shown to be ineffective against B . cepacia or B . pseudomallei [18 , 43] . The alpha-defensins HNP-1 and HNP-2 both demonstrated poor antimicrobial performance against B . thailandensis in this work . For HNP-1 , this is consistent with the literature on B . pseudomallei [55 , 56] . ( No previous work was found on HNP-2’s effect on Burkholderia pseudomallei . ) In addition , we tested HNP-3 and HNP-4 ( Fig 4B ) and found a similar lack of antimicrobial activity . This leads us to conclude that alpha-defensins in general do not exert strong antimicrobial activity against B . thailandensis . A fragment of hBD3 ( Peptide 4 from hBD3 ) was also ineffective against B . thailandensis . Thus , Burkholderia does seem to be highly resistant to both classes of defensins . It has also been demonstrated that both B . thailandensis and B . pseudomallei form biofilms in vivo [44 , 45] , which may be a virulence factor [45] . We were able to demonstrate that the cathelicidins LL-37 , D-LL-37 , NA-CATH , and SMAP-29 are capable of biofilm inhibition in B . thailandensis each at similar extents of ~50% inhibition at 3 μg/ml . This is in agreement with the published capability of LL-37 to inhibit biofilm formation in Pseudomonas [46] . In addition , we demonstrated the new result of the ability of D-LL-37 to disperse pre-formed biofilms . Thus , the biofilm inhibition we demonstrate in this work may be a crucial component of the activity of cathelicidin-derived peptides as possible therapeutics . Novel approaches to treatment for Burkholderia infections are critically needed , especially for treatment of melioidosis . A novel peptide-based treatment for melioidosis would ideally include both antimicrobial activity and biofilm inhibition , and may take the form of a topical application . In this work , we have demonstrated the effects of LL-37 , SMAP-29 , and NA-CATH as both antimicrobial and anti-biofilm peptides , and showed promising results of short , synthetic peptides , such as ATRA1 . We have also extended previous studies [35] showing here that an all-D-enantiomer of LL-37 , which is resistant to proteolytic degradation , maintains antimicrobial activity as well as significant anti-biofilm properties against B . thailandesnsis . The results of this study illustrating the susceptibility of B . thailandensis to cathelicidin-like peptide killing , resistance to defensins , and the ability of D- and L-LL-37 peptides to inhibit biofilm formation may provide a new understanding of the potential use for peptides , perhaps as topical applications , in melioidosis infection . | Burkholderia species such as B . pseudomallei , which causes melioidosis , and the model organism B . thailandensis are extremely resistant to antibiotics , including cyclic peptide antibiotics such as polymyxin B . Treatment for Burkholderia infections is impeded by this resistance , and new approaches are needed . We hypothesized that the cathelicidin NA-CATH from the Chinese cobra , Naja atra , and smaller derivative peptides ( ATRA peptides ) may have antimicrobial activity against Burkholderia . We therefore tested the bactericidal effects of the cathelicidin and its derivative peptides . We also wanted to determine whether the antimicrobial peptides exert anti-biofilm activity , although the role of biofilm as a critical virulence factor of Burkholderia has not yet been established . We found that the peptide ATRA-1A , as well as the stereo-isomer D-ATRA-1A , were able to kill B . thailandensis , and the full-length snake cathelicidin NA-CATH was able to both kill B . thailandensis and inhibit its biofilm formation , unlike the human-alpha defensin peptides HNP-1 and HNP-2 , and the small peptide derived from hBD3 . These results show that the NA-CATH antimicrobial peptide possess bactericidal and anti-biofilm activity against B . thailandensis , and suggest that these compounds should be tested for their effect against the more virulent strains of Burkholderia . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Snake Cathelicidin NA-CATH and Smaller Helical Antimicrobial Peptides Are Effective against Burkholderia thailandensis |
The 2014–2015 Ebola outbreak massively hit Guinea . The coastal districts of Boffa , Dubreka and Forecariah , three major foci of Human African Trypanosomiasis ( HAT ) , were particularly affected . We aimed to assess the impact of this epidemic on sleeping sickness screening and caring activities . We used preexisting data from the Guinean sleeping sickness control program , collected between 2012 and 2015 . We described monthly: the number of persons ( i ) screened actively; ( ii ) or passively; ( iii ) treated for HAT; ( iv ) attending post-treatment follow-up visits . We compared clinical data , treatment characteristics and Disability Adjusted Life-Years ( DALYs ) before ( February 2012 to December 2013 ) and during ( January 2014 to October 2015 ) the Ebola outbreak period according to available data . Whereas 32 , 221 persons were actively screened from February 2012 to December 2013 , before the official declaration of the first Ebola case in Guinea , no active screening campaigns could be performed during the Ebola outbreak . Following the reinforcement and extension of HAT passive surveillance system early in 2014 , the number of persons tested passively by month increased from 7 to 286 between April and September 2014 and then abruptly decreased to 180 until January 2015 and to none after March 2015 . 213 patients initiated HAT treatment , 154 ( 72% ) before Ebola and 59 ( 28% ) during the Ebola outbreak . Those initiating HAT therapy during Ebola outbreak were recruited through passive screening and diagnosed at a later stage 2 of the disease ( 96% vs . 55% before Ebola , p<0 . 0001 ) . The proportion of patients attending the 3 months and 6 months post-treatment follow-up visits decreased from 44% to 10% ( p <0 . 0001 ) and from 16% to 3% ( p = 0 . 017 ) respectively . The DALYs generated before the Ebola outbreak were estimated to 48 . 7 ( 46 . 7–51 . 5 ) and increased up to 168 . 7 ( 162 . 7–174 . 7 ) , 284 . 9 ( 277 . 1–292 . 8 ) and 466 . 3 ( 455 . 7–477 . 0 ) during Ebola assuming case fatality rates of 2% , 5% and 10% respectively among under-reported HAT cases . The 2014–2015 Ebola outbreak deeply impacted HAT screening activities in Guinea . Active screening campaigns were stopped . Passive screening dramatically decreased during the Ebola period , but trends could not be compared with pre-Ebola period ( data not available ) . Few patients were diagnosed with more advanced HAT during the Ebola period and retention rates in follow-up were lowered . The drop in newly diagnosed HAT cases during Ebola epidemic is unlikely due to a fall in HAT incidence . Even if we were unable to demonstrate it directly , it is much more probably the consequence of hampered screening activities and of the fear of the population on subsequent confirmation and linkage to care . Reinforced program monitoring , alternative control strategies and sustainable financial and human resources allocation are mandatory during post Ebola period to reduce HAT burden in Guinea .
Human African trypanosomiasis ( HAT ) is a vector born parasitic disease due to the infection by T . brucei gambiense in Western and Central Africa and T . brucei rhodesiense in Eastern Africa . In its late stage , HAT is generally considered to be fatal if left untreated and is responsible for a heavy burden in affected areas . It is one of the most neglected tropical diseases and , until recently , the treatment relied on ancient and toxic drugs [1] . In Western Africa , most cases of T . brucei gambiense infection are found in remote sparse active focuses such as the three major ones situated in the mangrove ecosystem of coastal Guinea[2] , namely from North to South: Boffa [3–4] , Dubreka [4] and Forecariah [4–6] . Of note , these three districts were among the most affected by the epidemic of Ebola virus disease that severely hit Guinea in 2014–2015 [7] . Previous studies have demonstrated the impact of the recent Ebola outbreak on health systems of affected countries with an unprecedented magnitude on HIV care or malaria control activities in Guinea and Liberia [8–13] . First , the Ebola crisis has weakened an already fragile healthcare system by the re-allocation of the available resources to fight Ebola . In addition , health-care workers who were in first-line have paid a heavy toll with 1 . 5% deaths among doctors , nurses , and midwives compared to 0 . 02% in the general population of Guinea [14] . Second , Ebola burden has led to a drastic reduction in healthcare center attendances [9] . The high transmission rates of Ebola virus in healthcare centers had raised a justifiable fear against the health system in general with dramatic consequences[15 , 16] . Since HAT control activities are generally vulnerable and poorly supported in terms of human or financial resources , one might expect that the Ebola epidemic has deeply impaired HAT control as well . Therefore , we aimed at investigating the impact of the Ebola outbreak on HAT screening and caring activities in the three coastal Guinean focuses of Boffa , Dubreka and Forecariah .
The Guinean national HAT control program is devoted to reducing the burden of sleeping sickness through a comprehensive action: ( i ) fight against the vector with targeted vector control using insecticide impregnated targets; ( ii ) improving human infection diagnosis by reinforcing passive screening with the implementation of new rapid diagnostic tests at peripheral level ( health posts and centers ) and conducing targeted active screening campaigns; ( iii ) treatment and follow-up of patients diagnosed with HAT . Our analysis focuses on screening and caring ( HAT diagnosis confirmation and subsequent treatment and follow-up ) purposes . Our study was carried out in three districts of coastal Guinea , namely from north to south Boffa , Dubreka and Forecariah ( Fig 1 ) . The Boffa [3 , 4] and Dubreka [4] districts are located at the north of the capital city Conakry and Forecariah [4–6] district is located at the south of Conakry . All districts are situated in a mangrove ecosystem favoring the growth of the HAT vector population . The area mainly hosts Soussou and Baga ethnic groups . Inhabitants have outside activities such as irrigated rice growth , salt extraction , wood cut , and fishing [3] exposing them to high contacts with tsetse flies . In 2005 , the prevalence of HAT in the area was estimated to be about 1% [17] . The number of newly reported HAT cases was 78 in 2013 and increased to 106 cases in 2016[18] . An unprecedented Ebola outbreak struck Guinea from December 2013 to April 2016 with more than 3 , 800 persons infected of whom 2 , 500 died [19] . The epidemic started in South East Guinea region and reached Conakry and coastal Guinea around March 2014 . Many healthcare centers including HAT treatment centers were requisitioned for the fight against Ebola ( Fig 2 ) . We performed a retrospective analysis of the data of all the people screened and/or treated for HAT in the three districts of Boffa , Dubreka and Forecariah within the frame of the Guinean HAT National Control Program ( PNLTHA ) routine activities . For that purpose , we used available data collected between February 2012 and October 2015 in two electronic databases: ( i ) the ‘HAT treatment database’ collecting prospectively sociodemographic , clinical , parasitological , therapeutic and follow-up information of the patients initiating HAT treatment in the study centers between January 2012 and October 2015; ( ii ) the ‘HAT screening database’ recording active testing campaigns activities since 2012 and passive screening activities only since 2014 . Regarding Ebola , we used previously published data reporting monthly cases in Guinea between March 2014 and October 2015 [19] . Data deposited in the Dryad repository , https://doi . org/10 . 5061/dryad . 036ds [20] . HAT-infected persons were recruited during active screening campaigns throughout the entire districts or by passive detection in persons presenting spontaneously to the three HAT treatment centers based in the Forecariah , Dubreka and Boffa cities and , as from 2014 , some people presenting at peripheral health centers of the same districts who underwent decentralized screening before the interdiction of blood sampling due to Ebola . Indeed , early in 2014 , HAT passive detection capacities were reinforced by the implementation of a passive surveillance system in which SD-Bioline rapid diagnostic tests [21] were made available at the level of the endemic district’s peripheral health centers ( approximately 30 per disease focus ) . Additional cases were also actively searched by targeted screening of the neighborhood of previous cases . Positive serological suspects were referred to the endemic district laboratories for parasitological study and if confirmed , treatment was performed . Individuals with positive Card Agglutination Test for Trypanosomiasis ( CATT ) underwent lymph node puncture when relevant and /or blood sampling for direct diagnostic confirmation according to previously described procedures [22] . Cerebrospinal fluid collection by lumbar puncture in confirmed cases allowed disease staging and treatment guidance [22] . Patients with 5 or less white blood cells ( WBC ) /ml of CSF were considered as stage 1 HAT patients whereas those with 6 or more cells were classified as stage 2 patients . The persons diagnosed with HAT were admitted in HAT treatment centers situated close to the endemic district laboratories . Boffa , Dubreka and Forecariah centers have a capacity of respectively 17 beds , 16 beds and 4 beds . After a treatment preparation phase consisting in pain relief , deworming and administration of an anti-malaria treatment , pentamidine isethionate ( 4 mg/kg ) was administered intramuscularly during 7 days to patients at the stage 1 of the disease . The NECT regimen ( nifurtimox at the dosage of 15 mg/kg/day divided in three oral intakes for 10 days and eflornithine 400 mg/kg/day administered in two intravenous infusions for 7 days ) was used for patients with stage 2 disease . Post-treatment follow-up visits were planned 3 , 6 and 12 months after treatment completion according to national procedures applicable at that time . We evaluated the impact of Ebola outbreak on HAT screening activities by analyzing overtime the number of persons screened during active campaign and the number of persons screened passively during the 2014–2015 period ( data were not available for passive screening before 2014 ) . To evaluate the impact of Ebola outbreak on caring activities , we first analyzed overtime the overall number of patients initiating HAT treatment and the number patients who attended the follow-up visits 3 and 6 months after HAT treatment was completed . Then , we compared sociodemographic , clinical , treatment and follow-up variables before and during Ebola outbreak using Fisher exact tests . The period referred as ‘before Ebola period’ ran from February 2012 ( beginning of available data in the “HAT treatment database” ) to December 2013 ( the first cases of Ebola appeared in late december 2013 in “Guinée forestière” ) and the ‘Ebola period’ extended from January 2014 ( first plain month after the first Ebola cases in Guinea and beginning of the “HAT screening database” ) to October 2015 ( end of available data ) . We estimated Disability Adjusted Life-Years ( DALYs ) before and after the Ebola crisis , using the same methodology than the 2010 Global Burden of Disease study , without application of age-weighting and discounting [23] . We weighted average DALYs for the following 3 sub-groups: ( i ) reported HAT cases before Ebola; ( ii ) reported cases during Ebola; ( iii ) under-reported cases during Ebola . The latter group refers to HAT cases that were not reported during the Ebola period compared to what has been observed before the Ebola outbreak , assuming that HAT incidence remained stable during Ebola and the decrease in the number of newly diagnosed HAT cases was attributable mainly to Ebola outbreak . Those assumptions are in line with previous reports from WHO [18] . Reported deaths and life expectancy at the age of death were used to estimate the Years of Life Lost ( YLLs ) in both sub-groups ( i ) and ( ii ) . Under-reported deaths were generated from binomial distributions with assumptive case fatality rates of 2% , 5% and 10% corresponding to conservative , average and pessimistic scenarios . Years Lived with Disability ( YLDs ) were estimated using disability weighting of 0 . 21 for early disease stage , 0 . 35 for late advanced stage; treatment duration of 1 month and time to death for untreated patients at 3 years [23] . We computed 95% confidence intervals around estimates using bootstrap ( 1 , 000 patients resampling , for detailed methodology see S1 Text ) . Statistical analyses were performed using SAS 9 . 3 ( SAS Institute , Cary , North Carolina , USA ) . All HAT patients described in the framework of this study were diagnosed and treated according to the national health and WHO policy . Agreement was obtained from the Ministry of Health of Guinea to use the National Control Program database ( 2012–2016 ) to assess the impact of Ebola on sleeping sickness . Data were anonymized prior to the analysis . All the investigations were conducted in accordance with the declaration of Helsinki . This work fulfills the STROBE criteria .
Fig 3A shows the monthly number of persons screened during active campaigns . Overall 32 , 221 persons were screened for HAT during 6 active campaigns led since February 2012 . The last active screening survey was performed in December 2013 during the follow-up of previously treated patients at their home . A total of 291 family members or neighbors were tested for HAT during this activity . No other active screening campaign was led out after that , during the Ebola period . Among the persons screened in the 2012–2013 period , 26 , 950 ( 84% ) , where from the Boffa district as the result of an elimination project initiated in 2012 in this area[24] . Three lesser campaigns were led in September 2013 with 2 , 810 persons screened in Dubreka , in October 2013 and November 2013 with respectively 2 , 299 and 453 persons screened in Forecariah . Overall 1 , 836 persons were tested passively for HAT between January 2014 and October 2015 . Fig 3B shows the monthly number of persons tested during this period . Passive testing at peripheral heath centers was performed at a low level in Dubreka and Forecariah districts from January 2014 to April 2014 as the passive surveillance system was reinforced . In Boffa , activities started slightly later in May 2014 . After April 2014 , the number of persons tested passively rapidly increased to reach a peak of 286 persons in September 2014 . After September 2014 , the number of tests abruptly decreased to a plateau of about 180 persons until January 2015 . From there , the number of tests rapidly decreased to no testing after February 2015 in Forecariah and after March 2015 in Boffa and Dubreka with the interdiction to perform Rapid Detection Testing ( RDT ) in peripheral health centers . The referral rate of individuals testing positive at a health center was also very low as only 18% ( 13 of 70 serological suspects ) were indeed seen for confirmation at the HAT treatment centers ( of whom 7 were confirmed as HAT patients ) . Unfortunately , we were not able to compare this parameter with pre-Ebola period . Between February 2012 and October 2015 , 213 patients were treated for HAT , 154 ( 72% ) before and 59 ( 28% ) during the Ebola outbreak in coastal Guinea ( Fig 1 ) . Of note , all patients with confirmed HAT initiated the treatment . During the whole study period , the mean number of HAT patients treated monthly was 5 ( Confidence Interval 95% ( CI95% ) : 3–7 ) . This number was 7 ( 95%CI: 3–11 ) before the Ebola period and 3 ( 95%CI: 2–3 ) during the Ebola period ( S1 Table ) . The number of patients treated for HAT was the highest in March 2012 and March 2013 , with 35 and 30 patients respectively , corresponding to large active screening campaigns led in the Boffa focus ( Fig 3C , Table 1 ) . Among these patients , 104 ( 49% ) were recruited during active campaigns and 107 ( 51% ) through passive screening ( Fig 3D , Table 1 ) . All the patients initiating the treatment during Ebola period were recruited passively ( 100% vs 32% before Ebola , p<0 . 0001 ) ( Fig 3D , Table 1 ) . Regarding the activities of the different treatment centers , 143 ( 67% ) , 48 ( 23% ) , and 22 ( 10% ) treatments were initiated at Dubreka , Boffa and Forecariah , respectively . The proportion of patient treated in the Dubreka center significantly increased during the Ebola period ( 75% vs 64% before Ebola , p = 0 . 048 ) ( Fig 1 , Fig 3C , Table 1 ) as the Forecariah and Boffa centers were requisitioned in May and September 2014 respectively . Overall , 130 patients ( 61% ) were male and 161 ( 76% ) were >18 years old . Of 201 patients with available data , 115 ( 57% ) had inside occupational activities in mangrove area . During EVD period , significantly less patients aged < 18 years old initiated treatment ( 14% vs 28% before EVD , p = 0 . 031 ) ( Table 1 ) . During the Ebola period , significantly more patients were diagnosed with stage 2 disease compared to the pre-Ebola period ( 96% vs . 81% , p = 0 . 0039 , Table 1 ) . If we consider missing values ( n = 26 ) as failure to complete HAT therapy , more patients did not complete their treatment during Ebola than before ( 25% vs . 11% , p = 0 . 0129 ) . Of note , the 2 ( 1% ) patients who died during the study period were admitted in a comatose state and passed before NECT could be initiated . The proportion of patients attending the 3 months and 6 months post-treatment follow-up visits ( centralized at the Dubreka center ) decreased from 44% to 10% ( p<0 . 0001 ) and from 16% to 3% ( p = 0 . 017 ) respectively , before and during Ebola period . To account for uncertainty concerning the split date chosen ( January 2014 ) between pre-Ebola and Ebola periods , we performed a sensitivity analysis with two different split dates ( December 2013 and March 2014 ) . The results remained similar ( see S2 Table and S3 Table ) . Only 1 death was reported for both periods , i . e . ; 1 ( 1% ) and 1 ( 2% ) before and during Ebola respectively ( Table 1 ) . The 2 patients who died were admitted in a comatose presentation and passed out before the therapeutic regimen could be administered . Hence , assuming a case fatality rate ( CFR ) of 2% ( similar to what has been observed among HAT cases reported during the Ebola period ) among the under-reported cases ( cases that should have been reported based on previous reports ) , the estimated number of deaths increased from 1 before Ebola to 3 during Ebola ( Table 2 ) . In alternative scenarios based on CFR of 5% and 10% , the number of deaths during Ebola increased to 6 and 10 respectively . YLLs was 44 . 7 ( 42 . 0–47 . 5 ) before Ebola and increased after Ebola up to 116 . 1 ( 110 . 8–121 . 4 ) , 232 . 2 ( 225 . 0–239 . 7 ) and 413 . 7 ( 403 . 5–423 . 9 ) for under-reported CFR of 2% , 5% and 10% respectively . The YLD estimate increased from 4 . 0 before Ebola to 101 . 4 during Ebola for all scenarios . Finally , the DALYs were estimated to 48 . 7 ( 46 . 7–51 . 5 ) before Ebola and increased to 168 . 7 ( 162 . 7–174 . 7 ) , 284 . 9 ( 277 . 1–292 . 8 ) and 466 . 3 ( 455 . 7–477 . 0 ) during Ebola assuming a CFR in under-reported HAT cases of 2% , 5% and 10% respectively ( Table 2 , for detailed estimations see S4 Table ) .
Our study suggests a major impact of the Ebola outbreak on HAT screening activities in the Western Africa most active focuses of Boffa , Dubreka and Forecariah in costal Guinea . All active screening campaigns had to be abandoned during the Ebola outbreak and passive screening activities were also impacted despite new efforts to avail rapid diagnostic tests in peripheral health centers in active foci . The number of person screened passively at health centers was first reduced by half during the early phase of the Ebola outbreak to zero after the outbreak peak . Nevertheless , some patients kept coming by themselves to the only remaining functional treatment center in Dubreka throughout the Ebola period . Even if we were not able to demonstrate a direct impact of Ebola on the referral rate of serological suspects ( people with a positive screening test ) to HAT centers for diagnostic confirmation and subsequent treatment , the number of patients initiating HAT therapy was reduced by 2/3 during Ebola , with 96% of them diagnosed at a late stage of the disease and a poor post-treatment retention in follow-up . It is unlikely that this drop could be explained only by a fall in HAT incidence . Hence , Ebola outbreak contributed to worsening the burden of HAT in Guinea , given DALYs were increasing from 48 . 7 to 217 . 5 in our conservative scenario . This increase in DALYs might have reached up to 466 in the most pessimistic scenario assuming a CFR of 10% among under-reported HAT cases during Ebola outbreak , in line with previous data from WHO [25] . Active screening campaigns , aimed at reducing the human reservoir of trypanosomes are very important for the control of HAT in the hardly reachable mangrove ecosystem of costal Guinea where access to healthcare is limited . The mass screening campaign planned in the Boffa focus in March 2014 had to be abandoned during its awareness phase after the first Ebola cases have been officially declared in Guinea . Prior to the Ebola period , such campaigns provided 68% of the patients treated for HAT and enabled the detection/treatment of persons in the early disease stage 1 , when no or only none-specific symptoms are present . Targeting these individuals is of up-most importance as , remaining active , they represent a major source of infection for the vector [26] , and because the treatment outcome is by far much favorable . Initially , for evident safety reasons and to avoid population gatherings , and then because of population mistrust toward the health system , all active screening activities had to be abandoned throughout the Ebola period , thereby hindering the effort to reduce the T . brucei gambiense reservoir and global HAT burden through early diagnosis and treatment . Only 2 ( 4% ) patients were diagnosed in stage 1 during the Ebola period as compared to 29 ( 19% ) before Ebola ( Table 1 ) . This sharp deficit highlights the fact that an important number of tsetse infective individuals were left undiagnosed during the Ebola outbreak . It is yet too early to assess precisely the impact of this on T . brucei gambiense transmission levels , but it is noteworthy that 79 HAT patients were recently diagnosed in the one and only Boffa focus where active screening activities could recently be implemented again ( May and October 2016 ) . This sharp increase of HAT prevalence , reaching up to 5% in several villages of the focus , is probably largely attributable to the three-year absence of active screening in these areas ( data from Guinean national HAT control plan , to be published later ) . With the inability to perform active screening , all medical activities to control HAT in costal Guinea during the Ebola period had to rely on the passive detection of patients . Although the data on the number of persons passively tested for HAT in the three endemic focuses were scarcely available before 2014 , passive cases ( self-presenting at HAT treatment centers ) represented 32% of HAT patients in the pre-Ebola period ( Table 1 ) . Data on passive screening were computerized and systematically collected after 2014 when the capacities of Boffa , Dubreka and Forecariah health districts were reinforced by the settlement of a passive surveillance system in which serological suspicion was made at the level of local health centers ( # 30 per disease focus ) and parasitological confirmation was done at the district treatment centers . The scale-up of reinforced passive screening activities has unfortunately coincided with the Ebola outbreak resulting in a reduction by half in the number of persons tested during the early epidemic . This can be partly explained by the fear of the population to attend health structures , leading to a sharp attendance decrease . In this regard , it is noteworthy that less than 20% ( 13/70 ) of the persons who tested positive to the HAT rapid test at the peripheral health center level indeed showed up at one of the districts confirmation laboratories . This phenomenon was amplified by the fact that the HAT confirmation/treatment centers of Forecariah and Boffa were requisitioned early during the Ebola outbreak leaving Dubreka as the only functional center . Hence the mean distance to seek for HAT confirmation and treatment was increased and discouraged many clinical suspects . The use of rapid tests in peripheral health centers was then progressively abandoned with the interdiction of taking capillary blood samples in these areas . Interestingly , significantly less people aged < 18 years were diagnosed with HAT during Ebola ( 14% vs . 28% , p = 0 . 031 ) . Maybe parents were less prone to let their children go outside ( or to accompany them to health centers ) during this period . Although late stage diagnosis is a classical feature of HAT passive diagnosis [4] , the proportion of patients diagnosed through passive screening with stage 2 disease has increased sharply from 57 to 97% during the Ebola period ( S5 Table ) . This illustrates another indirect impact of Ebola on HAT which was to increase the time length between infection and diagnosis in passively screened patients . Even with the well tolerated NECT regimen , the treatment of advanced infection remains hazardous [27]: greater efforts are required to raise the patient’s health status before initiating anti-trypanosome therapy; advanced stage patients require a constant attention from the medical teams and they often need to stay under observation for several days/weeks after treatment completion before they can be discharged . HAT diagnosis and treatment are provided freely in Guinea . Nevertheless , increase of hospitalization length can represent a heavy economic burden for affected families as food is not supplied and the patients need guardians to look after them . Economic losses are particularly high when the patient’s homes are located far away from the treatment centers . This was the case for many patients during the Ebola period as Dubreka became quickly the only available HAT treatment center in Guinea . Another impact of the fear of Ebola among communities on the patient care is that the proportion of patients coming for follow-up visits after treatment was significantly reduced , even if the new WHO 2014 guidelines which were no longer recommending systematic follow-up visits for HAT treated patients had not yet been implemented in Guinea . Assessing the impact of Ebola on HAT public health outcomes was not possible with the data available . However , it is noteworthy that no difference was observed in the lethality for the reported cases denoting the tremendous efforts of the medical staff to maintain acceptable treatment condition despite facing major challenges . DALYs estimates using the available data have shown a significant impact of Ebola . Even if these estimates rely on many assumptions due to the lack of evidence , the impact of Ebola outbreak was already substantial with the conservative hypothesis of a similar 2% CFR for reported and under-reported HAT cases during Ebola . Few studies have estimated DALYs for HAT infection , mainly addressing T . brucei rhodesiense sleeping sickness [28 , 29] . We however , used comparable DALYs estimation methods and were more conservative about the CFR in under-reported cases in accordance with the lower morbidity of T . brucei gambiense sleeping sickness [28–30] . Our study has several limitations . First , we used distinct databases with different periods of data collection for active screening , passive screening and treatment . Thus , we were not able to statistically compare the trends in screening activities variation between periods , especially for passive screening . The result of passive screening activities should be interpreted with caution since the improvement of data collection may have induced reporting bias . In addition , results on screening activities before Ebola indirectly derived from the data of the patients treated for HAT may be subject to selection bias . Second , we have no data on adverse events during HAT treatment or post treatment sequelae which combined with mortality may have better captured treatment outcome . Third , our analysis was limited to before and during Ebola periods since the data for post Ebola period were not fully available . Hence , we certainly not have captured all the burden due to HAT cases left undiagnosed , since T . brucei gambiense HAT is a medium-long term evolution disease . But the DALYs estimation is a way of appreciating this burden despite this limit ( even if subject to approximations ) . Post-Ebola data should comfort our findings by showing expected increasing HAT transmission after Ebola epidemic ( preliminary data from Guinean national plan against HAT , to be published later ) . Our findings are consistent with previous studies on the impact of Ebola on HIV care in forest Guinea and Liberia [9 , 11] and indicate an important impact also on HAT control and patient care in costal Guinea . Importantly , it is very likely that the deficit of active screening campaigns and the progressive failure of HAT passive detection have left many undiagnosed T . brucei gambiense infected persons in endemic areas . This may have created in turn favorable epidemiological conditions for disease burden enhancement in some areas . Increased awareness efforts toward HAT endemic communities as well as the revival of the passive surveillance system together with large active screening campaigns allowing early HAT diagnosis and treatment are thus crucial and timely to more fully evaluate the impact of Ebola on HAT transmission and avoid possible dramatic bursts of disease prevalence in endemic foci . Targeted vector control measures , as those implemented previously in part of the Boffa focus [24] may also help speeding–up the reduction of transmission levels and stay in line with the 2020 elimination goal [31] . Finally , to limit the consequences of such a crisis in the future and make HAT control programs more resilient , it appears crucial that “less vulnerable” strategies such as vector control be generalized and reinforced , alongside with an ambitious and sustainable ( in terms of human and financial resources allocation ) active screening campaigns program , in order to reduce both transmission and human reservoir . As for Ebola , the involvement of community leaders could probably be a good way of increasing both awareness and adhesion of the population , as well as limiting the costs , thereby improving the efficiency of the entire program . | This work was conducted in coastal Guinea , the last focus in Western Africa where the transmission of Human African Trypanosomiasis ( HAT ) is still very active . The Guinean government and his partners are conducting HAT control activities to reduce the burden of this neglected tropical disease and , as set-up by WHO , to eliminate it as a public health problem by 2020 . Unfortunately , control efforts were deeply impaired during the Ebola outbreak that stroke the country in 2014–15 . The aim of the study was to evaluate the impact of this unprecedented outbreak on HAT screening and caring activities . A major impact was the interruption of all active screening activities which aim , in addition of detecting and treating patients , is to clear the human reservoir of parasite and decrease transmission . Passive surveillance and diagnosis were also severely affected due to the fear of the population to come to the endemic district confirmation and treatment facilities and to the progressive banning of using rapid test in peripheral health structures , as well as the requisition of healthcare workers and facilities for the fight against Ebola . As a consequence , only 59 HAT patients were diagnosed and treated during the Ebola outbreak ( from January 2014 to October 2015 ) as compared to 154 before the outbreak ( from February 2012 to December 2013 ) . This potentially high number of undiagnosed human reservoir of trypanosomes may have contributed in turn to increase transmission levels . A rapid revival of HAT control activities in Guinea is thus vital in order to stay in line with the 2020 elimination goal and to avoid possible bursts of the disease . | [
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"... | 2017 | Impact of the Ebola outbreak on Trypanosoma brucei gambiense infection medical activities in coastal Guinea, 2014-2015: A retrospective analysis from the Guinean national Human African Trypanosomiasis control program |
The absence of a functional ATP Binding Cassette ( ABC ) protein called the Cystic Fibrosis Transmembrane Conductance Regulator ( CFTR ) from apical membranes of epithelial cells is responsible for cystic fibrosis ( CF ) . Over 90% of CF patients carry at least one mutant allele with deletion of phenylalanine at position 508 located in the N-terminal nucleotide binding domain ( NBD1 ) . Biochemical and cell biological studies show that the ΔF508 mutant exhibits inefficient biosynthetic maturation and susceptibility to degradation probably due to misfolding of NBD1 and the resultant misassembly of other domains . However , little is known about the direct effect of the Phe508 deletion on the NBD1 folding , which is essential for rational design strategies of cystic fibrosis treatment . Here we show that the deletion of Phe508 alters the folding dynamics and kinetics of NBD1 , thus possibly affecting the assembly of the complete CFTR . Using molecular dynamics simulations , we find that meta-stable intermediate states appearing on wild type and mutant folding pathways are populated differently and that their kinetic accessibilities are distinct . The structural basis of the increased misfolding propensity of the ΔF508 NBD1 mutant is the perturbation of interactions in residue pairs Q493/P574 and F575/F578 found in loop S7-H6 . As a proof-of-principle that the S7-H6 loop conformation can modulate the folding kinetics of NBD1 , we virtually design rescue mutations in the identified critical interactions to force the S7-H6 loop into the wild type conformation . Two redesigned NBD1-ΔF508 variants exhibited significantly higher folding probabilities than the original NBD1-ΔF508 , thereby partially rescuing folding ability of the NBD1-ΔF508 mutant . We propose that these observed defects in folding kinetics of mutant NBD1 may also be modulated by structures separate from the 508 site . The identified structural determinants of increased misfolding propensity of NBD1-ΔF508 are essential information in correcting this pathogenic mutant .
CF is the most common autosomal inherited disease with high morbidity among Caucasians . CF patients have altered epithelial ion transport that leads to decreased hydration of epithelial surfaces in the gut , kidney , pancreas , and airways [1] . Decreased surface liquid volume impairs mucociliary clearance which in turn leads to respiratory bacterial infection [2] , [3] . Chronic pulmonary damage caused by bacterial infection dramatically decreases patients' life expectancies . The absence of a functional ABC protein , CFTR , from apical membranes of epithelial cells is the basis of this pathophysiology in cystic fibrosis [4] , [5] . CFTR is a multidomain , integral membrane protein containing two transmembrane domains , two nucleotide-binding domains ( NBD1 and NBD2 ) , and a regulatory region ( R domain ) ( Figure 1 ) . Although more than 1 , 400 mutations are known in CFTR ( http://www . genet . sickkids . on . ca/cftr ) , approximately 90% of CF patients carry the allele with deletion of the codon for phenylalanine at position 508 [6] , which is located in the first nucleotide-binding domain ( NBD1 ) ( Figure 1 ) . Experimental studies suggest that the CFTRΔF508 may be arrested at two stages during its biogenesis . First , the loss of the Phe508 backbone may shift a fraction of the NBD1s of nascent CFTRΔF508 off the wild type folding pathway , causing misfolding and eventual rapid degradation [7]–[9] . Interestingly , recent studies show no significant structural difference between the wild type and mutant NBD1 structures nor in their thermodynamic stabilities [10] . Second , the absence of the Phe508 side-chain prevents the correct post-translational assembly of all CFTR domains [11] . The detailed structural origin of the perturbed kinetics of NBD1 leading to its co-translational arrest is unknown . Nucleotide-binding domains of ABC proteins are highly conserved in sequence and structure . NBDs contain a typical F1 ATPase core subdomain , which consists of an α-helix surrounded by antiparallel β-sheets [9] , [12] . This region contains the conserved Walker A and B motifs that are involved in binding ATP . The α-helical subdomain contains the ABC-signature motif important for ATP hydrolysis ( Figure 1 ) . From X-ray structures of bacterial transporters , the α-helical subdomain is also known to mediate contact with the transmembrane domains [13] , [14] . Folding of multidomain proteins is aided by molecular chaperones to prevent and correct improper ( non-native ) associations between solvent-exposed hydrophobic regions . Smaller single-domain proteins correct and prevent formation of improper contacts through a sequence of partial folding-unfolding events en route to the native state . This sequence of partial folding-unfolding events reflects the ability of single-domain proteins to self-chaperone their folding . In NBD1 , the attenuated refolding of the recombinant ΔF508 mutant is consistent with the notion that Phe508 reduces the activation energy of NBD1 folding in vivo as well as in vitro [11] . Lowering of the activation energy increases the folding rate , which in turn reduces the folding time for NBD1 . Reduction of the folding lessens the propensity of NBD1 to correct the malformed contacts in the intermediate states . Here we propose that Phe508 deletion decreases NBD1's self-chaperoning capability . To investigate the effect of the Phe508 deletion on the stability , dynamics and kinetics of NBD1 , we performed equilibrium dynamics simulations and folding simulations of NBD1-WT and NBD1-ΔF508 . Our analysis shows that there is no significant difference in their stability and equilibrium dynamics , which agrees with experiments . However , even in the presence of correcting mutants ( G550E , R553Q , and R555K ) [10] , [15]–[17] in our model of NBD1-ΔF508 , we still observe a significant change in dynamics at the folding transition . We further explore the difference in the folding transition by performing 300 folding simulations each for NBD1-WT and NBD1-ΔF508 . We also perform simulations of another mutant NBD1-F508A to serve as control . These simulations allow the comparison of the mutant and wild type folding probabilities , their intermediate states , the structures of these intermediate states , and their folding pathways . Finally , we identify contacts between residues in NBD1 critical to its folding dynamics that are perturbed by Phe508 deletion , thus increasing the propensity of NBD1-ΔF508 to misfold .
To determine the equilibrium dynamics and stabilities of the wild type and mutant NBD1 , we perform equilibrium simulations ( 106 time units∼0 . 5 millisecond [18] ) of wild type and mutant NBD1 using discrete molecular dynamics [19] , [20] ( see Methods ) . From the equilibrium simulations , we calculate the thermal denaturation curve of both NBD1-WT and NBD1-ΔF508 ( Figure 2 ) and observe two stable thermodynamic states , folded and unfolded . In agreement with previous experimental studies by denaturation experiments [7]–[9] , the stabilities of wild type and ΔF508 NBD1 are not significantly different . The slope at the transition temperature of the wild type ( Tm∼0 . 68 ε/kB ) is 9838 kb and the slope at the transition temperature of the mutant ( Tm∼0 . 70 ε/kB ) is 16201 kb ( ε∼1–2 kcal/mol and kB is the Boltzman factor; see Methods for further discussion on units ) . This shift in slope at the transition temperature indicates a difference in folding cooperativity of NBD1-WT and NBD1-ΔF508 and therefore a difference in folding kinetics . Folding is a stochastic process , thus to investigate in detail the difference in folding kinetics and dynamics of NBD1-WT and NBD1-ΔF508 , we perform 300 folding simulations on each of the structures . Starting from fully unfolded chains of NBD1-WTand NBD1-ΔF508 , we progressively reduce the temperature of the system to simulate thermal folding ( see Methods ) . We find the folding probability [21] ( number of runs that lead to the native structure/number of total folding simulations ) of wild type to be 33±3% while that of the mutant is 13±2% ( see Methods ) . The ratio of NBD1-WT and NBD1-ΔF508 correlates with the ratio of their folding yields derived from folding experiments . Folding yields of NBD1-WT is approximately twice that of NBD1-ΔF508 in the temperature range 10°C to 22°C [9] . Folding simulations of our control structure NBD1-F508A yield a folding probability of 26±4% which is intermediate to that NBD1-WTand NBD1-ΔF508 . This folding probability value is in agreement with experimental studies showing intermediate folding efficiencies and maturation levels of NBD1-F508A relative to NBD1-WT [9] , [11] . To investigate the molecular origin of the difference in folding yields and probabilities , we map the folding pathways of NBD1-WT , NBD1-F508A , and NBD1-ΔF508 by identifying their metastable folding intermediate states . The folding intermediate states of a folding trajectory are exhibited as peaks in the energy probability distributions ( Figure 3; Figure S1 ) . Thus , dominant intermediate states in the folding pathways are peaks in the average energy probability distributions ( see Methods; Figure 3 ) . The average energy probability distributions of wild type and the mutant are significantly different ( Kolmogorov-Smirnov test; P-value<1 . 4×10−292 ) , which suggests a significant difference in the folding kinetics of wild type and mutant NBD1 . The dominant intermediate states are listed in Table S1 . The average fraction of native contacts of NBD1 structures in an intermediate state follows a distinct distribution ( Figure S2 ) , thus , an intermediate state identified using energy as the folding reaction coordinate , forms a distinct collection of NBD1 conformations . We find that some intermediate states are accessible only by either NBD1-WT ( S6 and S9 ) or NBD1-ΔF508 ( S5 and S10 ) , further suggesting that Phe508 deletion leads the mutant to off-folding pathways ( see below ) . While states S2 , S3 , S4 , S7 , and S8 are both traversed by NBD1-ΔF508 and NBD1-WT , their time occupancies ( length of time NBD1 spends in an intermediate state ) are different ( Figure 3B ) . Since time occupancies are proportional to the free energy barriers between intermediate states , these observations suggest that the Phe508 deletion significantly perturbs the NBD1 folding free energy landscape . To determine the difference between the sequence of folding events of the wild type , ΔF508 , and the F508A control , we estimate the probability of transitions between intermediate states ( see Methods and Figure S3 ) . The difference in transition probabilities of NBD1-WT , NBD1-ΔF508 , and NBD1-F508A is shown in Figure 4 . The transition probabilities show some states accessible only to either wild type or mutant NBD1 . The difference in state accessibilities between the two indicates a difference in contact pattern formation ( nucleation events ) , which could cause the observed difference in folding yields . We calculate the most dominant folding pathways in wild type and mutant NBD1 . The most dominant path in wild type follows a sequence of transition Unfolded→S10→S8→S7→S5→S4→S1 , while the dominant path in the mutant follows the sequence of transitions Unfolded→S9→S8→S7→S6→S4→S1 . Thus , NBD1-WTand NBD1-ΔF508 undergo different sequences of folding events . Because of the reduction in dimensionality of the folding process when energy is used as a reaction coordinate , each intermediate state represents an ensemble of NBD1 structures . To identify the primary structural characteristics of each intermediate state , we clustered structures in the corresponding state and calculated the frequency of contacts formed between pairs of residues ( Figure 5; see Methods ) . In all intermediate states , we find the most notable structural difference between NBD1-WT and NBD1-ΔF508 occurs in the S7-H6 loop . For example , P574 interacts with Q493 in wild type but not in the mutant . Also , F575 interacts with F587 in the mutant but not in wild type ( Figure 6 ) . This pattern of contact formation reflects the difference in NBD1-WT and NBD1-ΔF508 crystal structures that is embedded in the interactions defined according to structure . Additionally , residue pairs that have similar interactions ( i . e . , attractive or repulsive ) in the wild type and mutant crystal structures still exhibit different contacts in the folding intermediate states . These results show that the pattern of transient contact formation in the wild type is also perturbed by Phe508 deletion . This class of residue pairs include Q525/E585 and C524/I586 . We observe a number of folding trajectories reaching native energies ( ∼630 ε ) and within a 2 . 5 Å root-mean-square deviation ( RMSD ) with respect to the native structure , but the resulting topological wiring of the secondary structures is incorrect . The “miswiring” consistently occurs in the H5-S6 loop . Interestingly , this H5-S6 loop is in the immediate neighbourhood of the loop containing Phe508 . This suggests “weak” regions in NBD1 that are intrinsically prone to misfolding . To verify that the identified contact pairs ( Q493/P574 and F575/F587 ) found in the S7-H6 loop are indeed critical in the kinetics of NBD1 , we revert their interactions in NBD1-ΔF508 to their interactions in NBD1-WT and perform folding simulations . In the case of the Q493/P574 pair , the residues are in close proximity in NBD1-WT but not in NBD1ΔF508 , thus we changed the interaction between Q493 and P574 in NBD1ΔF508 from repulsive to attractive to mimic a possible rescuing mutation . Folding simulations of “rescued” NBD1-ΔF508 yield a folding probability of 19±2% . On the other hand , residues F575 and F508 are in close contact in NBD1-ΔF508 but not in NBD1-WT , thus we reverted their interaction in NBD1-ΔF508 from attractive to repulsive . Folding simulations of the second “rescued” NBD1-ΔF508 yield a folding probability of 20±2% . These folding probabilities of the two “rescued” NBD1-ΔF508s are higher than the 13±2% folding probability of the original NBD1-ΔF508 , which supports our findings that the contacts between Q493 and P574 and between F575 and F587 are indeed critical to NBD1 folding .
Our results reveal the intrinsic property of NBD1ΔF508 to fold improperly and raise the possibility of redesigning NBD1ΔF508 to rescue it from misfolding . In case of the contact that is found in wild type but not in the ΔF508 mutant ( e . g . , Q493/P574 ) , one can find amino acid substitutions that promote interaction between this pair of residues ( Q493/P574 ) . On the other hand , for the contact found only in the ΔF508 mutant but not in wild type ( e . g . , F475/F587 ) , candidate rescue mutants are those that destabilize the interaction between this residue pair ( F475/F587 ) . Knowing the molecular details of the altered folding in the case of the mutant domain also provides a basis for design of small molecules to correct the most prevalent and pathogenic mutation in CFTR .
To access time scales of NBD1 folding , we use a simplified protein model but still maintain important features of the protein such as side-chain packing . Amino acid residues were modelled as follows: ( 1 ) glycines are represented by three beads ( -N , Cα , C′ ) ; ( 2 ) phenylalanine , tyrosine , tryptophan , and histidine by five beads ( -N , Cα , C′ , Cβ , Cγ ) , and ( 3 ) all other residues by four beads ( -N , Cα , C′ , Cβ ) [24] . This protein model successfully described protein aggregation [24] . In the simulations , we use PDB ID: 2BBO , PDB ID: 1XMI and PDB ID: 1XMJ [10] as models of NBD1-WT , NBD1-F08A , and NBD1-ΔF508 , respectively . The missing loop between E403 and L436 in both wild type and mutant NBD1 is reconstructed using a loop-search algorithm in SYBYL ( Tripos Assoc . Inc , St . Louis , MO ) . Using discrete molecular dynamics [19] , [20] , long equilibrium simulations at various temperatures were performed to investigate the equilibrium dynamics of the CFTR NBD1 . Interactions between beads were defined using the Go̅-model [25] . In the Go̅-model , interactions between residues are determined from the native structure of known NBD1 crystal structures . Pairwise , square-well interactions were assigned between beads in the model according to contacts formed in the native state . Specifically , two residues are said to be in contact if their atoms ( excluding hydrogen ) are within a distance of 4 . 5 Å . The strength of the interaction between residues in contact ( denoted hereon as ε ) defines the energy units . Physically ε∼1–2 kcal/mol , which is approximately a contribution to protein stability from a hydrogen bond . The temperature is measured in units of ε/kb , where kb is the Boltzmann constant . The time unit ( tu ) is estimated to be the shortest time between particle collisions in the system ( ∼0 . 1 nanosecond ) . From long equilibrium simulations of 106 tu , we were able to access the long time-scale dynamics of the CFTR NBD1 in the order of 0 . 5 millisecond . Each equilibrium simulation consumed approximately 300 CPU hours . We perform 300 folding simulations for each NBD1-WT , NBD1-F508A , and NBD1-ΔF508 . Starting from fully unfolded chains , the temperature of the system is progressively reduced to allow NBD1 to fold to its native structure . Folding simulations proceeded until τmax∼60 , 000 tu ( time units ) , which is chosen to be longer than the typical folding time of the studied sequences [21] . A similar criterion was employed in the studies calculating the folding probability of proteins [26] . The NBD1 structure in a folding run is considered folded when ( 1 ) its energy is less than or equal to −620ε ( the energy of the native state ) , ( 2 ) its structure is within 2 . 5 Å RMSD from the native , and ( 3 ) the structure possesses correct topological wiring of the secondary structure elements . To estimate the error in folding probabilities , each folding trajectory is considered a Bernoulli trial with a binary outcome , folded or unfolded . The variance of a Bernoulli process is σ2 = p ( 1−p ) /n , where p is probability and n is the total number of trials . To identify the positions of intermediate states , a sum of multiple Gaussian curves is fitted to the average energy probability distribution of successfully folded runs . ai , bi , and ci are the center , standard deviation and height of the ith Gaussian curve , respectively . We estimate probability of transitions between states by counting the trajectories that underwent such transition . The sum of probabilities of paths emanating from a given state is normalized to 1 , which physically means that the system always exits from its current intermediate state . The transition probabilities represent independent conditional probabilities , thus the most likely path from the unfolded state to the native is estimated by multiplying the probabilities of the traced edges . We calculated a contact matrix for each structure in the intermediate state . An element of the contact matrix is 1 when two residues were within 4 . 5 Å or 0 otherwise . Dominant contacts between pairs of residues in NBD1 are determined from the average contact matrix of all the structures . | Deletion of a single residue , phenylalanine at position 508 , in the first nucleotide binding domain ( NBD1 ) of the Cystic Fibrosis Transmembrane Conductance Regulator ( CFTR ) is present in approximately 90% of cystic fibrosis ( CF ) patients . Experiments show that this mutant protein exhibits inefficient biosynthetic maturation and susceptibility to degradation probably due to misfolding of NBD1 and the resultant incorrect interactions of other domains . However , little is known about the direct effect of the Phe508 deletion on NBD1 folding . Here , using molecular dynamics simulations of NBD1-WT , NBD1-F508A , and NBD1-ΔF508 , we show that the deletion of Phe508 indeed alters the kinetics of NBD1 folding . We also find that the intermediate states appearing on wild type and mutant folding pathways are populated differently and that their kinetic accessibilities are distinct . Moreover , we identified critical interactions not necessarily localized near position 508 , such as Q493/P574 and F575/F587 , to be significant structural elements influencing the kinetic difference between wild type and mutant NBD1 . We propose that these observed alterations in folding kinetics of mutant NBD1 result in misassembly of the whole multi-domain protein , thereby causing its premature degradation . | [
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] | 2008 | Diminished Self-Chaperoning Activity of the ΔF508 Mutant of CFTR Results in Protein Misfolding |
The effects of somatic mutations that transform polyspecific germline ( GL ) antibodies to affinity mature ( AM ) antibodies with monospecificity are compared among three GL-AM Fab pairs . In particular , changes in conformational flexibility are assessed using a Distance Constraint Model ( DCM ) . We have previously established that the DCM can be robustly applied across a series of antibody fragments ( VL to Fab ) , and subsequently , the DCM was combined with molecular dynamics ( MD ) simulations to similarly characterize five thermostabilizing scFv mutants . The DCM is an ensemble based statistical mechanical approach that accounts for enthalpy/entropy compensation due to network rigidity , which has been quite successful in elucidating conformational flexibility and Quantitative Stability/Flexibility Relationships ( QSFR ) in proteins . Applied to three disparate antibody systems changes in QSFR quantities indicate that the VH domain is typically rigidified , whereas the VL domain and CDR L2 loop become more flexible during affinity maturation . The increase in CDR H3 loop rigidity is consistent with other studies in the literature . The redistribution of conformational flexibility is largely controlled by nonspecific changes in the H-bond network , although certain Arg to Asp salt bridges create highly localized rigidity increases . Taken together , these results reveal an intricate flexibility/rigidity response that accompanies affinity maturation .
The variable region of an antibody is composed of a structurally conserved fold that contains six complementarity-determining regions ( CDRs ) , also known as hypervariable regions . The six CDRs , three on the light chain ( L1 , L2 and L3 ) and three on the heavy chain ( H1 , H2 and H3 ) , are known to be responsible for the majority of antibody-binding interactions . Antibody evolution starts with the assembly of germline ( GL ) antibodies in B and T cell progenitors through the recombination of V , D , and J gene segments [1] . Theoretically , V- ( D ) -J recombination could generate 2 . 3 × 1012 antibody variable domains [2] , which is far less than the number of epitopes on foreign antigens to which one could be exposed . Therefore , the GL antibodies undergo further cycles of somatic mutations for affinity maturation ( AM ) and specificity improvement as the immune response proceeds , which can produce an astronomical number of unique antibodies . A variety of biochemical and structural studies reveal that the same germline gene-encoded antibodies allow promiscuous binding to diverse antigens , and even the same antigens by quite different somatic mutations [3–5] . Structural diversity in the antigen-binding site accounts for the immense breadth of binding of the antibody repertoire . Two hypotheses , conformational flexibility and the induced-fit models , are commonly invoked to explain the conformational changes of antibodies during affinity maturation . Conformational flexibility assumes GL antibodies retain a degree of structural plasticity in their backbone in order to bind a number of different unrelated antigens , a capacity referred to here as polyspecificity [3 , 6] . In contrast , the induced-fit model supposes that conformational changes are induced as antigens binding to the Ab [7–9] . Regardless of the explanation , it is clear that flexibility/rigidity is changed , which is closely related to the binding affinity and specificity of antigens [4 , 5 , 10–13] . There is much evidence to suggest that mature antibodies , especially within the CDRs , are inherently more rigid than their GL precursors . Lipovsek et al . [14] demonstrated that constricting the flexibility of CDRs with inter-loop disulfide bonds enhanced the affinity of immunoglobulin interactions . Schmidt et al . [15] studied a broadly neutralizing influenza virus antibody using long-scale molecular dynamics and demonstrated that maturation rigidifies the initially flexible heavy-chain CDRs , which accounts for most of the affinity gain . Jorg et al . [16] applied three-pulse photon echo spectroscopy and molecular dynamic to explore the flexibility of mature 4-4-20 antibody and found that the binding site of the mature antibody is significantly rigidified compared to that of the GL , and that the increased rigidity occurs via increased coupling within and between CDR loops and the antibody framework . Finally , Manivel et al . [17] proposed that more unfavorable entropy changes are associated with ligand binding within GL antibodies compared to AM . Although genetic and biochemical studies have revealed the nature and origin of the sequence diversity of antibodies , the mechanisms by which the somatic mutations change the flexibility of the antibody-binding site is not well understood . Accurate assessment of the flexibility of the CDRs might be particularly important to further understand the thermodynamics of immunoglobulin binding . Flexibility of the CDRs is related to the polyspecificity by providing the capacity of a single binding site to bind different ligands . Molecular dynamics ( MD ) is commonly used to quantify protein “flexibility” at a very detailed level . However , quantifying protein motions characterized by MD trajectories using standard metrics such as root mean squared fluctuations ( RMSF ) or assessing essential dynamics by principal component analysis capture atomic motions with large amplitudes . While the phrase “flexibility” is often used interchangeably with mobility , there is a technical difference . For example , an α-helix constitutes a rigid substructure , yet it can simultaneously be highly mobile if its position as a rigid body undergoes large fluctuations . Conversely , a flexible region may serve as a hinge point to facilitate relative motions , but the hinge itself need not be mobile . Although flexibility and mobility are distinct properties , this distinction is typically not made in the literature . Flexibility is characterized by network rigidity as a direct mechanical property of molecular structure . The Distance Constraint Model ( DCM ) characterizes protein flexibility in a thermodynamically appropriate way [18 , 19] . The DCM has been successfully applied for many protein systems such as RNase H [20] , periplasmic binding proteins [21] , thioredoxin [22] , lysozyme [23 , 24] , and β-lactamase [25] . Collectively , these results reveal that conformational flexibility is very sensitive to perturbation ( e . g . , mutation and ligand binding ) . Moreover , these flexibility changes frequently propagate over long distances . Recently , we characterized the effects of mutation to single chain Fv ( scFv ) fragments of the anti-lymphotoxin-β receptor antibody using the DCM [26] . Statistically significant changes in the distribution of both rigidity and flexibility within the molecular structure is typically observed , where the local perturbations often lead to distal shifts in flexibility and rigidity profiles . In this report , we similarly characterize the effects of somatic mutations on the flexibility/rigidity changes by analyzing three GL-AM antigen-binding fragment ( Fab ) pairs . Interestingly , CDR H3 loop is rigidified after affinity maturation in all three cases . We observe a rich mixture of increased rigidity and flexibility along the backbone , and many of these changes are significantly long-ranged . In many instances specific hydrogen bonds or salt bridges that form in regions where there is tight side chain packing play an important role in rigidifying CDRs during the maturation process . The accompanying loss of conformational entropy due to this increase in rigidity near the mutation site is an enthalpy-entropy compensation mechanism that the DCM captures well through network rigidity . In addition , molecular couplings that describe flexibility and rigidity correlations between residues are frequently enhanced by somatic mutations . The structural plasticity of GL antibodies and associated trends in how rigidity and flexibility profiles redistribute upon maturation likely represent general mechanisms used by the immune response and could be used to guide design high affinity and selective antibodies for desired function .
The DCM is defined in terms of an all-atom free energy decomposition ( FED ) scheme combined with constraint theory . Atomic structure is mapped onto a graph where vertices represent atomic positions and edges describe intramolecular interactions that fix the distance between atomic positions . This graph defines a mechanical framework that is characterized by its constraint topology . A Pebble Game ( PG ) algorithm identifies all rigid and flexible regions [27 , 28] , which can provide statistically significant explanations of intramolecular couplings [29] . An ensemble of graphs is considered to account for fluctuations in constraint topologies due to the breaking and forming of H-bonds and packing interactions . The DCM generates a Gibbs ensemble of graphs , where each graph is weighted by a Boltzmann factor given by xp ( −βG ) . The free energy of a graph is calculated from a FED where each constraint is associated with a component enthalpy and entropy . The total enthalpy of a graph is the sum over all enthalpy contributions . However , as described below , the total entropy accounts for nonadditivity [23 , 30 , 31] due to network rigidity . Within the minimal DCM [18 , 32] the number of native-like torsion constraints , Nnat , and number of H-bond constraints , Nhb specify a macrostate . Native torsion states have lower energies and entropies relative to disordered torsion states , meaning they correspond to good packing interactions . As a result , protein stability is described in terms of both intramolecular packing and the H-bond network . Note that salt bridges are considered to be a special case of H-bonds . The two order parameters , ( Nhb , Nnat ) , define a macrostate of a protein in terms of its constraint topology , from which a free energy functional is constructed as: G ( Nhb , Nnat ) =U ( Nhb ) −usolNhb+vnatNnat−T[Sconf ( Nhb , Nnat|δnat ) +Smix ( Nhb , Nnat ) ] ( 1 ) where U is the intramolecular H-bond energy , usol is an average H-bond energy to solvent that occurs when an intramolecular H-bond breaks , vnat is the energy associated with a native-like torsion , Sconf ( Nhb , Nnat ) is the conformational entropy and Smix ( Nhb , Nnat ) is the mixing entropy of the macrostate associated with the number of ways of distributing Nnat native-torsions and Nhb H-bonds within the constraint topology . Three phenomenological parameters , {usol , vnat , δnat} , effectively account for overall structural shape and solvent interactions . Conformational entropy , Sconf , is calculated over the set of independent constraints identified by the PG using: Sconf ( Nhb , Nnat ) =R〈∑t∈hbqtγt+Qnatδnat+Qdisδdis〉graphs ( 2 ) where the index t spans over all H-bond constraints in the input structure , and each H-bond has a qt of either {0 , 1 , 2 , 3 , 4 or 5} to count the number of distance constraints that are independent based on the PG . Note that in the mDCM , each H-bond is modeled using five distance constraints . Hence , it is possible that all five constraints are independent ( i . e . qt = 5 ) or all five constraints are redundant ( i . e . qt = 0 ) or any range in between if the t-th H-bond is present , and qt = 0 if the t-th H-bond is not present ( i . e . broken ) . For each independent H-bond distance constraint , Rγt is the conformational entropy contribution . The details of these calculations depend greatly on the number of H-bonds present in the protein and where they are distributed . Moreover , there is a strong dependence on the number and location of torsion constraints within the protein . The macrostate stratifies the total number of H-bonds and total number of native torsions . All native-like and disordered torsion constraints respectively contribute Rδnat and Rδdis to the conformational entropy when they are independent . Taking advantage of the degeneracy , the variable Qnat is the total number of native-like torsions that are independent and Qdis is the total number of disordered torsions that are independent . The various qt , Qnat and Qdis values in Eq ( 2 ) are calculated for each mechanical framework ( graph ) using the PG , and the conformational entropy is obtained as an ensemble average over many graphs as denoted by the averaging brackets [18] . Monte Carlo sampling is used to sample networks at each macrostate value ( Nhb , Nnat ) . Typically , 200 samples per macrostate provide enough sampling to obtain sufficiently accurate statistics . Lastly , there is a critically important step that must be executed when determining if a constraint is independent or redundant . When the PG is used to calculate whether a constraint is independent or redundant during a recursive process of building the PG graph one constraint at a time [28] , the constraints are placed in preferential order from lowest to highest component entropies . With this preferential ordering , the calculation of conformational entropy provides a lowest possible upper bound estimate . Conceptually , total conformational entropy reflects the minimal set of the most constrictive yet independent interactions . Solvation free energy contributions are modeled by the phenomenological usol and vnat parameters [33] that are conjugate to the intramolecular H-bonds and packing order parameters respectively . While mutations are known to quantitatively affect solvation free energies [34] , the same usol and vnat parameters are used throughout because the changes are not expected to be large here due to the overall structural similarity across the dataset . We created a dataset of three pairs of GL-AM antibody Fabs that include anti-fluorescein ( FA ) , anti-CD3 T-cell receptor ( CA ) and esterase catalytic ( EA ) antibodies ( Table 1 ) . In this dataset , except for the GL Fabs of FA and CA , all the X-ray structures are available . The structures of GL Fabs of both FA and CA were modeled using their corresponding AM structures as templates by SCWRL4 [35] . The melting temperatures ( Tm ) of FA ( AM ) , CA ( GL ) and CA ( AM ) are available . S1 Table summarized the potential germline genes of the three antibodies based on the bioinformatics analysis of the closet human germline sequences by the alignments of the sequences of maturated antibodies with those from the germline sequence database . Across the three antibodies , only one or two segments may come from the same genes and other segments are from different GL genes , meaning the three example systems do not belong to the same GL family and suggesting wide applicability of our results . S2 Table shows the experimentally measured binding affinities of the three antibodies in the dataset with their antigens . The affinity increases from GL to AM is at least 32-fold in FA and 7 . 3 fold in EA . The GL CA antibody does not bind antigen , whereas the affinity for the AM antibody is Kd = 0 . 64 μM . The number and identity of mutations between AM and GL are summarized in S3 Table . We employed molecular dynamics ( MD ) simulations to generate an ensemble of conformations ( 10 representative structures ) for subsequent DCM analysis . The advantage of using multiple structures instead of a single structure is that sensitivity to structural artifacts is diminished and uncertainties can be estimated [26] . Each structure in the dataset was simulated for 100 ns using Gromacs 4 . 5 . 5 [36 , 37] in the NVT ensemble with the AMBER99SB-ILDN force field [38] . The structures were solvated by adding 10 . 0 Å of TIP3P water [39] in a cubic box ( counter ions are also added to neutralize charge ) . Before production , the systems were minimized for 5 , 000 iterations in Gromacs , followed by 1 ns of NPT and 1 ns of NVT equilibration . Pressure ( 1 atm ) was regulated using the extended ensemble Parrinello-Rahman approach [40] and temperature ( 300 K ) was controlled by a Nose-Hoover temperature coupling [40 , 41] . A nonbonded cutoff of 10 . 0 Å was used , and Particle-Mesh-Ewald [42] accounts for long-range electrostatic interactions . All bonds to hydrogen atoms in proteins were constrained using LINCS [43] , whereas bonds and angles of water molecules are constrained by SETTLE [44] , allowing for a time step of 0 . 002 ps . The phenomenological parameters , {usol , vnat , δnat} , are ideally obtained by fitting to experimental heat capacity curves from DSC . In our two recent reports [26 , 45] , we established parameter ranges for various antibody fragment sizes and the parameters are relatively conserved within the same antibody fragment . In addition , we showed that the mDCM could be parameterized in the absence of experimental heat capacity curves assuming the melting temperature is known or estimated . Therefore , because of the lack of experimental Cp curves for our dataset we obtained the parameters for the six antibodies by two ways . If the Tm value of the antibody is known , we fit the structure to a presumed similar experimental Cp curve from a commercial anti-VEGF antibody Fab , Bevacizumab , which has one peak corresponding to the Tm at 347K [46] . Prior to the fitting , the Tm of the target-experimental Cp curve was shifted to the true experimental Tm ( 326K ) [47] For the antibodies without experimental Tm , we used the same parameters from FA ( S4 Table ) . In addition to calculating thermodynamic properties , the mDCM calculates a number of mechanical properties that are ensemble averaged . Taken together , the mDCM produces Quantitative Stability/Flexibility Relationships ( QSFR ) of the protein . For example , large extended rigid sub-structures , punctuated by flexible loops , are prevalent at low temperatures , whereas the protein is primarily flexible in the denatured ensemble at temperatures greater than the Tm defined by the heat capacity peak . The backbone Flexibility Index ( FI ) and the Cooperativity Correlation ( CC ) serve as useful QSFR metrics for characterizing mechanical properties within a protein [48] . The FI is an ensemble average over the quantity fi = ( hi−li ) that is calculated for each constraint topology as follows . When the i-th rotatable bond can rotate within a flexible region , the number of rotatable bonds that can rotate ( distinct hinge motions ) within that flexible region is counted , and denoted as H . The number of independent disordered torsions within that flexible region is also counted , and denoted as A . The value hi = A/H represents the density of independent degree of freedom ( DOF ) within that flexible region , and it is assigned to all H rotatable bonds within . Conversely , if the i-th rotatable bond is locked within an over-constrained region , the total number of rotatable bonds that are locked are counted and denoted as L . The number of redundant constraints within that over-constrained region is also counted , and denoted as B . The value li = B/L represents the density of redundant constraints within that over-constrained region , and it is assigned to all L locked bonds within . In the special case that B = 0 , the locked bond is called isostatic , but this distinction is lost in FI due to ensemble averaging . The CC matrix is calculated similarly to FI; however , mechanical couplings are being tracked . That is , for a given constraint topology , the decomposition of regions as described above also yield which pair of rotatable bonds are in the same flexible region or same rigid region . If the i-th and j-th rotatable bond are in the same flexible region , the matrix element CCij = hi ( recall hi = hj ) . If they are in the same rigid region , the matrix element CCij = –li ( recall li = lj ) . If the pair of rotatable bonds are not within the same distinct region , the matrix element CCij = 0 and this pair of rotatable bonds are not correlated . The size of the CC matrix representing the backbone is nominally 2N·2N because the phi and psi torsions are tracked along the backbone . However , generally the CC matrix is slightly smaller in size because proline has only the psi rotatable bond . Although the backbone rotatable bonds within a residue can be averaged to arrive at a N·N matrix , the CC matrix that we typically use , as is the case here , show all rotatable angles . There are multiple ways to ensemble average mechanical properties and other physical observables . Note that because Boltzmann factors weight macrostates differently within the free energy landscape , the most probable constraint networks depend on temperature . In this report , we average over all macrostates corresponding to the native basin to focus on equilibrium fluctuations in the folded protein at T = Tm . As such , both the FI metric and the CC matrix represent the average native state characteristics at T = Tm . In this report , residue numbering is based on the Kabat scheme [49] . The QSFR properties are calculated for each representative structure , and a second average over 10 representative structures is performed with a weighting that is based on cluster size . To compare mutant QSFR properties to the wild type , we use a Z-score ( Eq 3 ) to discern differences between the GL and AM results across the 10 representative structures . The value of 10 corresponds to the number of representative structures considered . Cluster weightings are included when calculating the averages and standard deviations of a quantity over the 10 representative structures . Using a conservative Z-score cut-off , statistically significant changes are deemed to occur when |Z-scores| are greater than 2 . 33 , corresponding to a p-value of 0 . 01 . Further , large changes are deemed to occur when |Z-scores| are greater than 3 . 33 , which corresponds to a p-value of 0 . 0005 . That is , the odds of a moderate change occurring by random chance are 1 in 100 , and the odds of a large change occurring by random chance are less than 1 in 2300 . No change is assigned when Z-scores are between ±2 . 33 .
Three GL-AM antibody pairs including anti-fluorescein , anti-CD3 T-cell receptor and esterase catalytic antibodies were compiled for rigidity/flexibility analysis . For each pair we compare the AM sequence to the GL sequence via sequence alignments ( Fig 1 ) . The numbers of mutations located in the Fab and CDRs with respect to the GL sequence are 12/6 , 9/6 and 9/5 for FA , CA and EA , respectively , indicating that 50% or more of the mutations occur in CDRs . Fig 2 shows the binding modes of the antigens in their AM antibodies . It is noted that antigen fluorescein and hapten 5- ( para-nitrophenyl phosphonate ) -pentanoic acid are located in similar sub-regions of the binding site , which facilitate them to interact directly with the CDR-H3 and CDR-L3 loops . Similarly , one epitope loop of antigen CD3 inserts into the same sub-regions , while others make contact with CDR-H1 and CDR-H2 loops . S1 Fig summaries the changes of amino acid propensity during affinity maturation . An apparent trend is that charged residues are favorable in the mature sequences while polar residues are unfavorable ( S5 Table ) . Our previous study of the stability and flexibility of wild-type and stable mutants of scFv using mDCM indicate that an average over the most weighted ten representative structures sampling by MD reduces the statistical variance in mean QSFR properties to a point that is less than the level of accuracy that can be expected from the employed phenomenological mDCM underlying the calculations [26] . In addition , the modeled structures require further conformational refinement . Thus , we performed MD on all six Fab structures to generate a set of representative conformations for mDCM analysis . Root mean square distances ( RMSD ) of Cα atoms are plotted in Fig 3 . In all six cases the fluctuations within the constituent Fv and Fc regions from the Fab are well converged across the 100 ns trajectory ( S2 Fig ) . The same is true in each of the individual Ig-folds . While the overall RMSD values approach 8 Å in the GL and AM trajectories , these large fluctuations are simply due to reorientations between the Fv and Fc regions ( cf . Fig 3C ) . Due to the immense flexibility within the linker regions , these rearrangements are continuously present in the native ensemble as equilibrium fluctuations , and these fluctuations have been noted previously by both simulation and experiment [50 , 51] . Therefore , these particularly large fluctuations do not indicate poor convergence , and no added benefit would be achieved by simply simulating the systems longer . Moreover , it is worth noting the pronounced Fv/Fc rearrangements occurring in both the GL and AM trajectories eliminates the mutant modeling process as the cause of these fluctuations . It is interesting to note that the FA , one of two anti-hapten systems , has the largest domain-domain fluctuations; antibodies raised to haptens tend to be more susceptible changes in conformation upon binding than anti-protein antibodies [52] . On the other hand , the catalytic EA antibody has the smallest global RMSD fluctuations , so it is impossible to conclude anything with respect to the conformational fluctuations based on this hapten vs . protein antigen distinction . A total of 2 , 000 evenly spaced frames from each trajectory were clustered using the KCLUST module [53] from the MMTSB tool set [54] based on the RMSD of all heavy atoms . Table 1 summarizes the number of conformations represented by each cluster . We adjust the cluster radii to maintain around 20 total clusters , where the ten largest represent 92 to 99% of the total conformations . A representative structure is identified as the centroid from each of the ten largest clusters , which are then subsequently energy minimized and used as input to the mDCM . A weighted average of all mDCM properties is taken over the ten representative structures , where the total number of structures within the cluster containing a given representative structure defines its weight . After MD simulation and clustering , H++ [55] is employed to account for protonation state fluctuations by calculating ionization properties by considering residue pKa values followed by a final minimization [56] . Backbone flexibility as described by the flexibility index ( FI ) for each antibody within the dataset is shown in Fig 4 . Positive FI values indicate flexibility , whereas negative values indicate rigidity . Across the alignment , most secondary structure elements are determined to be rigid , whereas intervening loops are flexible . The termini and linker regions ( residue 112–120 ) between the VH and CH domains shows considerable flexibility , which is in agreement with the large fluctuations within domains observed by molecular dynamics ( Fig 3 ) . Despite this overall qualitative similarity , there are quantitative differences throughout . To assign statistical significance to the observed changes , we recast the differences between the GL and AM antibodies as Z-scores . The Z-scores for each GL-AM pairs are plotted against residue number in S3 Fig where all rigidity/flexibility differences are classified as no significant change ( |Z-score| < 2 . 33 ) , moderate change ( 2 . 33 < |Z-score| < 3 . 33 ) and large change ( |Z-score| > 3 . 33 ) . Table 2 counts the number of residues with altered rigidity/flexibility . Across the Fabs and CDRs , the overall number of residues with increased rigidity ( both are 52% ) is slightly higher than increased flexibility ( both are 48% ) . These percentages indicate that both the Fab and antibody-binding site , as a whole , maintains a global balance between rigidity and flexibility during affinity maturation . Therefore , Le Châtelier’s principle , stating that an equilibrium shift will occur to offset the perturbation and a new equilibrium is established , can be applied as a rule of thumb to make credible predictions of mutation effects on protein flexibility . That is the effects of affinity-improved mutations on the rigidity⇔flexibility equilibrium within the native state ensemble manifest themselves through enthalpy-entropy compensation as the protein structure adjusts to restore the global balance between the two . It is also interesting to highlight that increased rigidity in CDR-H3 is observed in all three AM antibodies . The increase in CDR-H3 rigidity is in good agreement with a backbone entropy study for immunoglobulin ( CDRs ) from the crystal structures of 34 low-affinity T-cell receptors and 40 high-affinity Fabs . Specifically , it has been demonstrated that loss of backbone entropy in CDR3 correlates significantly with the kinetic and affinity constants of the 74 selected complexes [11] . CDR-H3 likely plays a critical role in determining the evolution of antibodies because junctional amino acids introduced by imprecise joining in the combinatorial rearrangement of VH , DH , and JH genes provide increased diversity of CDR-H3 [57] . Interestingly , structural analysis ( Fig 2 ) shows that the antigens in all three AM antibodies contact directly with the CDR-H3 , which suggests that the lower entropic penalty upon binding due to increased rigidity of CDR-H3 is most likely related to the affinity maturation and specificity to a specific antigen . Similar results were presented by Manivel et al [17] who found that antibody maturation essentially reflects modulations in entropy-control of the association , but not dissociation , step of the binding [12] . Another consistent change in all the three cases is the increased flexibility of CDR-L2 . From a structural viewpoint , the CDR-H3 loop is located in the center of the Ag-combining site facing CDR-L2 , it is possible that this loop not only affects the flexibility but also controls the angle between the VH and VL domains . Zimmermann et al . [10] reported that the whole antibody-binding site is rigidified during affinity maturation while our study indicates that an increase in rigidity only occurs in the CDR-H3 loop , but CDR loops in the light chain tend to become more flexible during affinity maturation . The Z-scores are mapped to structure using the same stratification of changes indicated above ( cf . Fig 5 ) . Note that changes tend to occur primarily in loop regions of the variable domains especially in CDRs . In FA , the CDR-H3 loop is moderately rigidified , whereas H1 and L2 both become flexible . In CA , both H1 and H3 loops become rigid , whereas L2 becomes flexible . In EA , all three heavy chain CDRs become rigid , whereas all the light chain CDRs become flexible . Taken together , our results show a wide array of flexibility and rigidity changes , but general trends of the VH domains becoming more rigid and the VL domains becoming more flexible is observed . It is also interesting to point out that while all of the mutations occur in the Fv fragment , there are changes in the Fc in all three cases . The mix of increased rigidity and flexibility occurring in the CL domain of FA is particular noteworthy ( cf . Fig 5A ) . Since the H-bond network ( HBN ) is a critical component to protein rigidity , we characterize the changes that occur in the HBN in response to somatic mutation to better understand the observed rigidity differences . We track H-bonds across the MD trajectories by comparing densities between GL and AM in each pair . The propensity of a H-bond to form between a specific potential donor-acceptor pair is measured by the fraction of occurrence of this H-bond over the 2 , 000 frames . For example , if a H-bond occurs in all 2 , 000 frames , the propensity of the H-bond is 1 , whereas the value is 0 . 5 is assigned if it only occurs in half of the frames . The propensity for a residue to be involved in H-bond formation is defined as the sum of all H-bond propensities formed by its atoms . The HBNs for the GL and AM antibodies are provided in S4 Fig . The HBN differences are greater outside of secondary structures , while secondary structure H-bonds are more similar . Not surprisingly , this suggests that the preservation of secondary structure H-bonds are largely responsible for backbone flexibility to be well conserved , and why FI aligns well with secondary structure elements . Conversely , the largest H-bond differences involving side chains elucidate significant differences in rigidity properties . That is , a change in a handful of critically placed side chain H-bonds can drastically alter mechanical linkage properties . Fig 6 plots the difference of H-bonds for each residue between the GL and AM antibodies along the antibody sequences . Positive values represent more H-bonds formed in the AM antibodies , and negative values indicate more H-bonds in the GL antibodies . In the H chain , 27 residues gain at least one H-bond by somatic mutations and seven of them increase by two; simultaneously , 14 residues lose at least one H-bond , and three lose at least two H-bonds in the AM forms . Changes in the L chain are skewed in the opposite direction—20 residues gain at least one H-bond by somatic mutations ( four residues gain two ) , while 21 residue lose H-bonds ( 7 of which decrease by two or more ) . This indicates that the somatic mutations significantly enhance the HBN in the H chain , but slightly weaken it in the L chain , which parallels the overall rigidity/flexibility changes . To further characterize the relationship between the HBN and flexibility , we compare the Z-score versus the difference in H-bond propensity for each residue ( S5 Fig ) . An important feature that cannot be over emphasized is that local H-bond propensity along the backbone does not correlate well to backbone flexibility . In particular , residues with significant rigidity/flexibility typically do not possess significant H-bond propensity changes due to the long-range nature of network rigidity . That is , rigidity changes from H-bond differences propagate through the network to affect distal residues mainly because H-bonds crosslink constraint topology . This crosslinking property of H-bond constraints distinguishes their effect from torsion constraints that model the influence of residue packing . Nevertheless , as shown in the figure , a local increase or decrease of two or more H-bonds at a specific residue location is a statistical indicator for a concomitant increase in rigidity or flexibility at that location . However , this statistical bias holds only for extreme outliers since most propensity changes are well within a two H-bond variation , and even for these outliers , exceptions remain . This result shows that the complexity of rigidity/flexibility changes is directly linked to the details of the HBN ( or the entire constraint network ) as a whole , rather than a local backbone characterization of the HBN . Some detailed cases are now considered and discussed . Fig 7 demonstrates how mutations lead to significantly increased rigidity/flexibility by forming/breaking H-bonds . In FA , mutation VL H39R forms two new H-bonds to the VH Asp106 leading to increased rigidity within CDRs H3 ( Fig 7A ) . In CA , the VH S31R and VH Y101D mutations form two new H-bonds leading to increased rigidity within CDRs H2 and H3 , respectively ( Fig 7C ) . Finally , in EA , mutation VH N56D forms two new H-bonds to the VH Arg50 leading to increased rigidity within CDRs H2 ( Fig 7E ) . Therefore , in two of the three cases , the increased rigidity of CDR-H3 is mainly due to the local strengthening of the HBN . These local changes cause the whole loop to become rigid , meaning adjacent residues also become more rigid . This result demonstrates that the increased rigidity of CDR H3 could be obtained by introducing mutations directly forming H-bonds with the residues in this loop , which is in agreement with the multi-constraint design study for a set of antibodies that suggests proposed amino acid mutations along the CDR H3 loops for increasing the rigidity of the CDR H3 loop in the bound conformation to reduce its mobility [58] . By contrast , the increased rigidity of H3 in EA is not directly caused by an identified set of new H-bonds that form locally . Rather , increased rigidity in H3 is a consequence of a multitude of small but well distributed changes throughout the protein that supports the propagation of rigidity to H3 . Note that flexibility within L2 increases significantly in all three cases . In EA , the VL D55H mutation leads to the loss of two strong H-bonds between VL Asp55 and VL Arg46 ( Fig 7F ) . Conversely , in FA and CA there are no significant local HBN differences . Increased flexibility upon affinity maturation is also observed in non-CDR loops . For example , increased flexibility in a pair of CA loops is associated with the loss of H-bonds between VL R60H and VL Asp81 ( Fig 7D ) . Similarly , a non-CDR loop in the H chain ( residue 74–79 ) of FA becomes more flexible due to the VH S32Ymutation within H1 ( Fig 7B ) . In the process of pinpointing specific H-bond differences that are responsible for the observed changes in flexibility , it is worth noting that Arg-to-Asp salt bridges do play an important role . In some cases , somatic mutations introduce both donor and acceptor ( e . g . the VH S31R and VH Y101D pair of mutations in CA ) or just one ( e . g . , the VL H39R in FA interacts with an Asp present in the GL antibody ) . Surprisingly , there are even examples of changes in Arg-Asp salt bridges occurring at positions that are not altered by affinity maturation , indicating that global changes in the network from the mutations occurring elsewhere lead to these new favorable interactions . For example , the salt bridge between VH Arg74 and VH Asp76 of FA is lost due to an aromatic mutation introduced within H1 that drastically affects the carboxylate side chain position of Asp76 . Interestingly , it has been previously demonstrated that Arg-Asp salt bridges are the most frequent type occurring within antibody-antigen interfaces [59] . Keeping in mind that rigidity and flexibility fundamentally characterize different aspects of protein dynamics , Fig 8 shows the RMSFs of the three GL/AM antibodies . In FA and CA , the RMSFs of the GL CDR-H3 loops are both slightly higher than those of AM CDR-H3 , revealing that the increased rigidity therein is accompanied by a decrease in motion . However , the corresponding RMSFs in EA actually increase upon maturation , meaning that rigid-body motions are present . S6 Fig shows the comparison of changes in flexibility and mobility by plotting the ΔRMSFs against ZSCORE . It is observed that decreased mobility can be consistent with decreased flexibility . This is especially true for the H chain of CA . A few residues with ΔRMSFs < -1 Å in the AM form possess ZSCORE < -2 . 3 . However , in general , there is no significant correlation between the changes of mobility and flexibility , underscoring the differences between the two views of protein dynamics . Cooperativity correlation ( CC ) plots characterize mechanical couplings between residue pairs , providing a snapshot of allostery . It is worth pointing out that a particular rigid cluster can itself be very mobile as a rigid body , indicating the motion of all residues therein are highly correlated through the rigid body movement . When a pair of residues is flexibly correlated , random thermal motions of one residue is readily channeled into pathways dictated by how flexibility propagates through the protein to other residues , and vice versa . The rigidity network analysis highlights pathways defined by the native state ensemble of constraint topologies , but the mobility of atoms is not determined . Note , however , that molecular contacts can decrease mobility within flexible regions . As an analogy , a rigidity analysis would characterize the wiggling of fingers on a single hand as partly correlated , whereas the finger motions from two separate hands are uncorrelated . However , if the hands are clasped , the mobility of all fingers is greatly diminished due to being packed in an interlaced fashion . Thus , the CC-plot identifies channels of communication that are intrinsic to the skeletal structure of the protein , but the amplitude of motions that run through these channels is not quantified . In other words , thermodynamics and mechanics are quantified in QSFR , not kinetic properties . The CC plots of the three antibody pairs are provided in S7 Fig , revealing that different domains are often flexibly correlated and the CDRs within each domain can be highly correlated as well . These correlations are expected to be important for function . In addition , except for the GL form of EA , the VH domain is primarily composed of one large rigid cluster , punctuated by several flexible loops . Conversely , the VL domain is significantly more co-flexible throughout the dataset . The CH domains are similar to the VH domains , but slightly more co-rigid . Across the dataset , the most co-rigid domains are the FA and CA CL domains . Changes within CC highlight the sensitivity of rigidity properties to mutation , which is consistent with a number of our prior works [20 , 21 , 60] . Fig 9 plots ΔCC values represented by Z-scores per pixel for each of the antibody structures . Blue coloring indicates residue pairs that are more likely to be rigidly correlated , whereas red indicates residue pairs more likely to be flexibly correlated . Somatic mutations considerably increase rigid correlations between the CDRs of the VH domain such as the CDR-H1 and H3 in CA and all the three H-chain CDRs in EA . Meanwhile flexible correlations between different domains are enhanced . This result is consistent with a previous covariance analysis of molecular dynamic trajectories demonstrating that antibody motions in both CDRs and framework regions are correlated and that this correlation is stronger in the AM antibody [16] . Our results provide additional evidence that the correlations are enhanced during maturation , which makes sense due to the belief that these couplings are related to antigen specificity [61] . Note that most of the increases in backbone rigidity occur at locations where increased rigidly correlations also occur , indicating that the observed H-chain CDR rigidity increases are coupled . This leads to a cooperative mechanism that results in increased antigen specificity and affinity . Starting from the GL antibody , the maturation process accumulates multiple mutations by repeated affinity maturation triggered by a specific antigen . GL antibodies are often polyspecific [6 , 62–64] , which is likely due to increased conformational flexibility . For example , comparisons bound and unbound GL antibodies show much greater conformational changes compared to the conformational changes that occurs within the corresponding AM pair [3] . Keeping with this , our results demonstrate that conformational flexibility is an intrinsic property of GL—specified CDR H3 sequences , and significant conformational change within the antibody-binding site especially restraining the H3 loop is the principal consequence of affinity maturation in the three considered examples . Antibody maturation typically accumulates multiple mutations ( 10–20 ) in the course of the conventional immune response by iterative mutation and selection triggered by a specific antigen . As expected , the large sets of GL AM mutants have greater collective responses compared to the relatively small number of mutations characterized in our previous study of the anti-lymphotoxin-β receptor antibody variants [26] . That is , successive cycles of mutations during the maturation process are needed to substantially alter flexibility characteristics of the Fab . Note that many somatic mutations across the dataset are located in the unstructured loops , which are more flexible than secondary structures ( Fig 4 ) . This suggests that the preferred introduction of mutations in GL loops during maturation is a primary driving force to the observed difference . Interestingly , the observed changes across our dataset seldom significantly alter global flexibility properties within the antibody Fab and the antibody-binding site ( Table 2 and Fig 5 ) . For example , while VH is rigidified , the VL domain typically becomes more flexible . This near “zero-sum game” implies that the change of conformational diversity of antibody evolution again follows Le Châtelier’s principle [26] . That is , counteracting changes in rigidity and flexibility will occur at remote sites to globally restore the balance between rigidity and flexibility within protein structures [65] . Strikingly , our results show that both the co-rigid and co-flexible couplings between residues are enhanced during evolution , which suggest higher specificity require tighter collaboration between different structural components . This likely explains , at least in part , why multiple mutations are required for affinity maturation . Computation-guided affinity maturation is an appealing approach toward antibody engineering . Currently , most efforts focus on improving the association between receptor and substrate by optimizing their interactions using force-field-based energy functions [66–70] . However , the current success rate is rather modest because of the inaccuracy of force fields and complexity of interaction network . The mechanism of evolution-mediated conformational changes provides common features of affinity-matured antibodies that could be tracked to guide the design of high affinity antibodies by introducing local and distal mutations . One can imagine that simply introducing local mutations that rigidify the desired CDR loops to fix the optimal binding site conformations could increase the binding affinity to an antigen . This is partially true because the local neighborhood of a mutation will accommodate the new residue respecting local geometrical constraints and network constraints imposed by the protein . However , the final effect on rigidity and flexibility is a mix of both strengthening and weakening effects that occur over both short and long distances . Given that the molecular details that involve multiple mutations during evolution are complicated , a rapid high throughput computational method that does not rely on local propensity properties is required . The mDCM has provided insight into the process , and may provide a useful tool to assess the effects of mutations within antibody design algorithms . In ongoing work , we are characterizing how mutations affect the allosteric response to surface interactions within antibody fragments . These characterizations reveal a similarly diverse set of antibody-substrate interactions , meaning that antibody maturation likely also has a significant effect on intramolecular couplings . Future work will determine if this is the case . | Antibodies are protective proteins used by the immune system to recognize and neutralize foreign objects through interactions with a specific part of the target , called an antigen . Antibody structures are Y-shaped , contain multiple protein chains , and include two antigen-binding sites . The binding sites are located at the end of the Fab fragments , which are the upward facing arms of the Y-structure . The Fab fragments maintain binding affinity by themselves , and are thus often used as surrogates to student antibody-antigen interactions . High affinity antibodies are produced during the course of an immune response by successive mutations to germline gene-encoded antibodies . Germline antibodies are more likely to be polyspecific , whereas the affinity maturation process yields monoclonal antibodies that bind specifically to the target antigen . In this work , we use a computational Distance Constraint Model to characterize how mechanical properties change as three disparate germline antibodies are converted to affinity mature . Our results reveal a rich set of mechanical responses throughout the Fab structure . Nevertheless , increased rigidity in the VH domain is consistently observed , which is consistent with the transition from polyspecificity to monospecificity . That is , flexible antibody structures are able to recognize multiple antigens , while increased affinity and specificity is achieved—in part—by structural rigidification . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"and",
"Discussion"
] | [] | 2015 | Rigidity Emerges during Antibody Evolution in Three Distinct Antibody Systems: Evidence from QSFR Analysis of Fab Fragments |
The AmtB channel passively allows the transport of NH4+ across the membranes of bacteria via a “gas” NH3 intermediate and is related by homology ( sequentially , structurally , and functionally ) to many forms of Rh protein ( both erythroid and nonerythroid ) found in animals and humans . New structural information on this channel has inspired computational studies aimed at clarifying various aspects of NH4+ recruitment and binding in the periplasm , as well as its deprotonation . However , precise mechanisms for these events are still unknown , and , so far , explanations for subsequent NH3 translocation and reprotonation at the cytoplasmic end of the channel have not been rigorously addressed . We employ molecular dynamics simulations and free energy methods on a full AmtB trimer system in membrane and bathed in electrolyte . Combining the potential of mean force for NH4+/NH3 translocation with data from thermodynamic integration calculations allows us to find the apparent pKa of NH4+ as a function of the transport axis . Our calculations reveal the specific sites at which its deprotonation ( at the periplasmic end ) and reprotonation ( at the cytoplasmic end ) occurs . Contrary to most hypotheses , which ascribe a proton-accepting role to various periplasmic or luminal residues of the channel , our results suggest that the most plausible proton donor/acceptor at either of these sites is water . Free-energetic analysis not only verifies crystallographically determined binding sites for NH4+ and NH3 along the transport axis , but also reveals a previously undetermined binding site for NH4+ at the cytoplasmic end of the channel . Analysis of dynamics and the free energies of all possible loading states for NH3 inside the channel also reveal that hydrophobic pressure and the free-energetic profile provided by the pore lumen drives this species toward the cytoplasm for protonation just before reaching the newly discovered site .
The transport of ( NH4+ ) ammonium and/or ( NH3 ) ammonia ( we will refer to both of these species together as Am ) across biological membranes is a homeostatic necessity in both prokaryotes and eukaryotes [1] . In the case of many different plants , bacteria , and fungi , Am serves as a readily available nitrogen source for biosynthetic purposes . On the other hand , at high concentrations , it becomes cytotoxic , especially in animal cells . The family of Am transport proteins—ammonium transporters ( Amt ) in plants and bacteria , methylamine permeases ( MEP ) in yeast , and rhesus ( Rh ) proteins in animals—serves to facilitate the permeation of Am across the membrane . Plant [2–5] and yeast [6 , 7] Amt/MEPs as well as many bacterial [8–10] Amts take in Am in a membrane electrochemical potential–dependent manner in order to utilize it . In humans , the related Rh proteins are split into two groups: erythroid ( RhAG , RhD , and RhCE ) —expressed on the erythrocyte surface [11 , 12] where they perform immunogenic and structural roles , and nonerythroid ( RhCG , RhBG , and RhGK ) —expressed in the kidneys , liver , and testes where they aid in disposal of ammonium and regulation of pH [13 , 14] . Many years of study have shown that while members of the Am transporter family share homologous sequences and structures , it does not necessarily follow that they conduct Am using the same mechanism [15] . Whether particular members of the family transport Am in its ionic ( NH4+ ) or “gas” ( NH3 ) form remains a subject of debate [15] . It is also suggested that some members of the family transport H+ and NH3 in a coupled fashion [4 , 10 , 16] . Recently , X-ray diffraction studies [17 , 18] have revealed the atomic structure of the bacterial Am transporter , AmtB , from Escherichia coli , providing a quantum leap in our understanding of the permeation of Am in the form of NH3 . Even though its tomato plant homolog , LeAMT1;2 does not appear to share the NH3 transport mechanism , the fact that AmtB's human ( kidney ) homolog does transport NH3 rather than NH4+ means that these new crystallographic structures will provide an excellent starting point for understanding the structural basis of Am transport via its “gas” intermediate [15] as it pertains to human physiology . In fact , the newly available structural information on AmtB has already spurred comparative structural and functional modeling of human Rh proteins [19] . In the membrane , AmtB exists as a homo-trimer , with each monomer consisting of a right-handed eleven-helix bundle [17 , 18] . The center of each monomer forms a narrow hydrophobic pore connecting cytoplasmic and periplasmic depressions ( vestibules ) . The diffraction studies revealed a NH4+ binding site ( named “Am1”[17] ) on the periplasmic depression where the cation donates a hydrogen bond to the hydroxyl oxygen of a Ser residue ( S219 ) and is putatively stabilized by cation–π interactions with aromatic residues ( W148 and F107 ) . Though the narrow hydrophobic pore ( lumen ) connecting the inner and outer vestibules of the X-ray structure was seen to accommodate three poorly ordered NH3 binding sites ( named Am2 , Am3 , and Am4 , respectively [17] ) , the opening from the periplasmic vestibule to the pore was apparently blocked by two stacked Phe sidechains ( F107 and F215 ) , rendering the means of NH4+ deprotonation and translocation unclear . The fact that the pore was lined by two His residues ( H168 and H318 ) hydrogen bonded to one another led to the suggestion that NH4+ can penetrate deep into the pore up to the middle site , Am3 , before undergoing His-mediated deprotonation to form NH3 [17] . However , no site or putative mechanism for reprotonation of NH3 to form a cation was found . Nor was any binding site for NH4+ found in the cytoplasmic vestibule . It was also found very unlikely that water occupies the hydrophobic pore , further supporting the notion that AmtB transports NH3 instead of NH4+ . Since AmtB passively transports Am along an electrochemical gradient and was not seen to undergo conformational changes upon occupying different permeant species , it was deemed safe [17] to consider the family of Amts as “channels . ” The new structural knowledge of AmtB has facilitated computational studies [18 , 20–23] aimed at understanding aspects of Am transport through the channel . Of particular interest in these studies is an Asp residue ( D160 ) on the periplasmic side of the channel , which is highly conserved across Amt/MEP/Rh proteins . Mutational studies have shown this residue to be functionally crucial for Amts as well as MEP and Rh proteins [24 , 25] . Although it was proposed that D160 likely participates in the periplasmic binding site for NH4+ [24] , X-ray structures revealed that the carboxylate side chain does not participate in the Am1 binding site , is sequestered from water , and interacts closely with amide N-H groups of an outer loop ( connecting helices M4 and M5 ) , hence playing an indispensable structural role in luring NH4+ from the periplasm by forcing it to interact with outwardly directed carbonyl oxygens [17 , 18] . A computational free energy perturbation study by Luzhkov et al . [22] demonstrated that the charged form of D160 is stabilized by 0 . 3–5 . 1 pKa units in the absence of a cation at site Am1 . In the presence of a cation at Am1 , the charged form of D160 was seen to be even more stable ( by 9 . 2 pKa units ) . Performing the alchemical mutation , D160N , in agreement with experimental mutational studies [24 , 25] gave rise to a loss of binding for NH4+ at site Am1 , suggesting that the presence of D160 stabilizes cation binding via through-space electrostatic interactions . However , the simulations of Luzhkov did not evaluate the structural role of D160 in facilitating cation sequestration using backbone carbonyls in the M4–M5 loop as suggested by structural studies [17 , 18] . Such an evaluation would , necessarily , involve long simulations of the mutant channel . Thus , it is probably most accurate to say that D160 plays both an important role in stabilizing cation binding at site Am1 as well as stabilizing the M4–M5 loop for recruiting NH4+ from the bulk , and that its structural and cation-stabilizing roles likely cannot be considered separately . Computational studies performed by Lin et al . and Nygaard et al . led to the suggestion of an entirely different role for D160 as a proton acceptor from NH4+ , with either water [20] or the carbonyl oxygen of A162 [23] as an intermediary , based on qualitative inspection of molecular dynamics ( MD ) trajectories . The simulation studies of Luzhkov et al . and Lin et al . suggested that NH3 is highly favored over NH4+ at the first internal binding site , Am2 , while Nygaard et al . suggested that NH4+ must bind to site Am2 before becoming deprotonated , as suggested by Khademi et al . [17] . The available structural data and somewhat disjoint mechanistic proposals based on computational studies lead to questions about the specific details ( NH4+ recruitment , deprotonation to form NH3 , and subsequent NH3 translocation and reprotonation ) of Am transport through AmtB: if NH4+ loses a proton somewhere between the Am1 and Am2/Am3 binding sites , exactly where and how does the deprotonation occur ? What role do the blocking Phe residues ( F107 and F215 ) play ? What role does water play in the periplasmic and cytoplasmic vestibules , and can it permeate the channel ? Given that once Am exists as NH3 in the interior of the channel , the driving force for translocation of NH3 toward the cytoplasmic vestibule to become re-protonated is unclear . What drives this uncharged species toward the cytoplasmic vestibule during the conduction event ? Why are the internal NH3 binding sites ( Am2 , Am3 , and Am4 ) poorly ordered in the X-ray structure of AmtB , and ( on a related note ) can all three binding sites be occupied simultaneously ? Where and by what means does NH3 become protonated on the cytoplasmic side of the pore ? And finally , is there a cytoplasmic binding site for NH4+ ?
Analysis of our simulated system showed that the large dipole moment ( ∼2 , 000 Debye—see Figure S1A ) of the AmtB trimer has an interesting impact on the local ionic concentration at the membrane/electrolyte interface ( Figures 2 , S1 , and S2 ) , which comprises a dielectric response to the protein's electric field . While NH4+ adsorption at the membrane surface was not affected significantly , Cl− adsorption was . Overall , the local concentration of cation near the periplasmic vestibule ( just beneath the lipid headgroup phosphate plane ) was observed to be ∼16 times greater than that near the cytoplasmic vestibule . On the other hand , Cl− was very unlikely to approach either vestibule , and its equilibrium concentration was ∼3 times smaller on the periplasmic side than on the cytoplasmic side ( Figure S2 ) . Hence , without any external electrochemical gradient , we see that the channel is built for recruiting cations from the periplasm at neutral pH on a mesoscopic scale . In addition we see that the channel inhibits anionic adsorption to the periplasmic membrane surface—another mechanism of enhancing cation recruitment . To glean information about cation interaction with the channel interior , as well as specific binding interactions , we combined data from local z-dependent ion density profiles with umbrella sampling data ( Materials and Methods ) to calculate the potential of mean force ( PMF ) governing the transport of a single NH4+ molecule along the channel ( z- ) axis . The PMF profile , shown in Figures 3 and S3 , details the free energy required to bring a single NH4+ molecule to any point within the channel from the bulk electrolyte on either side of the membrane . It clearly shows two free energy minima ( binding sites ) —one on either side of the membrane . While approaching either site from the bulk , the PMF indicates that NH4+ overcomes a very small barrier ( ∼2 RT on either side ) at z ≈ ± 2nm ( z = 0 nm is taken to be the center of mass of the trimer backbone ) . The site on the periplasmic side ( at z = 1 . 07 nm ) of the membrane ( labeled Am1 in Figure 3 ) is at precisely the location of site Am1 in the X-ray structures [17 , 18] , although there are slight differences in the specific interactions . At this site , NH4+ forms hydrogen bonds with the backbone carbonyl oxygen of A162 and the sidechain hydroxyl oxygen of S219 . The “other half” of the cation donates hydrogen bonds to a shell of water molecules , which is stabilized , in turn , by hydrogen bonds to the polar sidechain of Q104 . Just below the cation , F107 provides a “floor” for the Am1 binding site . We also note a feature in the free-energy profile ( circled in blue at z = 1 . 342 nm ) slightly above what is normally referred to as site Am1 . This feature shows a slightly lower free energy than Am1 , and corresponds to interaction with backbone carbonyl oxygens of D160 , F161 , and A162 , which usher NH4+ into Am1 . Figure 3 shows that the carboxylate of D160 accepts hydrogen bonds from the hydroxyl and amide nitrogen of T165 , and from the backbone amide nitrogen from G164 and G163 . These persistent interactions support D160′s structural role in orienting the carbonyl groups of the M4–M5 loop to recruit NH4+ from the periplasm . This is not to say that D160 does not stabilize the cation at the lower site , Am1 , via through-space electrostatic interactions as suggested by Luzhkov et al . [22] , but that its loop-stabilizing role ( for recruitment of cations ) is likely not separable from its electrostatic role . Nygaard et al . noticed in their simulations that NH4+ can occupy different substates ( so-called Am1a or Am1b ) within the Am1 binding site [23] . While , indeed , these substates may exist and contribute to the free-energy profile in Figure 3 , the fact that the Am1 free-energy well is rather smooth implies that the free-energy barriers and differences between any set of substates would not be observable in a macroscopic ( experimental ) binding assay . In fact , the barrier ( and free-energy difference ) between Am1 and the feature circled in blue in Figure 3 is less than 1 RT , which implies that both features should be considered together in any discussion of ion recruitment and binding . Thus , it would make sense , from a free-energetic perspective , to refer to both of these “substates , ” together , as the periplasmic binding site . The PMF in Figure 3 also reveals a cation binding site on the cytoplasmic side of the membrane ( labeled Am5 at z = −1 . 37 nm in Figure 3 ) whose existence was previously unknown according to the latest diffraction and computational studies . A NH4+ molecule at this new site , Am5 , shows an equivalent level of stability to one bound at Am1 , is mostly hydrated ( usually by three to four water molecules ) , and donates strong hydrogen bonds to the carboxyl oxygen of D313 and the hydroxyl oxygen of S263 . A global view of the PMF for NH4+ translocation across the hydrophobic pore between the inner and outer vestibules reveals that NH4+ permeation would be extremely rare ( Figure S3 ) . The free energy barrier for NH4+ entering the lumen from either the periplasm or cytoplasm is prohibitive ( >50 RT to reach the NH3 sites Am2 , Am3 , or Am4—see Figures 3 and S3 ) . The free-energy barrier preventing complete translocation of NH4+ is astronomical ( ∼100 − 150 RT , see Figure S3 ) , and is located near the center of the pore around the central NH3 binding site , Am3 . We also calculated the PMF profile associated with translocation of a single NH3 molecule from the hydrated periplasmic vestibule to the cytoplasmic vestibule ( see Figure 4 ) . The profile produces three minima ( sites ) that were found in exactly the NH3 positions of Am2 ( z = 0 . 17 nm ) , Am3 ( z = −0 . 27 nm ) , and Am4 ( z = −0 . 68 nm ) from diffraction studies [17] , thus we label them accordingly . Small “oscillations” in the profile are observed at z ≈ 0 . 4 − 1 . 0 nm corresponding to NH3 passage across the phenyl groups , F107 and F215 , forming the “ceiling” of the pore . A similar feature is observed on the cytoplasmic end of the pore just after site Am4 at z ≈ −1 . 1 nm corresponding to NH3 passage across the phenyl side chain of F31 , forming the “floor” of the hydrophobic pore ( lumen ) . Residues H168 and H318 are seen to form stabilizing interactions with NH3 at each binding site ( Figure 4 , bottom ) . The most stable site at single NH3 occupancy is seen to be Am4 , at −3 . 46 RT units . The barrier for bringing NH3 from Am4 to one of the NH4+ sites ( Am1 or Am5 ) is drastically lower ( ∼9 − 16 RT , see Figure 4 ) than the barrier for bringing NH4+ to any one of the NH3 sites ( Am2 , Am3 , or Am4 , see Figure S3 ) . The structure of the pore was seen to be structurally invariant regardless of whether a single NH3 molecule occupied Am2 , Am3 , or Am4 ( Figure 5 ) . Unlike other channels such as the K+ channel , whose conformation and activity is highly dependent upon the occupancy of permeant species [30 , 31] , the AmtB channel architecture is stiff enough to maintain the structure of all three binding sites ( Am1 , Am2 , and Am3 ) , regardless of the occupancy of the pore . This makes our analysis of the pore convenient , because it means that the positions of the binding sites do not change even at higher occupancies . Thus , we may rely on a 1-D PMF ( Figure 4 ) to enumerate the possible NH3 loading states . Since we know the free energy as a function of the transport axis in the case of either the protonated or unprotonated form of Am , it is possible to glean the apparent pKa of NH4+ ( pKa ( z ) ) at all points along the transport pathway . This will be particularly useful in identifying precisely where its deprotonation occurs , and may even tell us how . The free-energy cycle in Figure 6A implies that we may calculate pKa ( z ) using Equation 1 ( assuming RT units ) , where pKa ( bulk ) is the pKa of NH4+ in the bulk ( known experimentally to have a value of 9 . 25 ) , far away from the membrane surface , , is the free energy for the alchemical reaction NH4+ → NH3 in the bulk , far from the membrane , and is the free energy for the same reaction at a particular position , z , along the transport axis ( “dp” stands for “deprotonation” ) . We used the method of thermodynamic integration to obtain the necessary free-energy values ( shown in Table 1 ) leading to the appropriate difference curve from the PMF profiles in Figures 3 and 4 ( see Materials and Methods ) yielding pKa ( z ) . An explanation of the meaning of “apparent” pKa ( or −log Ka ) may be useful to the unacquainted reader . For a position , z , along the transport axis , the equilibrium constant for the proton dissociation event , NH4+ → NH3 + H+ , is K = [NH3]z[H+]z/[NH4+]z , where the square brackets denote populations ( or concentrations ) . This quantity is not particularly useful experimentally ( due to the ill-defined nature of the local pH or [H+]z ) or in conventional MD simulations ( due to the inherent design of water models , which represent only neutral pH ) . Thus , we turn to the “apparent” equilibrium constant , Ka = [NH3]z[H+]bulk/[NH4+]z to describe the favorability for Am to exist in its protonated or unprotonated form . This allows us to use the fixed value for pH for the bulk , which is well-represented by our force field , pHbulk = 7 . With this definition , a ( de ) protonation event will occur at the so-called “equivalence point , ” when the apparent pKa = pHbulk , or , in other words , when pKa = 7 . One must note that although , normally , the auto-ionization of water provides a range of possible values for the pKa of 0–14 in aqueous solution , in a nonaqueous environment ( say , in the absence of water or in an environment where water is nonbulk-like ) this range does not apply . The resulting NH4+ pKa profile is shown in Figure 6B . Interestingly , the apparent pKa is shifted upward by ∼5 units near the membrane surface and around sites Am1 and Am5 . More precisely , using values from Table 1 , the shift in pKa is +4 ( ±2 ) units at site Am1 ( in agreement with a previous calculation [22] ) and +5 ( ±3 ) units at site Am5 . This upward shift , disfavoring deprotonation , is not unexpected for several reasons . First , any region that stabilizes or binds NH4+ will necessarily foster the protonated form of Am . This is true , not only for the binding sites , Am1 and Am5 , but also for the membrane surface itself ( see Figures 2 and S2 ) , which is seen to bind cations strongly—a result commonly seen at the membrane/electrolyte interface [26–29] . In addition , water has been shown to be highly polarized near the membrane surface and shows very different electrostatic properties than seen in the bulk [32] . The above arguments rationalize the upward shift in pKa near the membrane surface and stabilization of Am in its protonated form at the Am1 and Am5 binding sites . Note that at some regions in the curve the apparent pKa is seen to have values larger than 14 , which violates limits implied by the auto-ionization of water . At these regions , RT units , and deprotonation may be considered impossible . In addition , it is worth noting that the uncertainty accumulated in the thermodynamic integration calculations ( Table 1 ) and in shifting the PMF profiles leads to an uncertainty in the pKa shift of ∼3 units . In the center of the hydrophobic pore containing sites Am2 , Am3 , and Am4 ( Figure 6B , vertical red bars ) , we see that the pKa of NH4+ becomes negative , again violating limits implied by water auto-ionization . In this region , RT units , which implies that it is impossible for Am to exist in its protonated form . In fact , the pKa profile indicates that Am must exist as NH3 well before reaching site Am2 on the periplasmic end , and well after leaving site Am4 on the cytoplasmic end of the pore ( see Figure 6B , in between the vertical green bars ) . This result contrasts with qualitative analyses of static X-ray [17] or dynamic [23] channel structures , which posit that NH4+ can reach site Am2 before becoming deprotonated . The equivalence point is where one would expect to find Am in the form of NH3 and NH4+ with equal probability , and is marked by the two green vertical bars in Figure 6B ( we also show the bars in Figures 3 and 4 ) . At neutral bulk pH , this is where deprotonation of NH4+ occurs . The width of each bar covers the range of certainty ( ∼3 pH units ) for which the apparent pKa is equal to the bulk pH , or , in our case , where pKa ( z ) = 7 . Since the pKa ( z ) profile is extremely steep in this region , we may narrow down the deprotonation regions to very specific locations for both the periplasmic and cytoplasmic sides , despite the uncertainty in the pKa profile . On the periplasmic end of the channel , the site of deprotonation is immediately beneath site Am1 at z = 0 . 77 ± 0 . 08 nm ( with respect to the center of mass of the trimer backbone ) . This site coincides exactly with the position of the phenyl side chain of F107 ( see Figure 6C ) . As NH4+ travels from site Am1 toward the cytoplasm , its pKa drops so steeply that it must become NH3 before passing the second phenyl group , F215 . Based on Figure 4 , the process of bringing NH4+ from site Am1 to the deprotonation region corresponds to crossing a free high-energy barrier of ∼15 RT units , which supports the experimental observation of very slow Am translocation rates of 105–108 ns/molecule [18] . If we observe the average hydration number of Am as a function of the transport axis ( Figure 6D ) , we can rationalize the extremely unfavorable ( 15 RT ) barrier to translocation and drastic drop in apparent pKa . In the bulk , NH4+ is hydrated by six water molecules , and at site Am1 ( vertical gray bar at z ∼ 1 . 07 nm ) it loses two to three water molecules from its hydration shell to form hydrogen bonds with A162 and S219 ( Figure 3 ) , but as the external electrochemical gradient pushes it toward the deprotonation site , its hydrogen bonds are stripped away such that eventually the only available acceptors are one to two water molecules , and the carbonyl oxygens of A162 and F215 . The transition state for the NH4+ to NH3 transformation at the deprotonation site may be most accurately represented ( given our classical molecular description ) by Figure 7 , where the F107 phenyl group is seen to allow passage by rotating in response to the presence of the cation . In this configuration , NH4+ is stripped to three hydrogen bonds with one water molecule and the carbonyl groups of A162 and F215 , and must release one of its protons in order to favorably continue toward the cytoplasm as NH3 . Previous works have suggested that the proton is donated to A162 [23] and , eventually , to D160 [20 , 23] . However , at this deprotonation site , NH4+ has access to periplasmic vestibular water , which connects to the bulk in a continuous fashion ( Figures 6D and 7 ) . Thus , it is much more likely to pass its proton directly to water , so that the proton may escape directly to the periplasm in the form of hydronium ( cutting out the “middle man” ) . This conclusion is strongly supported by ( 1 ) the fact that water ionizes more easily than a carbonyl group; ( 2 ) the fact that at the transition state ( Figure 7 ) the carboxylate of D160 is persistently engaged in hydrogen bonds with the hydroxyl and amide nitrogen of T165 , and the amide nitrogens of G163 and G164; and ( 3 ) the finding that these strong interactions shift the pKa of the D160 sidechain downward by ∼9 pH units , making its protonation effectively impossible [22] . Therefore , it appears that the role of D160 is not to accept a proton , but , as mentioned in structural analyses [17 , 18] , to force recruitment of cations from the bulk by orienting periplasmic M4-M5 loop carbonyl groups , and by electrostatically stabilizing NH4+ at site Am1 [22] . We again emphasize that these roles for D160 are likely not mutually exclusive . It is also interesting to note that Figure 6D shows that the average hydration number of Am at the periplasmic deprotonation region ( vertical green bar at 0 . 77 nm ) drops immediately from 1–2 to 0 when NH4+ becomes NH3 , as if this transition immediately allows Am to shed its final water to favorably enter the hydrophobic pore . In this sense , even though the reaction occurs in a condensed phase , it is much like the reaction that would occur when transferring Am from aqueous solution to a gas phase . The process of bringing Am from site Am1 ( as NH4+ ) to site Am2 ( as NH3 ) is summarized in Figure 8A . After NH4+ is dehydrated and deprotonated at the periplasmic deprotonation region ( near F107 ) , it continues as NH3 , completely dehydrated ( Figure 6D ) , and moves across sites Am2 , Am3 , and Am4 . The consistent slope in the PMF ( Figure 4 ) , although all three NH3 binding sites provide stable sites , pushes NH3 toward the most stable site , Am4 , on the periplasmic end of the hydrophobic lumen . Here , further passage is hindered by the phenyl group of F31 . This sidechain , forming the “floor” of the hydrophobic lumen provides an ∼10 RT free-energy barrier ( see Figure 4 ) for NH3 reaching the cytoplasmic ( de ) protonation region at z = −1 . 13 ± 0 . 07 nm ( see the corresponding green bar in Figure 6B–6D ) . This region nearly coincides with the position of the phenyl group of F31 ( Figure 6C ) . The nature of the barrier to reprotonate NH3 is seen to be similar in origin to that for deprotonation of NH4+ near F107 . Namely , it requires work to force NH3 toward the water-accessible cytoplasmic vestibule just beneath the F31 phenyl “floor” of the lumen . Presumably , larger NH3 luminal occupancy ( i . e . , more than 1 NH3 ) would lower the barrier to reprotonation due to mutual destabilization of NH3 molecules ( we discuss this later ) . The protonation of NH3 to form NH4+ at the reprotonation region ( green bar in Figure 6D ) is coupled to a gain in hydration from 0 ( coordinating NH3 ) to 2–3 ( coordinating NH4+ ) water molecules and the gain of one hydrogen bond to the hydroxyl oxygen of S263 ( see Figure 6D and Figure 8B3 ) . Thus , again , the only plausible species involved in the protonation event is water . The process of bringing Am from site Am4 ( as NH3 ) to site Am5 ( as NH4+ ) is summarized in Figure 8B . Given the results presented thus far , it is clear that phenyl side chains play a key role at both the periplasmic and cytoplasmic ( de ) protonation regions . At the periplasmic ( de ) protonation region , the presence of the phenyl sidechain from F107 , though it rotates to allow passage , provides a constriction in the Am pathway such that most of its hydrogen bond acceptors ( including water ) are removed ( Figure 7 ) . Given the sequence of events observed in Figure 8A , it appears that once NH4+ donates a proton to neighboring water and moves slightly farther into the lumen , the F107 side chain guards against hydration while the second phenyl side chain from F215 rotates to allow passage of NH3 . Together , F107 and F215 act as a “double doorway , ” aiding to ensure dehydration of the passing Am species . In a similar manner , at the floor of the lumen , F31 guards against hydration from the cytoplasm ( Figure 8B ) . During 27 . 5 ns of equilibration and 25 ns of production MD with no intraluminal Am species , neither water nor NH4+ was seen to spontaneously enter the hydrophobic lumen ( see Figure S4 ) . During umbrella sampling simulations where NH4+ was forced to sample the lumen , it was seen to carry along anywhere from zero to three water molecules ( compared with six in the bulk , see Figure 6D ) . However , as mentioned before , the free-energy barrier for NH4+ entering the lumen from either the periplasm or cytoplasm is prohibitive ( >15 RT to reach the deprotonation region and >50 RT to reach the NH3 sites Am2 , Am3 , or Am4 , see Figures 3 and S3 ) . On the other hand , NH3 is quite “happy” to be completely dehydrated as it occupies the lumen ( see Figures 4 and 6D ) . Therefore , we can conclude that the NH4+ ion's ability to deprotonate to form NH3 imparts Am with the ability to permeate the AmtB channel in a selective fashion . That is , permeation of Am or methylated Am will be greatly preferred over other aqueous inorganic ionic species because deprotonated Am ( or methylated Am ) can favorably occupy the hydrophobic lumen . Based on our calculations , we can expect prohibitive free energy barriers ( >50 RT ) for permanently charged species to enter the lumen . Thus , without a doubt , this is the most robust rationale for the AmtB channel's display of selective permeability for Am over all permanently charged species ( e . g . , Family IA and IIA cations such as K+ or Na+ ) , and is the most plausible source of the “Am-sensing” property of this channel [24] . With that being the case , the Phe residues , F107 , F215 , and F31 , play a role in selective permeation , not by providing a larger relative stability at the periplasmic/cytoplasmic binding sites ( Am1/Am5 ) for NH4+ over other cations , but by aiding in the removal of water at the deprotonation region of the transport pathway . Whether other ions can competitively inhibit binding , and thus recruitment , of NH4+ to the periplasmic site , Am1 , is a different question altogether ( aside from selective permeability ) . However , this appears not to be an issue since experimental studies do not observe significant competitive inhibition of methyl-Am uptake by alkali cations in Amt/MEP/Rh transporters [6 , 33] . Recent calculations [22] reported slight selectivity of site Am1 for NH4+ over monovalent Family IA cations , rationalizing experimental observations of mild Am uptake inhibition . With only mild selectivity at site Am1 , it appears that the hydrophobic lumen is the most Am-selective feature of the channel . The finding that Am sheds its water before entering the hydrophobic lumen as NH3 , where it remains dehydrated , contrasts with a recent computational study by Nygaard et al . , which reported that the interior lumen of the hydrophobic pore is hydrated [23] . In that study , two 10-ns simulations , each with different neutral tautomeric states of the intraluminal His pair , H168 and H316 , both demonstrated hydration of the lumen all the way up to the periplasmic phenyl “stack” provided by F107 and F215 . The water entered the lumen in the form of a highly ordered , hydrogen bonded “water wire , ” a very commonly observed arrangement in hydrophobic pores [34 , 35] , which entered the pore via the cytoplasmic vestibule [23] . This observation led the authors to suggest that proton conduction by single-file water might provide a mechanism for H+ exchange with H168 and H316 . In contrast to the study of Nygaard , our MD simulations never displayed this hydration event in any one of the three channels in the AmtB trimer , despite 52 . 5 ns of dynamics ( combining both equilibration and production runs ) . It is possible that a difference in choice of force field ( see Materials and Methods ) may have been the origin of these contrasting results . However , since the study of Nygaard et al . included only Cl− counterions so as to achieve a net neutral system charge ( no electrolyte ) [23] , we find it more probable that the main cause for our different observations is inclusion of NH4Cl electrolyte in our simulated system ( measured to be 158 ± 5 mM in the bulk , see Figure 2B ) . We say this because past work has shown that simulated systems utilizing 3-D periodicity with full Ewald sums for treatment of electrostatic interactions and comprising membrane-bound proteins possessing a significant dipole moment ( greater than ∼300 Debye ) can display unexpected “water-ordering” artifacts [36 , 37] . This water-ordering phenomenon is constant in the bulk , can propagate all the way into the pores of membrane-bound channels , and is caused by the infinite array of persistent dipoles ( caused by the membrane-bound protein in the central simulation cell ) represented by a fully periodic system in 3-D . Normally , the largest dipole component of a membrane-bound protein will fall along the membrane normal ( or transport axis ) . Thus , one way to remove the effect due to the electric field produced by the infinite , 3-D periodic dipolar array is to remove the periodicity in this direction ( the membrane normal is usually taken to be the z-dimension ) , and to simulate in a slab ( effectively 2-D or xy- ) geometry [36] . There are other ways to avoid this artifact . For example , one can add electrolyte to the system ( as we have done ) so that it bears the dielectric response to the protein , rather than the water [37] . One may also choose to use a cutoff treatment of electrostatic interactions [37] , although other artifacts are associated with this method . The addition of counterions alone , though it is important to assure a neutral system charge , does not appear to be enough to avoid the effect [36 , 37] . Nor does the addition of generous layers of water in bulk in an attempt to screen the effect [37] . The dipole moment of the AmtB trimer is several times larger ( ∼2 , 000 Debye , see Figure S1A ) than the membrane proteins for which this phenomenon was first observed , and thus is an excellent candidate for providing an electric field to which the surrounding simulated system must respond . In our system , we observe that the dipole moment of the electrolyte precisely counteracts the dipole of the protein ( Figure S1A ) , leaving a net zero dipole moment for the entire system ( Figure S1B ) . Without the electrolyte , we can expect the water to bear the majority of the burden of the dielectric response to the protein . The next obvious question is: Given that the net force of an electric field on a dipolar molecule is zero , why might this cause water to enter the hydrophobic lumen of AmtB ? Recently , much attention has been given to the occupancy of water in hydrophobic spaces provided by macromolecular assemblies , including narrow hydrophobic pores , cavities , and the hydrophobic membrane interior [34 , 35 , 38 , 39] . It has been shown that , indeed , electric fields directed parallel to the axis of a narrow hydrophobic pore can make a water-filled state favorable , whereas in the absence of a field , it would be unfavorable [35] . In addition , electric fields directed along the bilayer normal can cause water to penetrate the hydrophobic portion of the lipid membrane itself and form water-filled pores—a process known as electroporation [38] . In either case , the electric field induces a preferred water orientation , which increases the probability of observing ordered water defects either at the membrane surface ( in the case of membrane electroporation ) or at the hydrophobic pore entrance ( in the case of pore hydration ) . This appears to increase the probability that water enters the pore . It is also known that hydrophobic pore hydration occurs in an “all-or-none” fashion , well-represented by two-state models [34 , 35] . Once water enters a hydrophobic pore , we can expect that it will become filled . That is , one would assume , unless there is some feature that blocks full permeation . It would appear that the phenyl groups of F107 and F215 provide such a “blocking” feature . The three NH3 binding sites within the hydrophobic lumen proved to be highly disordered in the X-ray structure [17] , leading to uncertainty in understanding whether the sites were occupied alternately with one another during the conductance mechanism , or partially occupied such that at high-bulk Am concentrations they might be fully occupied . According to the PMF for NH3 at single occupancy ( Figure 4 ) , it appears that immediately after the deprotonation event , NH3 is most likely to jump across sites Am2 and Am3 to site Am4 . There it must either overcome a barrier ( ∼37 kJ/mol or ∼15 RT ) to reprotonate and escape back to the periplasm , or overcome a barrier ( ∼25 kJ/mol or ∼10 RT ) to reprotonate and escape to the cytoplasm . Multiple occupancy of the lumen will undoubtedly lower these barriers . We utilized additional thermodynamic integration calculations ( see Table 1 and Materials and Methods ) to draw a state map for the possible NH3-occupied states of the lumen , shown in Figure 9A . For brevity , we adopt a vector representation for the occupied state of the three-site lumen , ( O2 , O3 , O4 ) , where O2 , O3 , and O4 are binary digits indicating whether Am2 , Am3 , or Am4 , respectively , are occupied ( Oi = 1 ) or unoccupied ( Oi = 0 ) . Since NH3 most favorably occupies site Am4 at single occupancy , ( i . e . , state 001 ) , the map suggests that when a second NH3 enters , it will most likely occupy site Am2 ( i . e . , state 101 ) . Figure 9A suggests that NH3 binding at site Am4 when site Am2 is occupied ( the reaction 100 → 101 ) is 7 kJ/mol less-favorable than binding at site Am4 when no other site is occupied ( the reaction 000 → 001 ) . If we assume that the barrier for NH3 escape from Am4 to the cytoplasmic protonation region remains the same ( we will refer to this as the “barrier assumption” ) as calculated for single occupancy ( Figure 4 ) , then it would cost less free energy ( ∼18 kJ/mol or 7RT ) for NH3 to move on to the cytoplasm from state 101 . If NH3 were to dynamically move to a state where Am3 and Am4 are simultaneously occupied ( state 011 ) , we find that NH3 binding at site Am4 when site Am3 is occupied ( 010 → 011 ) is 28 kJ/mol less-favorable than binding at site Am4 when no other site is occupied ( 000 → 001 ) . And with the above barrier assumption , the translocation of NH3 to the deprotonation region from site Am4 would become favorable , requiring ∼−3 kJ/mol or −RT . Needless to say , at full occupancy ( state 111 ) , the same approximation suggests that passage of NH3 from site Am4 to the deprotonation region would be even more favorable ( ∼−10 kJ/mol or −4RT ) . Thus , our results suggest that doubly-occupied NH3 dynamics in the lumen can force the escape of NH3 at site Am4 to the cytoplasmic deprotonation region for successive binding to site Am5 and escape to the cytoplasm . The state most likely preceding the escape of NH3 to the deprotonation region is one where the lumen is occupied at sites Am3 and Am4 ( state 011 ) . We also note , however , that the free energy to attain full occupancy ( the reaction 011 → 111 ) is favorable ∼−9 kJ/mol or −4 RT . In fact , this reaction is more favorable than the escape of NH3 to the cytoplasm . Therefore , it is also possible for the fully occupied state , 111 , to precede escape of NH3 to the deprotonation region . Though , above , we have only discussed the process of NH3 translocation toward the cytoplasm , our results do not preclude translocation toward the periplasm , which is well-known to occur [40] . In fact , at full occupancy ( state 111 ) , it becomes more favorable ( by ∼−13 kJ/mol or −5 RT ) for NH3 to escape from site Am2 to the periplasmic deprotonation region ( i . e . , the reaction 111 → 011 ) than the same reaction at zero occupancy ( i . e . , the reaction 100 → 000 ) . Thus , our results are also consistent with the facilitation of gradient-dependent bidirectional NH3 transport . In Figure 9B , we show a brief demonstration of NH3 dynamics for the fully occupied state of the lumen . Although the channel is fully occupied , it appears that the qualitative trend in binding-site favorability represented by the PMF of Figure 4 for single occupancy persists . The interactions of NH3 with the lumen display a strong tendency to force NH3 toward the cytoplasmic binding site , Am4 . At times , the position of Am4 can be transiently occupied by two NH3 molecules ( see Figure 9B and 9C ) . We can expect slight differences in the free energetics and dynamics of NH3 in the lumen depending on the tautomeric form of the intraluminal histidine residues [23] . The tautomeric configuration represented by our simulations appears to favor translocation toward the cytoplasm , while the study of Nygaard et al . showed that the alternate form for H168 and H318 does not appear to favor translocation in either direction . It is possible that , together , these different forms may be used by the channel to facilitate translocation toward the cytoplasm or toward the periplasm , depending on the electrochemical gradient conditions . Thus , our results suggest that a periplasmic electrochemical gradient will push NH4+ from site Am1 to be deprotonated at the deprotonation region; the hydrophobic NH3 molecules begin to fill up the lumen , resulting in mutual destabilization or a buildup of “pressure , ” which in turn allows NH3 reprotonation to become favorable . This means that the rate-limiting step of Am permeation is the NH4+ deprotonation step at the periplasmic end of the channel , representing an ∼15 RT free-energy barrier ( see Figures 3 and 4 ) that must occur at the position of the F107 phenyl ring , before proceeding into the lumen , just underneath the F215 phenyl ring . Our results show that the global electrostatic nature of the AmtB channel inhibits anion binding to the periplasmic membrane and plays a large role in the recruitment of cations to the outer vestibule . PMF analysis verifies all crystallographically observed [17] Am binding sites and reveals a new cytoplasmic NH4+ binding site , Am5 . At this new site , NH4+ donates hydrogen bonds to D313 and S263 , as well as to 3–4 water molecules . Additional contributions near the periplasmic binding site , Am1 , were found to be due to the backbone carbonyls of the M4–M5 loop . As mentioned in structural analyses [17 , 18] , D160 plays an important structural role by ordering residues in the M4–M5 loop such that their backbone carbonyls may aid in recruiting periplasmic NH4+ . This role persists as NH4+ binds to site Am1 , and as it moves on to be deprotonated . The results of a recent study show that D160 stabilizes NH4+ bound to site Am1 [22] . This observation combined with our own results indicates that the structural role of D160 and its role in binding NH4+ are not mutually exclusive . In contrast to other recent computational studies [20 , 23] , our results do not support the hypothesis that D160 is involved in the deprotonation of NH4+ . Nor does it support the hypothesis of structural analyses [17] that NH4+ deprotonation is mediated by intraluminal His residues ( H168 and H318 ) . Rather , our pKa analysis of NH4+ shows that the deprotonation site must coincide with the position of the phenyl group of F107 . This phenyl group rotates as NH4+ passes and is stripped of most of its hydrogen bonds . At this point , only the backbone carbonyl groups of A162 and F215 and a water molecule serve as hydrogen bond acceptors . Given ( 1 ) that water ionizes more easily than a carbonyl group , ( 2 ) that the carboxylate of D160 is persistently engaged in hydrogen bonds with residues of the M4-M5 loop , and ( 3 ) that the apparent pKa of D160 is shifted downwardly by ∼9 pH units due to its stabilized interactions [22] , water is the only plausible candidate for accepting a proton from NH4+ at the deprotonation site . The proton has full access to the periplasmic bulk , and must therefore escape in the form of hydronium . Thus , the Am deprotonation mechanism of AmtB is due to stripping NH4+ hydration to a critical point , much like the deprotonation event that one would observe in transferring Am from a hydrated state to a gas state . Recently , a mutagenesis study showed that the twin luminal His residues ( H168 and H318 ) are essential for substrate conductance [41] . Javelle et al . offered two explanations for this result: ( 1 ) these His residues serve as proton acceptors for entering Am such that they may move across the lumen as NH3 , or ( 2 ) efficient conductance might require a “narrow mainly hydrophobic pore with a few precisely oriented hydrogen bond acceptor or donor functions for weak , stabilizing interactions with water/ammonia that still permit rapid diffusion . ” Our results suggest that the latter explanation ( 2 ) applies . The need of NH4+ for hydration , or some other form of hydrogen-bond stabilization , also manifests itself in our PMF analysis as an insurmountable free-energy barrier ( >50 RT ) , preventing passage across the phenyl group of F107 and approaching any site within the lumen . This result contrasts with existing hypotheses [17 , 23] that NH4+ can occupy the intraluminal NH3 sites , Am2 , or Am3 . Since Am has the rare ability to deprotonate , it may enter the lumen as NH3 . This is the origin of the Am-sensing ability of AmtB . Since most cations , such as Family IA and IIA ions , are permanently charged , their passage is expected to be disfavored , with a free-energy barrier of more than 50 RT . Due to the nature of the deprotonation mechanism we describe , fully ordered hydration of the lumen would inhibit the Am-sensing ability of AmtB , providing the possibility for luminal Am to exist in its protonated form . Although , in contrast to other simulations of AmtB [23] , the MD simulations of our work do not show any ordered luminal hydration ( i . e . , in the form of a water wire ) , they do not rule out the possibility of a few water molecules “sneaking” into the lumen . Such events would not be expected to inhibit NH4+ deprotonation ( and , thus , AmtB's sensing capability ) . Recent diffraction studies [17 , 41] clearly show electron density at the three luminal NH3 binding sites ( as shown in Figure 4 ) , but are unable to determine whether these sites are occupied by NH3 or water molecules . The diffraction study of Khademi et al . [17] could not find density corresponding to these sites for AmtB structures crystallized in the absence of 25mM AmSO4 . This suggests that these peaks are mostly due to NH3 rather than water . Upon the introduction of charged species to the hydrophobic lumen , we might expect water to enter . For example , in the very unlikely event that NH4+ enters the lumen , we show that it can carry water with it ( see Figure 6D ) . Recently , an X-ray diffraction study of an H168D mutant of AmtB showed electron density inside the lumen that might be attributable to water [41] . It was deemed most likely that this was due to the negative charge of the Asp side chain protruding into the lumen . Given that hydration of narrow hydrophobic pores in the absence of an electric field is known to be unfavorable [35] , the absence of ordered luminal hydration is most likely attributable to the presence of NH4Cl electrolyte in our simulated system , which , by responding to the macro-dipole of AmtB in the membrane , neutralizes the resulting electric field acting parallel to the transport axis . Similar “neutralizing” electrolytic responses to dipolar membrane proteins have also been observed in other work [37] . It appears that the periplasmic phenyl groups of F107 and F215 and the cytoplasmic phenyl group of F31 are major contributors to the maintenance of the dehydrated state of the AmtB lumen . This is most likely the reason why our NH4+ pKa calculations highlight the positions of F107 and F31 as deprotonation “landmarks . ” Once Am occupies the lumen as NH3 , in the singly occupied state , Am4 near the cytoplasm is the most stable site . As more hydrophobic NH3 is added to the lumen by means of NH4+ deprotonation from the cytoplasm , it may favorably become reprotonated from either a doubly occupied state ( where Am3 and Am4 are simultaneously occupied ) or a triply occupied state . Favorability for reaching the cytoplasmic reprotonation site is thus attained by a buildup of NH3 pressure in the lumen . In addition , the dynamics of NH3 within the lumen supports flow toward the floor of the lumen at the cytoplasmic site , Am4 . Since , at the floor of the lumen , reprotonation of NH3 occurs favorably at multiply occupied states , the rate-limiting step for Am permeation must be the deprotonation step at the periplasm , where NH4+ becomes dehydrated . The free-energy barrier for this step is ∼15 RT based on our PMF calculations . Though our study addresses mostly the translocation of Am toward the cytoplasm , the results we present are not inconsistent with the expected capability of bidirectional Am translocation . A previous study has found slight differences in the dynamics of NH3 in the lumen depending on the tautomeric form of the intraluminal histidine residues [23] . It is possible that , together , these different forms may be used by the channel to facilitate translocation toward the cytoplasm or toward the periplasm , depending on the electrochemical gradient . Based on our apparent pKa calculations , reprotonation of NH3 at the cytoplasmic end of the lumen was shown to occur upon hydration immediately after passing the phenyl group of F31 . No plausible donor other than water exists at the point where NH3 reaches the reprotonation site . Thus , reprotonation at the cytoplasmic end of the channel must occur in the reverse manner to deprotonation at the periplasmic end . Upon reprotonation , Am binds as NH4+ to site Am5 before escaping to the cytoplasm . Thus , the role of the newly discovered site , Am5 , is apparently to reduce the barrier to reprotonation by promoting hydration directly beneath F31 and by stabilizing NH4+ such that it does not escape back into the lumen as NH3 .
All MD simulations were performed using the GROMACS package [42 , 43] , with the OPLS force field for protein , ions , and NH3 [44 , 45] , the OPLS lipid parameters developed by Smondyrev and Berkowitz [46] , and the SPC water model [47] . Our choice in force field parameters was guided by the fact that the OPLS force field , when used with the SPC water model , has been shown to yield equivalent ( or better ) results than the standard TIP3P water model when determining solvation free energies of amino acid side-chain analogs [48] . Production runs used a time step of 2 . 0 fs . Configurations of the system were saved every 1 ps for later analysis . Periodic boundary conditions were applied in all three dimensions . The LINCS algorithm was used to constrain all bonds in the systems [49] . Long-range electrostatics were handled using the PME algorithm [50] , with a real-space cutoff of 0 . 9 nm . Other nonbonded interactions were truncated at 1 . 2 nm . The temperature was maintained at 298 K using the Nose–Hoover scheme with an oscillatory relaxation period of 2 . 0 ps . The pressure was maintained at 1 . 0 atm using the Parrinello–Rahman coupling scheme [51 , 52] with a barostat time constant of 2 . 0 ps . The rectangular simulation box was allowed to scale in size semi-isotropically to maintain pressure . Analysis of trajectories was performed using a combination of GROMACS analysis utilities and locally written scripts and programs . The initial configuration of the AmtB trimer system was generated by first equilibrating a hydrated POPC bilayer containing 450 lipids ( 225 lipids per leaflet ) and 29 , 889 water molecules for 4 . 0 ns . The area per lipid molecule was seen to converge to ∼0 . 62 nm2 . The X-ray structure of Khademi et al . ( PDB 1u7g ) was used as the initial structure of the AmtB trimer in our simulations [17] . All residues were mutated such that a wild-type representation of the protein was obtained . In addition , the missing residues , A1 and P2 , were added to each monomer to yield the full protein . All crystallographic water-molecule positions that coincided with the transmembrane portion of the trimer were stripped from the structure except for those deemed by Khademi et al . to be in “special positions . ” We also removed NH3 positions from the interior binding sites ( Am2 , Am3 , and Am4 ) , to be replaced later for other calculations , leaving only the NH4+ position at site Am1 for each monomer . Hydrogen atoms were then added to the protein , NH4+ molecules , and crystallographic water molecules . All residues were assigned their standard ( neutral pH ) protonation states , and the tautomeric forms of each His residue were assigned using ( geometric ) hydrogen-bonding criteria except for the intraluminal H168 and H316 residues , which were shown to share a proton , and were thus assigned the tautomeric form suggested by Khademi et al . [17] and shown in Figure 4 . The resulting structure used for subsequent equilibration contained three AmtB monomers , three NH4+ ions , and 486 water molecules . A previous computational study [23] supported the tautomeric forms of H168 and H316 used in our simulations ( shown in Figure 4 ) for the case where water is absent from the lumen . Since , for all of our simulations , water was not seen to enter the lumen , it would appear that our choice of tautomeric state is reasonable . It should be noted , however , that any conformational event observed involving these His residues during simulation ( including umbrella sampling simulations ) can only represent one of a few possibilities , since their protonation states must be permanently assigned . Of course , this is true of any simulation employing conventional force fields . The trimeric structure described above was inserted into the equilibrated POPC bilayer , and all overlapping lipid and water molecules in the hydrated bilayer were removed . Water molecules in the bulk were randomly replaced by 117 Cl− and 108 NH4+ . The net charge on each AmtB monomer was 2e , thus with 117 Cl− ions , and a total of 111 NH4+ ions ( three coming from the X-ray structure ) , the net charge of the system was neutralized . The resulting initial system contained the AmtB trimer , 117 Cl− ions , 111 NH4+ ions , 291 POPC molecules , and 28 , 099 water molecules ( a total of 117 , 276 atomic sites ) . The system was minimized using the method of steepest descent , and simulated for 1 . 5 ns using Berendsen temperature and pressure coupling [53] while imposing restraints on the protein to allow membrane , ions , and water to relax around the protein . Restraints on the protein were then released , and the system was equilibrated further for 1 ns , again using Berendsen coupling . We then switched to Nose–Hoover temperature coupling and Parrinello–Rahman pressure coupling , and thermalized the system further , for 27 . 5 ns . We show the time evolution of the center of mass of both Cl− and NH4+ for each leaflet of the biomembrane system ( Figure 10A ) . It is seen that this quantity is well-converged after the first 15 ns of the equilibration run . The protein root mean square deviation ( RMSD ) was seen to converge after the first 15 ns of total simulation time as well ( Figure 10B ) . After the equilibration run , a 25-ns production run was performed . From this simulation , we derived the partial densities of various species across the simulation box ( see Figures 2 and S2 ) and the hydration number of NH4+ across the simulation box ( see Figures 6 and S4 ) , to be used later , in conjunction with similar analyses of umbrella sampling trajectories . The average concentration of NH4Cl in the bulk as measured from the resulting partial densities was 158 ± 5 mM ( see Figure 2B ) . After the 25-ns production run , we placed three NH3 molecules into the luminal binding positions , determined from umbrella sampling simulations ( Figure 4 ) , within a single monomer of the final configuration . To demonstrate the dynamics of NH3 in the lumen , we performed 300 ps of simulation with the NH3 molecules harmonically restrained ( in the z-dimension only—with a spring constant of 5 , 000 kJ/mol/nm2 ) to their binding positions , and 700 ps of simulation with the NH3 molecules unrestrained . The relevant data from these simulations is shown in Figure 9B . The final structure after 27 . 5 ns of equilibration was taken as the starting structure for umbrella sampling simulations [54] . All umbrella sampling windows utilized the same simulation conditions that were used for the production MD run . Am species ( either NH4+ or NH3 ) were restrained to z-positions along the transport axis with respect to the AmtB trimer backbone ( Am was free to move in the xy-plane ) . Each window utilized a harmonic umbrella potential acting in the z-dimension only , with a spring constant of 3 , 000 kJ/mol/nm2 . Each umbrella simulation consisted of 150 ps of equilibration , followed by 150 ps of production MD over which statistics of Am occupancy along the transport axis ( z ) were recorded . Statistics from umbrella window simulations were combined and unbiased using the weighted histogram analysis method ( WHAM ) [55] to yield the probability , P ( z ) , that a given Am species occupies position z along the transport axis . The PMF in units of RT , where R is the gas constant and T is the temperature ( RT = 2 . 48 kJ/mol at 298 K ) , is then given by the Boltzmann relation W ( z ) = −lnP ( z ) . Given the size of the system and the large barriers required for translocation of the two Am species ( i . e . , involving displacement of Phe sidechains and ( de ) hydration barriers ) , we took a conservative , incremental approach to building the PMF for single NH4+ and NH3 translocation to ensure adequate relaxation of the system at each window simulation . In the first series of umbrella simulations , we sampled NH4+ along z from site Am1 ( z = 1 . 04 nm ) , outwardly , into the periplasmic solution ( z = 2 . 64 nm ) using windows of Δz = 0 . 04 nm ( a total of 41 window simulations ) . Next , we sampled NH4+ along z from just beneath site Am1 ( z = 1 . 00 nm ) , inwardly , into the lumen of the channel ( down to z = −0 . 20 nm ) using windows of Δz = 0 . 04 nm ( a total of 31 window simulations ) . In both series of simulations described above , the initial configuration of the ( i + 1 ) th umbrella simulation was taken from the final configuration of the ith umbrella simulation . Thus , each simulation within a series was performed consecutively rather than in parallel ( as is usually done ) to ensure proper relaxation of the system along the Am pathway . We then moved on to sample NH3 along its transport path starting within the channel lumen using two additional series of simulations . The initial configuration for these series was generated by removing the three Am1-bound NH4+ ions and three Cl− ions from the bulk solution ( to maintain net neutral system charge ) in the initial structure of the NH4+ umbrella simulations described above . A single NH3 molecule was placed at site Am3 ( the central luminal site , at z = 0 . 25 nm ) within the AmtB monomer . We then sampled NH3 along z from site Am3 ( z = 0 . 25 nm ) , outwardly , toward the periplasmic vestibule ( up to z = 1 . 35 nm ) using windows of Δz = 0 . 04 nm ( a total of 41 window simulations ) . Next , we sampled NH3 along z from just beneath site Am3 ( z = −0 . 29 nm ) , inwardly , toward the cytoplasmic vestibule ( down to z = −1 . 89 nm ) using windows of Δz = 0 . 04 nm ( a total of 41 window simulations ) . Again , each simulation within a series was performed consecutively . Finally , we sampled NH4+ translocation at the cytoplasmic end of the channel . The initial structure for these series of simulations was taken from the 27 . 5-ns equilibration run . The starting position of NH4+ was taken to be a position of NH3 near the phenyl group of F31 generated from umbrella sampling of NH3 translocation described above ( at z = −1 . 10 nm ) . NH4+ translocation was sampled outwardly , toward the periplasm ( up to z = −0 . 02 nm ) using windows of Δz = 0 . 04 nm ( a total of 28 window simulations ) . We also performed an inward sampling series of simulations , starting from z = −1 . 14 nm toward the cytoplasm ( down to z = −2 . 70 to nm ) using windows of Δz = 0 . 04 nm ( a total of 40 window simulations ) . After combining and unbiasing all NH4+ translocation umbrella statistics , we matched the resulting PMF at the periplasmic and cytoplasmic ends with the PMF derived from NH4+ densities provided by the 25-ns MD production run to extend the profile into the bulk region ( Figure 3 ) . The PMF was shifted such that its value in the bulk was zero . Both the NH4+ and NH3 PMFs were smoothed using window averaging . Taking into account all trajectories used in the construction of the PMFs in this work , including all umbrella simulations and the production run , a total of 91 . 6 ns were simulated . Production run configurations were saved every 1 ps , and statistics from umbrella sampling trajectories were taken every timestep , yielding an analysis of a total of 1 . 69 × 107 configurations for constructing the PMFs . The total simulation time mentioned above ( 91 . 6 ns ) does not take into account the equilibration run ( 27 . 5 ns ) , nor the thermodynamic integration calculations ( eight calculations comprising 2 . 1 ns each—see the discussion below ) , which , when combined , give the total simulation time of this study: 135 . 9 ns . We performed two types of free-energy calculations in this work ( results summarized in Table 1 ) : ( 1 ) to determine the free energy for mutating NH4+ to NH3 at particular locations along the transport axis , and ( 2 ) to determine the free energy for “turning off” all interactions between NH3 and the rest of the system at particular binding sites within the lumen of the AmtB channel . For each individual free energy calculation , the system topology was varied , using a coupling parameter , λ , from the initial state ( λ = 0 ) to the final state ( λ = 1 ) . All transformations were performed over a set of 21 simulations , each of 100-ps length and carried out at different fixed values of λ ( i . e . , λ = {0 . 00 , 0 . 05 , 0 . 10 , … , 0 . 95 , 1 . 00} ) . Data from the final 90 ps of each simulation was split into nine blocks , each 10 ps in length , thus providing nine sets of data ( 21 values each for 〈∂H/∂λ〉P , T , where H ( λ ) is the hamiltonian of the system ) . These sets of data were integrated from 0 to 1 to obtain the free energy of the transformation , The nine resulting values for the free energy were averaged to obtain the final value , and an upper bound for the uncertainty in this value was taken to be the standard deviation of the sample . The type 1 calculations were used in conjunction with the PMF profiles of Figures 3 , 4 , and S3 to determine the apparent pKa as a function of the transport axis ( Figure 6B ) . In these calculations , the NH4+ molecule ( λ = 0 ) of interest was transformed to an NH3 molecule bonded to a dummy atom ( λ = 1 ) , where the dummy atom had no interactions with the remainder of the system . The pKa calculation required ( see Equation 1 ) the free-energy difference , , for the NH4+ → NH3 transformation at some position in the bulk . For this transformation , we took z = 4 . 52 nm ( note that in the bulk , all positions are equivalent by definition ) . In addition , we calculated the free energy , , for the same transformation at two different positions along the transport axis ( z = 1 . 02 nm near site Am1 , and z = −1 . 32 nm , near site Am5 ) . This allowed us to shift the PMF profile for NH3 with respect to that of NH4+ ( which was previously shifted to have a value of zero in the bulk ) before subtracting one curve from the other to obtain ( see Equation 1 ) . Figure S3 shows two examples where the NH3 PMF profile was shifted according to the free-energy calculations . Figure S3A shows the result acquired when statistics from only one channel were combined for the derivation of the NH4+ PMF profile , and Figure S3B shows the result for the NH4+ PMF from umbrella sampling the translocation across two different AmtB monomers ( and combining the data from both monomers ) . The uncertainty in at sites Am1 and Am5 determined by thermodynamic integration had an upper bound in uncertainty of 7 kJ/mol ( see Table 1 ) . At these positions , this translates to an upper bound of uncertainty in the resulting pKa of 1 . 2 units . In addition , we note that the NH3 PMF profile could not be fit exactly to the points determined by thermodynamic integration ( blue points with error bars in Figure S3 ) . If , as an upper bound , we estimate the uncertainty to be 4 RT in the PMF profile , we obtain an additional uncertainty in the pKa profile of 1 . 7 units . Combining these estimates of uncertainty in the pKa calculation , we may give an upper bound in the determined pKa profile ( of Figures 6B and S3C ) of 2 . 9 units ( or ∼3 units , as mentioned in the text ) . The difference in the pKa obtained when utilizing 1-channel versus 2-channel sampling ( Figure S3 ) is obviously much greater than ∼3 units in the hydrophobic lumen of the channel ( ∼15–20 units around z = 0 . 1 nm , see Figure S3C ) ; however , in this region , the total pKa is about −45 units . Thus , regardless of the differences observed in this region , the apparent pKa of NH4+ is so low that it would be effectively impossible for Am to exist in its charged form . Despite any differences seen in the results from 1-channel and 2-channel sampling , we come to the same conclusion when determining the position where deprotonation and reprotonation of Am occur along its pathway through the channel ( green vertical bars in Figures 6B and S3C ) . All type-2 calculations were used to complete the occupancy state map in Figure 9A ( free-energy values shown in blue ) . In these calculations , the NH3 molecule ( λ = 0 ) of interest at a particular site in the channel was transformed into a “null” molecule ( λ = 1 ) , where in the “null” state the NH3 molecule had no interactions with any other molecule within the system and was harmonically restrained to its site . The value of 〈∂H/∂λ〉P , T , at λ = 1 , corresponding to the “null” state , was determined by linear extrapolation based on the tail-end values at λ = {0 . 85 , 0 . 90 , 0 . 95} . The free energy for each of these calculations is shown in Table 1 . The free-energy values shown in the state map of Figure 9A were derived using the sequences of reactions shown in Figure S5B–S5E . | Selective flow of ammonium manifests itself in a unique way in the case of the ammonium channel , AmtB , allowing it to interact closely with cytoplasmic signal transduction proteins in order to “sense” the presence of extracellular ammonium . Although it is well known that AmtB transports ammonia ( NH3 ) rather than ammonium ion ( NH4+ ) , it is unclear from the channel's atomic structure exactly where and how , along its pathway toward the cytoplasm , NH4+ becomes deprotonated to form NH3 , and reprotonated on the cytoplasmic end of the channel to form NH4+ to enter the cell . We use a combination of molecular dynamics simulation techniques to glean the thermodynamics associated with these key events in ammonium translocation . Our findings provide a novel perspective on how this family of channels indirectly controls ammonium protonation—by directly controlling its hydration . Such a perspective should lend new insight to interpretations of experimental data , and could possibly lead to new strategies in an envisioned future for the design of nanopores that can control the protonated state of permeant species . | [
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"molecular",
"biology",
"computational",
"biology"
] | 2007 | Deprotonation by Dehydration: The Origin of Ammonium Sensing in the AmtB Channel |
Obesity is defined by excessive lipid accumulation . However , the active mechanistic roles that lipids play in its progression are not understood . Accumulation of ceramide , the metabolic hub of sphingolipid metabolism , has been associated with metabolic syndrome and obesity in humans and model systems . Here , we use Drosophila genetic manipulations to cause accumulation or depletion of ceramide and sphingosine-1-phosphate ( S1P ) intermediates . Sphingolipidomic profiles were characterized across mutants for various sphingolipid metabolic genes using liquid chromatography electrospray ionization tandem mass spectroscopy . Biochemical assays and microscopy were used to assess classic hallmarks of obesity including elevated fat stores , increased body weight , resistance to starvation induced death , increased adiposity , and fat cell hypertrophy . Multiple behavioral assays were used to assess appetite , caloric intake , meal size and meal frequency . Additionally , we utilized DNA microarrays to profile differential gene expression between these flies , which mapped to changes in lipid metabolic pathways . Our results show that accumulation of ceramides is sufficient to induce obesity phenotypes by two distinct mechanisms: 1 ) Dihydroceramide ( C14:0 ) and ceramide diene ( C14:2 ) accumulation lowered fat store mobilization by reducing adipokinetic hormone- producing cell functionality and 2 ) Modulating the S1P: ceramide ( C14:1 ) ratio suppressed postprandial satiety via the hindgut-specific neuropeptide like receptor dNepYr , resulting in caloric intake-dependent obesity .
Obesity is a condition in which body weight , caused by excessive accumulation of stored body fat , is increased to the point where it becomes a risk factor for certain health conditions and mortality . Overweight and obese individuals are at an increased risk for hypertension , dyslipidemia , Type 2 diabetes , heart disease , stroke and certain forms of cancer . Unfortunately , obesity is a growing worldwide epidemic with over 1 billion of the global population either overweight or clinically obese . Our ability to understand the underlying mechanisms involved in the pathogenesis and progression of obesity are essential to developing new methods and approaches for combating this disease . In the present study , we describe a central mechanistic role for sphingolipids ( SL ) in the progression of obesity . SLs are a versatile class of bioactive lipids , which play roles in a variety of signaling pathways that regulate diverse cellular functions such as programmed cell death , proliferation , migration , membrane stability , host-pathogen interactions and the stress response [1]–[4] . The basic structure of SLs consists of fatty acid chains linked by amide bonds to a long-chain “sphingoid” base . Biological functionality of each SL species can vary based on fatty acid chain length , degrees of saturation , and head group modification . Despite previous research detailing the cellular action of these lipids , their role at the organismal level and their homeostatic regulation in vivo is now just becoming understood with the emergence of suitable complex genetic models for analysis . Ceramide , the metabolic hub of sphingolipid metabolism , has recently been associated with metabolic syndrome and obesity in humans as well as a variety of animal model systems [5] . For example , in obese insulin resistant humans , high levels of ceramide were detected in skeletal muscle tissue [5] . In obese leptin deficient ob/ob mice , ceramide levels were elevated in the serum [6] . Subsequent studies in these mice showed that pharmacological perturbation of de novo ceramide synthesis , using the serine palmitoyl-transferase inhibitor myriocin , induced weight loss and decreased fat storage [7] . This suggests that ceramide , and potentially other SL intermediates , are playing an active role in the pathogenesis of obesity . However , a gap in our knowledge still exists as to whether specific modulation of ceramide levels is sufficient to induce obese phenotypes . Here , we use Drosophila as a model organism to determine whether direct perturbation of sphingolipid metabolism is sufficient to induce obese phenotypes . We used genetic manipulation to cause accumulation or depletion of ceramide intermediates , as well as to modulate the sphingosine-1-phosphate to ceramide ratio ( also known as the S1P: ceramide rheostat ) . We demonstrate that genetic manipulations that cause direct ceramide accumulation induce obesity by two distinct mechanisms: 1 ) Dihydroceramide ( C14:0 ) and ceramide diene ( C14:2 ) accumulation lowered fat store mobilization by reducing adipokinetic hormone- producing cell functionality and 2 ) decreasing the S1P: ceramide ( C14:1 ) ratio suppressed postprandial satiety via the hindgut-specific neuropeptide like receptor dNepYr .
The rate-limiting step of de novo SL synthesis is catalyzed by serine palmitoyl-transferase ( SPT ) ( Figure 1A ) . In flies , SPT is encoded by the gene lace . Since homozygous null mutations of lace are lethal , we utilize transheterozygous lacek05305/2 mutants to perturb de novo SL synthesis [8] . These mutants exhibit substantially reduced lace transcript levels ( Figure S1A ) , with significant reductions in downstream SL intermediate levels , including total ceramide ( −50% ) , sphingosine ( −30% ) and S1P levels ( −48% ) relative to wild type ( wt ) flies ( Table 1 ) . Ceramide-reducing lacek05305/2 flies show significant reductions in whole fly triglyceride ( TG ) levels at 2 days ( −21% ) , 8 days ( −23% ) and 15 days ( −27% ) of age relative to wt flies ( Figure 2A ) . RNAi-mediated lace knockdowns showed comparable reductions in both lace transcript ( Figure S1E ) and mean whole fly TG levels ( Figure S2B ) . Similarly , TG levels in hemolymph extracted from 2 day old flies were 58% lower than wt flies ( Figure 2B , S2D ) . Survival under starvation on agar-only media was also perturbed in these flies , with a mean 50% survival time of 45 hours , compared to 60 hours in wt flies ( Figure 2C ) . Mean body weight in 2 day old female flies was also reduced by 12% in lacek05305/2 mutants ( 0 . 97 mg/fly vs . 1 . 10 mg/fly in wt ) ( Figure 2D , S2C ) . Mutant lacek05305/2 flies display fat body cell atrophy relative to wt flies ( Figure 2F ) . Both mean cell ( Figure 2L–M ) and lipid droplet size ( Figure S2E–G ) were reduced . Abdominal sections of lacek05305/2 mutants exhibited diminished adiposity characterized by less lipid positive staining then wt flies ( Figure S3A , S3C ) . Collectively , we conclude that lacek05305/2 mutants exhibit a lean phenotype relative to wt flies . Next , we utilized two independent feeding behavior assays to determine if the lean phenotype exhibited by lacek05305/2 mutants was dependent on changes in caloric intake . First , flies were subjected to 3 hours of starvation on agar-only media , and then transferred to Bromophenol blue stained food . The relative starvation-induced appetite response of flies was quantified as the percentage of flies which scored positive for feeding ( blue abdomens ) over time [9] . Second , flies were monitored in a capillary feeding ( CAFE ) chamber that allowed us to determine mean daily food intake and meal frequency [10] . No significant changes in starvation-induced appetite ( Figure 3A ) or postprandial meal volume ( Figure 3B ) were observed between lacek05305/2 mutants and wt flies . Furthermore , there were no differences in mean daily caloric intake ( Figure 3C ) , mean meal frequency ( Figure 3D ) or mean meal volume ( Figure 3E ) . Taken together , we conclude that lacek05305/2 mutants exhibit a caloric intake-independent lean phenotype . Downstream of lace and schlank ( Figure 1A–B ) , ifc encodes the enzyme sphingosine Δ-4 desaturase , which catalyzes the conversion of dihydroceramide into ceramide ( Figure 1A ) . Mutant ifc4 flies exhibit a 55% reduction in ifc transcript ( Figure S1B ) , and significant changes in the subspecies of each SL . Specifically , ifc4 mutant flies accumulate the C14:0 “dihydro” ( DH ) subspecies , with C14:0 dihydroceramide ( DHC ) , dihydrosphingosine ( DHS ) and dihydrosphingosine 1-phosphate ( DHS1P ) intermediate levels increased +306% , +83% , and +257% respectively . Conversely , levels of monounsaturated C14:1 ceramides ( −10% ) , sphingosine ( −15% ) and S1P ( −31% ) SL intermediates were reduced . Notably , these lines also accumulate the polyunsaturated C14:2 ceramide “diene” ( +190% ) and sphingosine “diene” ( +206% ) [11] . Mutant ifc4 flies exhibit increased whole fly TG at 2 days ( +58% ) , 8 days ( +50% ) and 15 days ( +85% ) ( Figure 2A ) . Both transheterozygous ifc4 mutants over deficiency and global ifc RNAi mediated knockdowns ( Figure S1F ) exhibit similar increases mean TG levels ( Figure S2A–B ) . Hemolymph TG levels were also increased ( +95% ) ( Figure 2B , S2D ) . Survival under starvation was enhanced , with a mean 50% survival time of 68 hours , compared to 60 hours in wt flies ( Figure 2C ) . Mean body weight was also increased by 9% ( 1 . 20 mg/fly vs . 1 . 10 mg/fly in wt ) ( Figure 2D , S2C ) . These flies also exhibit fat body cell hypertrophy ( Figure 2G , 2L–2M ) , increased fat body lipid droplet size ( Figure S2E–G ) , and increased abdominal adiposity ( Figure S3E ) . Collectively , we conclude that ifc4 mutants exhibit an obese phenotype relative to wt flies . In ifc4 mutants , the starvation-induced appetite response was slightly increased relative to wt flies ( Figure 3A ) . However , no significant change in relative post prandial meal volume was observed ( Figure 3B ) . Furthermore , ifc4 mutants do not exhibit significant changes in mean daily caloric intake , mean meal frequency or mean meal volume ( Figure 3C–3E ) in the CAFE relative to wt flies . Taken together , we conclude that ifc4 mutants exhibit a largely caloric intake-independent obese phenotype . The Sk2 gene encodes for the enzyme sphingosine kinase 2 ( Figure 1A ) , which phosphorylates sphingosine into S1P . Sk2KG050894 mutants exhibit an approximate 80% reduction in Sk2 transcript with substantial increases in total sphingosine ( +79% ) and total ceramide ( +55% ) levels . Conversely , S1P levels are undetectable using our method in these flies ( Table 1 ) . Mutant Sk2KG050894 whole fly TG levels increased at 2 days ( +65% ) , 8 days ( +72% ) and 15 days ( +101% ) ( Figure 2A ) . Both transheterozygous Sk2KG050894 mutants over their deficiency and Sk2 RNAi mediated knockdowns ( Figure S1F ) exhibit a similar increase in TG levels ( Figure S2A–B ) . Increased TG levels are also observed in hemolymph ( +75% ) ( Figure 2B , Figure S2D ) . Survival under starvation was enhanced , with a mean 50% survival time of 71 hours ( Figure 2C ) . Mean body weight was increased by 10% ( 1 . 21 mg/fly vs . 1 . 10 mg/fly in wt ) ( Figure 2D , Figure S2C ) . These results correlate with observed fat body cell hypertrophy ( Figure 2H , 2L–2M ) , increased mean lipid droplet size ( Figure S2E–F ) , and increased abdominal adiposity ( Figure S1A , S1F ) . Collectively , we conclude that Sk2KG050894 mutants exhibit an obese phenotype relative to wt flies . Obese Sk2KG050894 mutants exhibit a substantial increase in starvation-induced appetite response , where over 50% of these flies consumed food within the first hour post-starvation relative to just 15% of control flies ( Figure 3A ) . Furthermore , relative postprandial meal volume was increased ( +34% ) in Sk2KG050894 mutants relative to wt flies ( Figure 2B ) . Additionally , Sk2KG050894 mutants consumed an average of 34 . 7% more food per day than controls ( 1 . 55 ul/day vs . 1 . 15 ul/day ) ( Figure 3C ) , exhibiting increases in both meal frequency ( Figure 3D ) and size ( Figure 3E ) . Based on these results , we conclude that ceramide accumulating Sk2KG050894 flies exhibit caloric intake-dependent obesity . The committal step of SL degradation is catalyzed by S1P lyase , encoded by Sply , which irreversibly degrades S1P ( Figure 1A ) . Sply05901 mutant flies show substantial loss of Sply transcript ( Figure S1D ) , and accumulate all SL intermediates , especially S1P ( +260% ) ( Table 1 ) . Young Sply05901 mutant flies exhibit a lipid metabolic phenotype similar to that observed in wild-type flies , with no significant changes in 2- and 8- day whole fly TG levels , hemolymph TG levels ( Figure 2A–2B , Figure S2D ) , or body weight ( Figure 2D , Figure S2C ) . Transheterozygous Sply05901 mutants over their deficiency and global Sply RNAi knockdowns ( Figure S1H ) exhibit comparable whole fly TG levels ( Figure S1A–B ) . This correlates with no observable change in mean fat body cell size ( Figure 2I , 2L–M ) , lipid droplet size ( Figure S2E–F ) or abdominal adiposity ( Figure S3B ) . However , 2 day old Sply05901 mutants display a decrease in their starvation-induced appetite response ( Figure 3A ) with a concomitant 30% reduction in relative starvation-induced postprandial meal volume ( Figure 3B ) . Furthermore , these flies consumed significantly lower quantities of food ( −35% ) per day in the CAFE ( Figure 3C ) , which was the result of a decrease in both meal frequency ( Figure 3D ) and size ( Figure 3E ) . Interestingly , these results correlate with a reduction in starvation resistance ( Figure 2C ) This is possibly the result of fewer flies , on average , entering the starvation chambers in the fed state ( and with a lower postprandial meal volume ) . A 21% reduction in TG levels is observed by 15 days ( Figure 2A ) . This suggests that reduced caloric intake observed at 2 days leads to a reduction in TG stores , sometime between 8 and 15 days . In Sply05901 flies , where ceramide levels are elevated in the context of high S1P levels , it appears that the classic hallmarks of obesity associated with high ceramide levels , are mitigated through reduced caloric intake . Based on our results , the SL metabolites appear to regulate global energy metabolism by both caloric intake- independent and -dependent mechanisms . To understand this , we utilized two double mutant models that we hypothesized would combine components of each phenotype . The first double mutant , lacek05305/2; Sply05901 ( Figure S1A , S1D ) combined two mutations associated with reduced fat storage and reduced caloric intake respectively . If these mechanisms are distinct , then their effects should be additive and the double mutants should display an exacerbated lean phenotype . Mutant lacek05305/2; Sply05901 flies exhibit a substantial reduction in dihydroceramide ( −97% ) and ceramide diene levels ( −73% ) , as observed in lacek05305/2 flies ( Table 1 ) . These double mutants also exhibit a large increase in S1P levels ( +40% ) relative to wt flies , similar to Sply05901 mutants ( Table 1 ) . In this respect , we have shifted the S1P: ceramide ratio by both increasing S1P levels and reducing ceramides . As predicted , lacek05305/2; Sply05901 double mutants exhibit an exacerbated lean phenotype . Whole fly TG levels decreased at 2 days ( −31% ) , 8 days ( −40% ) and 15 days ( −47% ) ( Figure 2A ) . This correlated to a substantial reduction in starvation resistance ( 42 hrs . vs . 60 hrs . ) , body weight ( −11% ) ( Figure 2C–2D , Figure S2C ) , and abdominal adiposity ( Figure S1A , S1D ) . Fat body cell atrophy ( Figure 2J , 2L–2M ) was also observed , with a marked reduction in mean lipid droplet size ( Figure S2E–2G ) . Furthermore , lacek05305/2; Sply05901 double mutants exhibit a reduction in their starvation-induced appetite response with reduced post prandial meal volume ( Figure 3A–3B ) . These flies also displayed reduced mean daily food intake , meal frequency and meal volume in the CAFE ( Figure 3C–3E ) . The second double mutant , ifc4; Sk2KG050894 , ( Figure S2B–C ) combines two mutations associated with caloric intake-independent and dependent obesity . Again , if the mechanisms are distinct , the effects should be additive and in this case , these flies should display an exacerbated obese phenotype . Mutant ifc4; Sk2KG050894 flies exhibit increases in dihydroceramides ( +223% ) and ceramide dienes ( +62% ) , as observed in ifc4 flies ( Table 1 ) . Simultaneously , total S1P levels are decreased ( −43% ) , including undetectable levels of C14:1/16:1 S1Ps as observed in Sk2k6050894 flies ( Table 1 ) . In this respect , we have shifted the ceramide: S1P rheostat towards ceramide , by both increasing ceramide levels while simultaneously decreasing S1P levels . As predicted , these modulations of SL intermediate levels resulted in ifc4; Sk2KG050894 mutants exhibiting an exacerbated obesity phenotype . Whole fly TG levels were elevated at 2 days ( +115% ) , 8 days ( +140% ) and 15 days ( +184% ) ( Figure 2A ) . This correlated to a substantial increase in starvation resistance ( 84 hrs . vs . 60 hrs . ) , body weight ( +14% ) ( Figure 2C–2D , S2C ) , and abdominal adiposity ( Figure S1G ) . Fat body cells were hypertrophied ( Figure 2K–M ) , with elevated mean lipid droplet size ( Figure S2E–2G ) . Furthermore , ifc4; Sk2KG050894 double mutants exhibit increases in their starvation-induced appetite response and elevated starvation-induced post prandial mean meal volume ( Figure 3A–3B ) . These flies display elevated mean daily food intake , meal frequency and meal volume ( Figure 3C–3E ) . In order to correlate the sphingolipidomic profile of our mutants to changes in metabolic phenotypes , we performed a DNA microarray analysis of these mutants across 14 , 000+ Drosophila transcripts of the Drosophila genome . Heatmap and DAVID analysis revealed differential expression of apoptosis-related genes between lacek05305/2 and ifc4 mutants . Analysis showed that specific subsets of proapoptotic genes were downregulated while anti-apoptotic genes were upregulated in lace k05305/2 flies ( Figure 4A ) . Conversely , in the same gene subsets , ifc4 mutants showed increased expression of pro-apoptotic genes and decreased expression of anti-apoptotic genes ( Figure 4A ) . Recently , apoptosis-induced cell death of Adipokinetic hormone-producing cells ( Akhpc ) was shown to increase TG storage [12] . Akhpc make up the majority of the cells in the corpus cardiac ( CC ) located in the brain ( Figure 5A ) . The Akh peptide activates mobilization of TG stores from the fat body [12] . Interestingly , lacek05305/2 and ifc4 mutants display differential expression of the gene Adipokinetic hormone ( Akh ) . Hence , we hypothesized that regulation of apoptosis in Akhpcs might be involved in caloric intake-independent phenotypes . Akhpc-ablated flies display reduced Akh mRNA expression , elevated TG levels , live longer under starvation and exhibit five times higher TG levels after starvation-induced death relative to control flies [12] . Similarly , ifc4 flies exhibit reduced Akh mRNA expression ( Figure 5B ) , elevated TG levels ( Figure 2A ) , live longer under starvation ( Figure 2C ) and exhibit elevated TG levels after starvation-induced death ( Figure 5C ) . The opposite effect is observed in lacek05305/2 flies which showed a nearly 2 fold increase in Akh mRNA expression ( Figure 5B ) , reduced TG levels ( Figure 2A ) , a shortened life span under starvation ( Figure 2C ) and undetectable post-starvation TG levels ( Figure 5C ) . We utilized a GAL-4/UAS system to achieve Akhpc-specific RNAi mediated knockdown of lace and ifc mRNA . Akhpc-specific ifc knockdown ( Akh-g4/ifc RNAi ) was sufficient to induce a significant reduction in Akh transcript expression; with a concomitant increase in adult fly TG levels ( Figure 5D–5E ) . The magnitude of Akh mRNA loss and TG elevation was similar to those observed in ifc4 mutants ( Figure 5B , Figure 2B ) and global ifc RNAi knockdowns ( Figure S1F , S2B , S5F ) . This suggests that ifc regulates Akhpc function in an Akhpc autonomous manner . Akh-g4/ifc RNAi 3rd instar larvae also exhibited decreased expression of GFP in Akhp cells relative to controls , as observed in both the cell body and neuronal projections ( Figure 5F , 5H ) . In 70% of 3rd instar larvae , undetectable levels of GFP expression were observed , while the remaining 30% showed only very low levels of GFP expression ( Figure S6A , S6C , S6F ) . Notably , GFP expression was generally relegated to rounded cell bodies , with an absence of GFP-expressing neuronal projections . Semi-quantification of GFP expression in those larvae showed a reduction in both mean GFP positive area and optical density relative to control flies ( Figure S6G ) . These data correlated with a substantial reduction in Akh mRNA levels in Akh-g4/ifc RNAi 3rd instar larvae ( Figure S6H ) . Next , we examined Akhpc-specific knockdown of Akh itself ( Akh-g4/Akh RNAi ) , to determine if the loss of GFP expression might be due to inhibition of Akh-g4 expression in these constructs . All Akh-g4/Akh RNAi 3rd instar larvae examined expressed detectable levels of GFP ( Figure S6A–B , S6F ) , in spite of substantially reduced Akh mRNA ( Figure S6H ) . However , while mean GFP positive area was comparable to controls , a significant reduction in mean optical density was observed , suggesting that some GFP production is likely perturbed in these lines ( Figure S6G ) . These data suggest that the absence/reduction of GFP expression in Akhpc of Akh-g4/ifc RNAi 3rd instar larvae is not due to complete inhibition of GFP expression , but rather is due to the absence/perturbation of Akhpc . Previous reports have shown that ablation of Akhp cells can be carried out via overexpression of the genes Grim , Hid and Reaper , which utilizes activation of the caspase-dependent intrinsic apoptotic pathway to induce Akhp cell death [12] . These proteins are known inhibitors of the Drosophila Inhibitor of Apoptosis Protein1 ( dIAP1 ) , which inhibits the activity of proapoptotic caspases Dronc and Drice . If ifc knockdown induced Akhpc ablation , we hypothesized that overexpression of dIAP1 should rescue Akhp cell death as well as normalize reduced Akh mRNA and elevated TG levels . Akhp cell specific exogenous expression of dIAP1 ( Akh-g4/+; UAS dIAP1/+ ) increased Akh transcript expression and decrease TG levels relative to control flies ( Figure 5D–5E ) . In 3rd instar larvae , GFP expression was elevated as observed in both the cell body and axonal projections . ( Figure 5F , 5I ) . All Akh-g4/+;UAS dIAP1/+ 3rd instar larvae exhibited robust GFP expression , with marked elevations in both mean GFP positive area and optical density ( Figure S6A , S6D , S6F–G ) . These data correlated with a ∼2 fold increase in 3rd instar larval Akh mRNA expression ( Figure S6H ) . These findings provide strong evidence that Akhp cells are sensitive not only to induction , but also inhibition of the caspase-dependent apoptotic pathway during the larval stages of development . Furthermore , Akhpc specific overexpression of dIAP , in an Akhpc-specific ifc knockdown background , partially rescued Akhp cell viability and function . In 3rd instar larvae , although GFP expression was slightly less than in wt larvae , it was observable in 100% of samples ( Figure 5F , 5J ) . This correlated well with Akh mRNA and TG levels in these flies , where no significant change relative to wt flies was observed . ( Figure 5D–5E ) . Akhp cell specific lace knockdowns ( Akh-g4/+; lace RNAi/+ ) ( Figure 5D–5G ) , exhibited reductions in 2 day fly Akh mRNA ( Figure 5D ) and whole fly TG levels ( Figure 5E ) . In 3rd instar larvae , Akh GFP-expression was enhanced , with an observable increase in cell density and neuronal projections ( Figure 5G , S6A , SF6E ) . This observation could be attributed to an increase in both mean GFP+ Akhp cell area and optical density ( Figure S6A , S6E , S6F–G ) . These data correlated well with a marked increase in Akh mRNA levels , as was similarly seen in Akh-g4/+; UAS dIAP1/+ 3rd instar larvae . ( Figure S6H ) . Some data suggest that Sply may play a role in Akhp cell viability and function . First , expression levels of Akh mRNA in Sply05901 are slightly reduced compared to wild-type flies ( Figure S5 ) . Secondly , Sply05901 flies exhibit slightly elevated TG levels at 2 days ( +9% ) . However , these flies show no significant difference in post-starvation TG levels , suggesting that fat mobilization is largely unperturbed ( Figure S5 ) . Interestingly , Akhpc-specific Sply knockdowns ( Akh-g4/Sply RNAi ) exhibit an increase in TG levels ( +40% ) ( Figure S2B ) that correlates with a 50% reduction in Akh mRNA expression ( Figure S5D ) . These effects were not observed in global Sply knockdowns ( Figure S2B , S5D ) , suggesting that systemic factors ( possibly systemic S1P pools ) prevent loss of Akhpc viability and function . Next , we compared changes in global gene expression between caloric intake-dependent obese Sk2KG050894 flies and lean Sply05901 flies . Heatmap and DAVID analysis showed opposing expression in gene subsets regulating major lipid metabolic pathways , including fatty acid biosynthesis and oxidation , as well as oxidative phosphorylation ( Figure 4B ) . Sply05901 mutants exhibit downregulation of key fatty acid biosynthesis genes while showing upregulation of fatty acid oxidation and oxidative phosphorylation genes , suggesting a shift in basal gene expression towards a low energy , fat-burning , “unfed” state . Conversely , Sk2KG050894 flies exhibit upregulation of the FA biosynthetic gene fatty-acid synthase , with downregulation of fatty acid oxidation genes and oxidative phosphorylation genes ( Figure 4B ) . This suggests a shift in basal gene expression towards a high energy , fat-storing , “fed” state . Additionally , differential expression was observed for CG5811 , which encodes for the neuropeptide Y-like receptor ( dNepYr ) ( Figure S4A ) [13] . A set of pancreatic TG lipases appear to be cis-natural antisense transcripts ( cis-NATs ) of dNepYr in full overlapping orientation ( Figure S4A ) [13] . In adult flies , dNepYr transcript has been observed to be expressed almost exclusively in the hindgut ( Figure S4B ) , with lower levels of expression observed in the brain [13] , [14] . Mammalian neuropeptide Y receptors ( NPYR ) with enriched expression in the digestive system are generally associated with induction of post-prandial satiety and include NPYR 2 and 4 [15] . The dNepYr gene is expressed throughout development starting in early embryo ( 4–6 hr ) , with peak expression seen in late embryos ( 16 hr–24 hr ) and throughout adulthood ( Figure S4C ) [16] . Interestingly , dNepYr is upregulated in Sk2KG050894 and downregulated in Sply05901 mutants ( Figure 6A ) . Its expression also appears to be coordinated with cis-NAT pancreatic TG lipase transcripts , and likely constitutes a novel system of coordinated dietary TG degradation and satiety signaling ( Figure 6A , Figure S4A Therefore , we hypothesized that dNepYr signaling is associated with induction of satiety . To test this , we utilized RNAi-mediated dNepYr knockdown flies ( Figure S4D ) . These flies exhibit increased mean daily caloric intake ( Figure S4F ) with a concomitant increase in TG levels ( Figure S4F ) . These data suggest that dNepYr signaling is involved in post-prandial satiety . The recently characterized Drosophila gene CG40733 was found to encode two dRYamides that are expressed exclusively in the brain and the gut in Drosophila [17] . Both dRYamide1 and dRYamide2 were found to be strong ligands of dNepYr that negatively regulate feeding behavior [17] . Therefore , we examined the effects of dRYamide loss by characterizing CG40733 RNAi knockdowns . Global CG40733 RNAi knockdowns ( Figure S4G ) exhibit increases in both caloric intake ( Figure S4H ) and TG levels ( Figure S4I ) . Based on these findings , we designed experiments to pharmacologically rescue elevated TG levels in Sk2KG050894 mutants by administering synthetic dRYamide1 and dRYamide2 peptides ( 1∶1 ) . Sk2KG050894 mutants were more responsive to a lower concentration of dRYamides then wt flies . At 10 uM dRYamides , Sk2 mutants exhibited a reduction in TG levels relative to 0 uM fed Sk2KG050894 mutants , and were not statistically different to control flies fed either 0 uM or 10 uM dRYamide containing food ( Figure 6B ) . The dose dependent rate of TG decline in Sk2KG050894 flies was steeper ( m = −3 . 26 , R2 = 0 . 9924 ) than wt flies ( m = −2 . 41 , R2 0 . 95 ) ( Figure 6C ) . Furthermore , 10 uM and 100 uM dRYamide-fed Sk2KG050894 flies also exhibit a concomitant reduction in dNepYr expression to near control levels ( Figure 6D ) . Reductions in TG levels and dNepYr mRNA levels ( Figure 6D ) were also observed in wt flies administrated 100 uM dRYamide ( Figure 6B ) . At 100 uM dRYamide , wild type and Sk2KG050894 dNepYr mRNA levels were equally suppressed . However , 100 uM dRYamide-fed Sk2 mutants' exhibit significantly higher levels of TG relative to 100 uM dRYamide-fed wt flies , suggesting that increased caloric intake through perturbation of dNepYr expression is not the only contributing factor to the obesity phenotype . Next , we attempted to pharmacologically rescue elevated caloric intake in Sk2 RNAi-mediated knockdowns by administering 10 uM dRYamide to liquid food in the CAFE chamber . Sk2 RNAi knockdowns administered 10 uM dRYamide for 3 days exhibit a nearly 30% reduction in mean daily caloric intake ( Figure 7A ) and a 50% reduction in dNepYr expression ( Figure 7B ) . CG40733 knockdowns showed a similar but stronger effect , where caloric intake was reduced ∼38% ( Figure 7A ) and dNepYr expression levels were reduced by ∼80% at the same concentration ( Figure 7B ) . Conversely , dNepYr RNAi knockdowns exhibited no reduction in caloric intake ( Figure 7A ) . Next , we examined whether knockdown of Sply could rescue the dNepYr RNAi phenotype . To do this , we generated Sply;dNepYr RNAi knockdown flies ( Figure 7C ) . Sply RNAi mediated knockdowns exhibit ∼32% reduction in caloric intake ( Figure 7D ) . However , Sply; dNepYr double knockdowns exhibit a 63% increase in caloric intake ( Figure 7D ) and elevated TG levels ( Figure 7E ) . This phenotype is nearly identical to that of dNepYr RNAi knockdowns . Therefore , these data suggest that Sply suppression of caloric intake is non-overlapping and upstream of dNepYr signaling . Finally , we attempted to pharmacologically rescue caloric intake in Sk2KG050894 flies by administering the stable and potent S1P analog FTY720P [18] . Sk2KG050894 flies fed liquid food containing 10 uM FTY720P for 3 days exhibit a reduction in caloric intake to near control levels ( Figure 6E ) . Interestingly , administration of FTY720P also reduced expression of dNepYr in Sk2KG050894 flies to control levels ( Figure 6F ) . Thus , administration of FTY720P to Sk2KG050894 flies mimicked the effect of S1P on suppression of appetite and dNepYr expression . We have shown that the exacerbated lean phenotype in lacek05305/2; Sply05901 flies was due to the additive effects of reduced fat storage and reduced caloric intake . These flies also showed a 2 . 5 fold increase in Akh mRNA expression , undetectable post-starvation TG levels ( Figure S5C ) , and significantly decreased dNepYr mRNA expression levels ( Figure S5A ) . Conversely , obese ifc4; Sk2KG050894 double mutants exhibit reduced Akh mRNA expression levels ( Figure S5B ) , elevated post-starvation TG levels ( Figure S5C ) and elevated dNepYr mRNA expression levels ( Figure S5A ) . Thus , the additive effects of changes in Akhpc-mediated fat storage mobilization and dNepYr-mediated caloric intake underlie the exaggerated phenotypes of the double mutants . Perturbations of SL genes lead to changes in multiple upstream and downstream SL intermediates . Thus , it is difficult to determine which SL species are most correlated with the observed metabolic phenotypes . In order to identify these associations , we calculated Pearson correlation coefficients ( multiple r ) between normalized SL intermediate levels and phenotypes ( Figure 8 ) . First , these data show a strong positive correlation between stored TG levels ( 2 , 8 , 15 days ) and caloric intake ( r = 0 . 83 , 0 . 86 , 0 . 86; p<0 . 01 ) , as would be expected . Caloric intake also exhibited a strong positive correlation with dNepYr mRNA expression levels ( r = 0 . 83; p<0 . 01 ) . Hence , dNepYr mRNA and TG levels were also positively correlated , albeit to a lesser extent ( r = 0 . 64 , 0 . 68 , 0 . 66; p<0 . 05 ) . Conversely , Akh mRNA expression showed a negative correlation with TG levels ( r = −0 . 80 , −0 . 71 , −0 . 74; p<0 . 05 ) . The SL's that exhibited the strongest positive correlation with dNepYr mRNA expression were C14:1/C20:0 and C14:1/C24:0 ceramides ( r = 0 . 73 , 0 . 76; p<0 . 05 ) . Conversely , a strong negative correlation was observed with C14:1 S1P ( r = −0 . 63; p<0 . 05 ) . The ratio of total S1P: C14:1 ceramide displayed the strongest negative correlation to dNepYr mRNA expression ( r = −0 . 85; p<0 . 01 ) . Importantly , the S1P: C14:1 ceramide ratio also showed a prominent negative correlation with caloric intake ( r = −0 . 96; p<0 . 01 ) and to a lesser extent , TG levels ( r = −0 . 68 , −0 . 72 , −0 . 71; p<0 . 05 ) . Thus , these data establish a relationship between the S1P: ceramide ratio , dNepYr mRNA expression , caloric intake and TG stores , which are primary drivers of caloric intake-dependent phenotypes in Sply , Sk2 and their respective double mutants . No significant correlation was observed between sphingosines , dihydroceramides , or ceramide dienes with dNepYr mRNA expression levels ( Figure 8 ) . The SL's that exhibited the strongest negative correlation with Akh mRNA expression were dihydroceramides ( r = −0 . 92; p<0 . 01 ) . A negative correlation was also observed with ceramide dienes ( r = −0 . 77; p<0 . 05 ) . Additionally , dihydrosphingosine ( r = −0 . 71; p<0 . 05 ) and to a lesser extent C14:1/C20:0 ceramides ( r = −0 . 60; p<0 . 05 ) were also negatively correlated . Interestingly , C14:0 S1P showed a modest correlation ( r = −0 . 59; p<0 . 05 ) with Akh mRNA expression . Furthermore , the S1P: C14:2 ceramide ratio exhibited a stronger correlation to Akh mRNA expression then C14:2 ceramides alone ( r = 0 . 83; p<0 . 01 ) . Importantly , dihydroceramide species display a significant positive correlation with TG levels ( r = 0 . 86 , 0 . 79 , 0 . 81; p<0 . 01 ) . These data establish a strong correlation between specific ceramide subspecies , Akh mRNA levels and TG stores , which are primary drivers of caloric intake–independent phenotypes in lace , ifc and their respective double mutants . Additionally , these results suggest that S1Ps could exhibit a modest opposing role in these processes ( Figure 8 ) .
Using a systems biology approach , we were able to correlate specific changes in SL profiles with changes in metabolic phenotypes and metabolic gene expression . Our results show that the SL intermediates dihydroceramide and ceramide diene are involved in the regulation of fat storage via Akhpc-mediated lipid mobilization . Moreover , the ratio of S1P: ceramide is involved in the regulation of appetite and caloric intake via dNepYr-mediated satiety in flies ( Figure 9 ) . Our data suggest that sphingolipid metabolism is involved in regulating Akhpc viability and function via the caspase dependent intrinsic apoptotic pathway . Interestingly , these data also suggest that dihydroceramide and ceramide dienes are likely potent inducers of this pathway and may be involved in the regulation of Akhp cell viability and function . In flies , Akh regulates fat mobilization in the fat body [25] . In mammals , fat is mobilized in adipose tissue by catecholamines and in the liver by glucagon . It will be interesting by analogy to the fly to determine whether SL modulation of ceramide dienes , is involved in the regulation of glucagon production by alpha-pancreatic cells and/or catecholamine production by the adrenal gland . Our results showed a substantial reduction in the S1P: C14:1 ceramide ratio in Sk2 mutants . These data correlated with reduction in postprandial satiety via the hindgut-specific neuropeptide like receptor dNepYr , resulting in caloric intake-dependent obesity . Furthermore , these data show that dietary administration of dRYamide 1 and 2 was sufficient to overcome these effects through induction of dNepYr signaling . In addition , dietary administration of the S1P analog FTY720P was also effective at inducing satiety and downregulation of dNepYr mRNA expression . Conversely , we showed a substantial increase in the S1P: C14:1 ceramide ratio in Sply mutants . These flies exhibit suppressed appetites , which also appear to be dependent upon dNepYr . Sply;dNepYr double knockdowns exhibited increased caloric intake and TG levels comparable to dNepYr knockdowns . These data suggest that S1P acts upstream of dNepYr and induces satiety via dNepYr signaling . Since de novo S1P production is dependent upon the availability of both dietary saturated fats and ATP , it is an attractive candidate molecule for regulating satiety via dNepYr , which is expressed almost exclusively in the hindgut of the closed digestive system . In addition , a set of pancreatic TG lipases , which appear to be overlapping cis-NATs of dNepYr , exhibit co-expression patterns and thereby may constitute a coordinated system for dietary fat digestion and satiety .
All fly lines were propagated on synthetic yeast based media as described [8] . The following lines ( and relevant CG stock numbers ) as listed below: Canton S ( wild-type flies ) , yw , Sply05901 ( BL-11393 ) , lacek05305 ( BL 12176 ) , lace2 ( BL 3156 ) , Sk2k6050894 , ifc4 ( BL-1549 ) , UAS-dIAP ( BL-6657 ) , Global Actin 5c-Gal4 ( BL 4414 ) . All double mutants were generated using classic genetics in-house ( Herr , et al . 2003 ) . UAS-dNepYr-RNAi , UAS-lace-RNAi , UAS akh-RNAi , UAS-ifc-RNAi , UAS-Sk2-RNAi , UAS-Sply-RNAi , and UAS-CG40733-RNAi were acquired through Vienna Drosophila RNAi Stock Center Akh-gal4 , UAS-GFP driver lines were graciously provided by the lab of Dr . Ronald Kuhnlein . Nearest human homologues to fly sphingolipid proteins were determined using protein sequences from Flybase [13] . Blastp results for these sequences against the human proteome were performed at NCBI . Nearest human homologues were determined as the nearest hit based on identity and e-values . The accession number for these proteins was provided . The dNepYr sequence was provided by Flybase . Multiple sequence alignment was performed with ClustalW and the 7TM domain structural motif determined by Chou-Fastman plot . Measurement of major sphingolipid intermediates was performed using liquid chromatography tandem mass spectroscopy ( LC/MS/MS ) on a Thermo Finnegan triple quadrupole machine . Fly lipid extracts were prepared as previously described [26] . C18 standards of sphingosine , S1P and ceramide ( not present in Drosophila ) were acquired through Avanti Polar Lipids and used as internal standards in positive-mode multiple reaction monitoring ( MRM ) . MRM experiments were adapted from those previously described so that only major sphingolipid species previously identified in Drosophila were monitored , identified , and quantified [27] . Drosophila TG levels were determined using Infinity TAG Reagent kits as previously described [28] . Protein concentrations were determined using the Bradford-Lowry assay . TAG levels were calculated in micrograms of TG per milligrams of protein and then presented as a percentage of the control TG levels . TG levels represent a mean value of triplicate measurements of 50 flies , with corresponding standard deviations , performed six times . Fly stocks were cleared and newly emerged flies collected at the end of 6 hours . Each fly line was sorted into 10 food vials with 10 flies each , 5 males and 5 females , until 2 days of age . Flies were then transferred to agar-vials which provided only a supply of water . Survival rate was determined by the regular counting of non-responsive flies . The experiment was performed in triplicate , with each time point representing the mean based on 300 flies per line . These experiments were adapted from previous studies [9] . Fat cells were extracted from 1 day old ( ±6 hours ) flies , spilled into Ringer's buffer , fixed with 4% paraformaldehyde , stained with Nile red ( 10 ug/mL ) and DAPI ( 1 ul/mL ) . Fat cells were then immediately photographed using a Canon 1500 digital camera on a compound light microscope at 400×magnification . The area of each cell was calculated using ImageJ Imaging Software . Each value represented is the mean of 50 random fat cell measurements taken per fly with the corresponding standard deviation . The experiment was performed in triplicate with the data representing a final mean of 150 cells . Lipid droplet size was assessed in 10 representative fat body cells for each line based on mean fat body cell size . The area of the 10 largest lipid droplets within each cell was measured . A second measure of nile red positive area was performed using a preset color threshold across all FBs and lines . This area was quantified as a % area of the total cell area . Total RNAi was isolated from 25 whole flies with a Qiagen RNeasy Isolation kit as per manufacturer's protocol . Total RNA ( 1 ug ) was checked for quality and purity using gel electrophoresis and UV spectrophotometry . RNA was subsequently reverse-transcribed using an iScript cDNA synthesis Kit from Biorad as per manufacturer's protocol . Primers were designed using Perl Primer software and ordered from ValueGene . Standard PCR was used to test primer sets for single amplicon products . Annealing temperature of the primers was optimized and One-Step qPCR was carried out using a BioRad iCycler IQ . Housing keeping gene RPL-32 was used as a control . RNA was isolated from 25 female flies aged 2 days using a Qiagen RNA isolation Kit . Affymetrix chips were used with oligo targets for 14 , 131 known Drosophila genes . Statistical validation of duplicate experiments results were carried out using Affymetrix software Gene Spring . Heat mapping analysis of metabolic gene subsets was carried out using open source MeV software provided generously to the public by TIGR . The gene subsets selected were based on globally expressed Drosophila metabolic genes found to be homologous to mammalian genes by KEGG Pathways using DAVID ( david . abcc . ncifcrf . gov/ ) . Pearson correlation coefficients ( multiple r ) were calculated using the excel correlation data function to measure the degree of correlation between normalized SL metabolites and various metabolic parameters , including 2 , 8 and 15 day TG levels , caloric intake , Akh mRNA expression , and dNepYr mRNA expression . Coefficients were determined using relevant data points across all fly lines ( wt , lace , ifc , Sk2 , Sply , lace; Sply , and ifc; Sk2 ) normalized to wt levels . P-values were calculated using a standard t-test . | Obesity is characterized by excessive weight gain that increases one's risk for pathologies such as Type II diabetes and heart disease . It is well-known that a high calorie diet rich in saturated fats contributes to excessive weight gain . However , the role that saturated fats play in this process goes far beyond simple storage in fat tissue . Saturated fats are essential building blocks for the bioactive lipid ceramide . Accumulation of ceramide has recently been associated with obesity . However , it is not known whether its accumulation plays an active role in the induction of obesity . Here , we utilized genetic manipulation in Drosophila to accumulate and deplete a variety of ceramide species and other related lipids . Our results showed that modulation of ceramide and related lipids is sufficient to induce obesity through two distinct mechanisms: a caloric intake-dependent mechanism works through suppression of neuropeptide Y satiety signaling , while a caloric intake-independent mechanism works through regulation of hormone producing cells that regulate fat storage . These data implicate ceramides in actively promoting obesity by increasing caloric intake and fat storage . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Identification of Sphingolipid Metabolites That Induce Obesity via Misregulation of Appetite, Caloric Intake and Fat Storage in Drosophila |
The risk of human infection with sylvatic chikungunya ( CHIKV ) virus was assessed in a focus of sylvatic arbovirus circulation in Senegal by investigating distribution and abundance of anthropophilic Aedes mosquitoes , as well as the abundance and distribution of CHIKV in these mosquitoes . A 1650 km2 area was classified into five land cover classes: forest , barren , savanna , agriculture and village . A total of 39 , 799 mosquitoes was sampled from all classes using human landing collections between June 2009 and January 2010 . Mosquito diversity was extremely high , and overall vector abundance peaked at the start of the rainy season . CHIKV was detected in 42 mosquito pools . Our data suggest that Aedes furcifer , which occurred abundantly in all land cover classes and landed frequently on humans in villages outside of houses , is probably the major bridge vector responsible for the spillover of sylvatic CHIKV to humans .
Chikungunya virus ( CHIKV , genus Alphavirus , family Togaviridae ) is maintained in a sylvatic cycle in West Africa , where it is transmitted by a suite of sylvatic Aedes mosquito species among a group of reservoir hosts , including African green monkeys ( Chlorocebus sabaeus ) , patas monkeys ( Erythrocebus patas ) and Guinea baboons ( Papio papio ) , and possibly reservoir hosts in other orders of mammals [1]–[3] . Moreover , CHIKV has a history of emergence into humans followed by sustained human-to-human transmission , with the peridomestic mosquito Aedes aegypti serving as the primary vector [1] , [3] . Aedes albopictus also serves as a vector of CHIKV in the human cycle . Indeed , this species , which originated from Asia , is a rapidly expanding exotic species in the Americas , Europe and Africa [3] , [4] and was responsible for explosive CHIKV outbreaks in the Indian Ocean , Asia , Europe and Central Africa [1]–[6] . CHIKV infection results in an acute febrile disease accompanied by debilitating arthralgia that begins soon after infection but can persist for years [6]–[8] . CHIKV is usually confined to Africa and Asia . However recent transmission following the arrival of infected travelers has been observed in Europe [3] and there is considerable concern that CHIKV will invade the Americas , where both of its major peridomestic vectors are abundant and infected travelers have arrived from Asia and the Indian Ocean [9] . Although past studies have documented the ability of CHIKV to spill over from sylvatic habitats into humans in West Africa , little is known about the environmental factors that influence the risk of human infection or the participation of specific vector species in transmission from zoonotic reservoir hosts to humans . In eastern Senegal , amplifications of sylvatic CHIKV have been detected in mosquito pools in 1975 , 1979 , 1983 , and 1992 in the Kédougou region . During these amplifications , CHIKV was isolated there from humans ( one strain in 1975 and two strains in 1983 ) and monkeys ( Cercopithecus aethiops in 1972 , Papio papio in 1975 and Erythrocebus patas in 1983 ) [2] , [10] . Following the 2003 amplification , a human outbreak of CHIKV occurred in 2004 in Kedougou among Peace Corps volunteers . In Western Senegal , three epidemics of CHIK fever have also been reported in 1966 , 1982 , and 1996 [2] . All of these data indicate frequent infection of humans by sylvatic CHIKV in southeastern Senegal . This transmission to humans may occur due to the movement of people into foci of infection in the forest , or to the movement of infected sylvatic vectors into areas occupied by humans . There is a low probability that humans are infected in the forest itself , as humans frequent the forest during daytime while the vectors described above are active at night . However , humans could be infected by sylvatic vectors in other biotopes that they enter at dusk or at night for farming purposes , or while commuting between their place of work and their village . Nonetheless , vector movement seems the more likely explanation for human infection , as dispersal of sylvatic Aedes vectors , particularly Ae . furcifer , into villages is well documented in Senegal [11] , [12] and elsewhere in Africa [13] . In the current study , we sought to better understanding the environmental factors that influence the risk of human infection by CHIKV by rigorously testing the association between specific land cover elements and the abundance of Aedes vectors and of CHIKV infection of those vectors . We measured both the distribution and infection of vectors in multiple sampling plots within 5 different land cover classes ( forest , savanna , barren , agriculture and village ) and also the distribution and infection of these vectors within and among individual villages .
Our study was undertaken in the Kédougou Region of southeastern Senegal ( 12°33 N , 12°11 W ) close to the borders of Mali and Guinea ( Figure 1 ) . The area ( 1 , 650 km2; 30 km in north-south and 55 km in east-west direction; center coordinates ∼12°36′N , 12°18′W ) is located in the shield region of Senegal , with natural vegetation comprised of a mosaic of open savanna , woody savanna , outcrops of laterite ( bowé ) , and relictual gallery forest , the latter concentrated along valleys and rivers [14] . Deforestation for cultivation and human habitations , as well as desertification , has greatly reduced the forested area , as in many other sub-Saharan regions of Africa . Characterized by a tropical savanna climate [15] , the Kédougou region receives an average of 1 , 300 mm of total annual rainfall , with one rainy season from approximately May through November , and mean temperatures varying between about 25–33°C during the year ( Figure 2 ) ( http://www . worldclimate . com/ ) . The human population of the region is ca . 80 , 000 , of whom 55% are under the age of 20 . It is primarily rural ( 84% ) with a low density of inhabitants ( 4/km2 ) , mostly living in small , dispersed villages averaging 60 inhabitants . The economy depends on horticulture and cattle farming , along with hunting , gathering and harvesting wood for crafts , necessitating human contact with forests . The primate fauna of the region includes three species , Guinea baboons , patas monkeys , and African green monkeys , which are known reservoir hosts of CHIKV [1] , [2] . A six-stage sampling scheme , summarized in Figure 3 , was used to identify ten sampling sites in each of the five predominant land cover classes ( village , agriculture , barren , savanna , forest ) in the study area . Stage I aimed at minimizing spatial autocorrelation among data collected in any given land cover type and entailed the division of the study area into ten equally sized sampling blocks ( i . e . , 5 north and 5 south of the central east-west line ) , each of which would eventually contain one representative sampling site per land cover class . In Stage II , a land cover map was generated by means of a maximum likelihood supervised classification of Landsat 5 Thematic Mapper satellite imagery acquired on June 11 , 2009 ( WGS Path 201/Row 51 ) . Stage III entailed the extraction of only those areas from the land cover map that would likely be accessible in the field , and was accomplished by reducing the land cover map to a one-kilometer buffer around major roads . In Stage IV , three 2-hectare sites were randomly selected within each of the five land cover classes ( i . e . , strata ) , within each of the 10 blocks , and within the one-kilometer buffer zone around major roads . Of the 150 sites , only one site per land cover and block was retained for mosquito sampling . However , three potential sites were identified initially because accessibility and land cover map accuracy in those specific sites were unclear prior to actual inspections . Stage V involved field visits of the 150 sites: sites that were accessible and representative of the mapped land cover type were retained for the final sampling site selection process ( Stage VI ) ; sites that were either inaccessible or unrepresentative of the mapped land cover type were removed from the pool of potential final sampling sites . As a result of Stage V endeavors , Block A1 had to be removed entirely from subsequent analyses due to inaccessibility . To avoid losing 5 sampling sites , Block D2—the most complex and centrally located block—was subdivided into two sub-blocks and Stages IV and V repeated in each . Finally , in Stage VI , one sampling site per land cover class per block was selected randomly from the pool of potential final sampling sites identified in Stage V . Mosquitoes were sampled via human landing collections , the only effective method for sampling sylvatic Aedes and the most appropriate method for determining human risk of infection . Teams of three collectors working simultaneously in forest , savanna , agriculture , village and barren sites in a particular block from 6–9 PM , based on previous data on biting periodicity [12] , collected all mosquitoes that landed on their legs . In each of the ten forest sites , mosquitoes were collected at ground level by 3 collectors . Additionally , in eight of the blocks ( A2 , B1 , B2 , C1 , C2 , D1 , E1 and E2 ) , a 9 m high platform was erected to enable collection by an additional 3 persons in the forest canopy . In each village , mosquito sampling was conducted by 6 landing collectors per evening . Five houses were selected in the village , following a transect going from one periphery to the opposite periphery via the center ( one house in the center , one in each of the periphery sites , and one between each periphery and the center ) . Each sampling evening , one indoor and one outdoor collector were positioned at each house . On a given night , collectors would be set up at three houses on one half of the transect: one on the periphery , one at the middle point between the periphery and center , and one at the center . On the next night they were positioned on the opposite side to avoid bias due to possible vector confinement within villages . Sampling was performed monthly for 1 to 4 consecutive nights in each block . At the end of each collection evening , mosquitoes were frozen and then sorted on a chill-table using morphological identification keys established by Edwards [16] , Ferrara et al . [17] , Huang [18] , and Jupp [19] for the culicines and by Diagne et al . [20] for the anophelines . Mosquitoes were sorted into monospecific pools of up to 40 individuals and frozen in liquid nitrogen for virus detection attempts . The ovaries from a sample of the unengorged mosquitoes were dissected on a slide containing distilled water . The degree of coiling of ovarian tracheoles was then observed to determine whether the female was parous or nulliparous [21] . To attempt virus isolation , monospecific mosquito pools were homogenized in 2 . 5 ml of Leibovitz 15 cell culture medium containing 20% fetal bovine serum ( FBS ) and centrifuged for 20 min at 10 , 000× g at 4°C . For each homogenate , 1 ml of the supernatant was inoculated into AP61 ( Ae . pseudoscutellaris ) or Vero African Green kidney cells as described previously [22] . Cells were incubated at 28°C ( AP61 ) or 37°C ( Vero ) , and cytopathogenic effects recorded daily . Within 10 d , slides were prepared for immunofluorescence assay ( IFA ) against 7 pools of immune ascitic fluids specific for most of the African mosquito-borne arboviruses . Viruses were identified by complement fixation and seroneutralization tests by intracerebral inoculation into newborn mice , as approved by the UTMB Institutional Animal Care and Use Committee . For the real-time PCR assay , 100 µl of supernatant were used for RNA extraction with the QiaAmp Viral RNA Extraction Kit ( Qiagen , Heiden , Germany ) according to the manufacturer's protocol . RNA was amplified using real-time RT-PCR assay and an ABI Prism 7000 SDS Real-Time apparatus ( Applied Biosystems , Foster City , CA ) using the Quantitect kit ( Qiagen , Hilden , Germany ) . The 25 µl reaction volume contained 1 µl of extracted RNA , 2x QuantiTect Probe , RT-Master Mix , 10 µM of each primer and the probe . The primer and probe sequences used those of Weidmann et al . ( manuscript in preparation ) for CHIKV , including the primers RP-CHIK ( CCA AAT TGT CCY GGT CTT CCT ) and FP-CHIK ( AAG CTY CGC GTC CTT TAC CAA G ) and the probe P-CHIK ( 6FAM –CCA ATG TCY TCM GCC TGG ACA CCT TT- TMR ) . The following thermal profile was used: a single cycle of reverse transcription for 10 min at 50°C , 15 min at 95°C for reverse transcriptase inactivation and DNA polymerase activation followed by 40 amplification cycles of 15 sec at 95°C and 1 min 60°C ( annealing-extension step ) . Fluorescence was analyzed at the end of the amplification . For analysis of the distribution of vector species among land cover classes , the average per site of female mosquitoes/person/evening ( F/P/E ) was used as a measure of absolute abundance . Abundance data were log transformed ( log10 ( n+1 ) ) and analyzed using ANOVA followed by a Tukey-Kramer post-hoc test . In the case of Ae . africanus , there were too many zero values to conduct a valid ANOVA , so abundance data were recoded as present or absent in a designated site and compared using a contingency table analysis . Comparison of vector abundance between villages was conducted similarly . To analyze the distribution of each vector species in the periphery , middle and center of villages , the average abundance of a given species in each of the three regions of each of the 10 villages , collected outside of houses , was compared using ANOVA . For comparison of the abundance of all species in the periphery versus the center of the village , a paired t-test was used to compare the mean abundance , averaged across the 10 villages , of each of the 6 species at the periphery and center . Spatial patterns of vector abundance were assessed using both global and local measures of spatial autocorrelation . At the global level , we quantified spatial autocorrelation with standard and cumulative spatial correlograms of Moran's I [23] , i . e . , graphs of Moran's I coefficients on the ordinate plotted against distance classes on the abscissa . We used eleven distance classes ( 0 to 5 , 000 m , 5 , 000 to 10 , 000 m , 10 , 000 to 15 , 000 m , etc . for the standard correlogram and 0 to 5 , 000 m , 0 to 10 , 000 m , 0 to 15 , 000 m , etc . for the cumulative correlogram ) , a compromise between Sturge's rule [24] and a straightforward lag distance , and an inverse distance weighting scheme . To test the significance of individual Moran's I coefficients at the 0 . 05 level , we used 9 , 999 permutations and a progressive Bonferroni correction to account for multiple testing . A correlogram was considered globally significant at the 0 . 05 level if at least one of the autocorrelation coefficients was significant at the Bonferroni-corrected level [25] . All Moran's I coefficients were computed using PASSaGE [26] . Moran's I values range from −1 ( indicating dispersion ) to +1 ( indicating correlation ) . Negative values indicate negative spatial autocorrelation; positive values indicate positive spatial autocorrelation; a zero value indicates a random spatial pattern . At the local level , we quantified spatial autocorrelation with Anselin's [27] Local Indicators of Spatial Association ( LISA ) statistic using weights based on the four nearest neighbors , 9 , 999 permutations , and a 0 . 05 pseudo significance level . Statistically significant LISA statistics include two types of positive spatial autocorrelation ( HH = High values surrounded by High values; LL = Low values surrounded by Low values ) and two types of negative spatial autocorrelation ( LH = Low values surrounded by High values; HL = High values surrounded by Low values Parous and infection rates were compared using a contingency table analysis . The index of parous and biting was also calculated . Both analyses were conducted in StatView 5 . 0 ® ( SAS Institute , San Francisco , CA ) or JMP ® ( SAS Institute , Cary , N . C . ) . The pooled infection rate program ( PooledInfRate , version 3 . 0 , Center for Disease Control and Prevention , Fort Collins , CO: http://www . cdc . gov/ncidod/dvbid/westnile/software . htm ) was used to calculate minimum field infection rates with a scale of 1 , 000 and the 95% confidence intervals for the species found positive for CHIKV .
Between June 2009 and January 2010 , 39 , 799 mosquitoes were collected comprising 50 species within 6 genera ( Table 1 ) . Among host-seeking females of known or suspected CHIKV vectors , Ae . vittatus ( 22 . 98% ) , Ae . furcifer ( 18 . 66% ) , Ae . dalzieli ( 15 . 63% ) and Ae . luteocephalus ( 13 . 05% ) had the highest relative abundance and Ae . taylori ( 2 . 00% ) , Ae . africanus ( 1 . 71% ) and Ae . aegypti ( 1 . 24% ) had the lowest relative abundance . Absolute vector abundance showed considerable seasonal variation: Ae . vittatus , Ae . luteocephalus and Ae . aegypti reached their peak abundance in June at the beginning of the rainy season and declined drastically during the following months ( Figure 2 ) . Other species peaked twice between July and November 2009 . Indeed , Ae . africanus exhibited 2 peaks of roughly equal level in August and October . The patterns of precipitation and temperature over the mosquito sampling period are shown in Figure 2 . With a total precipitation of 1087 . 3 mm ( http://www . tutiempo . net/en/Climate/Kedougou/616990 . htm ) , 2009 had a lower rainfall compared to the average of 1263 mm between 1967 and 1990 ( www . worldclimate . com ) . Total vector abundance peaked at the start of the rains in 2009 in June and declined thereafter as rainfall increased and temperature decreased . However there was a second , albeit much smaller peak in November as rainfall dropped off abruptly and temperatures began to climb . Potential sylvatic CHIKV vectors also showed significant variation in their distributions among land cover classes ( Table 2 ) . All species were collected in all land cover classes , with the notable exception of Ae . africanus , which was absent from barren , agricultural and indoor village sites . A contingency table analysis showed a significant difference in the distribution of Ae . africanus among land cover classes ( χ2 = 25 . 9 , df = 6 , P = 0 . 0001 ) ; results of the remaining statistical comparisons of absolute abundance are listed in Table 2 . Importantly , all of the mosquito species showed significant differences in absolute abundance among land cover classes except for Ae . furcifer , which showed similar abundance in all five classes ( F = 2 . 13 , df = 6 , 61 , P = 0 . 062 ) . This species had it highest abundances in the forest-canopy and the village-outdoor . However Ae . furcifer preferred the village outdoor environment , and was significantly more abundant outdoors than indoors in villages . Moreover , compared to the others vectors , it also had the highest abundance in village environment both outdoor and indoor . Indeed , the ratio of the abundance of Ae . furcifer to Ae . dalzieli and Ae . taylori in village-outdoor was 4 . 5:1 and 146 . 0:1 , respectively . Aedes africanus , Ae . luteocephalus and Ae . taylori were most abundant in the forest , particularly in the forest canopy . Aedes aegypti was most abundant in the forest at ground level . Aedes vittatus was most abundant in barren , agricultural and ground level forest sites while Ae . dalzieli was most abundant in savannah . The global abundance of CHIKV vectors was comparable across all land cover classes but was significantly lower inside of houses in villages than in any other sites . As shown in Figure 4 , the spatial correlograms of Ae . aegypti , Ae . africanus , Ae . furcifer , Ae luteocephalus , and Ae taylori were not significant ( p>0 . 05 ) , indicating that the abundance of these vectors exhibited no global spatial autocorrelation . Ae . dalzieli exhibits significant positive spatial autocorrelation only in the first distance class and Ae . vittatus significant negative spatial autocorrelation in distance classes 3 and 4 . The standard correlogram for the abundance of all vectors suggests significant positive spatial autocorrelation in the first distance class; spatial autocorrelation in subsequent classes is not significant at the Bonferroni-corrected significance level . The cumulative correlograms suggest that most of the spatial autocorrelation in our vector abundance data occured in the first lag ( 0 to 5 , 000 m ) . The vectors with no global spatial autocorrelation generally exhibited the least amount of local spatial autocorrelation ( Figure 5 ) . Ae . aegypti exhibited some positive spatial autocorrelation ( LL: A2 urban and A2 savannah ) , Ae . africanus some negative spatial autocorrelation ( LH: A2 barren and B2 urban ) , Ae . furcifer mostly positive spatial autocorrelation ( HH: A2 barren , B2 urban , and B2 forest ) , and Ae taylori mostly negative spatial autocorrelation ( LH: in Block C1 ) . Aedes luteocephalus showed very notable clusters of positive spatial autocorrelation ( LL in Blocks D2 and D2′ ) and Ae . vittatus has mostly positive spatial autocorrelation ( HH in Blocks C1 and D1 and LL in Block D2 ) . The LISA map for abundance of all vectors ( Figure 5 ) showed that , when combined , there was essentially no local negative spatial autocorrelation . Spatial autocorrelation in the western half of the study area was mostly non-significant . Positive spatial autocorrelation clusters were quite common , with hot spots ( HH clusters ) limited to the northern half ( Blocks C1 and D1 ) and cold spots ( LL clusters ) to the east/southeast ( Blocks D2 , D2′ , and E1 ) . The majority of mosquitoes dissected were parous for all species ( Tables 3 and 4 ) . However , Ae . africanus showed the highest parous rate ( P<0 . 0001 ) , while Ae . vittatus had the lowest . The monthly parous rates of each vector , except Ae . africanus ( P = 0 . 06 ) , were significantly different and the highest rates were observed in October , November and December , when almost all females were parous ( Table 3 ) . The index of parous rate/biting rate increased from August to December except for a drop in November for Ae . taylori . All the vectors except Ae . furcifer , Ae . vittatus and Ae . luteocephalus had high and statistically comparable parous rates in the different land cover classes ( Table 4; P>0 . 1 ) . The highest parous rates for both Ae . furcifer ( P = 0 . 02 ) and Ae . vittatus ( P = 0 . 02 ) were in the village sites and the highest rates for Ae . luteocephalus were in the savanna and village sites ( P = 0 . 06 ) . Within villages , 5 , 573 mosquitoes were collected , representing 38 species within 6 genera; Table 2 shows absolute abundance of these species . Aedes furcifer ( 34 . 7% of the mosquitoes collected ) , Ae . vittatus ( 25 . 4% ) , Ae . minutus ( 13 . 1% ) , Ae . dalzieli ( 8 . 5% ) , Culex quinquefasciatus ( 5 . 9% ) , Ae . luteocephalus ( 2 . 4% ) and Ae . aegypti ( 3 . 1% ) had the highest relative abundance . Aedes taylori , representing only 0 . 3% of the mosquitoes collected , had the lowest relative abundance within the villages . None of the individual species differed significantly in their absolute abundance in the periphery , middle and center of villages ( Table 2 ) . However , when mean abundance of each of the six species of mosquitoes was compared at village periphery versus center , abundance was found to be significantly higher at the periphery ( paired t-test , df = 5 , t = 2 . 6 , P = 0 . 048 ) . Large and statistically significant differences in absolute vector abundance were observed among villages ( Figure 6 ) . Aedes africanus and Ae . taylori had low abundance and were collected at one village ( E1 ) and 5 of the 10 villages ( B1 , B2 , C1 , E1 and E2 ) , respectively . Absolute abundance of Ae . vittatus and Ae . dalzieli were highest in the village in block D2′ , while absolute abundance of Ae . aegypti was highest in the village in block D1 and that of Ae . luteocephalus was highest in the villages in blocks C1 and B1 . Aedes furcifer was least abundant in villages in blocks C2 and D2′ . In total , potential CHIKV vectors were present at all villages but were most abundant at the village in D1 ( Ngari ) and least abundant at villages C2 and D2′ . CHIKV was detected in 42 of the 4 , 211 mosquito pools collected from June , 2009 to January , 2010 . Table 1 lists the number of pools and CHIKV infection rates of mosquito species . The 42 infected pools were distributed as follows: Ae . furcifer ( 15 pools of females and 1 of males ) , Ae . taylori ( 5 female pools ) , Ae . dalzieli ( 4 female pools ) , Ae . luteocephalus ( 5 female pools ) , Ae . africanus ( 2 female pools ) and Ae . aegypti , Ae . metallicus , Ae . neoafricanus , Ae . centropunctatus , Ae . hirsutus , An . domicola , An . funestus , An . coustani , Mansonia uniformis and Cx . poicilipes ( 1 female pool each ) captured in September , October , November and December . No CHIKV was detected in mosquitoes collected in the other months . These data represent the first detection of CHIKV in Ae . metallicus , Ae . centropunctatus , Ae . hirsutus , An . domicola , and Cx . poicilipes , and the first observation of CHIKV in a male Ae . furcifer from Senegal . Mean infection rates among species differed significantly ( P<0 . 05 ) . Higher and statistically comparable infection rates were observed in Ae . furcifer males , Ae . taylori , Ae . centropunctatus , Ae . metallicus , Ae . hirsutus , An . domicola and Cx . poicilipes females ( P = 0 . 48 ) . Taking into account the temporal dynamics of CHIKV , the highest infection rates were those of An . domicola in October , Ae . centropunctatus in November and Ae . furcifer males in December . Detailed characterization of the CHIKV isolates and sequences will be described separately . CHIKV infection rates showed temporal and spatial variation . They were higher in December for Ae . furcifer , Ae . luteocephalus Ae . taylori and Ae . dalzieli . The differences were statistically significant except for Ae . taylori ( P = 0 . 42 ) and Ae . luteocephalus ( P = 0 . 2 ) . CHIKV was detected from mosquitoes collected in 8 of 10 blocks ( A2 , B1 , B2 , C1 , C2 , D1 , E1 , E2 ) and in all land cover classes ( Table 1 ) , including 7 forest ( 24 pools ) , 3 savanna ( 5 pool ) , 3 barren ( pools ) , 2 agricultural ( 4 pools ) and 3 village ( 5 pools ) sites . To assess variation among land cover classes , each site was coded as positive ( at least one CHIKV-positive pool ) or negative ( no CHIKV-positive pools ) . Based on this coding , there was no significant association between land cover class and presence of CHIKV ( χ2 = 8 . 0 , df = 4 , P = 0 . 09 ) . However , there was a significant difference among blocks ( χ2 = 17 . 7 , df = 9 , P = 0 . 04 ) , with CHIKV being detected in all land cover sites in block D1 , no land cover sites in blocks D2 and D2′ , and some but not all sites in the remaining blocks . There was a significant , positive correlation between total vector abundance and the number of CHIKV-positive pools across sites ( Spearman rank correlation , N = 50 , P = 0 . 003 ) .
The mosquito fauna of the Kédougou region is very diverse . Since the initiation of entomological studies in the area , over 102 species belonging to more than 7 genera have been collected [2] , [11] , [28] , [29] . This high diversity is due to the availability of a wide variety of larval habitats ( such as clean slow-running streams and ponds , temporary and semi-permanent pools , and small water collections on the ground or phytotelmata ) , vertebrate hosts , nectar sources , resting and mating places . However , the amount of diversity detected varies widely among studies , depending on specific sampling methods used ( human landing collections alone or with animal baited trap , and larval sampling ) and the time period and area covered . The goal of this study was to determine when and where humans may be exposed to sylvatic CHIKV infection and to identify the bridge vectors responsible for such spillover . To accomplish this , we measured the relative abundance and parity of all putative vectors across different land cover classes at the onset of , during , and immediately after the rainy season . Additionally we conducted detailed sampling within villages to assess exposure to vectors inside versus outside of houses and at the center versus the periphery of villages . The study was specifically designed to avoid spatial autocorrelation by random selection of sampling sites within larger sampling blocks , and as expected we detected minimal levels of such autocorrelation . We collected few potential CHIKV vectors inside houses , indicating an exophagic feeding behavior of these mosquitoes . However , these vectors actively sought human hosts in all land cover classes investigated . In the evening , when the vectors peak in landing rates [11] , [30] , humans are generally within villages , suggesting that most exposures to sylvatic arboviruses occurs within villages in this region . Additionally , the majority of mosquitoes we collected were parous , indicating that they were in their second or a subsequent gonotrophic cycle and thus had high vectorial capacity . The season increase in the index of parous rate/biting rate suggests little or no recruitment of new mosquitoes to the biting population in October , November and December . Parous rates of vectors were higher in villages than other land cover classes , so humans are at risk of being infected by sylvatic CHIKV in every type of land cover we sampled , but are at greatest risk while outside of houses within villages . Across all species , vector abundance was higher at the periphery of villages than in the center , suggesting that vectors invade villages from surrounding land cover types and that risk of infection may therefore be highest at the edges of villages . The unexpectedly high host seeking activity of mosquitoes in land cover classes where their known , preferred hosts ( humans and monkeys ) are not generally present , such as barren areas , suggests that they probably feed on other crepuscular or nocturnal vertebrates . These other species could also be involved in undocumented enzootic cycles of CHIKV in the Kédougou area , as has been suggested by associations of CHIKV with birds , bats and other mammals in Africa [2] , [31] , [32] , [33] . A more comprehensive understanding of the enzootic ecology of this virus in the region will require the identification of other potential vertebrate hosts and the description of their roles in the sylvatic cycle of CHIKV . Collection and identification of bloodmeals from feral , engorged vectors will be necessary to achieve this objective . We associated five mosquito species with CHIKV for the first time . These new associations may reflect the wide spatial and seasonal scope of our study , since all the previous studies of CHIKV in the Kédougou area focused on only one forest-gallery site and a few villages . Detection of CHIKV from a male Ae . furcifer in the Kédougou region during our investigation , and in Ivory Coast [34] , may suggest vertical transmission of this virus . Dengue and yellow fever viruses have also been detected in male Ae . furcifer and Ae . furcifer-taylori in Kédougou in previous studies [11] , [35] . The ecology of sylvatic Aedes mosquitoes in Africa has been well studied because of their role in the transmission of yellow fever virus [30] , [35] . We demonstrated that the distribution of some vector species , such as Ae . luteocephalus , Ae . taylori and Ae . africanus , was largely restricted to the forest canopy . This observation is consistent with most similar studies in East and West Africa [36] , [37] , although Ae . africanus was collected within human settlements and inside houses in southeastern Nigeria [38] , [39] . In combination with data suggesting that these mosquitoes feed only during the evening [12] , [30] , our data suggest that these exophilic species are primarily involved in the maintenance of the zoonotic , sylvatic cycle of CHIKV with little impact on spillover into humans . Aedes furcifer , in contrast , had high and comparable abundance in the forest canopy and in villages outside houses . It was the only species that frequently contacted humans in villages , corroborating previous observations [11] , [40] . Abundance of this species differed significantly among villages and occurred at lowest density in the two most developed of the ten villages we studied . This species is also the only one of the putative sylvatic vectors that is commonly infected with sylvatic arboviruses within villages in the area [11] , [40] . Thus it is likely that Ae . furcifer is the principal vector for spillover of sylvatic arboviruses into humans in this area . However , the extreme generalism of Ae . furcifer for different land cover classes is unusual , and we caution that investigation of the population genetics of this species is warranted before firm conclusions can be made about its role as spillover vector . The fact that the CHIKV was detected in 3 of the 10 villages , and that the distribution of CHIKV was significantly different among sampling blocks , suggests that the risk of transmission to humans may be localized or spatially or temporally heterogeneous . These findings also suggest the need to further characterize the different land cover classes in order to identify subclasses that could differ among blocks . Vector abundance showed a positive correlation with the number of CHIKV-positive pools detected at a site , but vector density may not be the only explanation for variation in the distribution of CHIKV , and therefore this phenomenon merits further study . For example , these three villages in which CHIKV was detected are the closest to gallery forests of the ten villages studied . Although Ae . dalzieli and Ae . vittatus were widely distributed within the study area ( in forest floor , savanna , barren and agricultural sites ) , and had high abundance in some villages , they have never been found infected with CHIKV within villages in the Kédougou area . Thus , these two species could be involved in virus dissemination from the forest to other land cover classes and could also play a role in potential secondary transmission cycles of the virus among as-yet unidentified species , but are unlikely to be important for spillover of sylvatic CHIKV . Aedes aegypti showed low human landing rates in all land cover classes . Previous studies have also found that Ae . aegypti did not land on humans in high numbers in the Kédougou area [11] , [12] . The low abundance of human-seeking Ae . aegypti females despite high larval population density of this species in villages is probably due to its zoophilic tendency in West Africa [41] , [42] . Indeed , only the sylvatic form , Ae . aegypti subspecies formosus , occurs in the Kédougou area [43] , and this subspecies is thought to feed mainly on wild animals other than primates . Thus , although Ae . aegypti aegypti is the main CHIKV epidemic vector worldwide [1] , [8] , [44] , Ae . aegypti formosus probably plays no major role in either maintenance of sylvatic cycle or spillover to humans in this area . In summary , our data give new insight into the temporal and spatial dynamics of the extraordinarily diverse guild of sylvatic CHIKV mosquito vectors in an area where , at regular intervals , this virus undergo amplifications in their animal reservoirs that result in spillover infection of humans . While many vectors may participate in maintenance of sylvatic CHIKV , Ae . furcifer is most likely to be responsible for spillover into humans due to its broad land cover preferences and rates of human contact within village perimeters . This information can be used to inform the local population of the places and times of greatest risk for exposure so that mosquito avoidance or protective measures can be implemented . The detection of CHIKV-infected mosquito pools only during the rainy season was expected , but the aggregation of infected pools in specific sampling blocks , rather than in particular land cover classes , was not . We recognize that limited sampling for only a few hours per day and during only one year could have resulted in some anomalous findings or biased results . Additional surveillance and further analysis will be needed to reveal the ecological factors that shape the distribution of CHIKV; our surveillance efforts in Kédougou are ongoing to accomplish this goal . | Chikungunya is a mosquito-borne virus that infects and sickens people in many tropical , urban regions of the world . This virus circulates in forest cycles of West Africa , where mosquitoes transmit it among non-human primates . It also infects humans via bridge vectors , mosquitoes that feed on both non-human primates and humans . To date , little is known about the environmental factors that influence the abundance and distribution of mosquito vectors that participate in the forest cycle of this virus or about specific mosquitoes that are likely to act as bridge vectors . We studied the distribution and abundance of mosquitoes potentially involved in the forest cycle in southeastern Senegal , as well as their infection by this virus . Satellite imagery was used to classify the region into the 5 most abundant land cover elements , and mosquitoes attracted to humans were collected in sites representing each land cover class . We found that Aedes furcifer , a mosquito that occurs in all land cover types and also enters villages to feed on humans , is probably the most important bridge vector between forest circulation and human populations . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"geography",
"ecology",
"earth",
"sciences",
"biology",
"microbiology"
] | 2012 | Landscape Ecology of Sylvatic Chikungunya Virus and Mosquito Vectors in Southeastern Senegal |
The ABO blood group influences susceptibility to severe Plasmodium falciparum malaria . Recent evidence indicates that the protective effect of group O operates by virtue of reduced rosetting of infected red blood cells ( iRBCs ) with uninfected RBCs . Rosetting is mediated by a subgroup of PfEMP1 adhesins , with RBC binding being assigned to the N-terminal DBL1α1 domain . Here , we identify the ABO blood group as the main receptor for VarO rosetting , with a marked preference for group A over group B , which in turn is preferred to group O RBCs . We show that recombinant NTS-DBL1α1 and NTS-DBL1α1-CIDR1γ reproduce the VarO-iRBC blood group preference and document direct binding to blood group trisaccharides by surface plasmon resonance . More detailed RBC subgroup analysis showed preferred binding to group A1 , weaker binding to groups A2 and B , and least binding to groups Ax and O . The 2 . 8 Å resolution crystal structure of the PfEMP1-VarO Head region , NTS-DBL1α1-CIDR1γ , reveals extensive contacts between the DBL1α1 and CIDR1γ and shows that the NTS-DBL1α1 hinge region is essential for RBC binding . Computer docking of the blood group trisaccharides and subsequent site-directed mutagenesis localized the RBC-binding site to the face opposite to the heparin-binding site of NTS-DBLα1 . RBC binding involves residues that are conserved between rosette-forming PfEMP1 adhesins , opening novel opportunities for intervention against severe malaria . By deciphering the structural basis of blood group preferences in rosetting , we provide a link between ABO blood grouppolymorphisms and rosette-forming adhesins , consistent with the selective role of falciparum malaria on human genetic makeup .
The ABO blood group system of carbohydrate antigen expression on the surface of human red blood cells ( RBCs ) is critically important in transfusion medicine . Several associations have been reported between the ABO blood group phenotype and relative risk of infectious diseases , including malaria [1]–[6] . In the case of Plasmodium falciparum malaria , recent studies have indicated that blood group O confers a protective effect against severe malaria [7]–[9] . The best-documented parasite determinant associated with the ABO blood group is rosetting , the capacity of infected RBCs to bind uninfected RBCs , which is consistently associated with severe malaria in African children [10]–[12] and is reduced inblood group O individuals [9] , [12]–[16] . The hypothesis that group O protects against severe malaria by virtue of reduced rosetting has received strong support in a case-control study in Mali [9] . Although the ABO blood group preference of rosetting has been long known , understanding of its molecular basis is still fragmentary . Rosetting is caused by a sub-group of PfEMP1 adhesins encoded by the large var gene family . The extracellular region of PfEMP1 comprises multiple adhesion domains called Duffy Binding-Like ( DBL ) and Cysteine-Rich Interdomain Region ( CIDR ) [17] . DBL and CIDR domains are classified into different major classes ( α to ε ) and sub-classes by sequence criteria , while the var genes can be classified into specific subfamilies that possess distinctive upstream and downstream flanking regions [18]–[21] . Efforts to unravel the molecular basis of PfEMP1-mediated rosetting are complicated by the mosaic structure of the var genes and the population diversity of var repertoires [20] , [22] . Nevertheless , the rosette-forming PfEMP1 adhesins described so far , namely IT4/R29 [23] , Palo Alto 89F5 VarO [24] , 3D7/PF13_0003 [25] and IT4/var60 [26] , belong to a specific sub-group called groupA/UpsA var genes and , interestingly , all four present a specific DBL1α1-CIDR1γ double domain Head region [19] . Analysis of pseudo-rosettes formed on the surface of COS cells or baculovirus-infected insect cells expressing individual PfEMP1 domains have mapped the RBC adhesion region to the N-terminal DBL1α1 domain [23] , [24] , [26] . Here , we sought to understand the structural basis of the blood group preference in PfEMP1-VarO rosetting . We show that Palo Alto 89F5 VarO parasites bind to RBCs with a marked preference for blood group A compared to blood groups B and O . The binding preference of the PfEMP1-VarO N-terminal domain NTS-DBL1α1 ( termed hereafter DBL1α1 , as we have shown that NTS is a structural component of this domain [27] ) , and the DBL1α1-CIDR1γ double domain ( called hereafter Head ) mirror the ABO blood group preference of VarO parasites . Direct binding of the Head region to blood group trisaccharides was demonstrated by surface plasmon resonance . A more detailed blood group analysis showed that polymorphisms influence binding , with much stronger binding to subgroup A1 than to subgroup A2 , and minimal binding to Ax ( weak variant of A ) RBCs . We determined the crystal structure of the double domain Head protein , the first structure of a multiple domain segment of PfEMP1 , which reveals numerous interactions between the DBL1α1 and CIDRγ domains , and shows specific structural features of the CIDRγ class that differ from the published CIDRα class [28] . Importantly , we clarified the structure of the NTS-DBL1α1 hinge region that was lacking in our previous structural analysis of the DBL1α1-VarO domain [27] . We show that this region is surface-exposed and critical for RBC binding . The structural information obtained from the functional Head protein was used for computer docking and site-directed mutagenesis in order to localize the RBC-binding site . This was mapped to a specific region of the DBL1α1 domain , which is structurally conserved between different rosetting variants and is located on the face opposite to that of the major heparin-binding site . This work identifies the interaction with the ABO group as pivotal in rosetting and associates , for the first time , P . falciparum rosetting with the A subgroups , consistent with a contribution of virulent malaria to the selection of ABO blood group polymorphisms . The molecular description of the RBC-binding site provides novel perspectives for the development of preventive or therapeutic measures to combat severe malaria .
VarO-mediated rosetting was dependent on the presence of a minimum of 5% human serum ( Figure S1A ) , consistent with findings with other laboratory lines [29]–[32] and field isolates [33] . VarO rosetting is not CR1-dependent , as the rosetting rate was not correlated with the CR1 expression level on the recipient RBCs ( Figure 1A ) , was unaffected by the anti-CR1 mAb J3B11 ( which reduces rosetting of the R29 line [34] ) ( Figure 1B ) , and , importantly , was unchanged when CR1 was cleaved with trypsin or chymotrypsin ( Figure 1C ) . Likewise , immunoglobulin binding , implicated in rosetting in some lines [32] , [35] , [36] , apparently does not come into play in VarO rosetting , as VarO-iRBC rosetting was unimpaired in Ig-depleted human serum ( Figure S1B ) and no IgG or IgM binding could be shown on the surface of VarO-iRBCs ( Figure S1C ) . Incubation of VarO-iRBCs with varying ratios of recipient group A and group O RBCs , differentially labeled using lipophilic fluorescent probes PKH67 or PKH26 , showed preferential binding to the former ( Figure 1D ) . Blood group A was also preferred to blood group B but rosette formation was more efficient with group B than with group O RBCs ( Figure 1D ) . This ABO blood group dependence , in particular the reduced binding to group O RBCs , is in line with previous observations with rosetting lines and field isolates [9] , [13] , [14] , [16] . Although VarO rosettes could be readily disrupted by low concentrations of sulphated glycosaminoglycans such as heparin [37] , they could not be disrupted by soluble blood group A or B trisaccharides used in the 20–40 mM range ( data not shown ) , consistent with reports on other rosetting lines [13] , [14] . Because VarO rosettes were resistant to mechanical disruption , we could not test the capacity of trisaccharides to inhibit the reformation of rosettes as done for some rosetting lines [13] , [14] . All six individual PfEMP1-VarO domains and the Head protein ( DBL1α1-CIDR1γ , corresponding to residues 2–716 ) were produced as soluble recombinant proteins in Escherichia coli ( Figures S2A and S2B ) . All proteins used in this study were monomeric , as judged by gel permeation chromatography ( not shown ) . Correct protein folding was ascertained by CD spectroscopy , analytical ultra-centrifugation ( Figure S2C ) and the capacity to induce surface-reacting antibodies ( Ab ) ( Table S1 ) . RBC-binding capacity was monitored by immunoblot and flow cytometry using specific polyclonal sera at dilutions that did not disrupt VarO rosettes . To monitor binding of the Head protein , we used mAb G8 . 49 , which reacted by ELISA with CIDR1γ ( Figure S3 ) . Equal molar amounts of protein were used in all assays . Binding was specific , as no signal was detected when the recombinant domain , the anti-VarO Abs or the secondary anti-IgG Abs were omitted . Of all six individual PfEMP1-VarO domains , only constructs containing the N-terminal DBL1α1 bound RBCs ( Figures 2A and 2B ) . This confirmed previous findings with PfEMP1-VarO domains expressed on the surface of infected Spodoptera frugiperda ( SF9 ) cells [24] and is consistent with reports by other groups [23] , [38] . TheHead protein bound more efficiently than DBL1α1 , the mean MFI being enhanced by about 1 log unit and the immunoblot signal consistently higher for the Head protein than for DBL1α1 . Comparison of binding efficiency of DBL1α1 constructs of varying length showed essentially similar binding for an 18-Cys construct ending with the canonical Cys ( 11 ) xxCys ( 12 ) doublet ( residues 2–471 ) and for the 20 Cys constructs ( residues 2–487 ) with two additional Cys residues at the C-terminus ( corresponding to the structurally complete DBL1α1 domain ) . Presence of a hexa-His tag at the C-terminus did not substantially modify binding ( data not shown ) . As the various recombinant domains had their putative N-glycosylation sites removed ( NxT/S mutated to NxA ) , we also produced recombinant DBL1α1 and Head with a wild-type ( wt ) coding sequence ( DBL1α1 ( wt ) and Head ( wt ) , respectively ) . Seroreactivity was similar for the wt and the corresponding mutated constructs ( Figure S4 ) but DBL1α1 ( wt ) and Head ( wt ) bound slightly more efficiently than their mutated constructs ( Figure 2 ) . RBC binding by mutated and wt constructs was heparin-sensitive and inhibited by rosette-disrupting mAbs ( Figures S5A and S5B ) . Presence of human serum moderately enhanced RBC binding , an effect that was more marked for the Head constructs ( 2 . 9–4 fold increase ) than the DBL1α1 constructs ( 1 . 4–2 fold increase ) ( Figures S5C and S5D ) . A similar enhancement was observed when foetal calf serum was added ( Figure S5C ) . This indicates that the serum-enhancing activity is not species-specific and , as such , is different from the requirement of human-specific serum for VarO rosetting in human cells . ABO blood group preference was explored for DBL1α1 , DBL1α1 ( wt ) , Head and Head ( wt ) using a panel of RBC donors . All four proteins bound more efficiently to blood group A ( N = 5 ) than to blood group B ( N = 3 ) RBCs and , in turn , to blood group B more efficiently than to blood group O ( N = 5 ) RBCs ( Figure 3A and data not shown ) . This indicates that the terminal α-1 , 3-linked N-acetylgalactosamine ( GalNAc ) of blood group A and , to a lesser extent , galactose ( Gal ) of blood group B are key determinants of the interaction . The ABO blood group dependence was specific as no such dependence was observed for binding of the P . vivax Duffy Binding Protein PvDBP , known to bind Duffy-DARC on the RBC surface ( Figure 3A ) . We next explored binding to subtypes of group A RBCs , which differ mainly in the quantity of the terminal α-1 , 3-linked N-acetylgalactosamine ( GalNAc ) antigen displayed: subgroup A1 RBCs express up to four times as many A epitopes as subgroup A2 , while Ax ( weak variant of A ) RBCs express very low amounts of terminal ( GalNAc ) [39] . Binding of DBL1α1 ( wt ) and Head ( wt ) was subgroup-dependent , with binding to A1 ( N = 5 ) being substantially higher than binding to A2 ( N = 5 ) , while binding to Ax ( N = 3 ) was minimal ( Figure 3B ) . Similar results were obtained when binding was monitored by immunoblot assays ( Figure 3C ) . Binding strongly correlated with the amount of A antigen displayed on the recipient cell ( Spearman coefficient correlation , rho = 0 . 96 ( p<0 . 0001 ) and rho = 0 . 93 ( p<0 . 0001 ) for DBL1α1 ( wt ) and Head ( wt ) respectively ) ( Figure S6A ) . Furthermore , when A2 RBCs were treated with α-N-acetylgalactosaminidase , which cleaves the GalNAc groups , binding was reduced even further ( data not shown ) . Binding showed an inverse relationship to the amount of H antigen displayed on the RBC ( Spearman correlation coefficient , rho = −0 . 67 ( p = 0 . 001 ) and rho = −0 . 70 ( p = 0 . 0006 ) for DBL1α1 ( wt ) and Head ( wt ) respectively ) ( Figure S6B ) . Levels of binding to A2 and B RBCs , which displayed similar amounts of H antigen , were similar . Consistent with this , removal of the terminal galactose from blood group B RBCs by treatment with galactosidase reduced their binding to DBL1α1 ( wt ) ( data not shown ) . Direct binding of the recombinant Head ( wt ) domain to the blood group A and B trisaccharides was demonstrated using a surface plasmon resonance assay with covalently immobilized trisaccharide-conjugated-Bovine-Serum-Albumin as ligands . In line with the observations made on red blood cells , the Head ( wt ) region appears to bind more efficiently to group A than to group B conjugates ( Figure 4 ) . This was consistently observed across a large range of protein concentration ( Figure S7 ) . Furthermore , pre-mixing Head ( wt ) with a 2-fold excess of heparin totally abolished binding to both blood group BSA-conjugated tri-saccharides ( Figure 4 ) . Together , these data show that binding is influenced by the amount and type of terminal glycan displayed on the RBC surface and is heparin-sensitive . The N-terminal DBL1α1 domain was identified as the minimal binding unit of the ABO blood group and the presence of CIDR1γ enhanced binding . In order to understand the molecular basis of these interactions , we solved the crystal structure of the double domain Head protein , since we had already determined the structure of the DBL1α1 domain [27] . The DBL1α1 structure we previously determined [27] was cleaved after residue 69 , which turned out to abolish RBC binding ( Figure S8 , lane 2 ) . The Head construct studied here , however , was prepared with the native , intact sequence . The Head crystallised only in the presence of heparin , as found earlier for DBL1α1 , with the best crystals diffracting to 2 . 8 Å resolution . The Head structure ( residues 12–487 ) was solved by molecular replacement and the polypeptide chain in the final model was traced for the entire sequence , with the exception of the first 18 N-terminal residues ( see Table 1 for refinement statistics ) . The overall shape of the Head is rather compact , with the CIDR1γ domain folding back upon the DBL1α1 domain , thus burying a significant surface at the interface ( Figure 5A ) . The structure of the Head protein allows a detailed description of all segments that were missing in our previous analysis of the single DBL1α1 domain , in particular the stretch of residues 53 to 71 and 83 to 94 ( Figure 5B ) . The NTS region contains two more helices , followed by a long surface-exposed loop that includes canonical cysteines Cys ( −1 ) and Cys ( 1 ) of subdomain 1 ( Figure 5B and 6 ) . An antiparallel β-sheet then connects to αH1 at the conserved PPR motif , which is very close to the previous DBL1α1 structure . In the latter structure , we were able to trace only a short peptide D72-F82 in the hinge region , which has a different conformation to that of the intact Head protein analysed here . The structure of the major heparin-binding site is essentially unchanged and all of the typical DBL motifs of subdomain 2 ( in green ) and part of subdomain 3 ( in blue ) are very close in the Head and the single domain DBL1α1 ( Figure 5B ) . Importantly , the structure of the PEXEL-like sequence of NTS is identical to that observed earlier [27] , thus confirming that this sequence is buried and important for the stability of the protein . There are also differences in the region in contact with the CIDR1γ domain . The C-terminal moiety of helix αH6 and the N-terminal region of helix αH7 , are displaced by up to 14 Å , while loop C357-D372 ( including helix αH6′ ) follows a different path . The RMS difference between the DBL1α1 Cα coordinates of the Head and single domain structures is 2 . 9 Å for the complete domain , but only 1 . 3 Å when the NTS-DBL1α1 junction and the C-terminal region in contact with CIDR1γ are excluded . The density connecting DBL1α1 and CIDR1γ is rather poor and allowed modelling the polypeptide backbone atoms only . From D504 onwards , however , the electron density becomes clear and the rest of the CIDR1γ domain could be traced unambiguously . The CIDR1γ-VarO domain ( 220 residues ) forms a rather compact structure , in contrast to the CIDR1α-MC179 structure [28] , which lacks the N-terminal part of the domain . In CIDR1γ-VarO , this region ( residues 496–570 ) contains a twisted antiparallel β-sheet composed of four strands ( β1-4 ) as well as three short α-helices ( H-2 , 310H1 , H-1 ) ( Figure 5A and Figure 6 ) , forming an N-terminal subdomain . The latter part of CIDR1γ is largely α-helical , with a pattern of helices similar to that of MC179 , though helix a is shorter and helices b and c together span the length of helix b in MC179 . The relative position of the three-helix bundle H1 , H2 , H3 and helices a , b , c is , however , quite different , giving CIDR1γ-VarO a much more compact shape ( Figures 7A and 7B ) . The first Cys residue of CIDR1γ-VarO ( C534 , canonical Cys ( 1 ) ) forms a disulfide bond with C614 ( canonical Cys ( 8 ) ) in helix H2 . In our construct , CIDR1γ-VarO contains 11 Cys residues ( 31 for the entire Head protein ) and thus there must be at least one free sulfhydryl group per molecule . The protein was therefore purified in the presence of 25 mM cystamine , which prevented the formation of covalent dimers . All Cys residues are engaged in disulfide bridges except one free Cys ( C604 , canonical Cys ( 5 ) of CIDR1γ ) ( Table S2 ) . This free Cys is indeed quite exposed , but the electron density is not sufficiently well defined to show a covalent modification . The MC179 protein included 17 additional residues at the C-terminal end , including C168 ( canonical Cys ( 10 ) ) , which paired with canonical Cys ( 5 ) [28] . In the VarO sequence there is a Cys doublet ( C722 and C723 ) in the corresponding site and since it was not obvious which of the two would form a disulfide with Cys ( 5 ) , we terminated our construct upstream of it at residue 716 . The polypeptide chain ends in close proximity of C604 ( canonical Cys ( 5 ) ) , however , and could conceivably form a disulfide bridge . We have been able to express a longer Head construct ending at V787 in soluble form but did not succeed in obtaining crystals . The crystals were prepared in the presence of heparin , which , although not visible in the electron density , may have induced the domains to come together or interfered with any dimer formation . We therefore compared the overall shape of the individual domains and the double domain using analytical ultracentrifugation ( AUC ) in the absence of heparin . When the hydrodynamic characteristics of the double domain were calculated from the crystal coordinates [40] , [41] , the results showed that the shape of the double domain in solution was very similar to that found in the crystal . Furthermore , the overall shape of the isolated CIDR1γ domain in solution corresponds to that in the crystal structure of the double domain ( Table S3 ) . Both the CIDR1γ and the Head protein are monomeric in solution and in the crystal unit cell , as was the case for the single DBL1α1 domain [27] . Given the preference of VarO iRBC , as well as the DBL1α1 domain and the Head , for binding to blood group A , we sought to localize its binding site in the two recombinant proteins . We initially used the collection of mutant constructs used for defining the heparin-binding site [27] ( Table 2 ) . Mut1 and Mut3 , whose affinity for heparin is reduced by more than 100 fold , bind to RBCs , whereas Mut2 and Mut4 , whose heparin binding is unaltered or only slightly affected , do not bind ( Figure S9A ) , indicating that the heparin-binding and RBC-binding sites do not overlap . In some protein preparations , spontaneous cleavage of the polypeptide occurred after residue R64 or R69 upon storage ( depending on the protein ) . Detailed analysis showed that only the full-length protein retained the ability to bind ( Figure S8 , lane 1 ) , thus indicating that the integrity of a surface-accessible region of the NTS moiety was essential for RBC binding . This is the region with a large difference between the crystal structures of the intact Head and the cleaved , single DBL1α1 domain . We therefore used the Head structure to guide additional site-directed mutagenesis . Mutation of residues from the hinge region between NTS and DBL1α1 ( Mut 10 , 11 , 12 and 13 , see Table 2 and Figure S9B ) disrupted binding , confirming its essential structural role . We used computer docking to localize the potential binding site ( s ) of the terminal trisaccharide of blood group A within the Head protein . The lowest energy solutions of the docked trisaccharide were very tightly clustered about a site that included residues K87 , T88 , D147 and K149 , corresponding to the region that is significantly different in the cleaved DBL1α1 domain ( Figures 5B and 8 ) . We therefore prepared a series of single or double mutants exploring this region of the structure . Because of the poor yields of the Head protein , these mutants were derived from the single DBL1α1 domain . This identified several key residues: K95 ( Mut17 ) , F145 , K216 ( Mut19 ) , whose mutation completely disrupted binding , and T88 ( Mut15 ) and D147 , K149 ( Mut18 ) , whose mutation reduced binding by more than 90% . Single mutations , R64 ( Mut14 , localized within the NTS domain ) and K87 ( Mut16 , located in the hinge region ) , reduced RBC binding by 50% ( see Figure S9C ) . Overall , this demonstrates that the site predicted by computer docking was indeed critical for binding . The binding site includes the NTS segment and residues from subdomains 1 and 2 ( Figure 6 ) . Significantly , this region is conserved between rosetting strains and , moreover , is located on the face opposite to the major heparin-binding site ( Figure 8A ) .
Our results identify the ABO blood group as the major VarO rosetting receptor on the host RBC and show that the presence of the CIDR1γ domain to form the Head region results in enhanced RBC binding , mimicking the ABO blood group preference of VarO rosetting . The crystal structure of the Head ( DBL1α1-CIDR1γ ) allowed mapping of the RBC-binding site to a structurally conserved region of rosette-forming PfEMP1 variants , involving residues from subdomains 1 and 2 , with contributions from the neighbouring NTS-DBL1α1 hinge region . The RBC-binding site is distal to the heparin-binding site , indicating that although heparin prevents binding of the adhesion domain to the RBCs , it does not directly compete with the ligand-receptor interaction to prevent rosette formation . Previous analyses of interactions at play in VarO rosetting showed that none of the common receptors of P . falciparum cytoadherence , such as CD36 , ICAM-1 , CSA , nor other potential receptors ( VCAM-1 , HABP1 , CD31/PECAM , E-selectin , Endoglin , CHO receptor “X” , and Fractalkine ) were implicated in the binding of RBC to VarO-iRBCs [42] , [43] . We show here that CR1/CD35 , shown to be a receptor for some rosetting lines ( including R29 [23] , [34] ) , is not involved in VarO rosetting . VarO rosetting shares with other rosetting lines three generic characteristics , namely an extreme sensitivity to sulphated glycosaminoglycans , the need for human serum and a marked ABO blood group preference characterised by reduced binding to group O RBCs . Our data indicate that the major determinant affecting VarO rosetting efficiency is indeed the ABO blood group . We explored VarO-iRBC binding characteristics using a monovariant culture of the Palo Alto 89F5 clone , in which >90% of the iRBCs were positively selected to express PfEMP1-VarO [24] . VarO-iRBCs preferentially bind to blood group A compared to blood group B , which itself is preferred to blood group O . An identical blood group preference profile was observed with both the DBL1α1 and Head proteins . Our dissection of blood group A preference using the recombinant domains provides , for the first time , a link between rosetting and common group A polymorphisms . The difference between the A1 and A2 subgroups is mainly quantitative , with A2 RBC displaying 4–5 times fewer blood group A determinants than A1 [44] , although they have some qualitative differences as well [45] , [46] . Our observation that DBL1α1 hardly bound Ax RBCs is a strong indication that copy number variation of the terminal glycan is the main cause of differences in the binding behaviour between the three subgroups . Binding to A2 was similar in intensity to binding to blood group B . As the A2 RBCs expressed about 5-fold more H antigen than the B RBCs ( Figure S6 ) , we conclude that the terminal α-1 , 3-linked N-acetylgalactosamine ( GalNAc ) of group A is preferred to the terminal galactose ( Gal ) of group B . This was further documented by surface plasmon resonance assays , in which we explored binding to trisaccharide-BSA conjugates immobilized on the surface of a sensor chip . Binding to trisacharide A was more efficient than to trisaccharide B ( Figure 4 ) , with apparent affinities in the micromolar range . This blood group preference is fully consistent with the VarO-IRBCs blood group preference although it should be mentioned that BSA-conjugates sub-optimally mimic the RBC-displayed blood group saccharides , which present multiple branched saccharides or tandem copies of the blood group determinant . The structure of the DBL1α1-CIDR1γ Head reported here is the first crystal structure of a multiple domain from PfEMP1 , as well as of a complete CIDR1γ domain . The structure of the DBL1α1 domain published earlier [37] is confirmed and we further show that the CIDR1γ-VarO domain forms extensive contacts with the DBL1α1 region ( Figures 9A and 9B ) . The interface between the DBL1α1 and CIDR1γ domains is stabilised by several charged interactions ( D367-D654 , N381-R518 , E389-Y521 , Y437-K550 ) and , furthermore , a significant hydrophobic surface on DBL1α1 is buried within this interface . A significant part of the CIDR1γ accessible surface ( 1367A2 of a total of 12477A2 ) is buried within the interface , including residues from its N-terminal half , as well as from the a–b–c helical motif ( Figure 9 ) . The interface is also highly conserved within the DBL1α1-CIDR1γ family of PfEMP1 adhesins ( Figure 9C ) . The three-dimensional structure of the Head allows a definitive assignment of interdomain boundaries . Thus the boundary between DBL1α1 and CIDR1γ domains lies within a rather flexible linker around residue 490 , indicating that the DBL1α1 domain terminates after the disulphide Cys13–Cys14 ( residues C477–C483of PfEMP1-VarO sequence ) . The CIDR1γ domain is more compact than the partial CIDRα structure published by [28] , not only in our double-domain crystal structure but also in solution as a single domain ( Figures 7 and S10 ) . This difference could be due to a number of reasons: CIDR1γ -VarOcontains additional 98 residues at the N-terminus missing in the MC179 construct ( which also lacked the preceding DBL1α domain ) [28] , the extensive contacts between DBL1α1 and CIDR1γ , or the dimer formation in the case of MC179 . The two CIDR domains are of different sequence classes , γ for VarO and α for MC179 . Moerover , the latter binds CD36 , which is not the case for VarO [42] , [43] . Both CIDR classes share a conserved arrangement of Cys and Trp residues in most of their sequence , with an additional disulphide ( canonical Cys ( 8a ) –Cys ( 8b ) ) in the γ class . The sequences are , however , much less well conserved in loop regions connecting both the helices and the subdomains , which could be another reason for the structural differences observed . The similarity between DBL and CIDR domains in their general architecture , noted earlier [28] , is also present in the VarO structure . Superposition of the CIDR1γ -VarO domain upon the DBL1α1-VarO domain matches not only helices H1 , H2 and H3 ( CIDR1 γ ) to αH6 , αH7 and αH10 ( DBL1α1 ) , but also helices a and b ( CIDR1γ ) to αH8 and αH9 ( DBL1α1 ) . Furthermore , strands β1 and β2 of CIDR1γ lie quite close to strands β-1 and β-2 of DBL1α1 ( Figure S10 ) . The similarity extends to the disulfide pattern as well , as suggested earlier [28]: canonical disulfides Cys ( 10 ) –Cys ( 11 ) and Cys ( 7 ) –Cys ( 9 ) of DBL1α1 overlap with disulfides Cys ( 7 ) –Cys ( 9 ) and Cys ( 4 ) –Cys ( 6 ) of CIDR1γ , respectively , while disulfide Cys ( 5 ) –Cys ( 10 ) of CIDR1γ lies close to Cys ( 8 ) –Cys ( 12 ) of DBL1α1 . Indeed , the N-terminal half of CIDR could be classified as equivalent to subdomain 1 of DBL domains , while the second , helical domain of CIDR corresponds more closely to subdomain 3 . In the MC179 structure , the region implicated in CD36 binding [28] lies near the N-terminal end of helix b . In the CIDR1γ -VarO structure , the equivalent region corresponds to the b–c connecting loop , which faces the H1–H2–H3 helical bundle . Within the triplet S662-I663-D664 of VarO , corresponding to the critical residues E108-I109-K110 of MC179 , S662 and I663 are buried by H1 and D664 forms a salt bridge with K591 from H1 . Interestingly , S662 and I663 are highly conserved among CIDRγ sequences , suggesting that this loop may have a common conformation in this domain class . If these residues were implicated in CD36 binding , they would be poorly accessible in the rosetting strains of PfEMP1 , which do not bind CD36 [43] . The structure of the Head provides critical information about the RBC-binding site . Computer docking and site-directed mutagenesis localized a blood group A binding site in a restricted area situated at the interface of subdomain1 and subdomain 2 in the vicinity of the NTS-DBL1α1 hinge region . This differs from the CSA-binding site localized on VAR2CSA DBL3X domain , which lies within subdomain 3 [47]–[49] , and is more in line with the location reported for P . knowlesi Duffy Binding Protein ( also a site engaging residues from subdomains 1 and 2 ) [50] or some of the sialic acid-binding sites of P . falciparum EBA175 [51] . The NTS-DBL1α1 hinge region , missing in our previous single domain structure , is highly exposed on the surface and proved to be crucial for the RBC-binding site . Indeed , cleavage of this sequence disrupted binding and mutations of this region reduced binding , without substantially affecting antigenicity ( recognition of all mutants by ELISA was essentially unimpaired , data not shown ) . This reinforces the conclusion that NTS is an essential functional and structural component of the DBL1α domain [37] . Importantly , the blood group A binding site and the major heparin-binding site that we mapped previously [37]are distant from each other on the surface , indeed on opposite sides of the molecule . Therefore , direct competition with binding to the receptor cannot be the reason why heparin disrupts rosettes and inhibits the binding of the recombinant domains to RBC and trisaccharide-BSA conjugates , contrary to one of the previously suggested hypotheses [27] . The other possible mechanism , namely that heparin could provoke the formation of oligomers that are no longer competent for receptor binding , remains an interesting possibility . The RBC-binding site could become inaccessible in heparin-aggregated adhesins , or several PfEMP1 molecules need to bind simultaneously to the ABO antigens displayed on the RBC surface for an efficient interaction to occur and this is prevented in the heparin complexes . We analysed the location of the RBC-binding site on the DBL1α1 domain with respect to the position of molecular signature tags used to classify var genes and associate them with either severe or uncomplicated malaria . Conserved tags , called positions of limited variability 1 to 4 ( PolV1-4 ) , had been identified [52] , [53] . The relative combination of PoLV motifs appears characteristic of specific var gene subsets . Figures 6 and 10 show the localization of the four PolV sequence tags with respect to the identified RBC binding site of PfEMP1-VarO . All PolV tags , except for PolV1 , are remote from the RBC-binding site ( Figure 10 ) . Normark et al . [54] identified specific PfEMP1-DBL1αamino acid motifs correlated with rosetting and severe malaria . One of the sequence signatures associated with “high rosetting” , namely H3 , maps close to the binding site identified here . Palo Alto 89F5 VarO has a H3 motif ( H3 K D K/A V E/Q K G ) located at the beginning of αH4 , which includes K216 , a residue critical for RBC binding ( Figures 6 and 10 ) . This motif is surface-exposed and located in close proximity of the RBC-binding site . The RBC surface displays several million copies of ABO blood group determinants carried on membrane glycoproteins and glycolipids . The ABH antigens lie on terminal branches of poly-N-acetylgalactosamines , each of which may carry several ABH determinants . Although the type of branching varies , the ABH determinants displayed on the RBC surface are very dense . It is possible that PfEMP1 binding involves interaction with more than one glycan per Head region . Furthermore , although both DBL1α1 domain and the Head region are monomeric in solution , we do not know whether binding is associated with oligomerization of the adhesion domain , as reported for other RBC-binding proteins with DBL domains such as P . falciparum EBA175 [51] and the P . vivax Duffy Binding Protein [55] . The DBL1α1-CIDR1γ Head is present in a small subset of var genes from group A , four of which are implicated in rosetting [23] , [25] , [26] . The RBC-binding site is conserved in other rosette-forming PfEMP1 variants such as R29 , PF13_003 and IT-var60 , indicating that data obtained here can be extrapolated to other lines and form the molecular basis of the extensively documented ABO blood group preference in rosetting [9] , [14]–[16] . The presence of CIDR1γ increases binding efficiency , as indicated by the approximately 1 log unit higher MFI in flow cytometry and the increased amount of protein bound , as visualised by immunoblotting . The exact role played by CIDR1γ , however , is still unclear . It is possible that its folding back upon the DBL1α1 domain provides a structural framework for more efficient binding and increased affinity , just as the multimodular PfEMP1-VAR2CSA has been shown to require a compact fold for activity [56] , [57] . The binding characteristics of the Head region resemble those of the infected red cells , except for the susbtantial residual binding in the absence of human serum and the similar enhancement by human and foetal calf serum . Serum enhancement of NTS-DBL1α1 andHead binding to RBCs may reflect a need to buffer the highly negatively charged RBC surface . As the serum component ( s ) implicated in VarO rosetting and Head region binding are unknown , we carried out all binding assays in the presence of human serum . VarO rosetting has an absolute requirement for human serum ( Figure S1A ) that cannot be replaced by foetal calf serum . As we show that binding of NTS-DBL1α1 and/orHead does not account for this human-specific serum dependency , we suppose that interaction of serum components with downstream PfEMP1-VarO domains might contribute to modulate binding of PfEMP1-VarO , possibly by increasing affinity and optimising binding characteristics , or that other RBC surface proteins ( eg . rifins or stevors ) come into play in rosetting as well . Further work is needed to clarify this question . This work provides the molecular basis underpinning the blood group preference of rosetting . The association between the ABO groups , rosetting and severe malaria [9] is a strong indication that rosetting , as a contributor to severe malaria , has exerted a selective pressure that has shaped population polymorphisms at the ABO locus and has contributed to their varying geographic distribution . The data reported here expand this framework to subgroups within the susceptible blood group A . Although the genetic basis of the A1 , A2 and other rare A subgroups is well established , the physiological consequences of such phenotypes and the selective advantage they provide are unclear . The lower prevalence of A1 blood group in populations of African descent compared to populations of Asian or Caucasian origin [58]–[60] is consistent with the hypothesis that P . falciparum rosetting has contributed to subgroup selection and gene spread of blood group A variants .
For ABO groups , blood donated by healthy volunteers was purchased from the Blood Bank Centre ( EFS , Rungis ) . Fresh or cryo-preserved and thawed RBCs of A1 , A2 or Ax subgroup were obtained from the reference cryobank reagents of the Centre National de Référence pour les Groupes Sanguins ( CNRGS - INTS , Paris ) . Blood samples with CR1 copy number variation were left-overs ( “fond de tubes” ) from healthy volunteers recruited by the URCA EA3798 ( Reims , France ) for clinical studies . Specific written consent was provided by each donor to use the left-overs for research . The Comité Consultatif pour la Protection des Personnes se prêtant à des Recherches Biomédicales of Champagne Ardenne approved the protocol . Supply and handling of human red cells followed the guidelines of the agreement between Institut Pasteur and the Etablissement Français du Sang . The quantification of CR1 copy number was assessed using biotinylated anti-CR1/CD35 mAb J3D3 , followed by a sequential labelling with streptavidin-phycoerythrin ( PE ) , biotinylated anti-streptavidin and streptavidin-PEantibodies as described [61] , [62] . The amount of A and H antigens displayed on RBC surface was determined by flow cytometry using the following monoclonal antibodies: mAb BRIC-145/9W2 ( mouse anti-A antigen , IgG1 ) and mAb MR3-517 ( mouse anti-H antigen , IgM ) , respectively , and confirmed using TransClone Anti-ABO1 ( IgM ) and TransClone anti-H1 ( IgM ) murine mAbs ( Bio-Rad Laboratories ) , respectively . Secondary goat anti-mouse IgG or IgM Alexa fluor 488-conjugated ( Molecular Probes , Invitrogen ) antibodies were subsequently used . To reduce agglutination of antigen-positive cells , RBC samples were fixed for 10 min at room temperature with 0 . 1% glutaraldehyde as described [61] before staining . For each sample , 50 , 000 events were collected using a BD-LSR1 flow cytometer ( Becton Dickinson ) and expression levels were analysed with the FlowJo 9 . 4 . 7 software . Monovariant cultures of the Palo Alto 89F5 VarO and It4/R29 parasites , procedures for rosette enrichment on ice-cold Ficoll , rosette dissociation with dextran sulphate , magnetic selection of mature iRBCs were as described [24] , [25] . Rosette reformation assays were carried out in RPMI with 10% AB human serum ( RPMI-HS ) 1 h at 37°C and the rosetting rate was calculated , after addition of Hoechst 33342 dye ( Molecular Probes ) for parasite nuclei staining , by determining the percentageof rosette-forming iRBCs present in the mature parasite population . For experiments exploring the parameters affecting rosetting , purified VarO-iRBCs were diluted in RPMI-HS in the presence of specific mouse mAbs or isotype controls , or diluted in a range of human serum concentrations or 10% Ig-depleted human serum . Ig depletion of the serum was achieved by incubating 3 mL of RPMI-HS with 1 mL of RMPI-equilibrated protein G Sepharose beads ( GE Healthcare ) . Depletion was assessed by Coomassie blue staining ( PageBlue Protein Staining Solution , Thermo Scientific ) of sodium dodecyl sulphate-polyacrylamide gel electrophoresis ( SDS-PAGE ) and immunoblotting using anti-human Ig . Enzyme treatment of recipient RBCs was carried out by incubating 5×107 RBCs ( washed twice in PBS , Phosphate Buffer Saline 1× , Invitrogen ) for 30 min at 37°C with 10 µg trypsin ( Sigma , T1005 ) or α-chymotrypsin ( Sigma , C4129 ) in a final volume of 100 µL . Enzymatic digestion was stopped by three extensive washes in RPMI-HS . Surface expression of CR1/CD35 was detected using anti-CR1 mAb J3B11 , followed by an Alexa fluor 488-conjugated goat anti-mouse IgG . Treated or untreated recipient RBCs were used in rosette reformation assays with magnetically enriched mature VarO-iRBCs . For ABO blood group preference assays , red cell membranes were labelled according to the manufacturer's instructions with the lipophilic fluorescent probes PKH67 or PKH26 ( Sigma Aldrich ) , both of which provide stable , clear , intense and reproducible fluorescent labelling of live red cells with no apparent loss of function [62] or antigenicity [25] . VarO-iRBCs were purified to >90% parasitemia by magnetic separation and diluted in the presence of varying ratios of recipient A , B and O RBCs differentiated by labelling alternately with PKH26 or PKH67 . After incubation in RPMI-HS for 1 hr at 37°C , the rosetting rate was evaluated by fluorescence microscopy . The Palo Alto VarO coding sequence ( GenBank EU9082205 ) was used to design synthetic genes with a recodoned sequence to restore a balanced A+T/G+C ratio with all predicted N-glycosylation NxT/S sites mutated to NxA , except for DBL1α1 ( wt ) and Head ( wt ) . DBL1α1 ( residues 2–471 ) and DBL1-long ( residues 2–487 ) were cloned in pMAL-p2X ( NewEngland Biolabs ) between the NdeI-NotI restriction sites . DBL1α1 ( wt ) ( residues 1–471 ) , Head ( residues 2–716 ) and Head ( wt ) ( residues 1–716 ) were cloned in pMAL-c2X ( NewEngland Biolabs ) between the BamHI-HindIII restriction sites . CIDR1γ ( residues 508–787 ) was cloned in pET15 ( Novagen ) between the NdeI-XhoI restriction sites . DBL2β ( residues 831–1206 ) and DBL3γ ( residues 1220–1578 ) were cloned in pET22b ( Novagen ) between the NdeI-HindIIIrestriction sites . DBL4ε ( residues 1608–2014 ) and DBL5ε ( residues 2025–2321 ) were cloned in pMAL-c2X between the EcoRI-SalI restriction sites . All constructs , apart from DBL1α1 and Head had an in-frame hexa-His tag at the C-terminus ( N-terminus in the case of CIDR1γ ) . A thrombin cleavage site was introduced by PCR before the hexa-His tag in the DBL2β , DBL4ε and DBL5ε constructs . The recombinant domains were expressed in Rosetta-Gami ( Novagen ) except for DBL3γ , DBL1α1 ( wt ) and Head ( wt ) , which were expressed in SHuffle ( New England Biolabs ) . Protein expression in Escherichia coli was carried out for 20 h at 20°C ( 16°C for the Head ) and purified by affinitychromatography ( TALON , Clontech ) , followed by size exclusionchromatography ( S200 or S75 16/60 , GeHealthcare ) as described [37] . In the case of MBP fusion proteins , MBP was cleavedusing Factor Xa ( Novagen ) and the recombinant domain purified as described [37] . Where necessary , the hexa-His tag was cleaved by thrombin ( Novagen ) as recommended by the manufacturer and the cleavage monitored by Western blot . For crystallisation , the Head protein was further purified on a HiTrap Heparin column ( GE Healthcare ) , followed by size exclusion chromatography ( S20016/60 , GE Healthcare ) in 20 mM Tris-HCl , 200 mM NaCl , pH8 . 0 and on a CM ion-exchange column with an NaCl gradient from 50 mM to 1 M NaCl . The protein in a final buffer ( 20 mM Tris-HCl , pH 8 , 200 mM NaCl ) was then concentrated to 10 mg . mL−1 . The PvDBP construct used as control for some experiments encompassed residues 199–515 of the P . vivax Duffy Binding protein . The domain was cloned in baculovirus , produced in HiFive cells , and purified as described [24] . Point mutations were made using polymerase chain reaction-based mutagenesis kits ( Quikchange Lightning Site-Directed Mutagenesis kit or Quikchange Multi Site-Directed Mutagenesis kit , Stratagene ) following the supplier protocols . Mutants Mut1-7 have been described previously [27] . Mutants 12 and 13 were custom made by GeneCust ( Luxembourg ) using DBL1α1 as template . Mutants 10 and 14–19 were custom-made by GenScript ( Hong Kong ) using DBL1α1 and DBL1α1 ( wt ) as template , respectively . All mutants were produced as MBP fusion proteins and purified as above . Antibodies to the individual PfEMP1 domains and the Head were produced by immunising OF1 mice , five mice per antigen , ( 6–8 weeks old mice , Charles River , France ) with 10 µg recombinant protein in Freund's Complete Adjuvant for the first injection and Freund's Incomplete Adjuvant for subsequent injections done at days 21 and 42 . Serum was recovered ten days after the third injection and stored at −20°C until used . Antibody titers were determined by ELISA on the cognate antigen [24] . Immunoblot reactivity with PfEMP1 was assessed on parasite extracts separated on 4–12% gradient SDS-PAGE ( Biorad ) under reducing and non-reducing conditions . Reactivity with the VarO-iRBC surface was assessed by fluorescence microscopy using a Leica DM5000B Fluorescence microscope [24] and flow cytometry using a BD-LSR1 cytometer [25] . Monoclonal anti-CIDR1γ antibody , mAb G8-49 , was isolated from an OF1 mouse ( as described inNato et al . [63] ) immunised with the Head protein by screening by ELISA and VarO-iRBC surface reactivity . Monoclonal IgG was precipitated with 50% saturated ammonium sulphate from ascitic fluid , centrifuged , and dialyzed against PBS . For each binding assay , 2 . 5×10−11 mole protein was incubated at RT for 30 min with 2×107 RBCs in 100 µL RPMI-HS . RBCs were separated from the incubation mixture by centrifugation through 200 µL 85% silicone DC550 ( Serva ) 15% Nujol ( Alfa Aesar ) as described [27] . After one wash in RPMI , RBCs were either processed for immunoblotting [37]or for flow cytometry analyses by incubating with a specific mouse polyclonal serum followed by Alexa fluor-488-conjugated goat anti-mouse IgG F ( ab′ ) 2 ( Molecular probes ) [25] . For flow cytometry analysis , the Head region was pre-incubated with 50 µg mAb G8 . 49 before addition of RBCs in the assay . 50 , 000 events were recorded using LSR1 flow-cytometer ( Becton Dickinson ) and analysed with FlowJo software . Assays were performed on a Biacore2000 instrument ( GE Healthcare ) in PBS buffer at 25°C . Trisaccharide A or B-conjugated BSA ( 6 atom spacer , average of 19 and 21 sugar residues per protein molecule for group A or B , respectively; Dextra Laboratories Ltd , UK ) and unconjugated BSA ( Sigma-Aldrich ) were covalently coupled to the linear polycarboxylate hydrogel surface of three independent flowcells of a HLC 200 m sensorchip ( Xantec bioanalytics ) , using the Amine coupling kit ( GE Healthcare ) . Immobilization densities of 2500 resonance units ( RU; 1RU = 1 pg . mm−2 ) were attained . A duplicate range of concentrations ( 10 nM–1 µM ) with or without a 2-fold excess of heparin ( heparin 5000; Sigma-Aldrich ) of Head ( wt ) was then flowed at 50 µL . min−1 over the three surfaces . The association and dissociation profiles were double referenced using the Scrubber 2 . 0 software ( BioLogic Software ) , i . e . both the signals from the unconjugated BSA reference surface and from blank experiments using interaction buffer instead of protein were subtracted . The shape of the profiles was suggestive of a complex binding mechanism that was not explored further in the context of this study . The protein ( 10 mg . mL−1 final concentration ) was mixed with heparin 5000 ( Sigma-Aldrich ) at a slight molar excess . Crystals were grown using the hanging-drop vapour-diffusion method: 1 µL of protein solution was mixed with 1 µL of reservoir solution ( 10% PEG 3350 , 200 mM NaCl , 100 mM sodium citrate , pH 8 . 3 ) and equilibrated against 0 . 5 mL of reservoir solution . Crystals appeared after 6–8 hours and continued to grow for about one week . The crystals were passed through a cryoprotectant solution ( 20% glycerol , 15% PEG 3350 , 200 mM NaCl , 100 mM sodium citrate , pH 8 . 3 ) and frozen in liquid nitrogen . Data were collected on the beamline PROXIMA1 at SOLEIL ( St . Aubin , France ) . All data were treated with XDS [64] , followed by SCALA from the CCP4 program suite [65] . The structure was solved by molecular replacement using the high-resolution structure of the DBL1α1 domain [37]Protein Data Bank ( PDB entry 2xu0 ) as a search model . Several cycles of refinement using Buster [66] and rebuilding the structure using Coot [67]allowed to identify density belonging to the CIDR1γ domain . This was fitted using Buccaneer from the CCP4 programme suite and further refined with Buster . The conserved three-helix motif from the CIDRα domain of the MC strain [28] was used to search for a similar motif in the electron density map to allow matching the sequence of the protein and to build the remaining structure . The refined coordinates and structure factors have been deposited in the Protein Data Bank ( PDB entry code 2yk0 ) . Structural figures were prepared withCCP4MG [68] . Computer docking of trisaccharides onto the Head structure was performed using Autodock version 4 . 2 [69] . Coordinates for trisaccharide A were taken from a complex in the Protein Data Bank ( PDB entry code 2obs ) . The search was performed over the surface of the region of the DBL1α1 moiety encompassing the NTS segment , subdomains 1 and 2 , in order to allow fine grid sampling ( 0 . 375 Å ) . Default values were usedfor all docking parameters , except the number of search runs , which was 100 . The 10 best solutions were retained . The protein samples ( 0 . 5–2 mg . mL−1 ) were analysed in a Beckman Coulter XL-I analytical ultracentrifuge . Detection of the protein concentration as a function of radial position and time was performed by optical density measurements at a wavelength of 280 nm or 250 nm for high concentration samples . All samples were in a 50 mM NaCl and 20 mM Tris pH8 buffer . All experiments were carried out in an An-Ti 50 rotor at 20°C at a rotor speed of 42 , 000 rpm for 8 hours . Sedimentation velocity analysis was performed by continuous size distribution analysis c ( s ) using Sedfit 12 . 0 [70] . Partial specific volume 0 . 726 mL . g−1 and 0 . 738 mL . g−1 for the CIDR1γ and DBL1α1-CIDR1γ respectively , viscosity 0 . 01013 Poise and density 1 . 00093 g . mL−1 were calculated using Sednterp 1 . 09 and used to analyze experimental data . Sedimentation coefficients at zero concentration were obtained by linear extrapolation to zero concentration of the sedimentation measured for each protein sample at different concentrations . Sedimentation coefficients were corrected for viscosity and expressed as values in water at 20°C . Theoretical sedimentation values were calculated with the programmes HYDROPRO 7c [41] and US-Somo [40] . Far-UV CD spectra were measured ( Aviv215 spectropolarimeter , Aviv Biomedical ) using a cylindrical cell with a 0 . 01 cm path length as described [37] . The spectra , corrected using buffer baselines measured under the same conditions , were normalised to the molar peptide bond concentration and path length as mean molar differential coefficient per residue . Blood donation in France includes an optional consent for use of part or all of the donation for teaching , research purposes or for preparation of specific reagents - ( see documenthttp://www . dondusang . net/content/medias/media1837_FsPssqwYyfXZQcm . pdf ? finalFileName=Document_dinformation_pr-don . pdf ) . All donors participating to this study had given their written consent for this use . Healthy volunteers donating RBCs with known CR1 copy number were recruited by the URCA EA3798 in Reims as part of an ongoing clinical study on CR1 . The samples used here were left-overs ( “fond de tubes” ) , for which specific written consent was provided by each donor for use in research on other diseases . The Comité Consultatif pour la Protection des Personnes se prêtant à des Recherches Biomédicales Est II approved the protocol ( Ref protocol CCP:11/603 , Ref Afssaps 2011-A00594-37 ) . Supply and handling of human red cells followed the guidelines of the agreement between Institut Pasteur and the Etablissement Français du Sang and the regulation of blood donation in France . The IMP Unit at IP was issued an Habilitation à manipuler du sang humain ( HS2003-3255; ref ND/LK/CC-11 . 68 ) . For animal use , the study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Institut Pasteur and complied with the European Union guidelines for the handling of laboratory animals ( http://ec . europa . eu/environment/chemicals/lab_animals/home_en . htm ) . The procedures were approved by the Institut Pasteur animal care and use committee . Animal care and handling was approved by the Ministère de l'Agriculture et de la Pêche ( rapport 107503056792 , clearance number 75–273 , issued to OMP ) and the protocols and procedures used by the Direction Départementale des Services Vétérinaires de Paris ( Ref . RL- 07031395-30701147 , issued to OMP ) . All animal experiments were planned in order to minimize mice suffering . Differences in mean fluorescence binding of DBL1α1 ( wt ) or Head ( wt ) between ABO RBCs or A subgroup RBCs were tested using the non-parametric Kruskall-Wallis test . The associations between recombinant protein binding and blood group A or H antigens expression levels found to be significant using the Spearman's correlation analysis were investigated by linear regression analysis . The statistical analyses were done with STATA software ( STATA Corp . Release 9 . 0 ) . For all tests , P values of less than 0 . 05 were considered statistically significant . VarO cDNA sequence: GenBank database accession number EU908205 High-resolution structure of the DBLα1 domain: Protein Data Bank entry 2xu0 Refined coordinates and structure factors of Head: Protein Data Bank entry 2yk0 | Rosetting , the capacity of infected red blood cells ( RBCs ) to bind uninfected RBCs , is a Plasmodium falciparum virulence factor . Rosetting is influenced by the ABO blood group , being less efficient with O RBCs . Although this preference may account for protection against severe malaria afforded by the O blood group , its understanding is fragmentary . We identify the ABO blood group as the main receptor for the rosetting Palo Alto VarO parasites , which display a marked preference for blood group A . Rosetting is caused by a sub-group of PfEMP1 adhesins . PfEMP1-VarO shares with other rosetting lines a specific NTS-DBL1α1-CIDR1γ Head region . We show that the Head region binds RBCs more efficiently than NTS-DBL1α1 and that ABO blood group polymorphisms influence binding of both domains . The 2 . 8 Å resolution crystal structure of the Head region reveals extensive contacts between the DBL1α1 and CIDR1γ domains , and shows structural features of the NTS-DBL1α1 hinge region essential for RBC binding . We localize the RBC-binding site to the face opposite to the heparin-binding site of NTS-DBL1α1 and document direct binding of the Head region to A and B trisaccharides These findings provide novel insights into the interactions established by malaria parasites with a prominent human blood group . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biomacromolecule-ligand",
"interactions",
"physics",
"materials",
"science",
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] | 2012 | Structural Basis for the ABO Blood-Group Dependence of Plasmodium falciparum Rosetting |
During the morphological process of sporulation in Bacillus subtilis two adjacent daughter cells ( called the mother cell and forespore ) follow different programs of gene expression that are linked to each other by signal transduction pathways . At a late stage in development , a signaling pathway emanating from the forespore triggers the proteolytic activation of the mother cell transcription factor σK . Cleavage of pro-σK to its mature and active form is catalyzed by the intramembrane cleaving metalloprotease SpoIVFB ( B ) , a Site-2 Protease ( S2P ) family member . B is held inactive by two mother-cell membrane proteins SpoIVFA ( A ) and BofA . Activation of pro-σK processing requires a site-1 signaling protease SpoIVB ( IVB ) that is secreted from the forespore into the space between the two cells . IVB cleaves the extracellular domain of A but how this cleavage activates intramembrane proteolysis has remained unclear . Structural studies of the Methanocaldococcus jannaschii S2P homolog identified closed ( substrate-occluded ) and open ( substrate-accessible ) conformations of the protease , but the biological relevance of these conformations has not been established . Here , using co-immunoprecipitation and fluorescence microscopy , we show that stable association between the membrane-embedded protease and its substrate requires IVB signaling . We further show that the cytoplasmic cystathionine-β-synthase ( CBS ) domain of the B protease is not critical for this interaction or for pro-σK processing , suggesting the IVB-dependent interaction site is in the membrane protease domain . Finally , we provide evidence that the B protease domain adopts both open and closed conformations in vivo . Collectively , our data support a substrate-gating model in which IVB-dependent cleavage of A on one side of the membrane triggers a conformational change in the membrane-embedded protease from a closed to an open state allowing pro-σK access to the caged interior of the protease .
Regulated Intramembrane Proteolysis ( RIP ) is a broadly used strategy to transduce information across lipid bilayers [1–3] . In many of these RIP pathways , proteolysis on one side of the membrane by a so-called site-1 protease leads to the activation of a membrane-embedded site-2 protease that cleaves and activates a substrate on the other side of the membrane , ultimately leading to the activation of gene expression . Although the signaling ( site-1 ) and intramembrane cleaving ( site-2 ) proteases have been identified in many of these pathways , how intramembrane proteolysis is regulated at the molecular level remains poorly understood . The zinc metalloproteases of the Site-2 Protease ( S2P ) family are among the most commonly used intramembrane cleaving proteases in RIP signaling pathways [4–6] . This family is composed of four subfamilies that share a conserved catalytic core [7] . Well-characterized members include the mammalian Site-2 Protease ( S2P ) [8] and E . coli RseP [9 , 10] , both from subfamily 1 , and the B . subtilis sporulation protease SpoIVFB ( B ) [11–13] from subfamily 3 . There are currently no characterized members of subfamilies 2 and 4 . Mammalian S2P is involved in signaling pathways that sense and respond to intracellular levels of sterols and misfolded proteins in the endoplasmic reticulum [5] . In both cases , the canonical two-step protease cascade results in intramembrane proteolysis of membrane-anchored transcription factors and their release into the cytoplasm , translocation into the nucleus , and activation of gene expression . E . coli RseP and orthologs in diverse bacterial phyla are involved in envelope stress response pathways [4 , 6] . These pathways commonly involve extracytoplasmic function ( ECF ) sigma factors that are held inactive by membrane-anchored anti-sigma factors . Specific envelope stresses trigger site-1 cleavage of the anti-sigma factor on the extracytoplasmic side of the membrane followed by RseP-mediated intramembrane proteolysis and activation of the ECF sigma factor . B . subtilis SpoIVFB ( referred to as B , for simplicity ) is involved in cell-cell signaling during the morphological process of sporulation [14] and is the subject of this study . In response to starvation , B . subtilis differentiates into a dormant spore [15–17] . The first morphological event in this process is the formation of a polar septum that divides the sporulating cell into a large mother cell and smaller forespore . Shortly after division , the mother cell membranes migrate around the forespore generating a cell within a cell ( Fig 1A ) . The mother then packages the forespore in protective layers while the spore prepares for dormancy . Upon spore maturation , mother cell lysis releases the stress-resistant spore into the environment . Throughout this morphological process , the mother cell and forespore follow distinct programs of developmental gene expression that are linked to each by cell-cell signaling pathways [18 , 19] . The final communication between the forespore and mother cell involves a RIP signaling pathway in which an inactive mother cell transcription factor ( pro-σK ) is proteolytically activated by the S2P protease family member B in response to a signal from the forespore [11 , 12 , 14] . The B protease is produced in the mother cell at an earlier stage in sporulation and localizes to the mother-cell membranes that surround the forespore [11 , 12 , 20] . B is held inactive at this site by two membrane proteins SpoIVFA ( A ) and BofA [14 , 21] ( Fig 1A ) . Upon the completion of engulfment , a site-1 signaling protease called SpoIVB ( IVB ) is produced in the forespore and secreted into the space between the two membranes that separate mother cell and forespore where it cleaves the extracytoplasmic domain of A [22–25] ( Fig 1A ) . This cleavage relieves inhibition imposed on B , triggering pro-σK processing and the activation of late mother cell gene expression . Thus , in this RIP pathway , the site-1 signaling protease ( IVB ) cleaves a negative regulator of the S2P family protease ( B ) . How A and BofA hold B inactive and how cleavage by IVB triggers relief of inhibition remain unclear . The mechanisms by which site-1 cleavage activates intramembrane proteolysis by S2P family members are largely unknown . The pathway for which regulation is most well understood is the one that employs E . coli RseP . Site-1 cleavage of the periplasmic domain of the anti-sigma factor RseA triggers RseP-mediated intramembrane proteolysis of RseA and activation of the ECF sigma factor σE [4] . RseP and S2P subfamily 1 members contain extracytoplasmic PDZ domains [7 , 26] . In vitro studies have found that the interaction between this domain and the exposed C-terminal residue of RseA generated by site-1 cleavage is critical for intramembrane proteolysis [27] . However , in vivo analysis indicates that this is likely to be only a part of the story [28] . In vivo , the PDZ domain on RseP appears to function as a size-exclusion filter to prevent substrates with large extracytoplasmic domains from gaining access to the active site of the membrane protease . In addition , a membrane-reentrant β-loop in RseP has been shown to participate in the recognition of the substrate transmembrane helix and its destabilization for presentation to the protease active site [29] . The absence of a high-resolution structure of a member of this subfamily has prevented a complete mechanistic picture of this regulatory pathway . The only published S2P structures come from Methanocaldococcus jannaschii ( mjS2P ) [30] , a member of subfamily 3 . Unlike S2P and RseP , the proteases in this subfamily lack extracytoplasmic PDZ domains . Subfamily 3 members contain six N-terminal transmembrane helices that make up the membrane protease domain followed by a C-terminal cystathionine-β-synthase ( CBS ) domain [7 , 31 , 32] . The mjS2P structures comprised the N-terminal protease domain that shares 29% identity ( 54% similarity ) with the membrane protease domain of the B . subtilis B protease . Two molecules of mjS2P were present in the asymmetric unit of the crystal forming an antiparallel pseudo-dimer . One of the monomers was in a "closed" conformation in which the active site was surrounded by transmembrane helices and thus inaccessible to substrate . The other protomer adopted a relatively open conformation , in which the first and sixth transmembrane segments were laterally displaced by 10–12 Å , generating a deep groove that runs the length of the molecule . Based on these findings , it was hypothesized that mjS2P could transition between closed and open states to allow substrates access to the active site of the protease , however the biological relevance of these conformations has never been established . Here , using the B . subtilis sporulation RIP pathway , we investigate this substrate-gating model . Our analysis provides evidence that A and BofA hold the membrane protease B in a closed conformation and IVB cleavage triggers a shift to the open conformation allowing pro-σK access to the interior of the protease .
In previous studies we used co-immunoprecipitation to investigate the interaction between B and its two negative regulators A and BofA [22 , 33] . To prevent activation of the membrane-embedded protease , these experiments were performed in strains lacking the site-1 signaling protease IVB . Although we could efficiently recover the processing complex , we never detected pro-σK in the immunoprecipitates . Prompted by the two structural conformations of the mjS2P protease domain [30] , we wondered whether A and BofA hold B in a closed conformation preventing an interaction with pro-σK and potentially IVB signaling could generate an open conformation allowing stable association between protease and substrate . To investigate this possibility , we repeated the co-immunoprecipitations comparing strains with and without IVB . To prevent pro-σK processing and release of the mature transcription factor , the B protease contained the catalytic mutant E44Q [11 , 12] . In addition , B ( E44Q ) was fused to GFP , which we previously found stabilizes the protease from degradation upon IVB signaling [33] . The strains were induced to sporulate and harvested at hour 4 of sporulation . A crude membrane preparation was solubilized with the non-ionic detergent digitonin and B ( E44Q ) -GFP was immunoprecipitated with anti-GFP antibody resin . The immunoprecipitated material was then analyzed by SDS-PAGE and silver staining . As reported previously [22 , 33] , we efficiently immunoprecipitated B-GFP and its regulator A ( Fig 1B ) . BofA is a small ( 9 kDa ) protein and is likely present in the dye front ( S1A Fig ) . In support of the idea that the processing complex is anchored in the mother cell membranes that surround the forespore by SpoIIIAH ( AH ) and SpoIIQ ( Q ) [34] , these proteins were also present in the immunoprecipitates ( Fig 1B and S1 Fig ) . Furthermore , consistent with published work showing that the Q protein is a substrate of the IVB signaling protease [22 , 35] , the level of co-precipitated Q was lower from the IVB+ strain . For unknown reasons , the levels of co-precipitated A were similar in the presence and absence of IVB , despite being a substrate of the signaling protease . Importantly , a protein band similar in size to pro-σK ( 27 kDa ) was present in the immunoprecipitate from the IVB+ strain but absent from the ΔIVB mutant ( Fig 1B ) . This protein was also absent in an immunoprecipitate from a IVB+ strain that lacked sigK ( Fig 1B ) . To determine whether this protein was indeed pro-σK , we excised the band from the silver-stained gel and the corresponding region from the ΔIVB and ΔsigK lanes , digested the proteins with trypsin , and analyzed the products by Mass Spectrometry . Three unique peptides ( see Methods ) corresponding to pro-σK were identified in the immunoprecipitate from the IVB+ strain while none was detected from the ΔIVB and ΔsigK immunoprecipitates . Finally , immunoblot analysis using anti-σK antibodies identified pro-σK specifically in the immunoprecipitate from the IVB+ strain ( Fig 1B ) . Collectively , these data suggest that stable association between pro-σK and the B-A-BofA processing complex requires the IVB signaling protease . To investigate whether a similar phenomenon could be observed in vivo , we used fluorescence microscopy , taking advantage of a functional fusion between pro-σK and the cyan fluorescent protein ( CFP ) ( S2E Fig ) . To simultaneously visualize the B membrane protease while also protecting it from degradation , we fused B ( E44Q ) to YFP . Importantly , a fusion of the wild-type B protease to YFP supported efficient sporulation ( S2E Fig ) and processing of both pro-σK and pro-σK-CFP ( S2A and S2B Fig ) . Strains harboring these fusions were induced to sporulate and then monitored by fluorescence microscopy over a sporulation time course ( Fig 2 and S3 Fig ) . Previous immunofluorescence microscopy and cell fractionation studies indicate that pro-σK non-specifically associates with membranes in vivo [36] . However , in sporulating cells lacking the IVB signaling protease , pro-σK-CFP appeared to localize in the mother cell cytoplasm with weak enrichment on the nucleoid , presumably due to non-specific interactions with the chromosome . We suspect pro-σK-CFP transiently associates with the lipid bilayer but this interaction is too short-lived to be detected in live cells . By contrast and as expected , in IVB+ cells harboring a wild-type B-YFP fusion , mature σK-CFP co-localized with the mother-cell nucleoid ( Fig 2B ) consistent with its proteolytic processing ( S2B Fig ) and association with core RNA polymerase [36] . Importantly , in >80% of the sporulating cells harboring wild-type IVB and the B ( E44Q ) catalytic mutant , pro-σK-CFP accumulated in the mother cell membranes surrounding the forespore ( Fig 2 , S3 and S4 Figs ) . This localization pattern provides further evidence that stable association between pro-σK and its processing complex depends upon IVB . Finally , pro-σK-CFP failed to localize in the outer forespore membrane in a strain harboring a catalytic mutant ( S378A ) of IVB [25] ( Fig 2 and S4B Fig ) indicating that IVB protease activity is required for the stable interaction between pro-σK and the signaling complex . B , A , and BofA reside in a multimeric membrane complex in the outer forespore membrane [33] ( Fig 1A ) . To investigate which of these factors tethers pro-σK to the complex , we analyzed pro-σK-CFP localization in sporulating cells lacking each of these components . In all cases , these strains possessed an intact IVB signaling protease and , other than the ΔB mutant , the B ( E44Q ) -YFP fusion . As previously reported [33 , 37] , in sporulating cells lacking A , BofA , or both , the B protease was no longer exclusively anchored in the outer forespore membrane and instead was distributed in all mother cell-derived membranes ( Fig 3 and S5 Fig ) . In these mutants , pro-σK-CFP was detectable in the membranes surrounding the forespore ( Fig 3 and S5 Fig ) . The pro-σK-CFP signal was reduced compared to wild-type presumably due to the lower levels of B ( E44Q ) present in the forespore membrane . However , the percentage of sporulating cells with forespore-associated pro-σK-CFP was similar to wild-type ( S4C Fig ) . Surprisingly , pro-σK-CFP did not appear to localize in the peripheral membranes of the mother cell . The results of experiments described in the next section suggest that some of the pro-σK-CFP fusion protein is present in these membranes but the signal is too weak to generate a membrane signal . Finally , in cells lacking the B protease , pro-σK-CFP localization phenocopied the IVB null and was largely present in the cytoplasm with some enrichment on the nucleoid ( Fig 3 , S4C and S5 Figs ) . Taken together with the data presented in Figs 1 and 2 , these experiments suggest that pro-σK specifically associates with the membrane-embedded protease and does so in a IVB-dependent manner . To determine whether additional sporulation-specific proteins were required for the association between B and pro-σK , we engineered strains to express pro-σK-CFP and B ( E44Q ) -YFP under IPTG control during vegetative growth . Exponentially growing cells were analyzed by fluorescence microscopy in a time-course after the addition of IPTG ( Fig 4 and S6 Fig ) . In cells lacking the B protease , pro-σK-CFP appeared cytoplasmic ( Fig 4A and S6 Fig ) . Co-expression of pro-σK-CFP and wild-type B-YFP in the absence of its negative regulators A and BofA resulted in the production of processed σK-CFP ( Fig 4B ) that co-localized with the nucleoid ( Fig 4A and S6 Fig ) . In cells expressing the B ( E44Q ) catalytic mutant , pro-σK-CFP appeared cytoplasmic with some localization at division septa and along the cytoplasmic membranes . Although the membrane signal was weak , we note that the pro-σK-CFP signal in cells that were in contact with each other along their length had no gap in fluorescence between them as compared to the gaps observed for cytoplasmic pro-σK-CFP in cells lacking the B protease ( Fig 4A ) . We further note that the pro-σK-CFP signal that appeared to be cytoplasmic in the B ( E44Q ) mutant was weaker and more pixelated than the signal observed in the cells lacking B , even though the levels of pro-σK-CFP were similar in the two strains ( Fig 4B ) . Both phenomena are consistent with the association of pro-σK-CFP with B ( E44Q ) at the cell membrane . Importantly , pro-σK-CFP remained largely full-length with relatively little free CFP liberated from the fusion ( S2C Fig ) . The cytoplasmic pro-σK-CFP signal was similarly weaker and more pixelated in sporulating cells lacking A , BofA , or both compared to the ΔB mutant in Fig 3 . We suspect that this reduction in signal similarly reflects an association between pro-σK-CFP and B ( E44Q ) in the peripheral mother-cell membranes despite our inability to directly detect the membrane signal . Finally , we investigated whether expression of A and BofA in vegetatively-growing cells producing wild-type B-YFP is sufficient to inhibit pro-σK-CFP processing and membrane association of the fluorescent fusion . As anticipated , pro-σK-CFP remained full-length ( Fig 4B and S2C Fig ) and failed to associate with the membrane ( Fig 4A ) . Collectively , these experiments support the model that A and BofA maintain B in a conformation that cannot interact with pro-σK and IVB-dependent signaling promotes a stable association between B and its substrate . The B protease , like other members the S2P group 3 subfamily , contains a C-terminal cytoplasmic cystathionine-β-synthase ( CBS ) domain [7] . These domains commonly bind ligands with adenosyl groups like ATP , AMP , S-adenosylmethionine , and c-di-AMP and regulate enzymatic or transport functions [31 , 32] . In vitro , the CBS domain from the B protease has been shown to bind pro-σK [13] . We therefore wondered whether the stable association between B and pro-σK in response to IVB signaling was mediated by the CBS domain . Consistent with this idea , a deletion of this domain was reported to abolish pro-σK processing in an E . coli expression system [13 , 38] . To investigate the role of the CBS domain in IVB-dependent signaling in B . subtilis , we generated a deletion ( BΔ85 ) that lacks the CBS domain and interdomain linker and a smaller 66 amino acid deletion ( BΔ66 ) that lacks the CBS domain but retains the linker [13 , 38] . In addition , we generated a 10 amino acid C-terminal deletion ( BΔ10 ) that was previously shown to be produced in E . coli at levels similar to wild-type but was impaired in pro-σK processing in this heterologous system [13] . All three deletions contained the E44Q catalytic mutation and were fused to YFP to monitor localization and help stabilize the proteins . All three fusion proteins localized properly ( Fig 5A ) and , based on fluorescence intensities , were produced at levels similar to the full-length protein ( S7B Fig ) . Importantly , in sporulating cells harboring either the BΔ66 or BΔ10 mutant , pro-σK-CFP accumulated in the outer forespore membranes ( Fig 5A and S7A Fig ) , consistent with a recent study in which B truncations similar to those used here were found to interact with pro-σK when co-expressed in E . coli [38] . In support of the idea that the CBS domain helps stabilize the interaction between B and pro-σK [13 , 38] , the pro-σK-CFP fluorescent signal around the forespore was reduced compared to wild-type , however and importantly the percentage of cells with forespore-localized pro-σK-CFP was similar ( S4D Fig ) . Moreover , we found that this localization pattern was dependent on the IVB signaling protein ( S8 Fig ) . By contrast and similar to previous work [13] , the BΔ85 mutant phenocopied the ΔB null . In line with our pro-σK-CFP localization data , the BΔ10 and BΔ66 truncations with an intact protease domain supported efficient sporulation while the BΔ85 mutant sporulated at levels similar to the null ( Fig 5B ) . Analysis of σK activity during a sporulation time course ( S9A Fig ) revealed that the BΔ66 and BΔ10 strains activate σK with kinetics similar to wild-type B . Furthermore , σK activity was dependent on IVB , indicating that these truncations are subject to inhibition by A and BofA and are activated by site-1 signaling . Finally , immunoblot analysis revealed that the BΔ66 mutant processed pro-σK to mature σK with slightly reduced efficiency compared to the matched wild-type control ( S9B Fig ) . Altogether , these data indicate that the CBS domain is not critical for the IVB-dependent forespore localization of pro-σK , pro-σK processing , or efficient spore formation . Thus , these results suggest that pro-σK associates with the membrane protease domain of B and raised the possibility that IVB triggers a transition from a closed to open conformation of the caged protease allowing stable association between pro-σK and B's catalytic center ( Fig 1A ) . Prompted by these data , we sought to explore the biological relevance of the two conformations observed in the mjS2P structures . We used homology modeling to predict the structure of the B protease domain in both open and closed states ( Figs 6A and 6B ) . Because the B protease domain shares relatively low sequence identity with mjS2P , we used evolutionary co-variation analysis to guide target-template alignment and validate the resulting models . Evolutionary co-variation analysis takes advantage of the fact that residues that interact with one another in a folded protein tend to co-evolve to maintain their interactions [39 , 40] . Analysis of mjS2P by this method using 5 , 290 S2P subfamily 3 orthologs identified extensive co-variation in amino acids that interact in the crystal structures ( S10 Fig ) . Importantly , the interactions between the two molecules of mjS2P that form the antiparallel pseudo-dimer in the asymmetric unit of the crystal were not observed by co-variation analysis ( S10 Fig ) , consistent with the anti-parallel interface being a crystallographic artifact . Co-variation analysis of B identified 81 evolutionary coupled residues ( 90% probability threshold ) ( S10 Fig ) . The analysis confirmed our assignment of sequence register in each of the TM segments and indicates that our homology models are reasonable proxies for the B structures . Examination of the models of the B protease showed that a hydrophobic membrane-reentrant β-hairpin is buried in the hydrophobic core of the protease in the closed state ( Fig 6A ) . Phenylalanine 66 sits at the tip of this β-hairpin , where it makes extensive interactions with a cluster of hydrophobic residues in the closed conformation while being mostly exposed in the open-conformation model ( Fig 6A and 6B ) . Specifically , F66 contacts V189 , F188 , W185 , L136 , and V128 in the closed state and only an interaction with F188 is preserved in the open state model . Interactions between F66 and these residues are predicted to occlude substrate and help stabilize the closed state of the lateral gate ( Fig 6A ) . The analogous residue ( I77 ) in the mjS2P protein is similarly buried in the core of the protein and is predicted to help stabilize the closed conformation . In the open conformation , F66 ( and I77 in mjS2P ) is displaced from the catalytic center and the first and sixth transmembrane segments have moved apart . To investigate whether the B protease domain adopts the closed conformation in vivo , we generated a B ( F66A ) mutant . Removal of the phenyl ring , which makes most of the hydrophobic contacts , is predicted to destabilize the closed state relative to the open , generating a constitutively active protease . In support of this idea , a strain harboring B ( F66A ) -YFP was capable of activating σK during sporulation in the absence of both the site-1 signaling protease IVB and an auxiliary signaling protease CtpB [22 , 41] ( Fig 6C and 6D and S11A Fig ) . The B ( F66A ) point mutant similarly supported pro-σK processing in the absence of IVB albeit with reduced efficiency compared to a wild-type IVB+ strain ( S11B Fig ) . In the presence of IVB , the point mutant had a modest sporulation defect ( S11C Fig ) , consistent with the pre-mature activation of σK observed in this background ( Fig 6D ) . Importantly , B ( F66A ) -YFP specifically localized in the membranes surrounding the forespore ( Fig 6E ) in a manner that depended on A and BofA ( S12 Fig ) , supporting the idea that the F66A substitution did not disrupt interactions between the protease and its negative regulators . Collectively , these data are consistent with the model that the B protease adopts open and closed conformations in vivo .
Reconstitution studies using recombinant B protease revealed that pro-σK processing requires ATP [13] . In line with this finding , the CBS domain of the B protease was shown to bind ATP in vitro [13] . Furthermore , as described above , a deletion of the C-terminal CBS domain did not support cleavage of a modified pro-σK substrate in an E . coli expression system [38] . Since CBS domains have been proposed to function as sensors of cellular energy status [31 , 32] it was hypothesized that the CBS domain on the B protease might couple σK activation to the energy status of the mother cell . Here , we report that the CBS domain contributes to the association between B and pro-σK and the efficiency pro-σK processing in B . subtilis but is not strictly required for either nor is it necessary for efficient spore formation . We cannot account for the discrepancies between our in vivo analysis and the in vitro reconstitution and E . coli expression data reported previously , however , other differences between B . subtilis sporulation and these heterologous systems have been reported [38 , 42] . In vitro studies with the purified CBS domain of B [13] and with the CBS module from the S2P from Archaeoglobus fulgidus [43] suggest that Mg2+-bound ATP favors monomerization . While it is possible that fusing YFP to BΔ66 influenced its ability to dimerize , we obtained similar results using a BΔ66 fusion to monomeric YFP and a YFP variant that retains the ability to dimerize ( S13 Fig ) . Nonetheless , it is possible that replacing the CBS domain with YFP could mimic the activated conformation of this domain . We note that a BΔ66 truncation that lacks the YFP fusion is non-functional , however we do not have antibodies to the membrane protease domain to assess the stability of this mutant . That being said , we favor a deterministic model in which the activation of σK is principally governed by the developing spore via the production and secretion of the IVB signal and that mother cell energy status could dictate the efficiency of pro-σK processing and consequently the rate at which σK-directed genes accumulate in the mother cell . The pro-σK processing pathway is distinct from the other characterized RIP signaling pathways in that the substrate of the intramembrane cleaving protease , pro-σK , is not an integral membrane protein and is not subject to sequential site-1 and site-2 cleavages . Yet , this pathway takes advantage of a 2-step proteolytic cascade to transduce information across the lipid bilayer . We wonder whether other RIP signaling pathways that employ members of the group 3 S2P subfamily function similarly . M . jannaschii does not appear to have a homolog of the A protein . However , it does encode a protein with homology to BofA and a IVB protease family member despite not being an endospore-forming organism . It will be interesting to identify native substrates of mjS2P and other members of this broadly conserved subfamily . In the case of pro-σK pathway , this variation on RIP signaling is perfectly matched with the morphological constraints that exist during sporulation . B , A , and BofA are produced in the mother cell during the morphological process of engulfment [20 , 21] , while pro-σK expression is subject to more stringent control [44 , 45] and only accumulates around the time when engulfment is complete [44 , 46] . Due to a membrane fission event at this late stage [47] , the mother-cell membranes that surround the forespore become topologically distinct from the peripheral membranes of the mother cell ( Fig 1A ) [48] . Integral membrane proteins produced in the mother cell at this stage are exclusively inserted in the peripheral membranes and are therefore unable to access the membranes surrounding the forespore [49] . Accordingly , to have access to the B protease , pro-σK must be a peripherally-associated , rather than an integral , membrane protein . Thus , regulation of intramembrane proteolysis is achieved by a site-1 signaling protease that instead cleaves a negative regulator of the S2P family member . As in the case with RseP and other S2P family members , a complete picture of the sporulation RIP signaling pathway awaits structural determination of B ( E44Q ) bound to pro-σK and the inhibited B-A-BofA signaling complex . With the recent advances in membrane protein crystallography using lipidic cubic phase and cryo-electron microscopy , these structures are now within reach .
All B . subtilis strains were derived from the prototrophic strain PY79 [50] . Sporulation was induced by resuspension at 37°C according to the method of Sterlini-Mandelstam [51] or by exhaustion in supplemented DS medium [52] . Sporulation efficiency was determined in 24–30 hour cultures as the total number of heat-resistant ( 80°C for 20 min ) colony forming units ( CFUs ) compared with wild-type heat-resistant CFUs . All sporulation assays reported were based on three or more biological replicates . Expression of B , A , BofA and pro-σK during vegetative growth was performed in LB . IPTG was added to a final concentration of 0 . 5 mM at an OD600 of 0 . 1 and samples were analyzed every 30 minutes post induction . Deletion mutants were generated by isothermal assembly [53] and direct transformation into B . subtilis . Tables of strains ( S1 Table ) , plasmids ( S2 Table ) , oligonucleotide primers ( S3 Table ) and descriptions of plasmid construction and isothermal assembly deletion mutants can be found online as supplementary material . Strains bearing the PgerE-lacZ fusion were sporulated by resuspension . 1 mL samples were collected by centrifugation every hour during sporulation and stored at -20 ˚C . Samples were processed by the method of Miller [51 , 54] , using ortho-nitrophenyl-β-D-galactopyranoside ( ONPG ) as substrate . β-Galactosidase specific activity was defined as the change in A420 per minute per milliliter of culture per OD600 × 1000 and is reported in Miller units . All β-Galactosidase assays reported were based on three or more biological replicates . σK activity during sporulation was also assessed on DSM agar plates containing 100 μg/mL 5-bromo-4-chloro-3-indolyl β-D-galactopyranoside ( X-gal ) . Whole-cell lysates from sporulating cells ( induced by resuspension ) or vegetatively-growing cells were prepared as described previously [34 , 55] . Equivalent loading was based on OD600 at the time of harvest . Proteins were separated by SDS-PAGE on 12 . 5% polyacrylamide gels , electroblotted onto Immobilon-P membranes ( Millipore ) and blocked in 5% nonfat milk in phosphate-buffered saline ( PBS ) -0 . 5% Tween-20 . The blocked membranes were probed with anti-σK ( 1:10 , 000 ) [37] , anti-σA ( 1:10 , 000 ) [56] , anti-GFP ( anti-10 , 000 ) [33] , or anti-SpoIIP ( 1:10 , 000 ) [57] diluted into 3% BSA in 1X PBS-0 . 05% Tween-20 . Primary antibodies were detected using horseradish peroxidase-conjugated goat , anti-rabbit IgG ( 1:20 , 000; BioRad ) and the Western Lightning reagent kit as described by the manufacturer ( PerkinElmer ) . At least two biological replicates were performed for each immunoblot . To resolve pro-σK-CFP ( 55 kDa ) and mature σK-CFP ( 53 kDa ) , lysates were separated on a 35 cm 10% polyacrylamide gel . Fluorescence microscopy was performed with an Olympus BX61 microscope as previously described [58] . Cells were mounted on a 2% agarose pad containing resuspension medium using a gene frame ( BioRad ) . Fluorescent signals were visualized with a phase contrast objective UplanF1 100x and captured with a monochrome CoolSnapHQ digital camera ( Photometrics ) using MetaMorph software version 7 . 7 ( Molecular devices ) . The membrane dye 1- ( 4-trimethylammoniumphenyl ) -6-phenyl-1 , 3 , 5-hexatriene p-toluenesulfonate ( TMA-DPH , Molecular Probes ) was used at a final concentration of 50 μM and exposure times were typically 500 ms . The DNA dye 4’ , 6-diamidino-2-phenylindole dihydrochloride ( DAPI , Molecular Probes ) was used at 2 μg/mL and exposure times were typically 200 ms . At least two biological replicates were performed for all microscopy experiment . Images were analyzed , adjusted and cropped using MetaMorph software . Immunoprecipitations were performed as described previously [59] . Briefly , 50 mL cultures were harvested at hour 4 after the initiation of sporulation by resuspension . Cell pellets were washed twice with 1X SMM ( 0 . 5 M sucrose , 20 mM MgCl2 , 20 mM maleic acid pH 6 . 5 ) at room temperature and then resuspended in 5 mL 1X SMM containing lysozyme ( 0 . 5 mg/mL ) . The suspension was gently shaken for 30 minutes at room temperature to generate protoplasts . Protoplasts were collected by centrifugation and flash-frozen in N2 ( l ) . Thawed protoplasts were disrupted by osmotic lysis with 3 mL hypotonic buffer ( Buffer H ) ( 20 mM Hepes pH 8 , 200 mM NaCl , 1 mM dithiothreitol , with protease inhibitors: 1 mM phenylmethylsulfonyl fluoride , 0 . 5 μg/mL leupeptin , 0 . 7 μg/mL pepstatin ) . MgCl2 and CaCl2 were added to 1 mM and lysates were treated with DNAse I ( 10 μg/mL final ) and RnaseA ( 20 μg/mL ) for 1 hour on ice . The membrane fraction was separated by ultracentrifugation at 35 krpm for 1 h at 4°C . The supernatant was removed , and the membrane pellet dispersed in 200 μL of Buffer G ( Buffer H with 10% glycerol ) . Crude membrane preparations were aliquoted and flash-frozen in N2 ( l ) . 100 μL of crude membranes were diluted 5-fold with Buffer S ( Buffer H with 20% glycerol and 100 μg/mL lysozyme ) , and membrane proteins were solubilized by the addition of the nonionic detergent digitonin ( Sigma ) to a final concentration of 0 . 5% . The mixture was rotated for 1 h at 4°C . Soluble and insoluble fractions were separated by centrifugation at 35 krpm for 1 h at 4°C . The soluble fraction ( the load ) was mixed with 20 μL of affinity-purified anti-GFP antibodies [22] covalently coupled to Protein A sepharose and rotated for 4 h at 4°C . The resin was pelleted at 5 krpm and washed five times with 1 mL of Buffer S + 0 . 5% digitonin . Immunoprecipitated proteins were eluted with 50 μL of sodium dodecyl sulfate ( SDS ) sample buffer ( 0 . 25 M Tris , pH 6 . 8 , 6% SDS , 10 mM EDTA , 20% glycerol ) and heated for 15 min at 50°C . The resin was pelleted and the supernatant ( the IP ) was transferred to a fresh tube and 2-mercaptoethanol was added to a final concentration of 10% . The immunoprecipitates were analyzed by immunoblot and SDS-PAGE followed by silver staining [59] . Individual bands were excised from silver-stained gels and trypsinized . Extracted peptides were then separated on a nanoscale C18 reverse-phase HPLC capillary column , and were subjected to electrospray ionization followed by MS using an LCQ DECA ion-trap mass spectrometer . Among the 5 peptides identified by MS , 3 ( YLEILMAK , FGLDLKK , EIAKELGISR ) were from σK . No σK peptides were identified from the same region of the gel in the controls . To generate homology models of the B protease , the sequence was first aligned to that of mjS2P using the HHPred server to generate an initial alignment for homology modeling [60] . This was further adjusted manually to correct a register error in the last transmembrane segment revealed by evolutionary co-variation analysis . The resulting modified alignment was used to construct a homology model in MODELLER [61] using the structures of mjS2P as a template ( PDB ID: 3B4R ) . Both conformations were modeled , using 3B4R chain A as the template for the open conformation , and chain B as the template for the closed conformation . Multiple sequence alignments ( MSA ) were generated for both the B protease ( SP4FB_BACSU , residues 1–210 ) and mjS2P ( Y392_METJA solved in PDB 3B4R , residues 1–224 ) . The MSAs were built using jackhmmer [62] , an iterative hidden Markov model-based sequence search tool , with 5 iterations querying against the April 2017 Uniref100 database [63] . For SP4FB_BACSU , the alignment contained 5 , 390 sequences ( 2 , 005 effective sequences after downweighting sequences with more than 80% identity ) , with 92 . 4% of the input residues covered with less than 30% gaps . Y392_METJA was aligned with 5 , 290 sequences ( 1927 effective sequences ) with 92% coverage at 30% gap allowance . Evolutionary couplings were then determined as previously described [39 , 64 , 65] . The full EVFold package and documentation can be found at https://github . com/debbiemarkslab/EVcouplings | Regulated Intramembrane Proteolysis is a broadly conserved mechanism for transducing information across lipid bilayers . In these signaling pathways a protease on one side of the membrane triggers the activation of a membrane-embedded protease that cleaves its substrate within or adjacent to the cytoplasmic face of the membrane . Site-2 metalloproteases ( S2P ) are the most commonly used intramembrane cleaving proteases in these pathways but the mechanism by which cleavage on one side of the membrane triggers intramembrane proteolysis remains poorly understood . Here , we provide evidence for a substrate-gating model in which an extracellular signaling protease triggers a conformational change in a S2P family member from a closed to an open conformation allowing its substrate access to the catalytic center of the enzyme . | [
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"en... | 2018 | Evidence that regulation of intramembrane proteolysis is mediated by substrate gating during sporulation in Bacillus subtilis |
The immunobiology underlying the slow acquisition of skin immunity to group A streptococci ( GAS ) , is not understood , but attributed to specific virulence factors impeding innate immunity and significant antigenic diversity of the type-specific M-protein , hindering acquired immunity . We used a number of epidemiologically distinct GAS strains to model the development of acquired immunity . We show that infection leads to antibody responses to the serotype-specific determinants on the M-protein and profound protective immunity; however , memory B cells do not develop and immunity is rapidly lost . Furthermore , antibodies do not develop to a conserved M-protein epitope that is able to induce immunity following vaccination . However , if re-infected with the same strain within three weeks , enduring immunity and memory B-cells ( MBCs ) to type-specific epitopes do develop . Such MBCs can adoptively transfer protection to naïve recipients . Thus , highly protective M-protein-specific MBCs may never develop following a single episode of pyoderma , contributing to the slow acquisition of immunity and to streptococcal endemicity in at-risk populations .
The central role of immunity is to prevent re-infection with the same organism . For example , long-lasting immunity prevents re-infections with , measles , smallpox , and pertussis [1–4] . For other organisms that exist as multiple strains ( such as influenza virus ) , infection does not lead to general immunity but induces life-long strain-specific immunity and neutralizing antibody responses [5] . In healthy individuals , an infection or antigen exposure activates B-cells to form germinal centres in secondary lymphoid tissue with T-follicular helper ( TFH ) cells providing factors for growth and differentiation . Within the germinal centre they become antibody-secreting cells ( ASCs ) and undergo class switching and somatic hypermutation with the affinity of the antibody increasing . After 7–10 days , memory B cells ( MBCs ) form and circulate to other sites in preparation for future antigenic challenge where they mount a rapid secondary response and long-lived plasma cells ( LLPCs ) form and migrate to the bone marrow [6] . Immunological memory depends on MBCs but the factors that regulate their formation are neither well known nor understood . Group A streptococci ( GAS ) are Gram positive cocci that infect either the naso-pharynx or the skin . It is estimated that in excess of 500 , 000 lives are lost prematurely each year as a result of GAS infections [7] . The epidemiology of infections is highly dynamic in at-risk impoverished communities , with streptococci of multiple strains sequentially infecting children , such as occurs in many remote Australian Aboriginal communities [8 , 9] . This results in extreme endemicity and very high rates of streptococcal-associated serious pathology , including rheumatic heart disease [10–12] , post streptococcal glomerulonephritis [13 , 14] and invasive GAS disease [15 , 16] . Infection does not lead to general streptococcal immunity . This is largely attributed to expression of virulence factors that impede innate immunity ( including those inhibiting complement , neutrophil chemotaxis and blood clotting ) [17] and the significant sequence diversity of the M-protein , itself a major virulence determinant and target of serotype-specific opsonizing antibodies [18–20] . There are over 150 distinct strains based on the serological M-types [21] and over 250 emm types , based on the amino-terminal sequencing of the M-protein gene [22] . Little is known about the induction of strain-specific antibody responses and immunity as a result of streptococcal infection . Reports from the middle of the last century showed the presence of M type-specific antibodies in the blood of children convalescing from GAS pharyngitis [23 , 24] . Wanamaker et al [25] demonstrated that the presence of type-specific antibodies correlated with a 6-fold reduction in the risk of homologous pharyngeal infection . Lancefield [26] then reported the persistence of strain-specific antibodies in some individuals 4–32 years after a GAS infection; however , other individuals who had a known GAS infection did not demonstrate any type-specific antibodies . There was no correlation between the persistence of these antibodies and the severity of infection . Recently , Bencivenga et al [27] reported that 1 of 2 subjects who had confirmed rheumatic fever 45 years previously had persistent opsonic antibodies against the infecting strain . It was postulated that the higher levels of antibodies in the single individual might have arisen due to additional infections resulting in boosting of antibody levels . To our knowledge this was the first report of persistence of antibodies following streptococcal pharyngitis since the Lancefield study of 1959 but neither study was able to elucidate the factors regulating immunity to pharyngeal infection . There are no other reports on what factors may influence immune induction and persistence . Even less is known regarding the acquisition and persistence of immunity resulting from streptococcal skin infection ( pyoderma ) . Bisno and Nelson [28] reported that only 2 of 17 children with pyoderma developed type-specific antibodies . In addition , poor anti-streptolysin O and anti-streptococcal NADase responses are observed after skin infection [29] . While pyoderma is prevalent in tropical settings , including amongst Indigenous peoples of Australia , and skin is a common portal of entry for invasive GAS ( iGAS ) infections [16 , 30] , no studies have investigated factors that regulate induction of immunity to GAS pyoderma in these or other populations . To ask whether a sub-optimal immune response to streptococcus might be contributing to the high endemicity of streptococcal infections in at-risk communities , we used a mouse model for pyoderma . We sequentially infected mice with the same or with different endemic and reference strains of GAS following which we estimated the streptococcal bio-burdens in skin and blood and correlated these with the serotype-specific antibody responses and with the development of memory B cell responses . The data reveal a novel aspect of GAS immunobiology that if applied to the human situation contributes to the excessively high rates of endemicity and GAS-related pathology in at-risk communities .
To assess induction of serotype-specific immunity , naïve BALB/c mice ( group A ) were repeatedly infected with the NS27 GAS strain ( emm type 91 ) via skin scarification [31] and the development of strain-specific antibodies and protective immunity were monitored ( Fig 1a ) . Following a single infection with NS27 , low levels of strain-specific IgG were detected ( Fig 1b ( i ) ) . These were assessed by ELISA using the strain-specific amino-terminal peptide of NS27 as the capture antigen [32] . IgG levels improved ( up to 8-fold ) following subsequent infections ( Fig 1b ( i ) ) . We observed that with each subsequent infection the levels of NS27-specific IgG , having risen , were then depleted in the serum , presumably as a result of binding the infecting organism . Within 3 weeks of a single infection , mice developed profound immunity with over 85% reduction in bacterial burden in skin and blood following a second infection ( Fig 1b ( ii ) and 1b ( iii ) ) . This level of protection improved even further following subsequent infections and correlated with the increase in NS27-specific IgG titers . To dissect the development of immunity following infection with different strains , mice were sequentially exposed to GAS strains of different emm types ( S1 Table ) ( NS27→NS1→88-30→BSA10 ) ( group B ) . Each infection was three weeks apart . We determined strain-specific antibodies by ELISA using the relevant amino-terminal peptides for each M-type [32] . Following the initial infection with NS27 , there was again a transient appearance of low levels of NS27-specific IgG ( Fig 1c ( i ) ) . Subsequent infection with NS1 did not boost NS27-specific IgG levels ( Fig 1c ( i ) ) ; similarly , infection with NS27 did not induce immunity to NS1 in skin ( Fig 1c ( ii ) ) or blood ( Fig 1c ( iii ) ) . Similarly , the second infection ( NS1 ) did not induce immunity to the third strain ( 88–30 ) and infection with that strain did not induce immunity to BSA10 ( Fig 1c ( i ) –1c ( iii ) . Thus , prior exposure to a given strain provided neither cross-strain-specific antibody nor cross-strain protective immunity , irrespective of the emm type of the infecting strains ( Fig 1c ( i ) –1c ( iii ) ) . To determine whether immunity to a given strain was serotype-specific we infected mice with the 2031 strain , which is an M1 serotype , and re-infected them with either same ( 2031 ) or a different strain ( 5628 ) , which is also an M1 serotype . We observed that mice infected with 2031 showed ≥ 90% reduction in bio-burden when subsequently infected with 5628 three weeks later ( Fig 1d ( ii ) and 1d ( iii ) ) . The second infection ( with either 2031 or 5628 ) further enhanced immunity to a third infection with 2031 ( Fig 1d & 1e ) . The level of immunity to 5628 was comparable to the immunity observed followed by two sequential 2031 infections ( Fig 1e ( i ) –1e ( iii ) ) . In summary , the data thus show that skin infection with one particular strain does not induce immunity to a different strain unless that strain has the same M-protein as the initial strain . Thus , at least for the strains used in this study , immunity induced following a skin infection and assessed three weeks later is entirely specific to serotypic determinants expressed on the M-protein . We then asked whether immunity to a given strain would be influenced by co-infection with multiple strains of GAS , as is commonly found in streptococcal-endemic areas [9] . To assess this , other mice from the experimental groups ( A and B ) were then challenged with a cocktail of GAS strains . The cocktail contained , in equal ratio , the four strains: NS27; NS1; 88–30 and BSA10 . We assessed strain-specific immunity following challenge by rendering the strains resistant to different antibiotics and then enumerating the number of resistant bacteria growing on antibiotic-laced plates ( S1 Table ) . Following challenge , the group that had received four sequential infections with NS27 demonstrated >95% reduction in the NS27 bacterial burden both in skin and blood in comparison to the naïve control mice ( p<0 . 01 ) ( Fig 2a ) . There was no reduction in the bacterial loads for the other strains present in the cocktail . The NS27 protection correlated with increases in IgG titers specific for the N-terminal peptide of the NS27 M-protein ( Fig 1b ) , and the numbers of M-protein-specific ASCs in the spleen , and LLPCs in the bone marrow of infected mice at the time of challenge ( Fig 2b and 2c ) . However , following cocktail challenge of group B ( which had received four sequential heterologous infections [NS27→NS1→88-30→BSA10] ) , we observed that mice were protected only against BSA10 with >80% reduction in the bacterial burden both in skin and blood ( p<0 . 01 ) ( Fig 2d ) . The protection correlated with significant numbers of BSA10-specific ASCs in the spleen ( Fig 2e ) . Very low numbers of LLPCs were detected in bone marrow at this stage ( Fig 2f ) . To exclude the possibility that combining various GAS strains in a cocktail might have affected the virulence of individual strains , the challenge study was repeated with each individual GAS strain . Thus , further mice ( groups A and B ) exposed to four sequential infections ( NS27→NS27→NS27→NS27 ) or ( NS27→NS1→88–30→BSA10 ) were challenged with each of the four strains individually . The results were consistent with the previous observations , demonstrating protection against NS27 only ( group A; Fig 2g ) or BSA10 only ( group B ) ( Fig 2h ) . To further confirm that this was not an artefact of the experimental design or a strain-specific observation , we repeated the cocktail challenge experiment but changed the order of prior GAS strain infections ( NS1→88–30→BSA10; or NS27→NS1→88–30; or NS27→88–30→NS1 ) . In each experimental scenario the mice were protected against only the last of the sequential infections ( p<0 . 01 ) ( S1 Fig ) . These observations suggested that either the strain-specific primary immune response to GAS was short-lived or that exposure to heterologous strains ablated immunological memory of previous infections . To address this , additional experiments were undertaken with two strains ( NS1 and NS27 ) . Mice received a single infection with NS1 before they were challenged with the same strain at varying times up to nine weeks later ( Fig 3a ) . Protective immunity was again assessed in skin and blood . We demonstrated that immunity was dependent on the duration of time between exposure and challenge ( Fig 3b ) . We observed 86% and 90% reduction in skin and blood bacterial burden respectively if mice were challenged within three weeks . However , protection was dramatically reduced ( 22–55% in skin and 28–64% in blood; p<0 . 05–0 . 01 ) if it was assessed beyond three weeks . We also measured the ability of the challenge infection to boost the IgG titers and the numbers of ASCs in the spleen and bone marrow . We observed that in mice which had a single infection 3 , 6 , or 9 weeks previously , a subsequent infection did not boost either ASC levels in the spleen nor serum antibody responses , suggesting that MBCs had not formed ( Fig 3c and 3d ) . The low level of protection that was observed ( e . g . ~25% protection at 9 weeks post infection ) can be explained by the presence of low numbers of antigen-specific LLPCs in the bone marrow ( Fig 3e ) . However , we observed that re-infection with the same strain did induce enduring strain-specific immunity to a further infection with 84–89% and 87–95% reduction in skin and blood bio-burden respectively persisting for more than 12 weeks following the initial infection , irrespective of whether or not there was an intervening infection with a different strain ( Fig 3b ) . Two sequential infections with NS1 ( three weeks apart ) significantly increased the numbers of splenic ASCs and their response to a further infection ( p<0 . 05–0 . 01 ) , and improved the NS1-specific IgG titers as well as the IgG response to a further infection ( p<0 . 05–0 . 01 ) , compared to mice that had received a single infection ( Fig 3c and 3d ) . The numbers of LLPCs in the bone marrow increased significantly as well ( Fig 3e ) . Similar observations were made when the experiments were conducted with NS27 GAS ( S2 Table ) . The data were consistent with the induction of MBCs following two infections that were then responsible for the boost in ASC numbers and the IgG titer following a subsequent infection . To confirm that these observations were not specific to the particular mouse strain used in this study , a set of identical experiments was carried out in outbred SWISS mice . We observed that similar to BALB/C mice , development of protective immunity in SWISS mice also required two sequential infections within 3 weeks ( S2a Fig ) . These mice also demonstrated the necessity for two sequential infections to develop enduring strain specific IgG and ASC responses ( S2b–S2d Fig ) . The data from both mouse strains were consistent with the need for two infections with the same strain to induce MBCs . Although a rapid antibody response following infection is a hallmark of MBCs ( Figs 3d and S2C ) , existing antibody from LLPCs can make the interpretation more difficult . To identify MBCs more accurately , spleen cells from previously infected BALB/c and control mice were adoptively transferred into naïve immunodeficient SCID mice . This protocol does not transfer LLPCs , which are resident in bone marrow , and as such the background effect of existing antibody is removed . Post transfer , serum antibodies and ASCs specific for the serotype of the infecting strain were analysed after one day ( background ) and again after six days ( to measure the ‘boost’ indicative of an MBC response ) ( Fig 4a ) . Antigen-specific IgG responses in recipient mice following single NS1 infections of donor mice 3 , 6 , or 9 weeks previously were extremely low at both days one and six ( Fig 4b ) . Thus , there were very few ASCs in the donor cells that were transferred and no MBCs . However , two sequential infections of donor mice with NS1 three weeks apart significantly improved the NS1-specific IgG boost response in the recipients at day six-post challenge ( p<0 . 05–0 . 01 ) ( Fig 4b ) . Similarly , following two infections of donor mice with NS1 , recipient mice demonstrated a significant boost in the numbers of ASCs in their spleens compared to recipients of spleen cells from donor mice with a single infection ( ≥ 600 vs <50 ) ( p<0 . 01 ) ( Fig 4c ) . MBCs persisted at unabated numbers for at least nine weeks after the second infection ( Fig 4c ) . Memory was not ablated by a heterologous infection . It should be noted that when the donor mice were re-infected after 3 weeks , the initial skin lesion had completely healed and there was no residual streptococcal bio-burden . To assess whether the recipient SCID mice were protected , we euthanized mice from each group on day six post-challenge and assessed protection in skin and blood by enumerating total colonies . We observed only limited protection ( <12% and <5% reduction in skin and blood burden respectively ) in recipients of spleen cells from mice that had a single infection ( Figs 4d and S3 ) . Nevertheless , we observed that spleen cells from donor mice that had been infected twice , three weeks apart ( 3 and 6 weeks; 6 and 9 weeks; or 9 and 12 weeks prior to transfer ) , were highly effective in protecting recipient mice with ~80% reduction in bacterial burden in skin and blood ( Figs 4d and S3 ) . It was noteworthy that the MBCs protected the SCID mice even though these mice did not have detectable antibodies at the time of challenge . We were curious if sequential infections would generate immunity to the conserved epitopes of GAS . A conserved peptide epitope in the C3 repeat region of the M-protein , ( J8 ) , is able to induce strain-transcending immunity to skin infection [31 , 33] . We thus asked whether the level of immunity following infection was related to the level of J8-specific antibodies in these mice . However , we observed that anti-J8 antibodies were not induced following single or multiple infections with the same strain or with different strains ( Fig 5a ) . Active infection can deplete antibodies from the serum , rendering interpretation of antibody data difficult during an infection [34]; however , the lack of an anti-J8 antibody response was mirrored by a complete lack of induction of anti-J8 ASCs in the spleen and LLPCs in the bone marrow ( Fig 5b and 5c ) . These data are consistent with the human experience where many years of high-level endemic exposure to GAS are required to generate even a low-level antibody response to this conserved epitope [35] . Interestingly , mice receiving sequential infections did generate antibody [33] and ASC responses to the IL-8 protease and streptococcal inhibitor of neutrophil chemotaxis , SpyCEP ( Fig 5b and 5c ) . Again , these data are in line with previous observations where anti-SpyCEP antibodies have been reported in IVIG [36] as well as in convalescent human plasma and , depending on titer , may have a role in immunity [33] .
Here , we demonstrate that skin infection with streptococci leads to profound , but short-term , strain- and serotype-specific protection . Infection does not lead to the generation of MBCs that protect against either homologous or heterologous strains . However , two homologous infections within a 3-week period , do establish specific MBC responses that are enduring and protective . We are not aware of previous examples in any system where two infections are required to induce durable strain-specific immunity . We used mice with superficial skin scarification to model the development of immunity in humans , where streptococcus is known to enter only broken skin . To mimic the situation in GAS-endemic areas , where individual skin lesions are often co-infected with multiple strains of GAS simultaneously [9] , mice were sequentially exposed to single or multiple GAS strains or were co-infected with multiple strains simultaneously . We found that immunity following a single infection was effective but short-lived . Four significant reports suggest the observations described here will translate into the human experience . Firstly there is the report of children not developing type-specific antibodies following known streptococcal infections [37] along with the report that antibodies from streptococcal pharyngitis , once induced , are long-lived [26] . Secondly , there is the report that only 12% of children with pyoderma develop antibodies to the infecting strain [28] . Then there is the detailed epidemiological study of McDonald et al [8] demonstrating that different strains of GAS move sequentially through at-risk Aboriginal communities in Australia , although more than one strain exists at one time and each strain persists for at least 6 months . While not studied specifically , this persistence suggests that children within the community are very slow to develop immunity , possibly due to the requirement for re-infection . An early study following GAS infections in early childhood reported the occurrence of new versus reinfections as 25 versus 50 per 100 per annum respectively , throughout the study period of 4 years [38] . Adoptive transfer studies showed that MBCs , after a second infection , were effective in controlling an infection even in the absence of circulating type-specific antibodies and LLPCs at the time of challenge . Spleen cells from singly infected mice could transfer only very limited protection to recipient mice from three to nine weeks post-infection ( <10% ) compared to spleen cells from donors that had been infected twice ( ~80%; Fig 4d ) . There was no evidence that spleen cells from mice singly-infected 3 , 6 , or 9 weeks prior to adoptive transfer and challenge contained MBCs as shown by the lack of any boosting in either these mice ( Fig 3 ) or in SCID mice that had received spleen cells from them ( Fig 4 ) following a challenge infection . In contrast , cells from doubly infected mice were shown to contain MBCs by their rapid response ( within six days ) with ASCs and the rapid production of antibody following challenge . Immunity induced by repeated infections with a particular strain , and mediated by MBCs , was strain-specific . An intermediate infection with a different strain did not lessen the degree of immunity to the doubly infecting strain . These data might in part explain the situation in the Aboriginal communities of northern Australia where the highest rates of acute rheumatic fever in the world have been reported [39] . The high prevalence of pyoderma ( ≥70% ) among children throughout the year with up to 14 genetically different GAS strains circulating at any one time [40] suggests a lack of natural immunity in these populations , consistent with our observations here . We did not determine the specificity of the MBCs mediating protection; however , following adoptive transfer into SCID mice and re-infection there was an anamnestic response of antibodies to the amino-terminal epitopes of the M-protein ( Fig 4b ) . We previously showed that vaccination with these epitopes induces highly effective bactericidal antibodies [32 , 41] . Infection with one strain did not induce immunity to a different strain with a different emm type; however infection with one strain did induce immunity to a different strain but with the same emm type . However , repeated infections with the same or different strains of GAS did lead to a broadening of the antibody repertoire as shown by the induction of antibodies to the IL-8 protease , SpyCEP [33]; and , depending on titer , these antibodies are known to play a role in protection [31 , 36] . Thus , it is likely that the protective MBCs would have multiple specificities , of which the amino-terminal region of the M-protein was dominant and most critical . However , one epitope that did not stimulate antibody production or MBCs in re-infected mice was the conserved vaccine candidate epitope of the M-protein , J8 , demonstrating that this epitope is entirely cryptic ( Fig 5a–5c ) . We do not know why MBCs are not induced after a streptococcal infection . However , isotype switching has occurred by three weeks and the antibodies are strongly protective , suggesting that germinal centres , from which MBCs originate , have formed . Hyper immune stimulation may explain our observations . Chronic infections with HIV or malaria parasites are known to expand populations of exhausted or atypical MBCs [42 , 43] , which are hypo-responsive to in vitro stimulation . Atypical MBCs have not been described for streptococcus , but it is possible that bacteraemia and antigen load have led to exhaustion of MBCs . Our observations that bacterial loads are much reduced during a second infection , where MBCs are formed , are consistent with that hypothesis . However , the experiences with smallpox and influenza argue against this . Smallpox is known to induce life-long immunity [3] and neutralizing antibodies persisted for life following the 1918 Spanish influenza epidemic [5] . In both these infections there was a very high antigenic load . Streptococcus is known to express numerous immune-modulating factors [17] and it is possible that these have directly impeded the TFH cells and/or the B cells within the germinal centre , blocking MBC development . GAS is human specific pathogen and therefore to study its pathogenesis an appropriate mouse model as well as animal-adapted GAS strains are critical . The pathology of the murine superficial skin challenge model used here resembles the histopathology of human pyoderma and has been instrumental in understanding pathogenesis and immunity following vaccination ( [31 , 33] . However , while serial passaging in mice enhanced the virulence of the strains used in this study , the role of plasminogen in the pathogenesis of GAS infection remains an important issue for all mouse experiments . GAS streptokinase activates human plasminogen , but not mouse plasminogen , enabling GAS to clear fibrin clots in the skin tissue and more readily enter the blood [44] . While this represents a limitation to the use of mouse models in the study of streptococcal immunity in general , the plasminogen system is only one of many factors exploited by GAS in invasion [45] . Possibly once GAS enters the blood this becomes less relevant . In our study GAS do enter the blood , thus demonstrating virulence in the absence of human plasminogen . It seems unlikely that this would explain the lack of induction of acquired immunological memory . A further limitation of mouse models in the study of streptococcal immunity is the lack of response of mice to GAS super-antigens . These play a central role in the pathogenesis of toxic shock syndrome in humans . However , toxic shock syndrome leads to immune ablation in humans and the lack of this syndrome in mice could not explain why mice do not develop immunity to GAS infection after a single infection . Thus , the data presented here paint a picture of streptococcal pathogenesis and endemicity that is more complex than previously thought . The antigenic diversity of the M-protein at its amino terminus was thought to be primarily responsible for immune evasion and virulence factors were known to interfere with innate immunity . While these are undoubtedly major impediments to the development of immunity in children , our data suggest that a child must be exposed on more than one occasion to the same strain for immunological memory to develop to that strain . We had previously hypothesized that antibodies to the conserved J8 epitope of the M-protein would contribute to natural immunity and with heavy persistent infection , most children do eventually develop antibodies to this region of the M-protein [35]; however , this acquisition is very slow and consistent with the data here that four sequential skin infections do not lead to the development of these antibodies . Thus it would appear that , along with antigenic diversity , the requirement for more than one infection with each serotype in order to induce memory represent the most important obstacles to developing acquired immunity to streptococci in early childhood . With repeated exposure over many years , antibodies to conserved antigens including SpyCEP [46] , C5a peptidase [47] , group carbohydrates [48] and the cryptic conserved epitopes of the M-protein [35] may develop and play significant roles in protection , which then becomes life-long .
All animal studies were reviewed and approved ( AEC protocol # Gly/07/14 ) by Griffith University’s Animal Ethics Committee in accordance with the National Health and Medical Research Council ( NHMRC ) of Australia guidelines . Specific pathogen free 4–6 week old female BALB/c , SWISS or SCID mice were sourced from the Animal Resource Centre ( Perth , Western Australia ) . The peptide J8 ( QAEDKVKQSREAKKQVEKALKQLEDKVQ ) was synthesized and conjugated to diphtheria toxoid ( DT ) as described previously [33] . The gene encoding M1 protein ( amino acids 13–455 ) was cloned into pGEX-2T ( GE Healthcare Life Sciences ) , incorporating a carboxy-terminal 6 x His-tag [49] . The amino terminus serotypic peptides for each GAS strain NS271-19 ( ADDHPGAVAARNDVLSGFSC ) , NS11-19 ( RVTTRSQAQDAAGLKEKADC ) , 88–301–20 ( DNGKAIYERARERALQELGPC ) and BSA101-19 ( NSKTPAPAPAVPVKKEATKC ) were as previously described [32] and were synthesized at Toth laboratory , University of Queensland ( Brisbane ) or at China Peptides Co . , LTD ( Jiangsu , China ) . All peptides were stored lyophilized or in solution at -20°C . GAS isolates NS1 , NS27 , 88-30 , BSA10 , 2031 were originally obtained from Menzies school of Health Research , ( Darwin , NT , Australia ) . The animal adapted derivative of GAS isolate 5628R was obtained from the Walker laboratory ( University of Queensland ) [50] . Details of each strain are given in ( S1 Table ) . All the isolates were passaged in mice . To allow for their selection during co-infection experiments , each strain was made resistant to a specific antibiotic by continually replating them with increasing concentrations of antibiotic on blood agar plates ( S1 Table ) . Naïve BALB/c ( inbred ) or SWISS ( outbred ) mice were infected with GAS via the skin route of infection as previously described [31] . To prepare challenge inocula , the GAS strains were grown in Todd-Hewitt broth ( THB; Oxoid , Australia ) supplemented with 1% ( wt/vol ) neopeptone ( Difco ) and in the presence of a specific antibiotic for each strain . The fitness of various GAS strains used in the cocktail was confirmed in vitro prior to in vivo challenge studies . For CFU enumeration , 10-fold serial dilutions of bacterial cultures were plated in replicates on blood agar plates consisting of the medium described above with 2% agar and 2% horse blood . The broth culture inoculum was adjusted to obtain the intended challenge dose . For sequential infections mice were given 1x106 CFU/mouse . Each sequential infection was followed for up to 3-weeks to confirm the clearance of bacteria from skin and blood prior to subsequent infection . Following sequential homologous ( NS27x4 ) or heterologous ( NS27-NS1-8830-BSA10 ) infections , mice were challenged with a cocktail of all the 4 GAS strains or each strain individually . For challenge experiments with individual strains , the cohorts of sequentially infected mice ( homologous or heterologous ) were divided into 4 groups and each group was challenged with one of the 4 challenge strains . To confirm serotype-specific immunity , in separate experiments , two distinct GAS strains 2031 and 5628 from the same emm serotype ( emm1 ) were used . For memory experiments two selected GAS strains NS1 and NS27 were used . In all experiments , age-matched naïve mice were used as challenge controls . Following each sequential infection , serum and tissue samples were collected at designated time points ( Fig 1a ) . On day 6 post each infection , a designated number ( n = 5 ) mice were euthanized to obtain skin and blood samples for CFU quantification . To allow for detection of current and previous GAS infections , specific antibiotic-laced blood agar plates were used through out the experiments . Similar procedures were employed following final GAS challenges . Six days post cocktail or individual challenge , designated numbers of mice were euthanized and skin and blood samples collected for bio-burden assessment . Where specified , for selected experiments , spleen and bone marrow were also harvested at the same time-point to assess the memory responses . ELISA was used for the measurement of antigen-specific IgG titers in serum , as described elsewhere [34] . Titertek PVC microplates ( MP biomedicals ) plates were coated with J8 or N-terminal serotypic peptide . Serum samples were assessed using 2-fold dilutions of 1:100 dilution of serum . Antigen-specific mouse antibodies were detected with HRP-conjugated goat anti-mouse IgG antibody ( Bio-rad Laboratories ) . SIGMAFAST OPD ( Sigma-Aldrich ) was employed as a HRP substrate and absorbance was measured at 450 nm . Antibody titers were defined as the highest dilution that provided an optical density reading at 450nm of > 3SDs above the mean optical density of control wells containing normal mouse serum . To quantify the number and location of antibody secreting cells ( ASCs ) , bone marrow and splenocytes of naïve or vaccinated-GAS infected and control mice were analysed at specific time-points by ELISPOT . Multiscreen-HA plates were coated with 5 μg/mL of J8 or N-terminal serotypic peptides ( NS27 , NS1 , 88–30 or BSA10 ) in an alkaline carbonate coating buffer , overnight at 4°C . Isolated spleen and bone-marrow cells were directly tested for IgG-secreting ASCs on these coated plates using published methods [51 , 52] . The use of J8 or N-terminal serotypic peptides allowed measurement of specific ASCs . To investigate memory responses following single or multiple homologous and heterologous infections , splenocytes from GAS infected BALB/c mice were adoptively transferred into naïve SCID mice . Spleens were mashed and passed through a 0 . 70 μm cell strainer to obtain single cell suspensions . RBC lysis was carried out using ACK lysis buffer ( 0 . 15 M NH4Cl , 10 mM KHCO3 and 0 . 1 mM Na2EDTA; pH 7 . 4 ) . Following two washes , the cells were counted , resuspended in 200 μL sterile PBS and transferred intravenously into SCID mice . Each mouse received one spleen equivalent . Mice were immunized s . c . at the tail base on days 0 , 21 and 28 with 30 μg of J8-DT adjuvanted with alum ( Alhydrogel [2%]; Brenntag ) at a 1:1 ratio ( 50 μl immunization dose ) /mouse . Serum samples were taken prior to and one week after each immunization . Vaccinated mice were also followed for longevity of J8-specific IgG responses until day 90 . At specific time-points designated numbers of mice were culled , spleen and bone marrow harvested and ASCs were enumerated . Data were analysed using GraphPad PRISM version 6 . 00 for Macintosh . Except where noted , data shown are mean ± Standard Error of Mean ( SEM ) . Statistical differences between two groups were determined using the non-parametric U test with p<0 . 05 considered to be statistically significant . ANOVA with a Tukey’s post-hoc method for multiple comparisons was used for pairwise comparisons . The p values p<0 . 05 was considered to be statistically significant . | GAS skin infections pose a significant health problem in the tropics . They are highly prevalent in developing countries as well as amongst the Indigenous populations of developed countries . In at-risk impoverished communities the epidemiology of GAS infections is very dynamic , leading to very high rates of streptococcal-associated serious pathology including rheumatic heart disease , glomerulonephritis and invasive GAS disease . Immunity to GAS takes over 20 years to develop and this has been attributed to sequence diversity of the type-specific surface M-protein . There are more than 250 different strains of GAS and it known that antibodies to the amino-terminal segment of the M-protein can kill organisms in a strain-specific manner in vitro . In the present study , using four different strains of GAS isolated from the skin lesions of Aboriginal patients in the Northern Territory of Australia , we make the discovery that skin infection does not induce long-lived type-specific immunity . However , following reinfection with the same strain memory B cells are generated and long-term strain-protective immunity then develops . The dependence on reinfection for the development of strain-specific immunity compounds with antigenic diversity of the M-protein and provides a rational explanation for the very slow acquisition of streptococcal immunity . | [
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"antibo... | 2016 | Streptococcal Immunity Is Constrained by Lack of Immunological Memory following a Single Episode of Pyoderma |
A farmer’s decision on whether to control a pest is usually based on the perceived threat of the pest locally and the guidance of commercial advisors . Therefore , farmers in a region are often influenced by similar circumstances , and this can create a coordinated response for pest control that is effective at a landscape scale . This coordinated response is not intentional , but is an emergent property of the system . We propose a framework for understanding the intrinsic feedback mechanisms between the actions of humans and the dynamics of pest populations and demonstrate this framework using the European corn borer , a serious pest in maize crops . We link a model of the European corn borer and a parasite in a landscape with a model that simulates the decisions of individual farmers on what type of maize to grow . Farmers chose whether to grow Bt-maize , which is toxic to the corn borer , or conventional maize for which the seed is cheaper . The problem is akin to the snow-drift problem in game theory; that is to say , if enough farmers choose to grow Bt maize then because the pest is suppressed an individual may benefit from growing conventional maize . We show that the communication network between farmers’ and their perceptions of profit and loss affects landscape scale patterns in pest dynamics . We found that although adoption of Bt maize often brings increased financial returns , these rewards oscillate in response to the prevalence of pests .
The European corn borer ( Ostrinia nubilalis ) ( ECB ) , a serious pest of maize , cost the American economy an estimated 1 billion US dollars annually at its worst in the early 1990s [1 , 2] . In 1996 , Bt maize , a transgenic crop that expressed insecticidal proteins from the soil-dwelling bacterium Bacillus thuringiensis , was introduced for control of the pest . Since then , farmers have had to choose whether to plant conventional or Bt maize ( Fig 1 ) . Their decisions rest on the economic viability of Bt , given that future infestations of ECB cannot be predicted . Specifically , farmers must predict whether increased returns from Bt will exceed the technology fee , a financial premium for buying the transgenic seed [3 , 4] . In some situations , farmers believe that the economics favor conventional seed; more than half of them believe that the price of Bt maize is too high to merit purchase [1 , 5] , particularly if their crops have not recently been infested . Hutchison et al . [1] showed that Bt maize generated an estimated $230 million annual benefit to maize growers in Illinois , Minnesota and Wisconsin . Much of this economic benefit ( 75% ) accrued to farmers who did not plant Bt maize; these farmers did not pay technology fees but still benefitted from the area-wide suppression provided by those farmers who cooperated to use Bt to reduce pest densities [1] . Other systems , such as cotton , have shown similar benefits from area wide suppression of pests [6] . As such , the control of ECB can be evaluated through game theory because the mechanisms of cooperation , such as reciprocity , reputation and spatial structure are embedded in the farmer networks that mediate the population dynamics of the pest [7–10] . The system is akin to a ‘snow drift’ game [8] . The snow drift game is a metaphor for a situation whereby the benefit that an individual , in this case a farmer , obtains for a given strategy depends on the actions of others . In particular , if a farmer chooses to grow conventional maize in a landscape where the pest is supressed by other farmers growing Bt maize , then this individual will benefit from the pest suppression without paying the technology fee . On the other hand , in a situation where the pest is not suppressed at landscape scale it is likely to be more profitable for an individual to grow Bt maize . When deciding whether to plant Bt maize , farmers negotiate between ‘expert’ and ‘local’ knowledge ( Fig 1 ) . For example , Kaup’s [5] hierarchy of influences showed maize-seed dealers and crop consultants appeared to have substantial influence , neighbors had moderate influence , and extension agents had little influence on the farmers’ decisions to plant Bt maize . More than 50% of farmers who anticipated having ECB problems chose to plant Bt maize . The results emphasize an important principle in pest control: farmers’ perceived risks , rather than actual losses , play an important role in pest management [5 , 11 , 12] . This principle of 'risk perception' is crucial . If farmers’ underestimate the risk of infestation and grow conventional maize then the pest will flourish and diminish yields . If on the other hand farmers exaggerate the risk and plant too much Bt maize then there is an increased risk that the pest will adapt to its new host and threaten the long-term production of maize . Here we build a framework for exploring the intrinsic feedback mechanisms between the actions of humans and the dynamics of pest populations in a structured landscape , and use the European corn borer in maize as an example . Our example is intended to demonstrate the plausibility of the framework and so is illustrative rather than predictive . Our models are kept simple to both aid the elucidation of our results and to reduce the runtimes of the simulations . This particular example was chosen because there is a rich source of data to support it . We build a mechanistic model of the population dynamics of ECB in a 700-km long strip of the US Corn Belt . The models are parameterised to reflect a maize system similar to that in the part of the US Corn Belt that passes through Minnesota and Wisconsin . The model of the population dynamics includes the life cycle , dispersal and ecology of the pest including its relationship with the pathogen Nosema pyrausta ( Microsporidia: Nosematidae ) , which is one of the most important natural enemies of the ECB; this parasite reduces the number of surviving offspring , and is cited as the primary reason for the observed cycle in the population density [13–16] . The landscape model is spatially-explicit and parameterized so that one half has similar county sizes , farm sizes , and density of maize crops to those in Minnesota and the other to those in Wisconsin . We show how this model captures the behavior of the ECB-population dynamics in the observed empirical data at a coarse spatial scale . Importantly , analysis of the model shows that even when the infected population is reduced to small numbers , it retains the capacity to recover and so the natural control persists . We then introduce a sociological layer to the model . We simulate the processes by which individual farmers decide whether to grow Bt maize or conventional maize . The decision is based predominantly on likely profit: the probability that a farmer will chose a given strategy is based on the information that he or she has on the profits achieved under Bt maize and conventional maize in recent seasons . For any given farmer , the source of this information will depend on the network of communication . Here we explicitly model four different networks of communication . In particular we explore how the form of the network affects the uptake of Bt maize over time , the pest population dynamics and the long term profits of the farmers in the landscape . We show that the form of the network impacts the feedback mechanism between pest populations and farmers' decisions that affect landscape-scale dynamics . We show that independent decision makers that follow similar heuristics and are influenced by the same circumstances can create an apparent coordinated response which affects ecological systems at landscape scales . This coordinated response is not intentional , but is an emergent property of the system .
We developed a model to explore the population dynamics of ECB and its natural enemy , the pathogen Nosema pyrausta , and the impact of ECB on maize crops in a landscape . This landscape was based on national agricultural census statistics from 1997 , 2002 and 2007 on county sizes , farm sizes and numbers , harvested areas and the area of maize grown in Wisconsin and Minnesota [17–19] . We used a grid of 300 x 1400 cells that equates to a 150km x 700km strip . Each cell represents 25 ha ( 0 . 5km x 0 . 5km ) , similar to the typical size of maize fields in the region . One half of the simulated landscape was parameterised to be similar to Wisconsin and the other to Minnesota . We partitioned the two states into counties , with county sizes reflecting the actual distribution of county sizes in each state . We defined farms as connected cells in which arable crops could be grown . The number of farms in each simulated county , and the distribution of their sizes , reflected the true distribution of arable land on farms in each state . Simulated farms were fitted into the county , along with uncropped areas at random ( see S1 Text ) . The landscape was generated stochastically and so is a realisation of a random process . Crops were assigned county by county . On average , maize accounted for 44% of the cropped area in Minnesota and 37% in Wisconsin [17–19] . Cropped cells were then allocated at random as maize or other . Each year , the proportion of maize in a given county was resampled , and cropped cells allocated again at random to maize or other . This process allowed for a proportion of fields to have maize crops grown consecutively and others to have rotations with a non-host crop for ECB . We made the simplifying assumption that ECB only develops in grid cells with maize . In each of these cells we use an abundance-based population model to describe the development of a population of ECB that is susceptible to the pathogen N . pyrausta and one that is infected . Our model did not include the effect of other natural enemies of ECB or climate , and so was not expected to accurately describe the historic dynamics of the ECB . Rather , its purpose was to capture the population cycle attributed to N . pyrausta and to simulate the effect of Bt maize on larval survival . In the model , eggs hatch into larvae that pass through five instar stages . The survival of the larvae through to pupation is density dependent . We assume that the Bt toxin reduces the number of larvae that reach instar 3 by 99 . 9% [20] . We do not consider insecticides as a control measure as these are considered largely ineffective because after the neonate stage , the ECB larvae are concealed within the maize plant , thus avoiding direct contact with an insecticide's active ingredients . Adults emerge following pupation , then disperse and mate , and then females disperse before oviposition and the cycle starts again . We assume two generations of ECB per year , as is typical in Minnesota and Wisconsin . The larvae from the second generation overwinter in stalks , and so their survival rate is lower than that of the first generation . Infection by N . pyrausta travels through both horizontal and vertical pathways . We assume that infected adult males do not pass infection to their young , but that females pass on infection to 85% of their eggs [21] . Infection passes horizontally through the population during the larvae stage when susceptible ( uninfected ) larvae come into contact with frass from infected larvae . The infection rate is modelled as density dependent . The survival of the infected population at each stage is smaller than the healthy population . The parameter values of the model were based on the body of work by Onstad and colleagues [12 , 21 , 22] ( see S2 Text for full model description ) . We modelled the dispersal of the populations in four stages: pre-mating dispersal , mating , post-mating dispersal of females , and oviposition . The dispersal functions represent the integration of the movement of moths over a period of days . The dispersal of insects is often modelled with an exponential dispersal kernel which has a mode at the origin . The literature [23–24] suggest that in the case of the corn borer , however , this may not be appropriate as instinct and environmental factors force large numbers of adults from their natal fields . For this reason , and for computational efficiency we chose to model dispersal using a beta distribution , which has a flexible mode . We assume dispersal is the same in all directions , and that at the boundary of the landscape the moths are reflected back . We base our dispersal estimates on observations in the literature which demonstrate seasonal differences in the dispersal of spring and summer adults [23–26] . Crop rotation and lack of adequate humidity in crops during the day time can force newly emerged adults to move from their overwintering field before initiating sexual activity [27] . The probability density function ( PDF ) that describes the pre-mating dispersal in spring has a mode of 10km and 90% of the population travelling less than 30 km . The dispersal of infected moths is reduced by 80% . Dispersal in summer is more conservative with a mode of 1km and 90% of the adult moths fly less than 15km . Under typical conditions , the pre-oviposition period has a mean of 3 . 6 days [14] . Thereafter the mean oviposition period is approximately 10 days with oviposition decreasing with time . During this time a female could cover a considerable area . We assumed that for spring the mode of the post-mating PDF was 35 km and that 90% of the population travel less than 60 km , and that in summer the mode was 5 km with 90% of the population traveling less than 30 km ( see Fig 2 ) . The model of the ECB population density expresses the cycle of infestation caused by N . pyrausta observed in the field data with a similar wavelength [2] . When Bt was introduced into the landscape , the cycle collapsed and the pest was suppressed in a way similar to observed patterns [2] ( Fig 3 ) . In the model , farmers growing maize face the decision of whether to plant Bt or conventional maize . As described above , the decisions on which type of maize to grow directly impacts the survival of the ECB larvae and so the population dynamics of the pest . Kaup [5] surveyed 4000 farmers in Wisconsin and Minnesota and found that the most common reasons for growing Bt maize were: ( i ) to increase yield; ( ii ) to control insects better; and ( iii ) they anticipated ECB problem . The most common reasons for not using Bt maize were ( i ) the price of Bt seed was too high; or ( ii ) no ECB problem was anticipated . Although growers may misconceive the financial impact of the drivers described above , these drivers imply a profit-based decision . Other factors including farm size , age , education and available market information have been shown to influence the adoption of GM crops and complex empirical models have been proposed to describe these effects on farmer decisions [28] . To both ensure the easy interpretation of our results , we chose to use a simple model based on perceived profit . We assumed that the decision process is driven by the financial impact of ECB , and that farmers make decisions based on recent years’ experience [5] . We used data from Wisconsin and Minnesota on the estimated benefit ( $ ha−1 ) from Bt maize and the increase in the area of Bt maize grown ( as a percentage of total maize grown ) between 1995 and 2009 to model the probability ( p ) of farmers changing cropping strategy ( Hutchison et al . , [1] ) . The following exponential function was used based on empirical and theoretical considerations: p=1−exp[−β ( rA−rF ) ]where rA>rFp=0otherwise . ( 1 ) Here β is a parameter , rF is the reward the farmer perceives was attained under the chosen strategy and rA is the reward the farmers perceives would have been attained under the alternative strategy , so that the difference rA−rF measures the perceived net benefit for Bt maize adoption . This model is not only more parsimonious than a more traditional logistic model , but also has better goodness of fit criteria ( see S1 Fig ) . Furthermore , the exponential model is a constant absolute risk aversion utility function for the representative farmer with parameters estimated to fit the observed state-level Bt maize adoption data and estimated benefit [29 , 30] . The parameter β quantifies farmer responsiveness to the perceived gain from Bt maize adoption ( or equivalently , ECB loss ) . The regression estimate for β was 0 . 0055 with a standard error of 0 . 00174 with no evidence to support separate parameters for each state . In practice it would be possible to influence farmer responsiveness ( i . e . β ) through subsidy , taxation or education . For example if farmers were encouraged to be cautious about returning to conventional maize then farmers growing Bt maize would be less responsive when they experienced an apparent benefit reduction . We used the fitted value ± three standard errors to define the range of values for β that we explored in our analysis . For each season , we sample an individual farmer’s decision from a distribution whereby the probability of changing strategy is p ( as defined in Eq 1 ) . This allows us to implicitly include a range of individual behaviors from the intransigent farmer who finds a preferred strategy and will not change , to the receptive farmer who will try new practices . It also implicitly includes other social factors which we do not explicitly account for . The farmer’s reward is given by the average financial reward from his maize fields calculated as r= ( Y−YL ) mP−F , ( 2 ) where Y is the expected yield in a ECB-free crop ( t ha−1 ) , YL is the loss in yield due to the ECB ( t ha−1 ) , mp is the crop price ( $ t−1 ) and F is the technology fee ( $ ) , which is the seed price difference between conventional and Bt maize . We do not include varietal effects that could modify yields slightly , but assume that all maize crops have the same expected yield ( 10 t ha−1 ) . We assume that this yield is reduced by ECB according to the function given in the supplementary information of Hutchison et al . , [1]: YL=Y0 . 021 ( 2 . 56x+5 . 65x ) 1 . 16[ ( 2 . 56x+5 . 65x ) 2+ ( 3 . 4+1 . 73x ) 2]0 . 29 , ( 3 ) where x is the average number of overwintering larvae per plant . To be consistent with the data used to parameterise the landscape model we assume F = 16 $ ha−1 and a crop price ( mp ) of 99 $ t−1 which are averages for Minnesota and Wisconsin between 1996 and 2009 [1] . Given that we can calculate the reward ( r ) for growing maize in any particular field we must consider how to calculate the reward the farmer perceives was attained under each strategy ( i . e . rF and rA ) . The reward for a given strategy may be calculated from the rewards obtained for this strategy over a given area of the landscape , i . e . a farmer’s perceived reward depends on the network of communication and how much credence the farmer gives to the information available to them . Kaup [5] showed that growers who had reported an insect problem in one year were likely to grow Bt maize in the next , which is consistent with farmers who grow other Bt crops [31] . In Kaup’s study the state-reported insect levels did not significantly influence behavior . Therefore we assume that a farmer perceives that the reward for their chosen strategy ( rF ) is given by the average reward from across their fields , taking no account of the success of that strategy in their neighborhood . To inform on the perceived reward from the alternative strategy we consider four networks of communication that we shall refer to as: ( i ) landscape-network; ( ii ) neighbor-network; ( iii ) Kaup-network and ( iv ) varying-response-network . There are two theoretical extremes: the first is where each farmer has information from across the whole landscape , akin to accessing web-based crop data . In this scenario the perceived reward for the alternative strategy is the average of the rewards for the alternative strategy across the landscape . We call this the ‘landscape-network’ . The second is where each farmer has information only from farms that neighbor their own , which may reflect how traditional farming decisions are made alone or within cooperatives . In this scenario the reward for the alternative strategy is given by the average reward that this strategy attains in farms that neighbor the farmer . We call this the ‘neighbor-network’ . Research shows that when farmers decide which varieties to grow they may consult family and friends , other farmers , commercial newsletters , county extension agents and university specialists . Kaup [5] reports that 40 . 2% of farmers acknowledged that a major reason to grow Bt was that it was recommended by their seed dealers or consultants . Similarly 7 . 9% of farmers acknowledged recommendation by a neighbor , and 3 . 4% acknowledged recommendation by university or extension agencies . Normalizing these percentages to sum to 100% , we simulate a communication network whereby a farmer has a probability of 0 . 78 of being influenced by a consultant , a probability 0 . 15 of being influenced by a neighbour and a probability of 0 . 07 of being influenced by a university . According to those probabilities each farm is assigned a communication network type . For those assigned to be neighbor-influenced we calculate the reward of the alternative strategy by averaging the scores of this strategy from farms within 1km . We assume consultants operate over a county , and so for farmers assigned to be consultant-influenced we calculated the reward as the average reward across a county . Finally we assume universities operate at the state level and so the reward for those assigned to be university-influenced is given by the average reward across the state . This network , which we refer to as the ‘Kaup-network’ , is arguably more common in today's farming environment than the two former scenarios . For each network we set the responsiveness parameter β ( Eq 1 ) to 0 . 0055 , 0 . 0003 and 0 . 0108 , which are the value fitted to the data , and that value ± three standard errors . Kaup [5] showed that if farmers had planted Bt in the past then they were more likely to use it in the future . This tendency is incorporated into the model by scaling β in Eq ( 1 ) so that farmers who have used Bt maize in the past are more responsive to loss of profit . Our final network , the ‘varying-response-network’ , incorporates a reluctance for farmers to change back from Bt-maize to conventional maize . It assumes a Kaup-network with the probability of a farmer switching to Bt maize , having previously tried it given by Eq ( 1 ) with β = 0 . 0055 otherwise β = 0 . 0003 . We ran each simulation for 100 seasons . At the end of each season the reward rF ( i ) is calculated for each farm i along with the perceived reward for the alternative strategy rA ( i ) . The probability that the farm strategy will change is calculated according to the farmer’s responsiveness to loss . This probability is used to determine if they change strategy . Crops are rotated and fields growing maize are assigned to Bt or conventional maize according to the calculated strategy .
To explore the behavior of the solutions of the model we considered the equations without the spatial component . Ignoring dispersal , the model equations ( listed in S2 Text ) reduce to the following set of difference equations: S˜ ( t ) =a ( S ( t ) +cP ( t ) ) e−αP ( t ) ν+S ( t ) +P ( t ) P˜ ( t ) =k[P ( t ) +b ( S ( t ) +cP ( t ) ) ( 1−e−αP ( t ) ) ]ν+S ( t ) +P ( t ) S ( t+1 ) =ω1a ( S˜ ( t ) +cP˜ ( t ) ) e−αP˜ ( t ) ν+S˜ ( t ) +P˜ ( t ) P ( t+1 ) =ω2k[P˜ ( t ) +b ( S˜ ( t ) +cP˜ ( t ) ) ( 1−e−αP˜ ( t ) ) ]ν+S˜ ( t ) +P˜ ( t ) ( 4 ) where S ( t ) and P ( t ) represent the number of susceptible and infected eggs in year t , for the first generation respectively and S˜ ( t ) and P˜ ( t ) are for the second generation . The first pair of equations describes the summer generation and the second pair the autumn-spring generation . Many of the parameters result from combinations of biologically meaningful parameters from the full model ( see S2 ) . Parameters a = 929 . 8 and k = 85 . 6 capture the population increase from births modulated by survival rates for susceptible and healthy populations respectively . Parameter c = 0 . 15 is the proportion of susceptible eggs produced by an infected female . The term ( 1−e−αP ( t ) ) determines the proportion of the healthy population that becomes infected , where α = 0 . 72 controls the infection transfer from the infected to susceptible population . Parameter b = 2 . 31 relates to the survival of this recently infected population . The carrying capacity parameter ν = 130 . 7 controls the density dependent survival of the larvae , parameters ω1 = 0 . 081 and ω2 = 0 . 02835 relate to the overwintering survival of the susceptible and infected populations respectively . Analysis of these equations shows three steady-states , i . e . solutions where the rates of change of healthy population ( S ) and the infected population ( P ) are zero: ( C1 ) [P* = 0 , S* = 0] , ( C2 ) [P* = 0 , S* = a2ω1−ν2a+ν] , and ( C3 ) [P* = P0 , S* = S0] , where both P0 and S0 are positive real values . Linearization around these points determines the behavior of the solutions of the equations [32] . The first steady-state ( C1 ) relates to the trivial solution whereby both healthy and infected populations become extinct; the second ( C2 ) relates to the solution where the infected population becomes extinct; and the third steady-state ( C3 ) relates to the solutions where both the healthy and the infected population densities are larger than zero and the total population cycles . It can be shown that ( C3 ) exists , implying that N . pyrausta survives in the system , for parameter combinations such that ω2 ( k+αbS^ν+S^ ) >1 , where S^=a2ω1−ν2a+ν . For the model parameters used , and a wide range around these parameters , the steady-state ( C3 ) always exists supporting the hypothesis that even if ECB is suppressed to low levels , the infected population will survive and the natural control given by N . pyrausta persists . Under the landscape-network simulation shown in Fig 4a and 4b , the percentage of Bt maize oscillates between approximately 1% and 95% over time . Larval populations are driven by the Bt adoption and oscillate similarly , with the largest levels prior to the maxima in the Bt cycle . Increasing farmer responsiveness to economic loss ( i . e . increasing the parameter β in Eq 1 ) increases the frequency and amplitude of the oscillation; reducing farmer responsiveness reduces the frequency and amplitude of the oscillation . The average larval density is held near or below the economic threshold ( 0 . 06 larvae per plant for the model parameterization reported here ) , however , in some parts of the landscape the density was much higher . The results from the Kaup-network are similar to the landscape-network , but with a slightly higher oscillation frequency and slight dampening ( see S2 Fig ) . In the neighbor network the solution slowly converges to a state where the proportion of Bt maize is approximately 0 . 67 in Minnesota and 0 . 24 in Wisconsin ( Fig 4c ) . The difference in adoption rate results because the neighborhood connections are stronger in Minnesota than in Wisconsin due to a greater density of farms in Minnesota . Indeed , in the simulated Wisconsin landscape , more farms are likely to be isolated and so have no neighbors growing Bt maize to compare profits with ( see Fig 5a ) . Simulated ECB populations in Minnesota are lower than those in Wisconsin , where adoption of Bt maize was smaller ( Fig 4d ) . Fig 5b shows the average number of overwintering larvae per plant in each cell for a single year of the simulation . The average numbers of larvae in Wisconsin reach larger levels , and even for isolated farms in Minnesota the pest is supressed by the larger amount of Bt maize grown in the surrounding area . For example between years 30 and 50 of the simulation shown in Fig 4 the maximum number of ECB in any cell was 8 . 12 larvae per plant for Wisconsin and 2 . 69 for Minnesota . The responsiveness of the farmer to loss ( parameter β ) affects the convergence rate with smaller values of β taking longer to converge . Results from the simulation where farmers were more responsive to loss from conventional maize if they had experience of growing Bt maize ( varying-response-network simulations ) are shown in Fig 4e and 4f . The simulation illustrates that adoption of Bt maize is more rapid than that of conventional maize . Table 1 lists the average losses ( $ ha−1 year−1 ) across the landscape between year 20 and 100 under each simulation , and the average proportion of the maize that is Bt . Initial years were excluded to allow the simulation to stabilize . Losses ( L ) were calculated from a baseline whereby conventional maize was grown in an ECB-free landscape , i . e . , L = YLmp+F , where YL is the yield loss caused by the ECB , mp is the crop price and F is the technology fee . These results are based on 10 realisations of each simulation . The average proportions of Bt maize are similar across the networks ranging between 0 . 41 ( when β = 0 . 0108 ) and 0 . 67 ( when β = 0 . 0003 ) . The standard deviation of the proportions of Bt maize were generally smaller for the less responsive farmers ( β = 0 . 0003 ) . For the values β considered , mean losses are least in the varying-response-network scenario and greatest in the neighbor-network scenario . We also simulated losses under scenarios where the proportion of Bt in the landscape was fixed at a given proportion , with the smallest simulated losses averaging 11 $ ha−1 year−1 with a proportion of Bt of 0 . 61 . The sensitivity of our results to model assumptions is discussed in S3 Text . To test the plausibility of the results from our model , we compared the observed and simulated dynamics of the relationships between loss incurred by growing conventional maize ( calculated as above ) and the percentage of maize that was Bt ( Fig 6 ) . The relationship between these two variables changes year on year depending on the corn borer population in the landscape . The dynamics observed in the data from Minnesota and the simulations for the varying-response-network are broadly similar ( Fig 6a and 6e ) . The percentage of Bt maize grown increases until it is not profitable to grow Bt , then farmers start to move back to conventional maize only to return to Bt maize as losses increase later . The period of dis-adoption shown in Fig 6a is unlikely to be solely driven by the farmers’ perceptions of loss from corn borer infestation as it coincides with a period where there was a drop in confidence for the marketability of Bt maize , however our analysis gives support to the hypothesis that farmers’ perceptions of loss might explain dynamics . The Minnesotan data shows a second small drop in adoption over a two year period when the losses reach −13 $ ha−1 thereafter there is a steady increase in the percentage of Bt maize grown with no relationship to loss . Observed dynamics for Wisconsin show slower uptake of Bt maize compared with Minnesota ( Fig 6b ) . This may reflect the fact that maize is grown on a much larger scale in Minnesota compared to other states including Wisconsin , which in turn may have implications for the way in which information is shared and how fields are managed in these states [33] . Similar to the neighbor network we also see that levels of Bt maize that initially control losses are subsequently less effective at the landscape scale and so the use of Bt is increased . No ECB resistance to Bt maize has been reported and so these changes in loss result from other factors such as climate or N . pyrausta .
Liu et al . [34] highlighted the importance of linking sociological influences to ecological systems . In our simulation we show how economic conditions can result in the suppression of a pest throughout a landscape . Our results accord with the findings of Bell et al . [2] who observed the impact of a coordinated response to ECB , and showed the planting of Bt maize in Minnesota led to a collapse in the cycle of ECB caused by N . pyrausta . In Wisconsin , however , where less Bt maize was grown , the cycle persisted . Similarly , Hutchison et al . [1] showed that farmers who grew conventional maize benefited from the area-wide suppression from Bt maize in the region . Our model shows a similar phenomenon , particularly exemplified in the neighbor-network simulation where a smaller proportion of Bt maize in Wisconsin resulted in a larger density of ECB compared with Minnesota , so that ECB population density continued to exhibit the N . pyrausta driven cycle . The landscape scale effects of the decisions made by individuals have been observed in other agricultural systems in which farmers’ decisions are influenced by social or economic factors or both and appear to be coordinated . The farmers’ behaviors results in substantial impacts on the population dynamics of species across landscapes . For example , Bianchi et al . [35] reported that coordinated changes in landscape composition negatively impact natural pest control , and Klein et al . [36] showed how agricultural intensification threatens wild bee pollination services at the landscape scale . In our example , we show that decisions made by farmers on an individual basis impact ECB populations and the profitability of growing maize in the landscape . These decisions are driven by a range of external influences , from the advice of neighbors to information from extension specialists . We showed that the form of the network and the farmer responsiveness to loss substantially impact the dynamics of the system at all trophic levels . Generally we found that Bt-maize adoption oscillated in response to the prevalence of ECB in the landscape , and that the communication network and responsiveness of the farmer to loss influenced the amplitude and frequency of this oscillation . As the scale of communication networks increased so did the rate at which change occurred . This phenomenon was observed by Lambin et al . [37] who reported that rapid land-use changes often result when global influences replace local drivers . For example the global markets demand for certain commodities may rapidly change landscapes from longstanding diverse land-use patterns to more uniform cropping . Of the networks we considered , the varying-response-network performed the best in terms of minimising losses and showed a reasonably constant proportion of Bt maize grown across time ( Table 1 ) . The farmers in this simulation had good access to information from across the landscape and were quicker to re-adopt Bt maize at the first sign of losses from ECB , yet slower to return to the more risky strategy of growing conventional maize . Importantly , our simulations show that to avoid extreme events some resistance to change must be inherent in the system . The varying-response-network did not outcompete the simulation with a fixed percentage of 61% Bt maize however . This outcome is compatible with the initial US-EPA resistance management requirements for ECB of at least 20% non-Bt maize planted each year , to serve as a refuge to maintain non-Bt selected susceptible moths in the landscape [1] . One aspect that we did not consider is that seed companies use market power to protect against the sales of Bt maize oscillating by selling the ECB-Bt maize seed bundled with other desirable seed traits and by reducing ECB-Bt maize prices so that farmers continue to buy the ECB-Bt-maize [38] . Similarly , seed dealers may promote Bt maize seed over conventional because they themselves receive a better rate of commission for Bt maize . The effect of such actions would be to inflate the reward farmers perceive is obtained from growing Bt maize , and so increase the adoption of Bt maize and drive the trajectories shown in Fig 6 to the right . Indeed any volatility in the price of seed or the harvested crop will impact the dynamics of the system . Increases in the price of maize or a reduction in the technology fee result would result in a lower tolerance to corn borer larvae . Another area not included in our analysis is the effect of farmer decisions on the evolution of resistance ECB to Bt maize . A recent review by Tabashnik et al . [39] found no evidence of a decrease in the susceptibility of ECB to Cry1Ab in Bt maize in the field . Others have used modelling to evaluate the effect of refuge planting strategies and including two or more toxins within a cultivar ( pyramided toxins ) on the rate of resistance evolution [22 , 40–42] . These studies aim to guide regulatory policy designed to mitigate the threat of resistance . It is generally held that the greater the density of Bt maize in the landscape the faster the evolution of resistance . It follows that within the context of farmer behaviour , social factors that increase the use of Bt maize in the landscape would increase the rate of the evolution of resistance . Increased resistance of ECB to Bt maize would in turn result in farmers seeking alternative methods of control perhaps in the form of new toxins , or cropping strategies . Our work has implications for other systems , whereby the ecology of a system is driven by individual decision makers following similar heuristics and experiencing similar influences . Examples include important systems where co-ordinated control can result in area-wide suppression of a pest or diseases . These systems typically involve insect pests that either cause damage to crops by herbivory ( e . g . Meligethes aeneus F , Spodoptera exempta Walker ) or act as a vector for disease [43] . The model framework presented here also has application to other areas such as disease prevention in a public health setting . There are clear parallels between landscape suppression of pests and diseases , and the herd immunity afforded when sufficient numbers of the population vaccinate . A number of modelling studies have been done to explore behaviour in the context of vaccination to try to understand the conditions that cause vaccine coverage to fall [44–46] . The conceptual difference between the vaccination studies and our study is that in our study the host of the insect pest is fixed in space and the insect moves across space , whereas in the case of human diseases the hosts move and transmit disease to one another . Our decision model was based on the farmers’ perceived profits . However , other social factors such as perceived food safety , the threat to non-target species and resistance management can effect decisions [47] . These factors often do manifest as economic factors but where they do not , they could be included in a model framework such as the one described by using opinion dynamics models [48] . Vaccination uptake is an example of a situation where often decisions are based on a perception of the safety rather than financial incentives ( 44 ) . By understanding the dynamics of farmer decisions we can determine how to manage better the system , through improved communication , subsidy or taxation , to achieve robust and cost effective area-wide control , while minimizing the risk of the evolution of resistance to control strategies . | A farmer’s decision on whether to control a pest is usually based on the perceived threat of the pest locally and the guidance of commercial advisors . Therefore , farmers in a region are often influenced by similar circumstances , and this can create a coordinated response to a pest . This coordinated response , although not intentional , can affect ecological systems at the landscape scale . Using the European corn borer as an exemplar system , we develop a framework to explore the feedback mechanisms between pest populations and farmers’ decisions . We show that the form of communication network and the farmers’ perceptions of profit and loss influence the decisions made on pest control . Our work has implications for other systems , whereby the ecology of a system is driven by individual decision makers following similar heuristics and experiencing similar influences . Indeed , by understanding the feedback mechanisms between pest populations and farmers’ decisions we can predict landscape-scale dynamics and determine how to manipulate these to sustain control . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | The Effect of Farmers’ Decisions on Pest Control with Bt Crops: A Billion Dollar Game of Strategy |
Gastrointestinal nematode infections , such as Haemonchus contortus and Mecistocirrus digitatus , are ranked in the top twenty diseases affecting small-holder farmers' livestock , yet research into M . digitatus , which infects cattle and buffalo in Asia is limited . Intestine-derived native protein vaccines are effective against Haemonchus , yet the protective efficacy of intestine-derived M . digitatus proteins has yet to be determined . A simplified protein extraction protocol ( A ) is described and compared to an established method ( B ) for protein extraction from H . contortus . Proteomic analysis of the H . contortus and M . digitatus protein extracts identified putative vaccine antigens including aminopeptidases ( H11 ) , zinc metallopeptidases , glutamate dehydrogenase , and apical gut membrane polyproteins . A vaccine trial compared the ability of the M . digitatus extract and two different H . contortus extracts to protect sheep against H . contortus challenge . Both Haemonchus fractions ( A and B ) were highly effective , reducing cumulative Faecal Egg Counts ( FEC ) by 99 . 19% and 99 . 89% and total worm burdens by 87 . 28% and 93 . 64% respectively , compared to the unvaccinated controls . There was no effect on H . contortus worm burdens following vaccination with the M . digitatus extract and the 28 . 2% reduction in cumulative FEC was not statistically significant . However , FEC were consistently lower in the M . digitatus extract vaccinates compared to the un-vaccinated controls from 25 days post-infection . Similar , antigenically cross-reactive proteins are found in H . contortus and M . digitatus; this is the first step towards developing a multivalent native vaccine against Haemonchus species and M . digitatus . The simplified protein extraction method could form the basis for a locally produced vaccine against H . contortus and , possibly M . digitatus , in regions where effective cold chains for vaccine distribution are limited . The application of such a vaccine in these regions would reduce the need for anthelmintic treatment and the resultant selection for anthelmintic resistant parasites .
Infections with blood-feeding gastrointestinal nematodes , such as Haemonchus contortus and Mecistocirrus digitatus , cause significant animal welfare and production losses globally [1] , [2] . The latter is an important blood-sucking nematode of cattle in Asia and Central America [3] . In Asia and Africa , where resource-poor small holder farming is more common , gastrointestinal nematode infections of livestock are ranked in the top twenty diseases of livestock affecting the farmers ability to maintain food security and contribute to economic growth [4] . Control of these parasites is currently achieved by the regular use of anthelmintics: However , this approach leads to the inevitable development of anthelmintic resistance [5] . In Tamil Nadu , India , a recent survey ( Dicker et al , unpublished ) found evidence of widespread inefficacy of albendazole , levamisole and ivermectin against H . contortus in sheep and goats . As such , novel control strategies , such as vaccines , are urgently needed to enable resource-poor small-holder farmers in Tamil Nadu to control parasite infections in their livestock to ensure their food security . Substantial progress has been made in identifying several antigens from H . contortus which , in their native form , stimulate sufficiently high levels of protective immunity ( 70–95% reductions in faecal egg output ) in the ovine host to indicate that vaccination is feasible [6]–[9] . Much previous work by other authors has focused on proteins or protein complexes expressed on the surface of the worm gut which are exposed to the blood meal and , hence , antibody ingested with it . The antigens generally , but not in all cases , show protease activity and the antibody is thought to mediate protective immunity by blocking the activity of enzymes involved in blood meal digestion within the parasite [10] . The recent increase in genomic data for nematodes such as Caenorhabditis elegans , H . contortus , hookworms and the trematode Fasciola hepatica has allowed identification of novel candidate vaccine antigens whilst proteomics analysis has aided in the identification of post-translational modifications which affect protein folding and protein immunogenicity [11]–[13] ( http://nematode . net/NN3_frontpage . cgi ) . Compared to H . contortus , very little is known about M . digitatus , with only 25 nucleotide sequences , 12 genes and 38 protein entries present on NCBI ( http://www . ncbi . nlm . nih . gov/ , 31st May 2013 ) and no information on the potential for vaccination as an alternative to anthelmintics . Proteomics has been used to investigate other potential vaccine candidates such as excretory/secretory products from adult Ostertagia ostertagi and H . contortus and larval Teladorsagia circumcincta [14]–[17] . No proteomic comparison has been made between extracts from different blood-feeding nematodes but this approach should readily identify if potential vaccine candidates are shared by related species . Given that anaemia and a reduction in weight gain caused by the haematophagous activity of adult stages seem to be the most important pathogenic effects of M . digitatus infection in calves and are similar to those observed during infection with Haemonchus placei in calves and H . contortus in sheep and goats [2] , [18] , we sought to compare native protein vaccine preparations , enriched for intestinal surface proteins by Concanavalin A lectin affinity binding [19] from H . contortus and M . digitatus using proteomics , and to evaluate the protective efficacy of the latter against H . contortus challenge in sheep as a prelude to vaccine trials in buffalo in India . Cross-protection has been previously shown to occur in trials conducted in sheep which had been immunized with native Ostertagia protein fractions but challenged with H . contortus; the Ostertagia antigens cross-protected efficiently against Haemonchus [20] , as such cross-protection between M . digitatus and H . contortus was believed to be likely .
All experimental procedures were approved by the Moredun Research Institute Experiments and Ethics committee ( Experiment number E31/12 ) and were conducted under the legislation of a UK Home Office License ( 60/3825 ) in accordance with the Animals ( Scientific Procedures ) Act of 1986 . Adult M . digitatus were collected post mortem from abomasa of cattle collected at an abattoir in Salem , India . Adult Haemonchus contortus were obtained from a donor lamb following standard methods as described in Smith & Smith [8] . All parasites were stored at −20°C in 1 X PBS until required . Protein extraction from M . digitatus was carried out in India with the resulting protein extract transported to Moredun Research Institute ( MRI ) whilst maintaining a cold chain; H . contortus proteins were purified at MRI . Proteins were extracted using a protocol based on the method in [21] . The parasites were washed several times in 1 X Tris buffered saline ( TBS ) at a ratio of 10 ml per g dried worms and the worm pellet then homogenised on ice using a chilled pestle and mortar followed by a chilled glass hand homogeniser directly in a 1 . 0% v/v Triton X-100 buffer . The homogenate was then centrifuged at 2500 X G for 20 mins at 4°C . The supernatant was removed and filter sterilised through a 0 . 45 µM filter before being mixed with ConA lectin-agarose ( Vector laboratories ) on a rotary mixer at 4°C for 1 hour . The Protein-ConA-agarose complex was allowed to settle under gravity at 4°C and the supernatant removed . This washing procedure was repeated on 3 occasions using a 0 . 25% v/v Triton X-100 buffer and then bound proteins were eluted by using a buffer containing 200 mM α Methyl-D-mannopyranoside and 200 mM α Methyl-D-glucopyranoside . The resultant protein solutions were subsequently passed through a 0 . 22 µM filter . The H . contortus extract made using this method was named Hc extract A . A second H . contortus extract was made following the method in [20] , [19] , and is referred to as Hc extract B . Briefly , with centrifugation between each step , adult parasites were extracted in PBS to remove water soluble proteins , then the resultant pellet extracted in PBS/Tween 20 to solubilise membrane-associated proteins with the final pellet solubilised with PBS containing 1 . 0%v/v Triton X-100 . The solution was pumped through a column containing ConA lectin-agarose ( Vector laboratories ) . After thorough washing in a 0 . 25% v/v Triton X-100 buffer , the column bound proteins were eluted using a buffer containing 200 mM α Methyl-D-mannopyranoside and 200 mM α Methyl-D-glucopyranoside . Protein concentration was estimated using a Pierce BCA Protein Assay Kit ( Thermo Scientific ) , according to the manufacturer's instructions . An aliquot of each of the M . digitatus and H . contortus protein elutions were concentrated using the Amicon Ultracel centrifugal filters ( Millipore ) with a 10 KDa cut off , before a final estimation of protein concentration was obtained . To determine the complete protein profile from each parasite , individual non-reduced Novex NuPAGE 4–12% Bis-Tris gels ( Life Technologies ) for M . digitatus and H . contortus in 1 X MOPS buffer ( Invitrogen ) were run at 200 V for 45 mins following the manufacturers' protocols . 2 . 19 µg and 3 . 29 µg M . digitatus protein and 2 . 11 µg and 3 . 17 µg Hc extract A were loaded in NuPAGE LDS sample buffer with the PageRuler Unstained Broad Range Protein Ladder ( Fermentas ) loaded alongside to allow estimation of protein band size . The gel was stained with SimplyBlue Safestain ( Invitrogen ) and de-stained with distilled water according to the manufacturer's instructions . Liquid chromatography-electrospray ionisation-tandem mass spectrometry ( LC-ESI-MS/MS ) was carried out on the proteins contained in one complete lane from each species at the MRI Proteomics facility using the method as described previously in Wheelhouse et al [22] to provide an estimate of the relative abundance of each protein . Mascot generic files were generated and submitted to a local database server , utilising ProteinScape version 2 . 1 ( Bruker Daltonics ) , to perform database searches against the NCBI non-redundant eukaryotic database ( http://www . ncbi . nlm . nih . gov/ ) and the NEMBASE4 nucleotide database ( http://www . nematodes . org/nembase4/index . shtml ) , using the MASCOT ( Matrix science ) search algorithm . The carbamidomethyl ( C ) modification was fixed whilst the Deamidated ( NQ ) and Oxidation ( M ) modifications were variable , peptide and fragmentation mass tolerance values were set at 0 . 5 Da , allowing for a single 13C isotope . Peptide matches were compiled into a protein list compilation ( PLC ) search result and the quality of proteins inspected manually . Proteins with three or more peptides , or two peptides and with a Molecular Weight Search ( MOWSE ) score greater than or equal to 90 , were deemed significant if at least two different peptides were observed to contain an unbroken run of 4 ‘b’ or ‘y’ ions . The NCBI or NEMBASE protein hit identity for all selected proteins ( those passing the quality checks ) was determined and the number of identical proteins in each of the databases for both H . contortus and M . digitatus determined . Proteins identified as mammalian , trypsin or keratin were removed from the analysis . Subsequently , to determine the identity of individual bands of interest , visible bands were excised from a second gel which had been loaded with 3 . 29 µg M . digitatus extract and 3 . 17 µg Hc extract A in NuPAGE LDS sample buffer and run as described above to provide identification of individual bands . These individual bands were subjected to LC-ESI-MS/MS and MASCOT searches against both the NCBI non-redundant eukaryotic database and the NEMBASE4 nucleotide database carried out , as described previously . The quality of the protein matches was manually inspected as described for the PLC results . A vaccine trial comparing the efficacy of the Hc extract A and M . digitatus protein extract against Hc extract B and an unvaccinated control group was undertaken , following standard methods as described in Smith and Smith [8] . Briefly , groups ( n = 7 ) of indoor housed , parasite free lambs , matched for sex and weight , were vaccinated sub-cutaneously three times , three weeks apart with a dose of 40 µg/mL of protein extract ( either Hc extract A , Hc extract B or M . digitatus extract ) in TBS with VAX Saponin adjuvant ( Guinness Chemical Products Ltd ) at a final concentration of 1 mg/mL . The unvaccinated control group received VAX Saponin adjuvant in TBS only . On the third vaccination day all lambs were challenged with 5000 H . contortus L3s suspended in water per os . From fourteen days post challenge , twice weekly faecal egg counts ( FECs ) using a modified technique as described in Jackson [23] , were carried out on faecal samples obtained per rectum . Individual cumulative FEC were estimated by utilising the area under the curve calculation with the linear trapezoidal rule . The mean cumulative FEC for each group was subsequently calculated . Sheep were euthanized on day 35 post challenge , when it was anticipated that all worms present had reached patency , and worms recovered following methods described in Patterson et al [24] . Mean total , male and female worm burdens were calculated for each group . Statistical analysis of the FEC and worm burden results was carried out following the guidelines set out in Coles et al [25] , data was analysed using Minitab ( version 15 ) . The non-parametric Kruskal-Wallis test , followed by Pairwise Mann-Whitney tests , with adjusted P values for multiple comparisons ( Bonferroni correction ) , was used to determine whether statistically significant differences in worm burdens and cumulative FEC were present between the vaccinated groups compared to the unvaccinated control group . S . E . M . , range and percentage efficacy ( P . E . ) for the group mean worm burdens and group mean cumulative FEC were calculated; the P . E . for each vaccinated group was calculated relative to the unvaccinated controls . 2 . 85 µg M . digitatus extract and 2 . 83 µg Hc extract A in NuPAGE LDS sample buffer were heated at 70°C for 10 mins , then loaded onto a 4–12% Bis-Tris gel ( Life Technologies ) in 1 X MES buffer ( Invitrogen ) and run at 200 V for 50 mins following the manufacturers' protocols . 8 µL PageRuler Prestained Protein Ladder ( Fermentas ) was run alongside the samples , before being removed with a scalpel . The gel was transferred onto a nitrocellulose membrane ( Invitrogen ) for 1 hour using the XCell blot module , washed twice in a 50 mM Tris , 2 . 5M NaCl , 0 . 25% Tween20 pH 7 . 4 buffer ( TNT ) before being blocked overnight in TNT . 200 µL sera from each Md extract vaccinated lamb taken 7 days after the third vaccination was pooled together then diluted 1 in 200 in TNT . The blot was incubated in the diluted sera for 1 hour then washed for 10 mins three times in TNT . Monoclonal mouse anti goat/sheep IgG-HRP1 ( Sigma-Aldrich ) was diluted 1 in 1000 and the blot incubated in it for 1 hour , followed by three 10 mins washes in TNT . Finally the blot was visualised by incubation in DAB reagent ( Sigma-Aldrich ) until bands became visible .
The relative abundances of different proteins are shown in Figure 1 , whilst the identities of individual protein detected in H . contortus and M . digitatus ( Figure 2 ) are shown in Tables 1 and 2 respectively . The most frequently identified hits in the H . contortus NCBInr database ( representing 22 . 7% of the results each ) were for aminopeptidase , such as H11 and apical gut membrane polyproteins including the P100GA and P46GA2 proteins ( Figure 1 ) . Aminopeptidases were also the most prevalent hit in the M . digitatus NCBInr database ( 37 . 5% ) , and the third most prevalent search result ( 12 . 9% ) in the H . contortus NEMBASE nucleotide database . Protein disulphide isomerases and aminopeptidases was the most frequently identified hit ( 25% each ) in the M . digitatus NEMBASE nucleotide database . The most frequently identified hit ( 32 . 3% ) in the H . contortus NEMBASE nucleotide database was protein disulphide isomerase , which also accounted for 13 . 6% of the protein identities from the H . contortus NCBInr database search . Zinc metallopeptidases were identified more often from the H . contortus database searches , representing 13 . 64% and 16 . 1% of the significant hits from the NCBInr and NEMBASE nucleotide searches , respectively , compared to the M . digitatus database searches ( 0% and 6 . 3% , respectively ) . In both the H . contortus and M . digitatus database search results , homologues of other potential vaccine candidates were identified frequently ( Figure 1 ) and included glutamate dehydrogenase [26] and the P100GA proteins [27] . Potential vaccines candidates only identified from H . contortus include a 24 kDa excretory/secretory protein [28] , aspartyl protease precursor [9] and P46GA2 [27] . Only one potential vaccine component was solely identified from the M . digitatus whole lane analysis; a galectin protein 5 identified from the NEMBASE4 nucleotide database search . Between 16 and 41 peptides identified as cysteine proteases ( including cathepsins ) were present in database searches from both M . digitatus and H . contortus; however none of the proteins passed the quality checks . The proteomic analyses of the individual bands from H . contortus and M . digitatus protein extracts , excised from a 4–12% Bis Tris gel ( Figure 2A ) emphasised the similarity between the two parasites , an observation enhanced by the demonstrable antigenic cross-reactivity of the two extracts ( Figure 2B ) . The putative identities for these protein bands are shown in Tables 1 and 2; further details of the proteomic results for these bands can be found in Tables S1 and S2 , respectively . Hc1 and 2 and Md4 are all putative zinc metallopeptidases whilst Hc4 , Md3 , 5 and 6 are all microsomal aminopeptidases or H11: It is possible that Md3 , at 220 kDa , is a dimer of either Md5 or 6 . Hc7 , at approx 47 kDa , and Md9 , at approx 45 kDa share a putative protein identity of P100GA whilst Hc8 and Md10 both migrated at approximately 40 kDa and had putative identities of aspartyl proteases . However , other proteins also gave significant matches to these bands indicating that each band may comprise more than one protein , so the identity of these bands could not be confirmed . Hc9 was identified as a Galectin 5 , whilst no M . digitatus bands were identified as galectins . Combined with the previous , whole lane , analysis this indicates Galectin 5 may be present in both species . Two H . contortus bands ( Hc3 and 11 ) and five M . digitatus bands ( Md1 , 2 , 11 , 12 and 13 ) returned results either as no significant hits or hypothetical or uncharacterized proteins . Figure 3 shows the average FECs for each group obtained from twice weekly per rectum faecal samples from day 14 to day 34 post challenge , with the error bars representing the standard error of the mean ( S . E . M . ) for each group . Both the H . contortus vaccine preparations ( Hc extracts A and B ) elicited similar levels of significant protection against H . contortus challenge . The percentage efficacy of the Hc extracts A and B , as determined by group average cumulative FEC , was 99 . 19% , and 99 . 89% respectively ( Table 3 ) with the reductions in total worm burdens being 87 . 28% and 93 . 64% respectively ( Table 4 ) . Both the H . contortus vaccine preparations appeared to be more effective against females than males , reducing the worm burden by 94 . 23% and 79 . 54% ( Hc extract A females and males ) and 98 . 46% and 88 . 48% ( Hc extract B females and males ) compared to the unvaccinated controls ( Table 4 ) . All cumulative FEC and worm burden reductions for Hc extract A and B vaccinated groups were statistically significant ( P<0 . 0167 ) compared to the unvaccinated controls , with the P value adjusted for multiple comparisons using Bonferroni correction . Figure 3 shows that , although there was an indication that vaccination with M . digitatus derived proteins against H . contortus challenge reduced the group mean FEC slightly; the cumulative FEC P . E . of 28 . 20% ( Table 3 ) was not statistically significant . The Md extract vaccinated group average FEC was always lower than the controls; between day 67 and 76 post infection , the FECR was between 24 . 3% and 39 . 2% compared to the unvaccinated controls ( Figure 3 ) . Average group male and total worm burdens were higher ( i . e . negative P . E . ) in the Md extract vaccinated group compared to the unvaccinated controls whilst the 14 . 62% reduction in group mean female worm burden was not statistically significant ( Table 4 ) .
The main aim of this work was to compare , using proteomic analyses , the major gut membrane proteins from the closely related haematophagous nematodes , H . contortus ( affecting sheep and goats ) and M . digitatus ( affecting cattle and buffalo ) , both of which impose significant constraints on livestock production in tropical and subtropical regions of the world . Gut membrane proteins have proven vaccine efficacy in H . contortus [6] , and a recent study showed that vaccination of calves with native parasite gut membrane glycoproteins obtained from H . contortus conferred protection against both H . placei and H . contortus [29] . This work , and a previous study [20] , indicate that good cross-nematode species protection could be stimulated by vaccination with gut membrane proteins derived from closely related species . Due to their similar haematophagous life cycle , it is possible that cross protection against M . digitatus could be achieved utilising a vaccine developed against H . contortus [2] , [29] . If vaccination is going to be a viable method of control for both H . contortus and M . digitatus for resource-poor small-holder farmers then the vaccine production method should be cheap without a requirement for specialist and expensive laboratory equipment . This enables local production with minimal dependency on an extensive cold chain . In this paper we describe such a method . Then , the resulting protein extracts from both M . digitatus and H . contortus were analysed by proteomics to determine whether similar , known candidate vaccine antigens were present in each extract . Finally the protein extracts were tested in a vaccine trial in sheep against H . contortus challenge . Several reports have described the purification of protective antigens from the intestine of Haemonchus , and they share the need for successive saline and detergent extractions , high speed centrifugation steps and specialist chromatography equipment [19] , [30]–[33] . Redmond et al [21] used a simple detergent extract followed by affinity chromatography over Con A lectin to isolate a variety of glycoproteins from C . elegans . ConA lectin has a strong affinity for the microvillar surface of the Haemonchus intestine [6] , [8] and was used in [21] to isolate similar antigens from C . elegans , antibody to which cross reacted with numerous H . contortus gut antigens . Here , we used the same technique to prepare antigen extracts from H . contortus and M . digitatus with the modification that the Con A lectin chromatography step was replaced by a simpler protocol of mixing the extract directly with the affinity medium , with an incubation and wash protocol , as described in the materials and methods . The proteomic analyses described underline that this simple method is effective for the isolation of an extract which is highly enriched for intestinal proteins from nematodes and that its composition did not differ obviously from extract B , by comparison to an analysis described by Sherlock [33] . The protein components of each vaccine were determined by both LC-ESI-MS/MS of whole gel lanes ( Figure 1 ) and on individual bands which were excised from the gels ( Figure 2A ) and then analysed using LC-ESI-MS/MS ( Table 1 , Haemonchus and 2 , Mecistocirrus ) . Analysis of the whole gel slices was performed to ensure all proteins present in the extracts would be identified , not just those in sufficient abundance to create a visible band on the gel , whilst the identity of individual bands was determined by excising these from a second gel . There was a broad agreement between these datasets but there tended to be more unidentified or hypothetical protein bands in the analyses from M . digitatus compared to H . contortus , probably as a result of a lack of specific sequence data for the former . The precise identification of M . digitatus proteins is likely to be hampered by low genomic coverage of M . digitatus and resultant peptide matches with lower percentage sequence coverage and MOWSE scores [34] . Nonetheless , the results of this analysis indicate that similar proteins were extracted from adult H . contortus and M . digitatus and , as such , further validation work on the individual proteins identified from M . digitatus would be a worthwhile step towards developing a multivalent native vaccine for use in areas where co-infection of livestock with Haemonchus species and M . digitatus occurs . Despite some differences in the migration pattern of the protein bands from M . digitatus and H . contortus , as shown in Figure 2 , the analyses here indicate that both extracts are quite similar in terms of protein functions identified . For example , aminopeptidases including H11 , zinc metallopeptidases , and protein disulphide isomerases were prominent in both extracts . Many of these proteins have been associated with varying levels of protection against H . contortus and other nematodes in vaccine trials [6] , [9] . H11 is an insoluble gut membrane glycoprotein of approx 110 kDa involved in blood meal digestion in H . contortus and is the most effective vaccine candidate in H . contortus , giving greater than 90% reduction in worm burden [30] . The presence of aminopeptidases , including H11 , in M . digitatus indicates that , as a blood feeder like H . contortus , it may also be amenable to the gut antigen vaccination approach . In addition , both extracts contained homologues of zinc metalloproteases , which had been shown previously to be a major component of a host-protective protein complex H-gal-GP [35] . Zinc metallopeptidases were prevalent in the H . contortus database searches , representing 13 . 64% and 16 . 1% of the significant hits from the NCBInr and NEMBASE nucleotide searches , respectively and were barely detectable in the M . digitatus equivalents ( 0% and 6 . 3% , respectively ) . Somewhat surprisingly , there were very few hits to cysteine proteases ( none of which passed the proteomic quality checks ) despite their apparent abundance in EST datasets from the intestine of H . contortus [36] , [37] . This anomaly may reflect differences in transcript abundance compared to translation into an actual protein . Gut derived cysteine proteases have been shown to be useful immunogens in H . contortus and in human hookworms , which are also blood-feeders [38] , [39] . Protein disulphide isomerases were particularly prominent in both datasets; they play important roles in protein folding , catalysing thiol-disulphide interchange which leads to protein disulphide bond formation . In C . elegans , they have a role in the formation of cuticular collagen network [14] . Although protein disulphide isomerases have been found in several nematodes and have been detected in ES [14] , [15] , [40] , no report has linked them to the induction of protective immune responses as yet . Other putative vaccine candidates which have been studied in less detail in parasitic nematodes and which have been identified in this current study include: Glutamate dehydrogenase , the apical gut membrane polyproteins ( P100GA and P46GA2 ) , and a 24 kDa excretory/secretory ( E/S ) protein . Glutamate dehydrogenase has been identified from H . contortus and T circumcincta using Thiol-Sepharose affinity chromatography but , despite being the major 60 kDa component of the TSBP extract [7] , it did not provide protection against infection [31] . Three proteins , P46GA1 , P52GA1 and P100GA1 are encoded by the same gene in H . contortus , initially being expressed as a polyprotein [27] and together giving 60% and 50% reductions respectively in worm burden and faecal egg outputs in goats [41] . In this analysis , P100GA2 was identified from both H . contortus and M . digitatus whilst P46GA2 was only identified from H . contortus . Finally , a 24 kDa excretory/secretory protein was identified only from the H . contortus protein extract . In H . contortus , this 24 kDa E/S protein , together with a 15 kDa E/S protein reduced FECs by 32–77% and adult worm burden by 64–85% when tested as a vaccine in sheep [28] . Subsequent to the proteomic analysis , a vaccine trial , comparing the efficacy of the Md extract , Hc extracts A and B against H . contortus challenge , was undertaken . Both H . contortus vaccine preparations gave statistically significant levels of protection against homologous H . contortus challenge , compared to the unvaccinated controls as measured by cumulative FEC and worm burden . The levels of protection ( reductions in FEC of 99 . 19% and 99 . 89% with Hc extract A and B respectively ) exceeding figures of 80% efficacy in 80% of the flock predicted by Barnes et al [42] as necessary to provide better protection against infection and disease than standard anthelmintic based control strategies . However , this prediction was made using a computer model based on Trichostrongylus colubriformis , a less fecund nematode . These data indicate that the simplified method used to produce Hc extract A could form the basis for a locally produced vaccine against H . contortus in regions , such as India , where effective cold chains for vaccine distribution are limited , with the proviso that sufficient worm biomass can be harvested , either from donor animals or abattoir material . All the vaccine preparations tested here were more effective against female worms than their male counterparts , probably reflecting the extra nutritional demands imposed by egg production [43] . M . digitatus is not native to the U . K . so performing a protection trial using a crude M . digitatus protein extract against homologous challenge was not possible . Therefore , a heterologous challenge with H . contortus , which is part of the same Trichostrongylidae family and is also haematophagous , was chosen to determine whether protection could be achieved with M . digitatus protein extracts [18] . Previously , cross protection trials have been carried out using protein extracts from Ostertagia ostertagi and Teladorsagia circumcincta against H . contortus challenge and using H . contortus proteins against T . circumcincta , Trichostrongylus axei and Cooperia oncophora challenge [20] , [44] . O . ostertagi proteins cross protected against H . contortus challenge by reducing FEC by 81–97% and worm burdens by 57–84% [20] . In comparison the results of cross-protection trials against T . circumcincta , Tr . axei and C . oncophora were mixed as H . contortus proteins did not provide any protection against infection with any of the aforementioned species , yet T . circumcincta proteins caused a significant reduction in FEC ( though no effect on worm burdens ) following challenge with H . contortus [44] . In this current trial , there was no effect on H . contortus worm burdens following vaccination with a M . digitatus vaccine extract and the 28 . 2% reduction in cumulative FEC was not statistically significant . However , it is notable that the FEC were consistently lower in the M . digitatus extract vaccinates compared to the challenge controls from 25 days post-infection . A trial is now in progress in India to determine whether M . digitatus proteins provide protection against M . digitatus challenge . The failure to obtain evidence of effective cross-protection was somewhat surprising given the evidence from prior studies discussed above and that the parasites are closely related and obligate blood feeders . Moreover , the immunoblot data shown in Figure 2 confirms that there is strong antigenic cross reactivity between the H . contortus and M . digitatus extracts . Perhaps the explanation lies in the relative abundance or absence of specific components when the two vaccine extracts are compared . For example , there is solid evidence that the zinc metallopeptidases contribute to vaccine-induced immunity against Haemonchus but these were much less abundant in M . digitatus ( Figure 1 ) and the 24 kDa Haemonchus ES protein , associated with strong protective immune responses [28] was not detected in the M . digitatus extract . | Parasitic worms infecting the intestines of grazing livestock cause economic losses and welfare problems . Infection is predominantly controlled by wormers , the indiscriminate use of which has led to drug-resistance problems in the worms infecting livestock on which many of the world's resource-poor farmers are dependent . New and cheap control methods are needed . Vaccination with protein extracts from the parasite Haemonchus contortus reduces the burden of infection and some work has indicated that cross-protection between closely related parasites is possible . Typically , these extracts are made using relatively sophisticated centrifugation and chromatography equipment as well as needing refrigeration capabilities . In this study , the authors show that equally efficacious extracts can be prepared using a very simplified protocol not requiring these specialist facilities . Proteomic analyses demonstrated the close similarity between protein extracts from both H . contortus and Mecistocirrus digitatus and vaccine trials in sheep showed that the simplified extraction protocol resulted in an equally efficacious vaccine compared to the more complex methods described prior to this work . Antigenic cross-reactivity was demonstrated between extracts from the two species; the M . digitatus extract gave a slight reduction in worm egg output when used to vaccinate sheep challenged with H . contortus . | [
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... | 2014 | Proteomic Analysis of Mecistocirrus digitatus and Haemonchus contortus Intestinal Protein Extracts and Subsequent Efficacy Testing in a Vaccine Trial |
In vitro cultures of endothelial cells are a widely used model system of the collective behavior of endothelial cells during vasculogenesis and angiogenesis . When seeded in an extracellular matrix , endothelial cells can form blood vessel-like structures , including vascular networks and sprouts . Endothelial morphogenesis depends on a large number of chemical and mechanical factors , including the compliancy of the extracellular matrix , the available growth factors , the adhesion of cells to the extracellular matrix , cell-cell signaling , etc . Although various computational models have been proposed to explain the role of each of these biochemical and biomechanical effects , the understanding of the mechanisms underlying in vitro angiogenesis is still incomplete . Most explanations focus on predicting the whole vascular network or sprout from the underlying cell behavior , and do not check if the same model also correctly captures the intermediate scale: the pairwise cell-cell interactions or single cell responses to ECM mechanics . Here we show , using a hybrid cellular Potts and finite element computational model , that a single set of biologically plausible rules describing ( a ) the contractile forces that endothelial cells exert on the ECM , ( b ) the resulting strains in the extracellular matrix , and ( c ) the cellular response to the strains , suffices for reproducing the behavior of individual endothelial cells and the interactions of endothelial cell pairs in compliant matrices . With the same set of rules , the model also reproduces network formation from scattered cells , and sprouting from endothelial spheroids . Combining the present mechanical model with aspects of previously proposed mechanical and chemical models may lead to a more complete understanding of in vitro angiogenesis .
How the behavior of cells in a multicellular organism is coordinated to form structured tissues , organs and whole organisms , is a central question in developmental biology . Keys to answering this question are chemical and mechanical cell-cell communication and the biophysics of self-organization . Cells exchange information by means of diffusing molecular signals , and by membrane-bound molecular signals for which direct cell-cell contact is required . In general , these developmental signals are short-lived and move over short distances . The extracellular matrix ( ECM ) , the jelly or hard materials that cells secrete , provides the micro-environment the cells live in . Apart from its supportive function , the ECM mediates molecular [1] and biomechanical [2] signals between cells . Mechanical signals , in the form of tissue strains and stresses to which cells respond [3] , can act over long distances and integrate mechanical information over the whole tissue [4] , and also mediate short-range , mechanical cell-cell communication [2] . How such mechanical cell-cell communication via the ECM can coordinate the self-organization of cells into tissues is still poorly understood . Here we propose a cell-based model of endothelial cell motility on compliant matrices to address this problem . A widely used approach to study the role of cell-ECM interactions in coordinating collective cell behavior is to isolate cells ( e . g . , endothelial cells isolate from bovine aortae or from human umbilical cords or foreskins ) and culture them on top of or inside an artificial or natural ECM ( e . g . , Matrigel ) . This makes it possible to study the intrinsic ability of cells to form tissues in absence of potential organizing signals or pre-patterns from adjacent tissues . A problem particularly well-studied in cell cultures is the ability of endothelial cells to form blood vessel-like structures , including the formation of vascular-like networks from dispersed cells and the sprouting of spheroids . To this end , cell cultures can be initialized with a dispersion of endothelial cells on top of an ECM material ( e . g . , Matrigel , collagen , or fibrin ) [5] , [6] , with endothelial spheroids embedded within the ECM [7] , [8] , or with confluent endothelial monolayers [9]–[11] . Although the conditions required for vascular-like development in these in vitro culture systems are well established , the mechanisms driving pattern formation of endothelial cells are heavily debated , and a wide range of plausible mechanisms has been proposed in the form of mathematical and computational models reproducing aspects of angiogenesis ( reviewed in [12]–[14] ) . Typical ingredients of network formation models are ( a ) an attractive force between endothelial cells , which is ( b ) proportional to the cell density , and ( c ) inhibited or attenuated at higher cellular densities . The attractive force can be due to mechanical traction or due to chemotaxis . Manoussaki , Murray , and coworkers [15] , [16] proposed a mechanical model of angiogenic network formation , based on the Oster and Murray [17] , [18] continuum mechanics theory of morphogenesis . In their model , endothelial cells exert a uniform traction force on the ECM , dragging the ECM and the associated endothelial cells towards them . The traction forces saturated at a maximum cell density . Namy and coworkers[19] replaced the endothelial cells' passive motion along with the ECM for active cell motility via haptotaxis , in which cells move actively towards higher concentrations of the ECM . Both models also included a strain-biased random walk term for the endothelial cells , but they found that it had little effect on network formation; the mechanism was dominated by cell aggregation . In their model based on chemotaxis , Preziosi and coworkers [20] , [21] assumed that cells attract one another via the secreted chemoattractant VEGF . Due to diffusion and first-order degradation , the chemoattractant forms exponential gradients around cells leading to cell aggregation in much the same way as that assumed in the Manoussaki and Namy models . These chemotaxis-based hypotheses formed the basis for a series of cell-based models based on the cellular Potts model ( CPM ) . Assuming chemotactic cell-cell attraction , and a biologically-plausible overdamped cell motility , the cells in these CPM models form round aggregates , in accordance with the Keller-Segel model of cell aggregation [22] . Additional assumptions , including an elongated cell shape [23] or contact inhibition of chemotaxis [24] are needed to transform these circular aggregates into vascular-like network patterns . Related network formation models studied the role of ECM-bound growth factors [25]–[27] and a range of additional secreted and exogenous growth factors [27] , and studied the ability of the contact-inhibition mechanism to produce three-dimensional blood-vessel-like structures [28] . Szabó and coworkers found that in culture , astroglia-related rat C6 cells and muscle-related mouse C2C12 cells organize into network-like structures on rigid culture substrates [29] , such that ECM-density or chemoattractant gradients are excluded . They proposed a model where cells were preferentially attracted to or preferentially adhered to locally elongated structures . As an alternative mechanism for “gel-free” network formation it was found that elongated cells can also produce networks in absence of chemoattractant gradients [30] . Paradoxically , despite the diverse assumptions underlying the mathematical models proposed for vascular network formation , many are at least partly supported by experimental evidence . This suggests that a combination of chemotaxis , and chemical and mechanical cell-ECM interactions drives network formation , or that each alternative mechanism operates in a different tissue , developmental stage , or culture condition . A problem is that one mathematical representation may represent a range of equivalent alternative underlying mechanisms . For example , a model representing cell-cell attraction cannot distinguish between chemotaxis-based cellular attraction [20] , [21] , [23] , [24] , attraction via haptotaxis [19] , direct mechanical attraction [15] , [31] or cell shape dependent adhesion [29] , [32] , because the basic principles underlying these models are equivalent [12] , [24] . As a solution to this problem , a sufficiently correct complete description of endothelial cell behavior should suffice for the emergence of the subsequent levels of organization of the system , an approach that requires that the system has been experimentally characterized at all levels of organization . The role of cell traction and ECM mechanics during in vitro angiogenesis have been characterized experimentally particularly well , making it a good starting point for such a multiscale approach . Endothelial cells apply traction forces on the extracellular matrix , as demonstrated by a variety of techniques , e . g . , wrinkle formation on elastic substrates [9] , force-generation on micropillar substrates [33] , and traction force microscopy [6] , [34] . Using scanning electron microscopy , Vernon and Sage [9] found that ECM ribbons radiate from endothelial cells cultured in Matrigel , suggesting that the traction forces locally reorient the extracellular matrix . The cellular traction forces produce local strains in the matrix , which can affect the motility of nearby cells [2] . Thus endothelial cells can both generate , and respond to local strains in the extracellular matrix , suggesting a feedback loop that may act as a means for mechanical cell-cell communication [2] and hence coordinate collective cell behavior . Here , we use a hybrid cellular Potts and finite element model to show that a set of assumptions mimicking mechanical cell-cell communication via the ECM suffices to reproduce observed single cell behavior [35] , [36] , pairwise cell interactions [2] , and collective cell behavior: network formation and sprouting .
First we set out to capture , at a phenomenological level , the response of endothelial cells to static strains in the ECM in absence of cellular traction forces . When grown on statically , uniaxially stretched collagen-enriched scaffolds , murine embryonic heart endothelial ( H5V ) cells orient in the direction of strain , whereas cells grown on unstrained scaffolds orient in random directions [37] . Because the collagen fibers make the scaffold stiffen in the direction of strain , we hypothesized that the observed alignment of cells is due to durotaxis , the propensity of cells to migrate up gradients of substrate rigidity [38] and to spread on stiff substrates [39] , [40] . In our model we assumed ( a ) strain stiffening: a strained ECM is stiffer along the strain orientation than perpendicular to it , such that ( b ) due to durotaxis the endothelial cells preferentially extend pseudopods along the strain orientation , along which the ECM is stiffest , giving cells the most grip . To keep the ECM mechanics simulations computationally tractable , we assumed an isotropic and linearly elastic ECM . With these assumptions it is not possible to model strain stiffening explicitly . We therefore mimicked strain stiffening by assuming that stiffness is an increasing , linear function of the local strain . Durotaxis was modelled as follows , to reflect the observation that focal adhesion maturation occurs under the influence of local tension [41]: At low local stiffness , we applied standard cellular Potts dynamics to mimic the iterative formation and breakdown of ECM adhesions , producing “fluctuating” pseudopods . However , if the stiffness was enhanced locally , we assumed that the resulting tension in the pseudopod led to maturation of the focal adhesion [41] , [42] , stabilizing the pseudopod as long as the tension persists . To mimic such focal adhesion maturation in the cellular Potts model , we increased the probability of extension along the local strain orientation , and reduced the probability of retraction ( see Methods for detail ) . Figure 1 A shows the response of the simulated cells to uniaxial stretch along the vertical axis . With increasing values of the durotaxis parameter ( see Eq . 8 ) , the endothelial cells elongate more . To test the sensitivity of the durotaxis model for lattice effects , we varied the orientation of the applied strain over a range and measured the resulting orientation of the cells . Figure 1 shows that the average orientation of the cells follows the orientation of the stretch isotropically . Thus the durotaxis component of our model phenomenologically reproduces published responses of endothelial cells to uniaxial stretch [37] . We next attempted to mimic the forces applied by cells onto the extracellular matrix , in absence of durotaxis . Traction-force microscopy experiments [34] , [39] show that endothelial cells contract and exert tensional forces on the ECM . The forces are typically directed inward , towards the center of the cell , and forces concentrate at the tips of pseudopods . A recent modeling study by Lemmon and Romer [43] found that an accurate prediction of the direction and relative magnitudes of these traction forces within the cell can be obtained by assuming that each lattice node i covered by the cell pulls on every other node the cell covers , j , with a force proportional to their distance , di , j . Because this model gives experimentally plausible predictions for fibroblasts , endothelial cells , and keratocytes [43] , we adopted it to mimic the cell-shape dependent contractile forces that endothelial cells exert onto the ECM . Figure 2 shows the contractile forces ( black ) and resulting ECM strains ( blue ) generated in our model by two adjacent cells . The traction forces and ECM strains become largest at the cellular “pseudopods” , qualitatively agreeing with traction force fields reported for endothelial cells [34] . The two previous sections discussed how the simulated cells can respond to and induce strain in the ECM in an experimentally plausible way . To test how the simulated cells respond to the strains they generate themselves , we studied the behavior of simulated , single cells in presence of both the cell traction mechanisms and the durotaxis mechanisms . During each time step , we used the Lemmon and Romer [43] model to calculate traction forces corresponding to current cell positions . Next , we started the finite element analysis from an undeformed matrix , calculating steady-state strains for the current traction forces . To simulate cell movement , which was biased by the local matrix strains using the durotaxis mechanism , we then applied one cell motility simulation time step , or Monte Carlo step ( MCS; the MCS is the unit of time of our simulation; see Methods for detail and Discussion for an estimate of the real time corresponding to an MCS ) . After running the CPM for one MCS we again relaxed the matrix such that the next step started with an undeformed matrix . Thus we currently did not consider cell memory of substrate strains . As Figure 3 and Video S1 demonstrate , in this model matrix stiffness affects both the morphology and motility of the simulated cells . On the most compliant substrate tested ( 0 . 5 kPa ) the simulated cells contract and round up , whereas cells spread isotropically on the stiffest substrate tested ( 32 kPa ) . Overall , the cellular area increases with substrate stiffness ( Figure 3 B ) . On matrices of intermediate stiffnesses ( around 12 kPa ) the cells elongate , as reflected by measurements of the cell length ( Figure 3 C ) and eccentricity ( Figure 3 D ) that both have maximum values at around 12 kPa . Such a biphasic dependence of cellular morphology on the stiffness of the ECM mimics the behavior of endothelial cells [39] and cardiac myocytes [36] in matrices of varying stiffness . The dependence of cell shapes on substrate stiffnesses is due to the transition from fluctuating to adherent pseudopods with increasing stiffness . Focal adhesions of cells on soft substrates all remain in the “fluctuating” state , irrespective of the local strains . On intermediate substrates , some pseudopods , due to increased traction , move to an extended state ( mimicking a mature focal adhesion ) , generating more traction in this direction . Hence an initial stochastic elongation self-enhances in a feedback loop of increasing traction and strain stiffening . Such a self-enhancing cell-elongation starting from an initial anisotropy in cell spreading has previously been suggested by Winer et al [44] . Extensions perpendicular to the long axis of an elongated cell do not occur since there is insufficient traction and the volume constraint is limiting . At matrices of high stiffness all pseudopods attempt to extend , mimicking the formation of static focal adhesion , until the volume constraint becomes limiting . This makes the cells spread more on stiff substrates than on soft substrates , with weaker volume constraints ( lower values of ) producing a stronger effect of substrate stiffness on cell shape and cell area ( Figure S1 ) . We also measured the random motility of the cells by characterizing their dispersion coefficients , which we derived from the mean square displacements of the cells ( Figure S2; see section Morphometry for detail ) . The dispersion coefficients show biphasic behavior , with the highest motilities occurring at around 12 kPa ( Figure 3 E ) . The biphasic dependence of the dispersion to substrate stiffness is in accordance with in vitro behavior of neutrophils [45] , and smooth muscle cells [46] . Here it is typically thought to be due to a balance of adhesion and actin polymerization , or due to the interplay between focal adhesion dynamics and myosin-based contractility [45] . In our model , the effect is more likely due to the appearance of eccentric cell shapes at intermediate stiffnesses; as a result , only the tips of the cell generate sufficient strain in the matrix to extend pseudopods , producing more persistent motion than the round cells at stiff or soft substrates . It will be interesting to see if a similar relationship between cell shape and cell motility holds in vitro . Thus the model rules for cell traction and stretch guidance based on durotaxis and strain stiffening suffice to reproduce an experimentally plausible cellular response to matrix stiffness . Strains induced by endothelial cells on a compliant substrate with low concentrations of arginine-glycine-aspartic acid ( RGD ) -containing nonapeptides can affect the behavior of adjacent cells [2] . On soft substrates ( 5 . 5 kPa or below ) the cells reduced the motility of adjacent cells , whereas on stiff substrates ( 33 kPa ) such an effect was not found . On substrates of intermediate stiffness ( 5 . 5 kPa ) , adjacent endothelial cells repeatedly attached and detached from one another , and cells moved more slowly in close vicinity of other cells , than when they were on their own . Because the extent to which cells could affect the motility of nearby cells depended on matrix compliancy , mechanical traction forces could act as a means for cell-cell communication [2] . To test if the simple strain-based mechanism represented in our model suffices for reproducing such mechanical cell-cell communication , we initiated the simulations with pairs of cells placed adjacent to one another at a distance of fourteen lattice sites corresponding to a distance of 35 µm , and ran a series of simulations on substrates of varying stiffness ( Figure 4 A and Video S2 ) . The cells behaved similar to the single cell simulations ( Figure 3 ) , with little cell-cell interactions at the lower and higher stiffness ranges . Consistent with previous observations [2] , cell pairs on substrates of intermediate stiffness ( 12 kPa ) dispersed more slowly than individual cells ( paired two-sample t-test at 5000 MCS , p<0 . 05 for 12 kPa ) , whereas individual cells and cell pairs dispersed at indistinguishable ( p<0 . 05 ) rates on stiff ( 14 kPa or more ) or soft ( 10 kPa or below ) substrates ( Figure 4 , B-D ) and Figure S3 ) . Also in agreement with the previous , experimental observations [2] , on a simulated substrate of intermediate stiffness ( 12 kPa ) the cells responded to the matrix strains induced by the adjacent cell by repeatedly touching each other , and separating again ( Figure 4 E ) . The contact duration of cells on soft and stiff substrates , when they get close enough to each other , are typically longer than for intermediate substrates . This behavior is also similar to observations in vitro[2] . As one might expect that strongly adherent cells will not repeatedly touch and retract , but rather stay connected upon first contact , we investigated the effect of cell adhesion on these parameters ( Figure S4 ) . Consistent with this intuition , for stronger adhesion , the contact count tends to be reduced and the contact durations tend to increase , but the overall trend holds: at intermediate matrix stiffnesses we continue to observe more frequent cell contacts than for more soft or more stiff matrices . Thus the observed pairwise cell behavior is primarily driven by durotaxis . Mechanical strain can also coordinate the relative orientation of cells . Fibroblasts seeded on a compliant gel tend to align in a head-to-tail fashion along the orientation of mechanical strain [47] . Bischofs and Schwarz [48] proposed a computational model to explain this observation . Their model assumes that cells prefer the direction of maximal effective stiffness , where the cell has to do the least work to build up a force . This work is minimal between two aligned cells , because maximum strain stiffening occurs along the axis of contraction . Interestingly , visualization of our model results ( Figure 1 C ) suggested similar head-to-tail alignment of our model cells at around 12 kPa . To quantify cell alignment in our simulations , we measured the angle α between the lines and , defining the long axes of the cells ( Figure 4 F ) . We classified the angles as acute ( ; i . e . no alignment ) or obtuse ( ; alignment ) . At matrix stiffnesses up to around 10 kPa , about one fourth of the angles α were obtuse , corresponding to the expected value for uncorrelated cell orientations . However , at 12 kPa and 14 kPa significantly more than a fourth of the angles α between the cell axes were obtuse ( 55/100 for 12 kPa , p<1×10−8 and 52/100 for 14 kPa , p<1×10−8 , binomial test ) , and for substrate compliancies of 8 to 16 kPa significantly more of the angles α were obtuse than for 4 kPa ( p<0 . 01 for 8 kPa , and p<1×10−12 for 10 kPa to 16 kPa; two-tailed Welch's t-test ) , suggesting that the mechanical coupling represented in our model causes cells to align in a head-to-tail fashion . After observing that the local , mechanical cell-ECM interactions assumed in our model sufficed for correctly reproducing many aspects of the behavior of individual endothelial cells on compliant matrices and of the mechanical communication of pairs of endothelial cells on compliant matrices , we asked what collective cell behavior the mechanical cell-cell coordination produced . When seeded subconfluently onto a compliant matrix ( e . g . , Matrigel ) , endothelial cells tend to organize into polygonal , vascular-like networks [5] , [6] , [49] , [50] . To mimic such endothelial cell cultures , we initialized our simulations with ( approximately ) 450 cells uniformly distributed over a lattice of 300×300 pixels ( 0 . 75×0 . 75mm2 ) , corresponding to a cell density of 800 endothelial cells per mm2 . In accordance with experimental observations on gels with low concentrations of collagen [6] or RGD-peptides [2] , after 3000 MCS networks had not formed on soft matrices ( 0 . 5-4 kPa ) or on stiff matrices ( 16-32 kPa ) ( Figure 5 A ) : The cells tended to form small clusters ( Figure 5 A ) . Interestingly , on matrices of intermediate stiffness after around 300 MCS the cells organized into chains ( 8 kPa ) or network-like structures ( 10 kPa and 12 kPa ) similar to vascular network-like structures observed in endothelial cell cultures [5] , [6] , [49] , [50] . The optimal stiffness ( ≈10kPa ) for network formation is slightly lower than the stiffness of the substrate ( ≈12kPa ) on which single cells elongate the most ( Figure 3 A ) . In comparison with a single cell , the collective pulling of a cell colony creates larger strains in the substrate . Consequently , the strain threshold inducing cell elongation is crossed at smaller substrate stiffness . Figure 5 B and Video S3 show a time-lapse of the development of a network configuration on a substrate of 10kPa . The cells organized into a network structure within a few hundred MCS . The network was dynamically stable , with minor remodeling events taking place , including closure and bridging of lacunae . Figure 5 C shows such a bridging event in detail . In an existing lacuna ( 1800 MCS ) stretch lines bridged the lacuna , and connected two groups of cells penetrating the lacuna ( 1980 MCS ) . The cells preferentially followed the path formed by these stretch lines ( 2150 MCS ) and reached the other side of the lacuna by 2400 MCS . Such bridging events visually resemble sprouting in bovine endothelial cell cultures on compliant matrices ( Figure 5 D , Video S4 , and [6] ) . To stay close to the experimental conditions used for the observations of pairwise endothelial cell-cell interaction on compliant substrates [2] that we compared the simulations of pairwise interactions with , in this experiment we used a 2 . 5 kPa gel functionalized with 5 µg/ml RGD peptide - a stiffness at which no network-formation is found in our simulations . Although we thus do not yet reach full quantitative agreement between model and experiment , note that network formation occurs at substrate stiffness of 10kPa on polyacrylamide matrices enriched with a low ( 1 µg/ml ) concentration of collagen [6] . We next asked if the mechanical model could also reproduce sprouting from endothelial spheroids [7] , [8] . Video S5 and Figure 6 shows the results of simulations initiated with a two-dimensional spheroid of cells after 3000 MCS . On soft ( 0 . 5–8 kPa ) and on stiff ( 32 kPa ) matrices the spheroids stayed intact over the time course of the simulation . On matrices of intermediary stiffness ( 10–12 kPa ) the spheroids formed distinct sprouts , visually resembling the formation of sprouts in in vitro endothelial spheroids [7] , [8] . On the 14 kPa and 16 kPa matrices the cells migrated away from the spheroid , with some cell alignment still visible for the 14 kPa matrices . Observation of a sprout protruding from a spheroid at 10 kPa suggests that a new sprout starts when one of the cells at the edge of the cluster protrudes and increases the strain in front of it . In a positive feedback loop via an increase in perceived stiffness the strain guides the protruding cell forward . The strain in its wake then guides the other cells along ( Figure 6 C ) .
In this paper we introduced a computational model of the in vitro collective behavior of endothelial cells seeded on compliant substrates . The model is based on the experimentally supported assumptions that ( a ) endothelial cells generate mechanical strains in the substrate [34] , [43] , ( b ) they perceive a stiffening of the substate along the strain orientation , and ( c ) they extend preferentially on stiffer substrate [37] . Thus , in short , the assumptions are: cell traction , strain stiffening , and durotaxis . The model simulations showed that these assumptions suffice to reproduce , in silico , experimentally observed behavior of endothelial cells at three higher level spatial scales: the single cell level , cell pairs , and the collective behavior of endothelial cells . In accordance with experimental observation [36] , [39] , the simulated cells spread out on stiff matrices , they contracted on soft matrices , and elongated on matrices of intermediate stiffness ( Figure 3 ) . The same assumptions also sufficed to reproduce experimentally observed pairwise cell-cell coordination . On matrices of intermediate stiffness , endothelial cells slowed down each other ( Figure 4 B ) and repeatedly touched and retracted from each other ( Figure 4 E and Video S2 ) , in agreement with in vitro observations of bovine aortic endothelial cells on acrylamide gels [2] . Also , in agreement with experimental observations of fibroblasts on compliant substrates [47] and previous model studies [48] the cells repositioned into an aligned , head-to-tail orientation ( Figure 4 F ) . The model simulations further suggest that these pairwise cell-cell interactions suffice for vascular-like network formation in vitro ( Figure 5 ) and sprouting of endothelial spheroids ( Figure 6 ) . The correlation between pairwise cell-cell interactions and collective cell behavior observed in our computational model parallels observations in vitro . Cells elongate due to positive feedback between stretch-guided extension and cell traction , as previously suggested by Winer et al . [44] . Elongated and spindle-shaped cells are considered indicative of future cell network assembly [6] . Our model suggests that the elongated cell shapes produce oriented strains in the matrix , via which cells sense one another at a distance . In this way new connections are continuously formed over “strain bridges” ( see , e . g . , Figure 5 C , D and Video S4 ) , while other cellular connections break , producing dynamically stable networks as illustrated in Video S3 . Such dynamic network restructuring was also observed during early embryonic development of the quail embryo [51] and in bovine aortic endothelial cell cultures ( Figure 5 D and [6] ) , but not in human umbilical vein endothelial cell cultures [23] , [50] . Also in agreement with experimental results , the collective behavior predicted by our model strongly depends on substrate stiffness . The strongest interaction between cell pairs is found on substrates of intermediate stiffness , enabling network formation [2] , whereas network assembly does not occur on stiffer or on softer substrates[6] . These agreements with experimental results are encouraging , but our model also lacks a number of properties of in vitro angiogenesis that pinpoint key components still missing from our description . We compared the simulation of pairwise cell-cell interactions with previous experiments conducted on polyacrylamide gels , functionalized with RGD ligands [2] , which have linear elastic behavior for small deformations [52]–[54] . Strain-stiffening of polyacrylamide gels has been reported for deformations over 2 µm [55] . Thus with pixels in our model measuring 2 . 5 µm×2 . 5 µm , strain-stiffening seems a reasonable assumption . Nevertheless , a possible alternative interpretation of the cell pair simulations is that the increased tension generated in pseudopods pulling on the matrix leads to a higher probability of focal adhesion maturation[41] , [42] . A further issue is that in our simulations , single cells dispersed somewhat more quickly on soft gels than on stiff gels ( Figure 3 E and Figure S2 ) . This model behavior contradicts experimental observations that endothelial cells move fastest on stiff substrates [2] . Another open issue concerns the time scales of our simulations . In the present paper time we use the Monte Carlo step as a ( computational ) unit of time . To estimate the actual time corresponding to 1 MCS , we scale the single cell dispersion coefficients shown in Figure 3 E to experimental dispersion coefficients of bovine endothelial cells on compliant substrates in vitro [2] . Reported dispersion coefficients of endothelial cells range from around ( on substrates of ) to around ( on substrates of ) ( as derived from the MSDs in Figure 3a , c in [2] and based on ; cf . Eq . 13 ) . The dispersion coefficients of single cells in our simulations are in the range of ( Figure 3 ) , assuming pixels of . Thus , based on fitting of single cell dispersion rates , the estimated length of 1 MCS is 0 . 5 to 3 seconds . The typical time scale of a vascular network formation simulation is around 3000 MCS ( Figure 5 ) , i . e . , to for these time scale estimates . In experiments , network formation takes longer , around 24 hr . Thus in our current model the time scales of cell dispersion and network formation do not match exactly . A possible reason of this discrepancy is the short persistent length of cell motility in standard cellular Potts models . To better match the time scales of single cells and collective cell behavior in our model , in our future work we will increase the persistence length of the endothelial cells by using the available cellular Potts methodology [56]–[58] , or model the subcellular mechanisms of cell motility in more detail , e . g . by including mean-field models of actin polymerization [59] , [60] . A further open issue is the interaction between substrate mechanics and cell-substrate adhesivity . Although the model correctly predicts the absence of network formation on stiff substrates , it cannot yet explain the observation that reducing the substrate adhesivity of the endothelial cells rescues network formation on stiff substrates [6] . On compliant gels endothelial cells must secrete fibronectin to form stable networks , whereas fibronectin polymerization inhibitors elicit spindle-like cellular phenotypes associated with network formation on stiff matrices , under conditions where networks do not normally form [6] . To explain these observations , straightforward future extensions of the model will include a more detailed description of cell-substrate adhesion , combined with models of ECM secretion and proteolysis [13] , [25] , [27] , [61] . The current model also assumes a uniform density ( i . e . , the infinitesimal strain assumption ) and thickness of the extracellular matrix , whereas under some culture conditions the endothelial cells have been reported to pull the extracellular matrix underneath them [62] , producing gradient in matrix density and/or thickness . To describe the role of viscous deformations of the extracellular matrix in morphogenesis , Oster and Murray [17] , [18] developed a continuum mechanical model of pattern formation in mesenchymal tissues . Their model assumed ( a ) that cells exert contractile forces onto the surrounding extracellular matrix , that will ( b ) locally deform the ECM , resulting in passive displacements of cells along with the ECM , and ( c ) produce density gradients in the ECM along which cells move actively due to haptotaxis . These mechanisms together produce periodic cell density patterns . Manoussaki et al . [15] and Namy et al . [19] applied this work to investigate mechanical cell-ECM interactions during angiogenesis , and demonstrated that the mechanism can produce vascular-like network patterns . In their model they also included an anisotropic diffusion term to simulate preferential movement along the local strain-direction , but the term was neither necessary nor sufficient for network formation . This finding contradicts our model in which strain-induced sprouting is the driving force of network formation and sprouting . The two models represent the two extremes of network formation on visco-elastic matrices . Here , the Manoussaki et al . [15] and Namy et al . [19] models represent patterning on viscous matrices , in which cellular traction forces pull the matrix together while inducing little strain or stress . Our model would represent elastic materials , in which pulling forces induce local strains . Future extensions of the model will include matrix remodelling ( e . g . , by assuming a matrix thickness field ) allowing us to study the full range of viscoelastic matrices . Apart from these biological issues , we made several mathematical simplifications that we will improve upon in future models of cell-ECM interactions . In the current model , for mathematical simplicity , we assumed that after each Monte Carlo step the matrix was undeformed again . Thus we currently did not consider cell memory of substrate strains . Further developments of the model presented here will improve on this issue , because actin filament dynamics are typically influenced by the past evolution of substrate deformations , e . g . , due to reorientation of matrix fibers [62] . For computational efficiency , we assumed linearly elastic materials and infinitesimal strain in the finite element simulations , and mimicked durotaxis via a perceived strain-stiffening ( Eq . 9 ) where cells perceive increased ECM stiffness due to local strain . In our ongoing work we are interfacing the open source package FEBio ( http://febio . org ) with the cellular Potts package CompuCell3D ( http://compucell3D . org ) . This will allow us to run our model with any ECM material available to users of FEBio , including strain-stiffening materials . Using an actual strain stiffening material may lead to longer-range interactions between cells , because locally stiffer regions may channel the stress between the cells [63] . A further technical limitation of our model is that we currently only run two-dimensional simulations , representing cells moving on top of a two-dimensional culture system . The ongoing interfacing of FEBio and CompuCell3D will pave the way for modeling cell-ECM interactions in three-dimensional tissue cultures . We also plan to model fibrous extracellular matrix materials in more detail . A quite puzzling aspect of vascular network formation and spheroid sprouting is that so many alternative , often equally plausible computational models can explain it ( reviewed in [12] ) . Including the present model , there are at least three alternative computational models based on mechanical cell-ECM interactions [15] , [16] , [19] , [31] , [64] , a series of models assuming chemoattraction between endothelial cells [20] , [21] , [23] , [24] , [65] , [66] and extensions thereof [25] , [27] , [67] , and models explaining network formation in absence of chemical or mechanical fields [29] , [30] , [32] . Each of the models explains one aspect of vascular network formation or a response to an experimental treatment that the other models cannot explain , e . g . the relation between spindle-shaped cell phenotypes and network formation [23] , [30] , the requirement of VE-cadherin signaling for network formation and sprouting [24] , [29] , the binding and release of growth factors from the ECM [25] , [26] , the role of mechanical ECM restructuring and haptotaxis [15] , [19] , [31] , the response of vascular networks to toxins [27] , or the role of intracellular signaling [57] . Among these alternative models , we must now experimentally falsify incorrect mechanisms , and fine-tune and possibly combine the remaining models to arrive at a more complete understanding of the mechanisms of angiogenesis . To this end , we are currently quantitatively comparing the kinetics of patterns produced by chemotaxis-based , traction-based , and cell-elongation based models with the kinetics of in vitro networks [23] , [50] . The resulting , more complete model would likely contain aspects of each of the available computational models and assist in explaining the conflicting results obtained from the available experimental systems , culture conditions , and in silico models of angiogenesis .
The CPM represents cells on a regular square lattice , with one biological cell covering a cluster of connected lattice sites . To mimic random cell motility , the CPM iteratively expands and retracts the boundaries of the cells , depending on the passive forces acting on them and on the active forces exerted by the cells themselves . These are summarized in a balance of forces , represented by the Hamiltonian , ( 1 ) The first term is an ( approximate ) volume constraint , with the actual volume of the cells , , a resting volume , and an elasticity parameter that regulates the permitted fluctuation around the resting volume . In contrast with the original formulation of the CPM [68] , the deviation of the cell from its target volume is taken relative to the target volume , by analogy with the ( non-dimensional ) engineering strain . Alternative , similar volume constraints can be chosen [67] . We use a value for all cells; the medium does not have a volume constraint . The second term represents cell-cell and cell-medium adhesion , where is the contact cost between two neighboring pixels , and , the Kronecker delta . Throughout the manuscript we use neutral cell-cell adhesion settings; at cell-cell interfaces , and at cell-medium interfaces , with and . In other words , cells have no preference for adhering to other cells or the medium . For these neutral cell adhesion parameter settings , cells will still adhere weakly to one another ( a remedy for this effect was proposed in [74] ) . Additional terms in the Hamiltonian represent the cells' responses to ECM mechanics , and will be described in more detail below . The CPM iteratively selects a random lattice site and attempts to copy its state , , into a randomly selected adjacent lattice site . To reflect the physical , “passive” behavioral response of the cells to their environment , the copy step is always accepted if it decreases the Hamiltonian . To account for the active random motility of biological cells , we allow for energetically unfavorable cell moves , by accepting copies that increase the Hamiltonian with Boltzmann probability , ( 2 ) where is the change in H if the copying were to occur , and parameterizes the intrinsic cell motility . It represents the extent to which the active cell motility can overcome the reactive forces ( e . g . volume constraint or adhesions ) in the environment . We assume that all cells keep the same motility and thus set to be constant throughout the simulations . During one Monte Carlo step ( MCS ) , we perform copy attempts , with equal to the number of sites in the lattice . To prevent cells from splitting up into two or more disconnected patches , we use a connectivity constraint that rejects a spin flip if it would break apart the retracting cell . A two-dimensional model describes the compliant substrate on which the cells move . Deformations are calculated using the finite element method ( FEM; reviewed in [70] ) . The FEM represents the substrate as a lattice of finite elements , , with each element corresponding to a pixel of the CPM . To obtain the finite element equations , the weak formulation ( associated with the total potential energy ) of the governing equations of the displacement of the substrate is set up , in order to obtain the finite element equations , ( 3 ) with stiffness matrix , displacement , and forces . The vector contains the displacements of all nodes , which are the unknowns that the FEM calculates based on the active forces exerted onto the material , presented in . In this paper only consists of traction forces that the cells apply onto the ECM , unless stated otherwise . In a two-dimensional analysis the forces are divided by the thickness they are working on . For this we assume an effective substrate thickness . We impose boundary conditions of at the boundary of the CPM grid , this means that the substrate is fixed along the boundaries . To a first approximation , in this work we consider an isotropic , uniform , linearly elastic substrate [48] , [75] and we apply infinitesimal strain theory: We assume that material properties , including local density and stiffness are unchanged by deformations . The global stiffness matrix is assembled from the element stiffness matrices ( see Supporting Text S1 and [70] ) , which describe the relation between nodes of each element , , ( 4 ) where —the conventional strain-displacement matrix for a four-noded quadrilateral element ( see Supporting Text S1 and [70] ) —relates the node displacements to the local strains , as , ( 5 ) The strain vector is a column notation of the strain tensor and is the material property matrix . Assuming plane stress conditions , ( 6 ) where is the material's Young's modulus , and is Poisson's ratio . Throughout this study , we use a Poisson's ratio and Young's moduli ranging from to , which are plausible values for most cell culture substrates [48] , [53] , [76] . For more details of the derivation of Eq . 3 , and the entries in , see Supporting Text S1 and [70] . As a reference configuration for the displacements we used an unstretched substrate , . Thus , after each Monte Carlo step ( during which the cells positions and shapes have changed ) the substrate is assumed to be undeformed , such that the stiffness matrix , , is constant in time . This prevents expensive calculations that would be necessary for recalculating in each iteration . Although the previous displacements do not influence the new deformation of the substrate , they are used as an initial guess for solving , in order to reduce the number of iterations necessary to converge to the FEM solution . To simulate cell-substrate feedback we alternate the cellular Potts model ( CPM ) steps with a simulation of the substrate deformations using the finite element method . We assume that cells apply a cell-shape dependent traction on the ECM and the cells respond to the resulting ECM strains by adjusting their cell shape . Using the CPM grid as the finite element mesh , the pixels of the CPM become four-node square elements in the FE-mesh . Adopting the model by Lemmon & Romer [43] , we assume that each node covered by a CPM cell pulls on all other nodes in the same cell , at a force proportional to distance . The resultant force on node then becomes , ( 7 ) where is the lattice spacing and gives the tension per unit length . This parameter has been scaled to , such that the total cell traction corresponds to experimentally reported values [77] . The resultant forces point towards the cell centroid , and are proportional to the distance from it ( Figure 2 ) . In this way a CPM configuration yields a traction force , which are collected in the forces for the finite element calculation . To calculate the resulting ECM strains , we solve for the node displacements with a preconditioned conjugate gradient ( PCG ) solver [78] , and derive the local strains using Eq . 5 . The reference configuration for the displacements is an unstretched substrate , . After a sufficiently accurate solution for the FEM equations has been obtained by the PCG solver , we run a Monte Carlo step of the CPM . After each MCS , which changes cell positions , the substrate is assumed to be undeformed again , for the sake of simplicity . Thus , the stiffness matrix , , is constant in time . We assume durotaxis , i . e . , the CPM cells preferentially extend pseudopods on matrices of higher stiffness ( e . g . , because of strain stiffening ) . By analogy with chemotaxis algorithms [79] at the time of copying we add the following durotaxis term to in response to the strain- and orientation-dependent ECM stiffness , ( 8 ) with for extensions and for retractions , is a parameter , , a unit vector giving the copy direction , and and , and and eigenvalues and eigenvectors of representing the principal strains and strain orientation . We use the strain in the target pixel when considering an extension , and for retractions we use the strain in the source pixel , . Thus we consider the strain in the ECM adjacent to the pseudopod . The sigmoid , with threshold stiffness , and , the steepness of the sigmoid , mimics maturation of focal adhesions under the influence of tension [41] . The tension in focal adhesions will increase with higher local matrix stiffness , , because the matrix will deform less easily . The sigmoid function starts at zero , goes up when there is sufficient stiffness , and eventually reaches a maximum . This means that a certain level of stiffness is needed to cause a cell to spread . Alternative forms of can be used: For an overview see Figure S5 . Due to limitations of our current finite element code and for reasons of computational efficiency , we assumed a linearly elastic , isotropic material in the FEM , thus precluding explicit strain stiffening effects in the FEM calculations . Instead , we implemented the effect of strain-stiffening in the cell response , where cells perceive increased ECM stiffness as a function of the principal strains and , ( 9 ) where sets a base stiffness for the substrate , and is a stiffening parameter . The indicator function indicates that strain stiffening of the substrate only occurs for substrate extensions ( ) ; compression ( ) does not stiffen or soften the substrate . To characterize the random motility of single cells and cell pairs , we measured the cells' mean square displacement , ( 10 ) with , the centroid of cell at Monte Carlo step ( “time” ) , given by ( 11 ) with , the set of coordinates of the lattice sites comprising cell at MCS , ( 12 ) and . The MSD is a reliable measure of random motility [80] and it can be directly compared with experimental data ( e . g . , [2] ) . The dispersion coefficient , defined as ( 13 ) is derived from the slope of the MSD , and is used as a measure of the motility of random walkers . The length , orientation and eccentricity of cells were estimated from the inertia tensors of the cells , defined as [81] , ( 14 ) Assuming cells are approximately ellipse-shaped , the length of cell is approximated as , with the largest eigenvalue of . The eccentricity of a cell is defined using the eigenvalues of the inertia tensor as , where are the eigenvalues of . An eccentricity close to zero corresponds to roughly circular cells and cells with an eccentricity close to unity are more elongated . The orientation of the cell is given by the eigenvectors of the inertia tensor . Bovine aortic endothelial cells ( BAECs ) ( VEC Technologies , Rensselaer , NY ) were cultured through passage 12 . Cells were kept at and 5% and fed every other day with Medium 199 ( Invitrogen , Carlsbad , CA ) supplemented with 10% Fetal Clone III ( HyClone , Logan , UT ) , 1% MEM amino acids ( Invitrogen ) , 1% MEM vitamins ( Medtech , Manassas , VA ) , and 1% penicillin-streptomyocin ( Invitrogen ) . Polyacrylamide hydrogels were synthesized as previously described [6] . Briefly , a gel mixture was prepared from MilliQ water , HEPES , TEMED ( Bio-Rad , Hercules , CA ) and a 5%:0 . 1% ratio of acrylamide to bis-acrylamide ( Bio-Rad ) to generate substrates with a Young's modulus of 2 , 500 Pascals . Polymerization was initiated by the addition of N-6- ( ( acryloyl ) amido ) hexanoic acid ( synthesized according to Pless et al . [82] ) and ammonium persulfate ( Bio-Rad ) into the gel mixture . Following polymerization , gels were incubated with RGD peptide ( GCGYGRGDSPG ) ( Genscript ) , followed by ethanolamine ( Sigma ) . Gels were stored in PBS overnight . Hydrogels were sterilized with ultraviolet light before cell culture . A T-75 flask with a confluent BAEC monolayer was seeded onto the hydrogels at 350 , 000 cells per gel ( approximately 1 , 375 cells per mm2 ) . The gels were maintained at and 5% for three days prior to imaging . After replenishing with fresh complete media , the cells on hydrogels were visualized with a Zeiss Axio Observer . Z1 inverted spinning disc microscope with a Hamamatsu ORCA-R2 digital camera . Images were captured every 30 minutes for 24 hours . | During the embryonic development of multicellular organisms , millions of cells cooperatively build structured tissues , organs and whole organisms , a process called morphogenesis . How the behavior of so many cells is coordinated to produce complex structures is still incompletely understood . Most biomedical research focuses on the molecular signals that cells exchange with one another . It has now become clear that cells also communicate biomechanically during morphogenesis . In cell cultures , endothelial cells—the building blocks of blood vessels—can organize into structures resembling networks of capillaries . Experimental work has shown that the endothelial cells pull onto the protein gel that they live in , called the extracellular matrix . On sufficiently compliant matrices , the strains resulting from these cellular pulling forces slow down and reorient adjacent cells . Here we propose a new computational model to show that this simple form of mechanical cell-cell communication suffices for reproducing the formation of blood vessel-like structures in cell cultures . These findings advance our understanding of biomechanical signaling during morphogenesis , and introduce a new set of computational tools for modeling mechanical interactions between cells and the extracellular matrix . | [
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"computatio... | 2014 | Mechanical Cell-Matrix Feedback Explains Pairwise and Collective Endothelial Cell Behavior In Vitro |
Stoichiometric models of metabolism , such as flux balance analysis ( FBA ) , are classically applied to predicting steady state rates - or fluxes - of metabolic reactions in genome-scale metabolic networks . Here we revisit the central assumption of FBA , i . e . that intracellular metabolites are at steady state , and show that deviations from flux balance ( i . e . flux imbalances ) are informative of some features of in vivo metabolite concentrations . Mathematically , the sensitivity of FBA to these flux imbalances is captured by a native feature of linear optimization , the dual problem , and its corresponding variables , known as shadow prices . First , using recently published data on chemostat growth of Saccharomyces cerevisae under different nutrient limitations , we show that shadow prices anticorrelate with experimentally measured degrees of growth limitation of intracellular metabolites . We next hypothesize that metabolites which are limiting for growth ( and thus have very negative shadow price ) cannot vary dramatically in an uncontrolled way , and must respond rapidly to perturbations . Using a collection of published datasets monitoring the time-dependent metabolomic response of Escherichia coli to carbon and nitrogen perturbations , we test this hypothesis and find that metabolites with negative shadow price indeed show lower temporal variation following a perturbation than metabolites with zero shadow price . Finally , we illustrate the broader applicability of flux imbalance analysis to other constraint-based methods . In particular , we explore the biological significance of shadow prices in a constraint-based method for integrating gene expression data with a stoichiometric model . In this case , shadow prices point to metabolites that should rise or drop in concentration in order to increase consistency between flux predictions and gene expression data . In general , these results suggest that the sensitivity of metabolic optima to violations of the steady state constraints carries biologically significant information on the processes that control intracellular metabolites in the cell .
Cells endure relentless variations in intra- and extra-cellular conditions . These perturbations propagate through the cell's metabolic and regulatory networks , leading to a diverse range of interdependent , transient responses in the abundance of metabolites , transcripts , and proteins [1]–[3] . In spite of these changing conditions , cells must efficiently allocate molecular resources through the metabolic network to guarantee homeostasis and enable self-reproduction . Understanding how biochemical pathways and regulatory circuits work together to achieve this robustness remains an open problem with major implications for systems and synthetic biology [4]–[7] . One approach to this question is to use genome-scale , constraint-based models of metabolism ( such as flux balance analysis , FBA [8]–[11] ) . These models rely predominantly on reaction network stoichiometry to provide a scalable , largely parameter-free method for linking individual reaction fluxes with global cellular properties , such as growth . Importantly , constraint-based models frequently assume that the cell has been optimized , through selective pressure and evolution , towards some cellular objective ( frequently captured in the biomass flux ) . The major drawbacks of constraint-based approaches ( in contrast to mechanistic models of metabolism [12] ) are the incapacity to predict metabolite concentrations and the difficulty of making inferences about the dynamics of the system , though recent efforts have made important contributions in overcoming some of these limitations [13] , [14] . Here , we show that some features of the behavior of intracellular metabolites are shaped by the interplay between the stoichiometric architecture of the metabolic network and the nutrient limitations imposed by environmental conditions , as well as the key role of metabolism as the conduit for allocating cellular resources towards growth . This link between structure and function of metabolism is hidden in a largely unappreciated aspect of the solution to flux balance models , namely the dual solution to the associated linear programming ( LP ) problem [15] . Our main results are threefold . First , we demonstrate how sensitivities to each steady-state constraint in FBA ( referred to as shadow prices , and often automatically calculated when solving an FBA problem [16]–[19] ) correlate negatively with experimentally quantified degrees of growth-limitation of a metabolite . Second , we show how the growth-limitation of a metabolite ( as captured by its shadow price ) provides a window onto the temporal response of that metabolite following an environmental perturbation . In particular , by examining a number of time-dependent metabolomics datasets , we observe that metabolites which have large negative shadow prices also exhibit little temporal variability following a perturbation . Third , we examine the broad applicability of shadow prices to other constraint-based approaches to modeling metabolism . We show that , by studying the shadow prices of a constraint-based model that incorporates high-throughput gene expression data , we are able to predict whether an intracellular metabolite accumulates or depletes . Taken together , our results suggest that shadow prices and “flux imbalance analysis” may find quite useful application in probing the behavior of metabolites using constraint-based modeling .
Flux balance analysis is a method for computing expected reaction rates in complex metabolic networks , and has been described in detail elsewhere [9] , [20] . The basic strategy of FBA is to identify steady state metabolic rates ( fluxes ) that satisfy a set of constraints , and maximize ( or minimize ) a given objective function . The main constraints are usually ( i ) mass conservation ( or flux balance ) at each metabolite node , due to the steady state approximation , and ( ii ) a set of inequalities associated with limitation of extracellular metabolites and empirical evaluations of irreversibility . Key inequalities are usually imposed on exchange reactions , i . e . source/sink reactions mediating the interaction between a cell and its surrounding environment . A canonical FBA calculation can be formally expressed as the following primal LP problem: ( 1 ) ( 2 ) ( 3 ) where S is the m ( metabolites ) by n ( reactions ) stoichiometric matrix , v is the vector of metabolic fluxes , vLB is a vector of lower bounds for all fluxes , vUB is a vector of upper bounds for all fluxes , b is the vector of the rates of accumulation/depletion of each metabolite , and c is the vector defining the contribution of different fluxes to the objective function . For intracellular reactions , the right-hand-side coefficients bi in Eq . ( 2 ) are typically assumed to be zero , capturing the assumption that all intracellular metabolites are at steady state . Our analysis is essentially centered on exploring how the cell would respond to deviations from null bi coefficients . Such a deviation implies a flux imbalance at metabolite i , and hence its accumulation or depletion . Importantly , this interpretation of Eq . ( 2 ) is not meant as a substitute for the underlying kinetics of the system . Such a flux imbalance may propagate through the metabolic network to influence the optimal value of the objective function Z . How can one quantify the sensitivity of the objective function to such flux imbalances ? What is the biological significance of these sensitivities ? In fact , every LP calculation can be reformulated in terms of a complementary problem known as the dual problem [15] , whose variables ( referred to as a shadow prices , λi ) specifically capture the change in the value of the objective function upon a unit change in the right-hand-side of a single constraint ( bi ) . The general formulation of the dual problem can be found in any linear optimization textbook ( e . g . [15] ) , and its specific formulation for FBA is described in detail in the Methods section . In practice , the shadow prices are typically provided in parallel to the primal variables by any LP solver upon solving Eqs . ( 1 ) – ( 3 ) . In analogy with the interpretation of shadow prices in economics and in line with prior work on shadow prices in constraint-based metabolic modeling [16]–[19] , FBA's shadow prices estimate the value of each metabolite to the global molecular budget of a growing cell ( Figure 1 ) . The interpretation of shadow prices is particularly interesting in the case of the canonical FBA objective function , i . e . maximization of the biomass flux ( Z = vgrowth ) . In this case , a shadow price corresponds to the change in the biomass flux when one of the intracellular metabolites deviates from steady state . Importantly , if a metabolite has a negative shadow price , this means that allowing additional outflow from this metabolite ( so that bi<0 ) will increase the maximal value of the biomass flux , implying that this metabolite is limiting for the biomass objective ( Figure 1 ) . In the remainder of this article , we test the hypothesis that shadow prices correlate with the magnitude of growth-limitation of a metabolite using experimental data , and explore the broader implications of shadow prices in modeling genome-scale metabolism . To explore the connection between shadow prices and growth limitation , we analyzed previously collected experimental data studying the relationship between intracellular metabolite abundances and growth-limitation in Saccharomyces cerevisae under continuous culture [21] . For three different conditions ( single nutrient limitation on glucose , nitrogen , and phosphate ) , and two auxotrophic mutants ( leucine and uracil ) the abundance of intracellular metabolites was quantified for several different dilution ( growth ) rates . Boer and colleagues [21] showed that the growth limitation of a metabolite could be quantified by measuring the change in metabolite abundance at different , increasing growth rates . In particular , metabolites with relatively low intracellular concentrations which increased in abundance as growth rate increased were found to be growth-limiting . In contrast , metabolites which relatively high concentration and which decreased in concentration as growth rate increased were described as “overflow” metabolites , and were not growth-limiting . To understand why we may expect such correlations , we can re-elaborate on the reasoning presented by Boer and colleagues in [21] . As described in [21] , we consider the simplest case , where growth is limited by the concentration of a single , growth-limiting nutrient M . The dependence of growth on this metabolite can be described by the classical Monod equation:where K is the half-saturation constant , is the growth rate , and is the maximum growth rate . As we derive in detail in Supplementary Text S1 , valuable intuition for the dependence of on M can be gained by considering the limiting cases M>>K and M<<K . In the first case , M is substantially larger than the half-saturation constant K . Then , the growth rate is relatively insensitive to changes in M , and it can be treated as non-growth-limiting . By calculating the dependence of M on in this limiting case , one findsThus , in this case , we would expect very small correlation between log and log M . As shown in [21] this correlation can even become negative due to feedback inhibition ( corresponding to points below the horizontal red line in Figure 2 ) . In the other limiting case , where M is much smaller than K ( and the growth rate is very sensitive to M , so M is very growth-limiting ) and we expect a positive correlation between and M ( corresponding to points above the horizontal red line in Figure 2 ) . The extent of this correlation ( together with its sign ) , constitutes a metabolite-specific metric for growth limitation , and corresponds to the abscissa in the graph of Figure 2 . Furthermore , this simple model is readily extendible to cases where many metabolites may simultaneously be limiting for growth rate , as shown in [21] . We compared the growth-limitation measurements for each metabolite identified as significantly growth-limiting or non-growth-limiting/overflow in [21] , to the corresponding shadow prices computed through FBA in silico experiments when maximizing for biomass production in the yeast model iMM904 [22] . For all natural nutrient limitations , we found an anticorrelation between shadow prices and growth-limitation: growth-limiting metabolites exhibit negative shadow prices , while non-growth-limiting metabolites exhibit small or zero shadow prices ( Figure 2 ) . Furthermore , for each individual nutrient condition , the more negative a shadow price was , the more limiting the corresponding metabolite was found to be in the original paper [21] . Although there is little support that the correlation between shadow prices and growth limitation is linear , we report both Spearman ( rank-based ) and Pearson ( linear ) correlations . These anticorrelations were strongest for nitrogen ( Spearman ρ = −0 . 74 , p-value = 2×10−5 , Pearson r = −0 . 77 , p-value = 1×10−5 ) and phosphate limitation ( Spearman ρ = −0 . 66 , p-value = 5×10−5 , Pearson r = −0 . 50 , p-value = 0 . 033 ) , where there were substantially more data points ( 12 and 17 metabolites experimentally identified as significantly growth- or non-growth-limiting in [21] , respectively ) than for glucose limitation ( Spearman ρ = −0 . 59 , p-value = 0 . 008 , Pearson r = −0 . 77 , p-value = 1×10−5 , 7 metabolites ) . In agreement with [21] , the growth-limiting metabolites in each condition reflect the corresponding nutrient limitation . In nitrogen limitation , we found many candidate growth-limiting metabolites , nearly all of which were amino acids . In glucose starvation , we found N-acetyl-glucosamine-1-phosphate ( a precursor for protein glycosylation ) and arginine to be among the most growth-limiting metabolites ( with the most negative shadow price ) . The main outlier in glucose starvation was glutamate , which had a negative shadow price ( i . e . predicted to be growth-limiting ) even though its concentration was experimentally observed to fall with increasing growth rate . The authors of [21] attributed this peculiar behavior of glutamate to the potential overabundance of nitrogen relative to carbon in extremely carbon-limited environments . Perhaps most interestingly , we found that the largest shadow prices occurred under phosphate limitation ( see Figure 2 , green dots ) , in agreement with the large growth-limitation ( in comparison to other conditions ) reported in [21] . It will be interesting in the future to investigate whether the apparent strength of growth-limitation ( as quantified by the magnitude of the shadow price ) plays a role in the extent to which these metabolites regulate the rates of enzymatic reactions . We repeated the statistical analyses above for two “lumped” datasets containing data from ( i ) all three natural nutrient limitation conditions ( glucose , nitrogen , and phosphate limitation; Spearman ρ = −0 . 87 , p-value 2×10−8 , Pearson r = −0 . 69 , p-value = 8×10−5 ) and ( ii ) all three nutrient limitation conditions , together with auxotrophies ( Spearman ρ = −0 . 70 , p-value = 2×10−13 , Pearson r = −0 . 28 , p-value = 0 . 006 ) . The results also remained valid when we only considered cytosolic metabolites , rather than metabolites from all compartments ( see Table S1 and Dataset S1 ) . Finally , we assessed whether the sign of a shadow price ( i . e . either zero or negative ) could be used as a predictive binary classifier for whether a metabolite is growth-limiting or non-growth-limiting . To do so , we calculated the Matthews Correlation Coefficient ( MCC ) [23] , a standard measure for the performance of a binary classifier . We found statistically significant agreement between the sign of a shadow price and its classification as growth-/non-growth-limiting , both when using metabolites from all compartments ( MCC 0 . 61 , p-value = 5×10−8 ) and only cytosolic metabolites ( MCC 0 . 81 , p-value = 1×10−7 ) . An important question in the above analysis , and in the calculation of shadow prices in general , is whether the possible alternative optima in the FBA optimization problem could give rise to degenerate shadow prices , and hence ambiguity in the comparison with experimental data . As described in detail in the Methods , we addressed this issue by recalculating each shadow price in a brute force way , i . e . by solving two additional LP problems where the right-hand-side of each steady-state constraint ( bi in Eq . ( 2 ) ) is incremented/decremented by a small amount ( as explored before in a different context [24] and in detail in the Methods ) . The shadow prices obtained from solving these two problems correspond to manual ( i . e . not obtained automatically from the LP solver upon solving the primal ) re-calculations of the sensitivity of the objective function to deviations from each steady-state constraint . We then compared the incremental shadow price , decremental shadow price , and the shadow price obtained directly from the LP solver , and found no instances of degeneracy in our shadow price calculations . Our results so far indicate , in line with our intuition and with prior work on duality in FBA [17] , [18] , that shadow prices may serve as quantitative measures of the sensitivity of growth rate to the abundance of an intracellular metabolite . In the next section , we investigate whether this sensitivity has implications for the transient dynamics of growth-limiting metabolites following a perturbation . Given the metabolite-specific associations between shadow prices and growth-limitation , we decided to investigate whether shadow prices could also aid in understanding other features of intracellular metabolites . In particular , we reasoned that if a metabolite is truly growth-limiting , then its concentration in the cell should be tightly controlled . If , in contrast , a growth-limiting metabolite's concentration is allowed to fluctuate or vary uncontrollably , this temporal variability would eventually propagate to growth rate and have potentially deleterious consequences . Our reasoning was further bolstered by recent studies of the metabolic response of Escherichia coli to sudden perturbations which demonstrated that the growth rate of cells responds remarkably quickly to changes in environmental conditions . In two experiments [25] , [26] , it was shown that a sudden change in substrate availability in the environmental media led to a rapid change in the growth rate . In [25] , a pulse of glucose to a glucose-limited chemostat culture of E . coli lead to a 3 . 7-fold increase in growth rate less than a minute . Similar results were observed in [26] for pulses of pyruvate and succinate . Based on our reasoning and on the two studies in [25] , [26] , we hypothesized that growth-limiting metabolites ( with very negative shadow prices ) should exhibit very little temporal variation in their concentrations in response to perturbations . In contrast , metabolites exhibiting large temporal variation should not be growth-limiting ( and have small or zero shadow price ) . We tested this prediction using multiple time-course metabolomics datasets for E . coli for different glucose and nitrogen perturbations [27] , [28] . We elected to use these datasets because they contained information for a large number of metabolites ( ∼70 unique compounds ) , enabling us to obtain reasonable statistical power . For each dataset , we calculated the temporal variation of a metabolite across the time course following the perturbation , using the coefficient of variation ( CV , the standard deviation of the time series , divided by its mean; see Methods for more details ) . Thus , a very large temporal variation corresponded to a circumstance when a metabolite's concentration changed substantially following a perturbation , and a small temporal variation indicated that a metabolite's concentration remained relatively constant post-perturbation . After calculating the temporal variation for each metabolite , we computed the metabolite shadow prices using FBA ( see Methods ) . The results of our analysis are shown in Figure 3 . In agreement with our expectations , shadow prices were found to be correlated with temporal variation in the five perturbations we studied . Metabolites with very large , negative shadow prices ( and thus very limiting for biomass production ) showed little temporal variation . Conversely , metabolites with the largest temporal variation were found to have comparatively smaller shadow prices . We again report both Spearman and Pearson correlations , although there is no a priori reason to expect linear correlations . The correlations were statistically significant for nitrogen upshift ( Spearman ρ = 0 . 26 , p-value = 0 . 02 , Pearson r = 0 . 22 , p-value = 0 . 04 ) , as well as the 4 different carbon perturbations ( glucose starvation , Spearman ρ = 0 . 21 , p-value = 0 . 05 , Pearson r = 0 . 21 , p-value = 0 . 045; acetate limitation , Spearman ρ = 0 . 37 , p-value = 0 . 002 , Pearson r = 0 . 36 , p-value = 0 . 002; succinate limitation , Spearman ρ = 0 . 23 , p-value = 0 . 04 , Pearson r = 0 . 18 , p-value = 0 . 08 , and glycerol limitation , Spearman ρ = 0 . 32 , p-value = 0 . 007 , Pearson r = 0 . 33 , p-value = 0 . 006 ) . These correlations were further substantiated using non-parametric permutation tests , described in the Methods , with results detailed in Table S2 . Despite these statistically significant correlations , a number of outliers ( i . e . , metabolites with relatively large , negative shadow prices and high temporal variation ) appeared in our results . Among the outliers under glucose limitation ( Figure 3B ) , the most notable were cyclic AMP ( a signaling molecule ) and acetyl-CoA . More interestingly , in both acetate and glycerol limitation , a repeated outlier was fructose 1 , 6-bisphosphate ( FBP ) . This metabolite was highlighted in one of the two papers from which we obtained the time-series data [28] . As the authors showed there , upon a sudden switch from glucose medium to either no carbon , acetate , succinate , or glycerol , the concentration of FBP dropped suddenly by 15- to 30-fold . This sudden drop in FBP , coupled with its role as an allosteric activator of PEP carboxylase , resulted in the buildup of PEP . This buildup enabled fast uptake of glucose when it re-appears in the media , where it is used as a phosphate donor for the import of glucose . Furthermore , FBP was recently identified as a candidate “flux sensor , ” i . e . a metabolite whose concentration may change in linear proportion to the flux through glycolysis , via its role as an activator of pyruvate kinase [29] . Thus , the aberrant behavior of FBP ( a negative shadow price , but high temporal variation ) may be related to its key role in affecting E . coli's response to glucose starvation and carbon limitation through allosteric regulation . To further corroborate our findings , we tested whether the differential dynamic behavior of mutant knockout strains could be captured through our analysis . We used additional metabolite time series available for the wild type and two knockout strains ( ΔGOGAT and ΔGDH ) of E . coli following nitrogen upshift in [27] . We replicated these knockouts in silico , and calculated the shadow prices . We performed two different analyses on this dataset: first , we looked broadly at the changes in shadow prices ( from wild-type to knockout ) for each of the two knockouts . As illustrated in Figure 4 , we found that for the ΔGOGAT strain , 22 metabolites showed a significant drop in shadow price , decreasing by a magnitude greater than one ( i . e . becoming more growth-limiting ) . The most drastic changes were found for lipids and precursors , like undecaprenyl phosphate , and UDP-D-glucoronate , both of which showed a drop in shadow price of 1 . 29 . In contrast , the ΔGDH knockout featured no metabolites with a substantial ( greater than 0 . 1 ) drop in shadow price . This absence of new growth-limiting metabolites in ΔGDH is consistent with the observation in [27] that the GS/GOGAT pathway dominates over GDH in nitrogen limitation . Interestingly , a subset of eight metabolites , all corresponding to glycolipids , showed a substantial increase in shadow price in ΔGDH ( corresponding to a relaxation in growth-limitation ) . Thus , while the “growth-limitation landscape” of the ΔGDH mutant , characterized by the most growth-limiting metabolites in the model , seemed relatively similar to that of the wild-type , the ΔGOGAT strain displayed substantially different growth limitations . Second , we tried to recapitulate the primary qualitative finding of the knockout study from [27]: that glutamine exhibited a substantial drop in temporal variation in ΔGOGAT in comparison to the wild-type and ΔGDH strains ( from a sudden increase and then return to steady state in wild type and ΔGDH strains to nearly no response in ΔGOGAT ) . When comparing the shadow prices of glutamine across the different strains , the shadow price of glutamine dropped from 0 to −0 . 08 , in both ΔGDH and ΔGOGAT . While the shadow prices of other metabolites tracked in the knockout experiments changed as well , glutamine exhibited the largest drop . Thus , despite the fact that an alternative pathway for nitrogen assimilation was present in each knockout strain , the knockout of either GDH or GOGAT led to an increase in growth-limitation of glutamine . This drop in shadow price was in qualitative agreement with the experimentally observed drop in temporal variation in the knockout strains ( from 0 . 85 in wild-type to 0 . 78 in ΔGDH and 0 . 32 in ΔGOGAT ) . Thus , the shadow prices associated with individual intracellular metabolites provide information not only about the extent to which each metabolite is limiting for growth , but also about its overall temporal variation following a perturbation . Importantly , in the current framework , shadow prices do not provide quantitative predictions about the speed at which metabolites respond , or the new steady-state concentrations they reach . Hence , shadow price analysis should not be treated as a substitute to explicit predictions from kinetics . While our results seem to hold across different experiments in E . coli ( i . e . different nutrient limitations and genetic modifications , albeit in a noisy manner ) , its general validity and mechanistic basis across different organisms and types of perturbations will require further scrutiny and will be an important aspect of future work . In particular , the availability of specific mechanistic models for the metabolic response to perturbations , coupled with higher temporal resolution data , would allow one to obtain more precise estimates of temporal variability , and hence better quantitative comparisons with shadow prices . So far , we have corroborated the notion that shadow prices are indicative of growth limitation , and demonstrated that shadow prices are even more broadly related to metabolite dynamic variability . As described above and in the Methods , shadow prices are dependent on the underlying stoichiometric model , and the specific environmental conditions . Correspondingly , in the analysis shown up to now , we have explored the relevance of shadow prices across different conditions ( different nutrient limitations and genetic modifications ) . There is , however , a third feature that shadow prices crucially depend on , i . e . the specific objective function used in the FBA optimization . Does the analysis of shadow prices have a meaning and an application for stoichiometric problems with radically different objective functions , or is it biologically interpretable only for the growth maximization objective ? To answer this question , we decided to explore the significance of shadow prices in a recently proposed optimization problem aimed at identifying genome-scale fluxes that minimize the inconsistency relative to a given set of gene expression data . This approach , pioneered with the GIMME algorithm [30] , and recently re-elaborated in the time-dependent TEAM method [31] , is a way of integrating gene expression data with stoichiometric models of metabolism , in order to obtain better predictions and understanding of cellular physiology . Instead of maximizing growth , GIMME and TEAM minimize the conflict between gene expression data and flux predictions using a penalty score ( see Methods ) . In particular , fluxes whose corresponding gene ( s ) exhibit low expression are penalized ( Figure 5A ) in proportion to how much lower the gene expression is , relative to a given gene-specific threshold ( see [31] and Methods ) . The cumulative penalty obtained from all these costs ( termed the Inconsistency Score , IS ) is minimized across the entire metabolic network [30] , [31] . This problem can be solved again using linear programming , in analogy to the FBA problem illustrated above: ( 4 ) ( 5 ) ( 6 ) ( 7 ) where c is a vector of reaction penalties , and the reaction flux vRMF is a required metabolic functionality ( RMF ) , some minimal , user-defined metabolic behavior which the model must reproduce ( for example , growth at a minimal rate or the secretion of a metabolite ) . One of the reasons the RMF constraint is imposed is to avoid the trivial solution v = 0 . As depicted in Figure 5B , shadow prices in TEAM have a different interpretation from shadow prices in FBA . A shadow price in TEAM is defined as the change in the inconsistency score IS when the steady-state constraint on one metabolite bi deviates from zero . It is reasonable to assume that some portion of the inconsistency between experimentally measured gene expression and TEAM's flux predictions is the result of imposing the steady state assumption in our model , while a metabolite may be truly accumulating or depleting during certain time intervals in the experiment . Allowing such a metabolite to violate the flux balance condition ( either accumulate or deplete ) should lower the inconsistency score . Then , if a metabolite's abundance is decreasing , we should expect the shadow price to be positive ( Shadow Price = negative change in IS/negative change in abundance ) . Conversely , if the metabolite's abundance is increasing , then we should expect the shadow price to be negative ( Shadow Price = negative change in IS/positive change in abundance ) . As illustrated in Figure 5B , TEAM's shadow prices should thus be informative of the direction of changes in metabolite abundance: metabolites with positive ( negative ) shadow price are expected to decrease ( increase ) in abundance . To test whether TEAM's shadow prices indeed could predict changes in intracellular metabolite abundances , we re-analyzed a transcriptomics [32] and metabolomics [33] dataset for the yeast metabolic cycle , previously integrated in FBA using TEAM ( see [31] ) . Our analysis ( see Methods for details ) showed a significant anticorrelation between TEAM's shadow prices and experimentally measured changes in metabolite abundance ( Spearman ρ = −0 . 41 , p-value = 9×10−6; Pearson r −0 . 31 , p-value = 8×10−4; Figure 5C ) . Notably , if we only consider those metabolites for which TEAM reported a nonzero shadow price , we correctly identify the direction of change ( e . g . increase or decrease ) in 55 of 63 metabolites in the dataset . We used this data in combination with the Matthews Correlation Coefficient ( used earlier to analyze data from Figure 2 ) as a measure of how well the sign of TEAM's shadow prices can be used to predict the accumulation/depletion of a metabolite . We found that the sign of the shadow price was indeed a good predictor of the direction of change of a metabolite's concentration ( MCC 0 . 68 , p-value 7×10−8 ) . Interestingly , among the incorrect predictions , many were for amino acids ( methionine , ornithine , proline ) . The failure of TEAM's shadow prices to predict changes in abundance for these compounds suggests that inconsistency with gene-expression data in pathways utilizing these metabolites may not be due to flux imbalances , and may instead indicate that other regulatory mechanisms are at play . Using the same sensitivity analysis developed in [31] and discussed in the Methods , we furthermore confirmed that the shadow price results reported above were insensitive to changes of the primary free parameter of TEAM , θ ( a measure for how high to set each gene's penalty threshold ) , within the range θ = 0 . 50–0 . 73 and θ = 0 . 78–0 . 88 . This range of thresholds is substantially larger than the range of θ's found to accurately recapitulate experimental data in our studies of Shewanella oneidensis using TEAM ( θ = 0 . 65 to θ = 0 . 72 ) [31] , suggesting our results here are robust to variations in θ . Thus , our analysis of flux imbalances in TEAM , a constraint-based approach based on an objective function radically different from the classical growth maximization of FBA , reveals that shadow prices have useful applications beyond conventional flux balance methods .
Constraint-based stoichiometric models of metabolism have become a widely used approach for characterizing and predicting cellular metabolic states [10] . The notion that steady-state constraints and a cell-level objective function provide an approximate quantitative understanding of the behavior of a population of cells has been subjected to experimental testing , and discussed at length in the literature [8] , [11] , [34] . Yet , other more subtle aspects of stoichiometric modeling , such as the potential power of shadow prices , had not been directly tested . Nor had the idea of flux imbalance been pursued as a link between the sensitivity analysis of FBA and the dynamics of metabolite pools . The results we have presented may seem at first glance surprising . How can a steady state solution convey information about the dynamical changes of metabolite pools ? The answer is that flux balance models are not simply steady state solutions to a dynamical system . Rather , they use constraints and optimality to predict how a cell should allocate its resources for maximal efficiency , given the underlying network architecture . It would be tempting to make the leap of inferring that the architecture itself truly constrains the dynamics , independent of parameters and regulation . Rather , we suggest that the stoichiometric architecture may dictate how regulation should evolve to guarantee robustness to temporary variations in the intracellular milieu . If the cell cannot allow itself to accumulate or deplete certain metabolites , without incurring a substantial penalty to growth , then the response to variations in these metabolite pools should be swift . This suggests that quick allosteric and post-translational metabolite-induced regulatory feedback should control the stability of these pools [35] , [36] and highlights the role the growth process itself may play in providing immediate feedback on metabolite pools by virtue of growth limitation [37] . Thus , we expect that an important challenge for future work will be examining our findings in light of newly reconstructed atlases of metabolic regulatory mechanisms [36] . A subtle but potentially important aspect of shadow prices and their biological interpretation in metabolic network models is the fact that they are defined only over a certain range , as dictated by the structure of the feasible space . These ranges capture how large a perturbation can be before the genome-scale optimal flux distribution changes sharply ( i . e . by moving to a different corner of the feasible space ) . In future research , it would be interesting to directly assess the potential existence of such discontinuities in the dynamical behavior of a perturbed metabolic network . In addition , the magnitude of the range of validity of a shadow price may be thought of as an additional tolerance metric for each individual metabolite , conveying the scale beyond which its response to a perturbation becomes unpredictable . Future models may test whether the extent to which a metabolite is regulated depends both on its shadow price , as well as this tolerance to large perturbations . The sensitivity of cells to variations in specific metabolite pools suggests a novel , metabolite-centric route towards the computational prediction of drug targets , e . g . for selectively affecting microbial pathogens or cancer cells . In addition to seeking enzyme gene deletions as a way to impair specific metabolic pathways [38] , one could instead impair the regulatory mechanism stabilizing metabolite pools to which growth is particularly sensitive . Notably , the shadow prices automatically generated upon solving the FBA problem would directly provide a prioritization list of the most sensitive target metabolites . It will be interesting to relate the metabolite-centric information obtained from shadow prices to prior quantifications of the importance of metabolites based on their producibility upon gene deletions [39] and on the sum of all incoming or outgoing fluxes around them [40] . Furthermore , one could consider how lethal gene deletions/perturbations , which often result in infeasible models for which shadow prices are not immediately available , can be treated using our framework . Another prospect for future studies will be to evaluate whether shadow prices may shed light on the interplay between evolution , regulation , and the sub-optimal behavior of cells . While most stoichiometric models still use maximization of growth as the central objective , a number of studies have suggested specific applications of alternative objectives . These include the Minimization Of Metabolic Adjustment [41] ( or its recent more robust variant , Minimization of Metabolites Balance [42] , based on metabolite turnovers instead of fluxes ) for describing the cellular phenotypes arising upon genetic perturbations prior to further regulatory or evolutionary optimization , and multi-objective Pareto optimality for studying how cells may sacrifice optimal growth in favor of tradeoff solutions [43] . In the same spirit as our ad hoc interpretation and analysis of TEAM's shadow prices , sensitivity of these optimization problems to their respective constraints may offer further insights into the cellular response to perturbations . Additionally , upon availability of comprehensive data on intracellular metabolite concentrations at multiple time steps , one could envisage implementing stoichiometric models that use explicit flux imbalances ( rates of accumulation/depletion ) as inputs to the constraint-based model . For example , our shadow price analysis with TEAM is readily extendible to cases where the rate of accumulation/depletion is known for one subset of metabolites , but unknown for another set ( e . g . for metabolites for which precise intracellular measurements are technically difficult ) . In such circumstances , for every metabolite for which appropriate data is available , the right-hand-side of the corresponding steady-state constraint ( e . g . bi ) could be adjusted accordingly . Finally , while the notion of flux imbalance analysis is not the first to bridge between the worlds of stoichiometry and metabolic dynamics [14] , [44] , it is the first to use a genome-scale modeling approach to make inferences about the qualitative response of metabolite concentrations to a perturbation . We do not know the mechanism which induces relatively fast changes in growth-limiting metabolites , when compared to non-growth-limiting metabolites . Indeed , an exciting prospect for future work will be bridging our findings with well-established schools of metabolic theory , including metabolic control analysis [44] , biochemical systems theory [45] , and structural kinetic modeling [46] , [47] . Compellingly , the dual of the FBA problem has also been suggested to constitute a window onto the thermodynamics of biochemical networks , with potential implications for understanding the energetics of metabolism [19] . Unifying these distinct threads , which independently derive dynamic and energetic meaning from the same mathematical framework , seems a worthwhile direction for future efforts .
We offer here a simple derivation of the dual problem to flux balance analysis . We begin by posing the primal FBA problem ( 8 ) where c , v , vLB , and vUB are vectors of length n , and S is the m×n stoichiometric matrix . For clarity and in contrast to the main text , we have formulated the FBA problem in vector notation ( including inequalities , to be interpreted component-wise ) . We associate with each set of constraints in the primal problem a single set of dual variables . For the steady state constraints , we assign variables ( a vector of length m , the shadow prices which we use throughout this work ) , for the constraints on the lower bounds of each flux , we assign variables ( a vector of length n ) , and for the constraints on the upper bounds of each flux , we assign variables ( a vector of length n ) . Then , following any standard text on linear optimization ( e . g . [15] ) one can obtain from Eq . ( 8 ) the dual problem ( 9 ) We implemented a number of measures to ensure that each shadow price used in our calculations was accurate and meaningful . In particular , we validated that the shadow prices obtained directly from the LP solver could not take on different values depending on whether a metabolite was accumulating or depleting ( i . e . that the shadow price was not degenerate , described below ) . To do so , we used brute-force techniques to validate that each shadow price reported by the solver was indeed the sensitivity of the objective function to each steady-state metabolite constraint . This process thus simultaneously helped ensure that our results were robust to alternative dual optima . In addition to the primal solution ( optimal fluxes ) , the Gurobi LP solver provides the corresponding dual solution to the FBA problem . The dual solution contains ( i ) the shadow price value relative to each metabolite steady-state constraint and ( ii ) the upper ( G+ ) and lower ( G− ) bounds for which these shadow prices are valid . These bounds indicate the maximum that the right hand side of each constraint may be perturbed while still maintaining the validity of each shadow price . First , we ensured that any calculated shadow prices had non-zero ranges of validity ( range = G+−G− ) . Any shadow prices which did not exhibit a minimal range εrange = 10−6 were discarded . Other tested values of εrange in the range 10−3 to 10−6 led to qualitatively identical results . Second , we ensured that alternate optimal solutions [48] did not impact the dual solution . Prior work has reported that degenerate solutions can lead to differences between the incremental shadow price λ+ ( the change in the objective function when the right-hand-side of a constraint is increased ) and the decremental shadow price λ− ( the change in the objective function when the right-hand-side of a constraint is decreased ) [24] . To ensure that this did not affect our shadow price calculations , we manually re-calculated the incremental and decremental shadow price for each metabolite for which we had experimental concentration data ( indexed by itest ) in the model using a perturbation procedure . This calculation was implemented by solving the two following optimization problems , in which the steady state constraint is positively or negatively violated at each individual metabolite: ( 10 ) ( 11 ) Here , the parameter p modulates how large we allow a steady-state constraint to be violated , while remaining in the range [ , ] ( where the are those defined above with reference to the range of each shadow price ) . Thus , 0<p<1 . We used p = 0 . 2 , although other choices of p yielded identical results ( we tried p = 0 . 5 and p = 0 . 9 ) . Upon solving the above optimization problems , the incremental and decremental shadow prices can be computed as the changes in the objective relative to the changes in the right-hand side terms , i . e . , respectively: ( 12 ) ( 13 ) where Z is the solution to the regular FBA problem ( i . e . the one without perturbations of the right-hand sides ) . We then ensured that the shadow price obtained from the solver deviated from and less than the error tolerance of the solver . In many cases , one of or was equal to zero ( i . e . the shadow price was only valid when perturbing in one direction ) . In these cases , we only manually calculated the shadow price corresponding to the valid direction . It is important to note that and are obtained through brute-force re-calculation of the shadow prices obtained directly from the solver . While they are laborious , they enable us to ensure that degenerate solutions do not adversely affect our results . In order to facilitate the implementation of degeneracy checking of shadow prices , we have provided the pseudocode below: CHECK_DEGENERACY ( S , LowerBound , UpperBound , Objective ) 1 # Run FBA and obtain four outputs: the optimal flux vector , the shadow prices for each metabolite , the incremental range over which each shadow price is valid , the decremental range over which each shadow price is valid , and the optimal value 2 [Flux SP SPUpRange SPDownRange OptVal] = Run_FBA ( S , LowerBound , UpperBound , Objective , RHSConstraints ) 3 p = 0 . 5 # p can take value between 0 and 1 4 # For every metabolite , check for degeneracy in the shadow price of the metabolite by changing one of the steady-state constraints from zero to a non-zero value within the range of validity 5 for i = 1…number of metabolites 6 if SPUpRange ( i ) >0: # if we can perturb up 7 RHSConstraintsPlus = RHSConstraints # Use a temporary variable 8 RHSConstraintsPlus ( i ) = SPUpRange ( i ) *p # Change one constraint 9 # NEXT: Solve FBA with new constraint ( incremental shadow price ) 10 [FluxPlus SPPlus SPUpRangePlus SPDownRangePlus OptValPlus] = Run_FBA ( S , LowerBound , UpperBound , Objective , RHSConstraintsPlus ) 11 end #end if 12 if SPDownRange ( i ) <0: # if we can perturb down 13 RHSConstraintsMinus = RHSConstraints; 14 RHSConstraintsMinus ( i ) = SPDownRange ( i ) *p; 15 # NEXT: Solve FBA with new constraint ( decremental shadow price ) 16 [FluxMinus SPMinus SPUpRangeMinus SPDownRangeMinus OptValMinus] = Run_FBA ( S , LowerBound , UpperBound , Objective , RHSConstraintsMinus ) 17 end # end if 18 # Compare manually calculated shadow prices ( if they exist ) to solver's 19 SPPlus ( i ) = ( OptValPlus – OptVal ) /SPPlusRange ( i ) *p 20 SPMinus ( i ) = ( OptValMinus – OptVal ) /SPMinusRange ( i ) *p 21 if |SPPlus ( i ) – SP ( i ) |>tolerance OR |SPMinus ( i ) – SP ( i ) |>tolerance 22 return ERROR # There is a degenerate shadow price 23 end #end if 24 end # end for In this work , all optimization problems were solved using the Gurobi optimization software [49] with an academic license . In all FBA problems , the objective was the wild-type biomass reaction in the most recent Escherichia coli metabolic model [50] . The yeast model iMM904 [22] was used for all growth limitation and TEAM simulations , with media formulations matching those described in the original publications . For all simulations relating to E . coli , we used the metabolic network reconstruction iJO1366 [50] . Growth medium compositions for all experiments simulated with the model were obtained from the corresponding experiment references . In all cases , the medium was based on the minimal salts medium [51] with 10 mM ammonium . For experiments from [28] , we removed glucose from the media formulations and replaced it with the appropriate limiting carbon source . In order to calculate temporal variation of metabolite , we use the coefficient of variation ( CV ) :where is the standard deviation of the measurements and is the mean . For all experiments from both publications , we calculated temporal variation using time points up to 30 minutes following perturbation . In the main text , we show that metabolites with large negative shadow prices exhibit little temporal variation , and metabolites with large temporal variation should exhibit small ( or zero ) shadow price . To further corroborate the significance of the anticorrelation between shadow prices and temporal variation illustrated in Figure 3 , we completed a nonparametric permutation test . For each experiment , the vector of shadow prices ( λ ) and vector of temporal variation ( CV ) of each metabolite were calculated . Then , the mean temporal variation ( mT ) and mean shadow price ( mS ) for the experiment were determined using λ and CV , respectively . We then computed the number of metabolites , poriginal , which exhibited a shadow price more negative than mS and a temporal variation larger than mT . These metabolites served as a proxy for the number of “incorrect” assignments made by our model . We generated 105 random permutations of λ and CV . For each permutation i , we calculated pi , the total number of metabolites satisfying the two criteria described above ( exhibited a shadow price more negative than mS and a temporal variation larger than mT ) . Then , we identified the proportion of permutations for which pi<poriginal ( i . e . the permuted data exhibited fewer incorrect predictions than the real data ) , reported in Table S2 . We repeated these tests using medians instead of means , with data reported in Table S2 . The penalty vector c quantifies the modeler's expectation that a reaction is metabolically active ( that is , that it carries flux ) to an extent that depends on the expression of its constituent genes . The c vector is calculated by assigning a penalty to each gene in the metabolic model , and then propagating these penalties to the reactions using the Boolean gene-to-reaction mapping provided in the model iMM904 [22] . The higher the value of the penalty ci for reaction i , the higher our confidence that the reaction is inactive . In contrast , reactions with c = 0 are expected to be active and carry flux . Importantly , each element of c is calculated using experimental measurements of gene expression . First , we describe how we assign a penalty to each gene g in the metabolic model . Gene penalties are determined by comparing the expression value of a gene with a predefined threshold . For each gene g , we created a cumulative distribution function ( CDF ) of all expression measurements for that gene ( using all gene expression data reported in [32] ) . Then , for a chosen percentile θ ( in Figure 5C , we use θ = . 88 ) , we use the CDF to calculate ( for each gene ) the expression value corresponding to that percentile . This was the gene's penalty threshold xg . For the purposes of this article , the primary difference between TEAM [31] and GIMME ( an algorithm upon which TEAM is based , see [30] ) is that TEAM assigns unique penalty thresholds to each gene in the metabolic model , while GIMME assigns a common penalty threshold to each gene . In [31] , we showed that these gene-specific thresholds substantially increase the accuracy of the algorithm . Once the gene expression penalty thresholds have been calculated , the penalty for each gene g , pg , is calculated:where EXPg is the expression of gene g . Thus , if a gene's expression is above the penalty threshold xg , that gene is assigned no penalty . In contrast , if its expression is found to be below the threshold , then its penalty is equal to the difference between the two . As described in [31] , we used the gene-to-reaction matrix provided in the metabolic model to map the vector of gene penalties p to a vector of reaction penalties c . An essential part of TEAM's formulation is a user-defined required metabolic functionality ( RMF ) . The RMF is a metabolic behavior ( such as growth or the secretion of a metabolite ) that TEAM must reproduce . It was observed in [32] that the population of yeast secreted acetate at the end of the oxidative portion of the metabolic cycle . We recreated in silico the environmental conditions of the experiments . We decided to use acetate secretion as our RMF flux . To do so , we first used FBA to identify the maximal amount of acetate that could be secreted by solving the optimization problem ( 14 ) Then , the minimal RMF flux vRMF , min was set to some proportion p of this maximal secretion rate . We used p = 0 . 3 , although other values of p yielded qualitatively similar results . Because the metabolomics and transcriptomics measurements were obtained from two distinct experiments in which the periods of the cycles were significantly different ( ∼8 hours vs . ∼12 hours , respectively ) , we used dissolved oxygen measurements ( DO ) ( which the authors of [32] repeatedly cited as representative of the population's location in the cycle ) to align timepoints from the two datasets . The experiments were otherwise comparable in terms of conditions and phenomena observed . All metabolomics data is represented in Figure 5C as fold changes . In order to validate whether the results using TEAM were dependent on our choice of penalty threshold , we applied a sensitivity analysis identical to the one described in [31] . We calculated the Spearman correlation for all possible percentile thresholds from to for the same expression and metabolomics time points as those in Figure 5C . As shown in Figure S1 , we found a large range of thresholds for which we obtained high accuracy and a significant correlation , confirming that our results were not highly sensitive to choice of penalty threshold . | Cellular metabolism is composed of a complex network of biochemical reactions that convert environmental nutrients into biosynthetic building blocks and energetic currency . Genome-scale mathematical models of metabolic networks focus largely on trying to predict the rates – or fluxes - of these reactions . By assuming that the concentrations of intracellular metabolites are at steady-state ( flux balance ) , and invoking optimality , these constraint-based methods for modeling metabolism have offered abundant insight into how metabolic flux is routed through the cell . Here we ask how cellular growth would respond to deviations from steady state ( flux imbalance ) of every possible intracellular metabolite . This question can be addressed through a sensitivity analysis inherent to linear optimization theory , known as duality . We show how some features of metabolite concentrations , such as their growth-limitation and their transient response , are captured by this sensitivity analysis . Our results suggest that , in addition to predicting fluxes , stoichiometric models offer a valuable route towards probing the metabolites themselves and their relevance to growth dynamics . | [
"Abstract",
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] | [
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] | 2013 | Flux Imbalance Analysis and the Sensitivity of Cellular Growth to Changes in Metabolite Pools |
Mycobacterium leprae infects macrophages and Schwann cells inducing a gene expression program to facilitate its replication and progression to disease . MicroRNAs ( miRNAs ) are key regulators of gene expression and could be involved during the infection . To address the genetic influence of miRNAs in leprosy , we enrolled 1 , 098 individuals and conducted a case-control analysis in order to study four miRNAs genes containing single nucleotide polymorphism ( miRSNP ) . We tested miRSNP-125a ( rs12975333 G>T ) , miRSNP-223 ( rs34952329 *>T ) , miRSNP-196a-2 ( rs11614913 C>T ) and miRSNP-146a ( rs2910164 G>C ) . Amongst them , miRSNP-146a was the unique gene associated with risk to leprosy per se ( GC OR = 1 . 44 , p = 0 . 04; CC OR = 2 . 18 , p = 0 . 0091 ) . We replicated this finding showing that the C-allele was over-transmitted ( p = 0 . 003 ) using a transmission-disequilibrium test . A functional analysis revealed that live M . leprae ( MOI 100∶1 ) was able to induce miR-146a expression in THP-1 ( p<0 . 05 ) . Furthermore , pure neural leprosy biopsies expressed augmented levels of that miRNA as compared to biopsy samples from neuropathies not related with leprosy ( p = 0 . 001 ) . Interestingly , carriers of the risk variant ( C-allele ) produce higher levels of mature miR-146a in nerves ( p = 0 . 04 ) . From skin biopsies , although we observed augmented levels of miR-146a , we were not able to correlate it with a particular clinical form or neither host genotype . MiR-146a is known to modulate TNF levels , thus we assessed TNF expression ( nerve biopsies ) and released by peripheral blood mononuclear cells infected with BCG Moreau . In both cases lower TNF levels correlates with subjects carrying the risk C-allele , ( p = 0 . 0453 and p = 0 . 0352; respectively ) , which is consistent with an immunomodulatory role of this miRNA in leprosy .
Leprosy is an ancient disease caused by Mycobacterium leprae . Once infected , the majority of individuals may clear the bacilli through a natural resistant response . Nevertheless , some patients could develop a latent infection that eventually evolves to one of the clinical symptomatic forms of leprosy . Peripheral nerves Schwann cells and skin macrophages are preferentially invaded , evoking a chronic infection that may take years to become active . Once the disease is established , a range of immune responses occur in spite of M . leprae been genetically conserved [1] . The spectral clinical manifestations are classified in a five-group system proposed in the 1960s by Ridley and Jopling [2] . A classic view of predominant Th1 for tuberculoid ( TT ) pole where a localized form of the disease is observed , in contrast to a major Th2 profile , where a disseminated form , called lepromatous ( LL ) pole is verified [3] . This classification system also comprises intermediate phenotypes , known as borderline , that interpose those two well characterized poles . Also , a variable percentage of the patients can experience an abrupt inflammatory episodes during the natural course of the disease , which are called type I ( reversal ) or type II ( erythema nodosum leprosum ) reactions [4] , [5] . Patients at the onset of the episodes exhibit high cytokine levels that are decreased once anti-inflammatory drugs are effective [6]–[8] , while genetic association might also be important [9] . Host susceptibility or protection is associated with the complex interaction between environment and genetic background , leading to different outcomes . Several publications aimed to understand the genetic contribution to leprosy risk or protection using different approaches including: twin studies , family-based linkage analysis , candidate gene association and genome wide association studies [10]–[13] . In fact , studies are linking or associating genes that have been generating a compelling amount of evidence to confirm the genetic influence in leprosy outcome . For instance , genes associated with innate immune response , like TLR1 , NOD2 , and PARK2 [11] , [14]–[16] or adaptive immune responses , such as IL10 , IFNG and LTA/TNF/HLA have been consistently associated with leprosy [9] , [13] , [17]–[19] . Recently , microRNAs ( miRNAs ) have been described as novel regulators of innate and adaptive immune responses , although a few data reported its involvement in leprosy . MiRNA genes are transcribed by RNA polymerase II [20] , resulting in a hairpin primary-miRNA ( pri-miRNA ) that is processed , in a cascade , by different RNAses [21] generating pre-miRNA , and finally the mature miRNA strand facilitating the miR-RISC ( RNA-induced silencing complex ) assembly [20] , [22] . The miRNAs control gene expression at post-translational level by pairing with 3′-untranslated regions [23] leading to mRNA cleavage or translational repression [24] . Given that , it is possible to assume that the presence of polymorphisms along double-stranded sequences can affect miRNA expression and gene silencing [25] . Genetic variants in miRNA precursors , miR-196a-2 ( rs11614913 C>T ) and miR-146a ( rs2910164 G>C ) have been associated with cancer and tuberculosis [26]–[30] . Here , we conducted a case-control and a family-based study to test these miRNA SNPs with leprosy susceptibility . Further , we performed functional studies using cell cultures and biopsies from skin and nerves to investigate miRNA mature expression form to define a genotype-phenotype correlation .
The case-control study includes a total of 1 , 098 individuals from Rio de Janeiro; of these , the 491 patients were recruited from the Souza Araújo outpatient unit , located at Fundação Oswaldo Cruz ( FIOCRUZ ) , Rio de Janeiro , Brazil . The data for 607 controls was obtained from a bone marrow donors' bank in Rio de Janeiro comprising of samples from local healthy individuals . A detailed presentation of this population has been described in Table S1 and elsewhere [16] , [31] . A replication population was also tested . Subjects for the family-based study were enrolled from Duque de Caxias , a hyper endemic city from the Rio de Janeiro state ( Table S2 ) . This population exhibited 97 nuclear families ( 426 subjects ) [31] . All patients were routinely diagnosed according to Ridley and Jopling criteria ( 1966 ) . Also , we adopted the World Health Organization ( WHO ) classification for treatment purposes , and patients were classified as paucibacillary/PB ( including TT and borderline-tuberculoid ) and multibacillary/MB ( including LL , borderline-lepromatous and borderline-borderline ) . Population characteristics according to the WHO classification and reactional status are summarized in Table S1 and S2 . All patients signed an informed consent and this project was approved by the institutional ethics committees from the involved institutions . Nerve biopsy samples were obtained at Souza Araújo outpatient unit . A detailed description of nerve samples and clinical forms was previously published [32] . To perform the correlation of TNF mRNA expression with miR-146a genotype we used 33 nerve samples ( 19 diagnosed with leprosy and 14 with other neuropathies ) . Among these specimens , we were able to determine miR-146a expression in 12 samples from leprosy patients and 7 from other peripheral neuropathies . In the group of neuropathies other than leprosy , our clinicians were able to accurately diagnose three out of 7 patients . Among those there was: chronic inflammatory demyelinating polyneuropathy ( CIDP , n = 2 ) ; and one case of systemic lupus erythematous . All undiagnosed patients returned to their neurological clinic for follow-up . Skin biopsies were obtained from patients who live in Rondonópolis ( Mato Grosso State , Brazil ) , enrolled and diagnosed by professionals from Instituto Lauro de Souza Lima ( Bauru city , São Paulo State , Brazil ) . These specimens comprise 54 skin samples , amongst which 17 patients were diagnosed as MB [borderline borderline ( BB ) = 10 , borderline lepromatous ( BL ) = 2 , LL = 5; distributed as 3 women and 14 men; mean age: 41 . 6±9 . 6] . Thirty seven patients were classified as PB [borderline tuberculoid ( BT ) = 21 and TT = 16; distributed as17 women and 20 men; mean age: 43 . 9±16 . 9] . The sample collection and procedures described in this work were approved by the Oswaldo Cruz Foundation ( FIOCRUZ ) and Instituto Lauro de Souza Lima ( ILSL ) ethics committees . All patients or their parents/guardians signed a written informed consent ( IRB protocol - Fiocruz 151/01 and ILSL 172/09 ) . M . bovis BCG Moreau strain ( obtained from Fundação Ataulpho Paiva , Rio de Janeiro , Brazil ) was cultured , for about 2 weeks , in Middlebrook 7H9 ( Invitrogen , Carlsbad , CA ) containing 0 . 02% glycerol and enriched with 10% ADC Middlebrook and 0 . 5% Tween-80 at 37°C as described elsewhere [32] . Live M . leprae from Instituto Lauro de Souza Lima ( Bauru , São Paulo ) was aseptically cultured in footpads of athymic NU/NU mice , purified and enumerated using methods described previously [33]–[35] . All infections experiments with live M . leprae were conduct at 33°C . A portion of live M . leprae was irradiated with ionizing radiation 10 kiloGray ( Acelétrica Ltda ) . THP-1 cells were purchased from American Type Culture Collection ( ATCC , Rockville , EUA ) and cultivated with RPMI-1640 ( LGC Biotecnologia , Brazil ) supplemented with 2 mM L-Glutamine , 100 U/mL penicillin , 100 µg/mL streptomycin and 10% heat-inactivated FBS ( HyClone Laboratories , Canada ) at 37°C , 5% CO2 . Before infection , cells ( 5×105/well ) were differentiated into macrophage-like cells ( mTHP-1 ) using 80 nM phorbol 12-myristate 13-acetate ( PMA , Sigma-Aldrich ) for 24 h . Then , mTHP-1 were washed with PBS ( 1× ) , which was replaced by fresh antibiotics-free medium . Subsequently , stimulation ( 3 , 24 , and 48 h ) was performed with irradiated or live M . leprae ( Multiplicity of Infection - MOI 10∶1 , 100∶1 ) at 33°C . After infection , total RNA was extracted as described below . PBMC from healthy donors were collected in K3EDTA-tube ( Labor Import Com . Imp . Exp . Ltda , Brazil ) , and isolated by Ficoll-Hypaque density gradient . After centrifugation ( 2 , 500 rpm , 30 min , 25°C ) , the interface containing mononuclear cells monolayer was collected , washed twice with PBS 1× , and cultivated in RPMI 1640 ( LGC Biotecnologia , Brazil ) supplemented with 10% human A/B RH+ serum ( Sigma-Aldrich ) . After isolation , PBMC were carefully selected according to miR-146a genotype ( rs2910164 G>C ) , as described below . Cells were infected with BCG Moreau strain ( MOI 10∶1 ) for 24 hours at 37°C . BCG was used as a surrogate model for M . leprae infections since it is a better inducer of TNF and also because this mycobacteria is able to induce miR-146a expression in vitro ( data not shown ) . The supernatant was collected and detection to TNF levels was evaluated by Enzyme-linked immunosorbent assay kit DuoSet ( R&D Systems , EUA ) according to the manufacturer's protocol . Samples optical density ( OD ) was taken at 450 nm and estimated based on a standard curve , ranging ( 15 . 6–1000 pg/mL ) . Measurements were performed in duplicate . Genomic DNA for the genetic study was extracted from peripheral blood or directly from PBMCs aliquots according to salting-out method as described [36] . RNA , and then DNA , from skin and nerve biopsy specimens were extracted using Trizol ( Invitrogen ) according to the manufacturer's instructions [37] . After DNA extraction , all samples were genotyped for SNPs as described below . Total amount of nucleic acids and purity were measured at NanoDrop ND-1000 ( Thermo Scientific ) instrument . The quality inspection of RNA was tested using agarose gel electrophoresis ( 1 . 2% ) of 200 ng of SYBR Green II-stained RNA visualized at a transiluminator system ( L-Pix Touch , Loccus Biotecnologia ) . Allelic discrimination was performed using TaqMan Genotyping Assay ( Applied Biosystems , CA , USA ) for miR-196a-2 ( rs11614913 C>T ) , miR-146a ( rs2910164 G>C ) , miR-125a ( rs12975333 G>T ) and miR-223 ( rs34952329 *>T ) SNP . DNA ( 10–50 ng ) amplification was performed in a final volume of 5 µL ( 2 . 5 µL of the TaqMan Genotyping Master Mix ( Applied Biosystems ) , 0 . 125 µL of the TaqMan primers and probes ) . For miRNA expression analysis we performed a pooled-RT by using a set of specific stem-loop primers for each target ( miR-146a , RNU44 , RNU48 ) as indicated by the manufactures ( TaqMan , Applied Biosystems ) . Briefly , non-denatured total RNA ( 200 ng ) was incubated with RT primer pool ( 0 . 02× ) , dNTP ( 2 mM ) , Superscript III ( 10 U/µL , Invitrogen ) , RNase inhibitor ( 0 . 253 U/µL , Invitrogen ) and first strand buffer ( 1× ) in a final volume of 15 µL . The cDNA obtained was diluted ( 1∶6 ) and 2 µL were subjected to real-time PCR reaction , at a final volume of 10 µL . For RPL13a and TNF expression , total RNA ( 500 ng ) was reversed transcribed following Superscript III manufacturer's instruction ( Invitrogen ) . Then , 5 µL of diluted cDNA ( 1∶5 ) was amplified by real time PCR using SYBR Green PCR Master Mix ( 1× ) and primers ( 0 . 5 µM ) at the final volume of 20 µL . Both genotyping and miRNA expression were run on a StepOne Plus thermocycler detection system ( Applied Biosystems ) . Specifically for TNF mRNA expression in nerve biopsies , data was retrieved from previous experiment and reanalyzed stratifying patients according to genotypes . Quantitative RT-PCR was performed using Biomark multiplex assays ( Fluidigm , CA ) as previously described [32] . After genotyping , we performed the Hardy-Weinberg equilibrium ( HWE ) analysis by chi-square tests . Then , we determined the genotypic , allelic and minor allele carriers frequencies , in order to perform comparisons between case and control groups . Genotypes and alleles with higher frequency were taken as baseline . The measure of allelic and genotypic association with leprosy was estimated by the Odds Ratio ( OR ) values generated after the application of a logistic regression model as described in detail elsewhere [31] . We also assessed an OR value adjusted for sex , ethnicity and age-at-onset for all comparisons evaluated ( leprosy per se , subgroup PB–MB , reaction per se , and types of leprosy reactions ) . We also performed a case-control analysis using age as a categorical variable , for that analysis we adjusted for sex and ethnicity . Case-control study statistical analysis was performed using the packages genetics and coin from open source software R version 2 . 12 . 2 ( available at http://www . R-project . org/ ) . MiR-146a allele-dose effect ( GC/CC ) was determined by the Cochran–Armitage trend test . The analysis of the family-based transmission/disequilibrium test ( TDT ) was performed with FBAT software , version 2 . 0 . 2c . The TDT allows exploring if miR-146a C-allele is transmitted , from heterozygous parents to its affected child [38] . The amount of transmitted alleles was determined in the software Haploview [39] . We first performed the TDT analysis considering all affected child , regardless of their clinical form . A second analysis was conducted to verify the allele transmission to affected child according to their PB/MB status . To test for statistical significance among subgroup analysis we performed heterogeneity testing determined by Cochran's Q statistic . Gene expression statistical analyses were done using Prism 5 ( GraphPad software ) . Two-tailed Mann-Whitney t-test was applied for two sample group comparisons . For multiple testing , Kruskal-Wallis test was used followed by Dunn's post-test . Data is presented as mean ± SEM , except for ELISA ( median ) . The value of p<0 . 05 was taken as statistically significant .
Allelic , genotypic and carrier frequencies were determined in both cases and controls and they did not deviate from HWE for SNPs miR-146a and miR-196a-2 ( Table 1 ) . The other two tested SNPs ( miR-125a and miR-223 ) proved not polymorphic in our population . No association was detected for miRSNP-196a-2 genotypic or allelic frequencies with leprosy per se . Nevertheless , the polymorphisms of miR-146a gene have a susceptibility effect to leprosy per se for genotypes ( GC ORadjusted = 1 . 44; p = 0 . 04 and CC ORadjusted = 2 . 18; p = 0 . 0091 ) . This effect was also observed for allelic frequencies and C-allele carriers ( ORadjusted = 1 . 47; p = 0 . 03 and ORadjusted = 1 . 56; p = 0 . 008; respectively ) . The genotypic OR values prompted us to investigate if it was directly proportional to the C-allele presence in each genotype ( allele-dose effect ) , which was confirmed by applying the Cochran–Armitage trend test ( χ2 = 96 . 6 , p = 2 . 2×10−16 ) . Interestingly , when we evaluated the influence of age-at-leprosy diagnosis in the association effect of miR-146a , we found a stronger effect in the subgroup correspondent from 25 to 34 years/old ( Figure S1 and Table S3 ) . Furthermore , we performed a comparison between controls and clinical forms , as stratified as PB and MB ( Table S4 ) . Once again , we could not find any association with miR-196a-2 . Nonetheless , for both comparisons ( PB vs . control or MB vs . control ) we observed the risk association of C-allele for miR-146a , which was more prominent in the PB group . Significance levels were maintained in different comparison levels in PB subgroup versus controls rather than MB subgroup ( Table S4 ) . But , the heterogeneity test demonstrated no statistical significance ( p-value = 0 . 40 ) A confirmation of the genetic finding for miR-146a was observed after TDT analysis ( Table 2 ) . Considering leprosy per se , twenty-eight over 41-affected informative patients , received the C-allele indicating its over-transmission ( p = 0 . 003 ) . When we stratified affected individuals according to their PB or MB classification , the TDT for PB patients revealed sixteen over twenty one-affected transmissions ( p = 0 . 01 ) . The number of transmissions of the C-allele for MB affected patients were not significant ( p = 0 . 23 ) ( Table S5 ) . These data confirm that miR-146a , in our replication sample , is associated with leprosy per se , however we could not provide sufficient evidence to infer subtype specificity . In order to evaluate if those miRSNPs could be associated with leprosy reaction episodes , we subdivided only our patient group in ( 1 ) controls , patients without occurrence of reactional episodes and ( 2 ) cases , patients who exhibited only one type of leprosy reactional ( LR ) episodes ( erythema nodosum leprosum , ENL or reverse reaction , RR ) . Those who have experienced both episodes were excluded from analysis . We could not observe any association between miRSNP-196a-2 and leprosy reactions ( data not shown ) . Although we found that CC genotypes showed borderline association with protection to leprosy reactions ( Table S6 ) , and ENL as outcome , no statistical significance was found after adjustment for the covariates gender , ethnicity and age ( Table S7 ) . Previous results have shown that specific pathways associated with pro-mycobacterial profiles that reprogram cellular environment to establish a suitable niche for bacterial survival are dependent on M . leprae viability [40] . So , we asked whether the functional role of miR-146a was dependent on live M . leprae . Irradiated M . leprae did not induce miR-146a expression ( data not shown ) , on the other hand , live M . leprae infection for 3 , 24 and 48 h at two different MOIs ( 10∶1 and 100∶1 ) induces miR-146a expression at 100 bacilli per cell ( Figure 1 ) . MiR-146a expression started at 24 h and was sustained until 48 h of infection . So far , we observed an associated SNP in a gene that was being up-regulated by M . leprae infection . Then , we decided to explore miR-146a expression in skin and nerve biopsy samples trying to correlate genetic , clinical and biological findings . Initially , we determined miR-146a levels in nerve leprosy patients ( L ) and performed a comparison with biopsies from patients with non-leprous ( NL ) neuropathies . We found that miR-146a is more expressed in leprosy nerve biopsies group than in NL biopsies ( Figure 2A ) . The examination of miR-146a expression according to host genotype revealed that carriers of C-allele were able to produce high levels of the mature miRNA ( Figure 2B ) in nerves . We determined miR-146a levels in skin biopsies from leprosy patients and performed a comparison between the MB and PB groups . Our results showed that biopsies from patients in both clinical forms express moderate-to-high levels of this miRNA , but no difference between MB and PB was detected ( Figure 2C ) . Stratification according to Ridley and Jopling ( R&J ) clinical forms ( LL , BL , BB , BT and TT ) did not show any differences in miR-146a levels ( data not shown ) . Furthermore , stratification by the risk allele ( C-carriers ) showed a tendency of augmented miR-146a expression in skin biopsies , although not significant ( Figure 2D ) . Comparisons indicate that miR-146a C-allele seems to induce higher levels of miR-146a , which was also increased in leprosy patients , although no clustering was observed between clinical forms . It was previously reported that miR-146a negatively regulates cytokines in primary peritoneal macrophages of mice , such as TNF [41] . Therefore , at this point , our hypothesis was whether the polymorphism associated with risk ( C-allele ) correlates to lower levels of TNF . For this , we analyzed the expression of TNF in nerve biopsies [32] and stratified according to the genotypes . As shown in Figure 3A , the presence of C-allele is associated with a reduction of TNF expression ( p = 0 . 045 ) , irrespective the disease type ( L or NL ) . Then , we tested if miR-146a genotypes could influence TNF secretion . For that purpose , the cells were either left uninfected or infected with BCG Moreau and we compared infected groups with different genotypes . As shown in Figure 3B , the presence of the C-allele was related with less TNF secretion when comparing it to its control , GG-infected genotype ( p = 0 . 0352 ) .
In this study we showed that a SNP in miR-146a ( rs2910164G>C ) , located in a cytokine cluster ( 5q31 ) associated with autoimmunity [42] , [43] and Crohn's disease [44] , was associated with leprosy susceptibility . Interestingly , live M . leprae up regulates this miRNA and carriers of the risk allele were also expressing more miR-146a . Furthermore , we were able to correlate lower levels of TNF with the presence of the risk allele . We also selected other two candidate miRNA SNPs ( miR-125a , miR-223 ) previously identified as associated with regulation of immune responses [45] , [46] , but neither were polymorphic in Brazilians . The miR-196a-2 were chosen based on their involvement in Crohn's disease [47] . Nonetheless , we could not find any association between miR-196a-2 and leprosy although a previous report provided evidence to a common genetic fingerprint in Leprosy and Crohn's disease [48] . It was reported that miR-146a ( rs2910164 ) GC polymorphisms plays an important role in papillary thyroid carcinoma while CC genotype are linked with risk and the reduction of survival in patients with glioma [49] . Controversial studies concerning susceptibility to cancer were investigated by a meta-analysis . They could not find a pattern between the SNP and the tumor type , conversely , the study pinpointed that there is an association between GG variant genotypes and increased risk of cancer among Asians [50] , maybe reflecting the heterogeneity of the disease . Considering mycobacteria infections , it was demonstrated that the G-allele has an association with pulmonary tuberculosis in different directions in Han ( protection ) and Tibetan ( risk ) populations [51] . Here , we provide consistent evidence of G>C miRSNP-146a associated with leprosy among Brazilians . Using two different study designs , case-control and family-based , we found that the C-allele was strongly associated with susceptibility to leprosy per se and age-at-diagnosis was an important adjustment for the association , which was also suggested previously in leprosy [52] , [53] . For all case-control comparisons , we tested the miRSNP-146a association considering sex and ethnicity with or without ( data not shown ) age as covariate , but the results remain unaltered after the inclusion of age-at-diagnosis correction . However , considering age subsets independently , a stronger association in the early-onset leprosy ( 25–34 years/old ) was detected . This last observation is consistent with the idea that the early-onset may reflect a stronger genetic effect [17] , [52] , [54] . Curiously , the genetic design using leprosy reactions as outcome suggested an association between CC genotypes and LR protection towards protection , although not statistically significant after correction considering or not the covariate age . MiR-146a mature form contributes to the reduction of TNF synthesis by down-regulation of adapter molecules IRAK1/TRAF6 through 3′UTR matching [55] . In THP-1 ectopically super-expressing miR-146a , Boldin and coworkers described that the exacerbated immune response was down-regulated by the reduction of TNF and IL-6 levels . Also , they found an uncontrolled autoimmune profile in miR-146a−/− knockout mice , as the animals were hyper-responsive to LPS challenge , producing high levels of those pro-inflammatory cytokines TNF , IL-6 [41] that was also in agreement with previous reports [56] , [57] and our results here . It was recently shown that M . bovis BCG induces miR-146a expression and regulates TNF levels [58] . In our model , only live M . leprae was able to stimulate miR-146a expression . A recent paper from Siddle and colleagues , identified some SNPs in miRNA genes as markers of expression of quantitative trait loci ( eQTL ) in dendritic cells infected with M . tuberculosis [54] , [59] . In fact , it has been proposed that SNPs along the strands that generate miRNAs can have great impact on both biogenesis of mature miRNA as well as the gain or loss of function of a particular miRNA [25] , [60] . The miRSNP-146a is localized in the precursor strand and involves a shift of G∶U pair to C∶U mismatch . Jazdzewski showed that miR-146a expression was lower in the presence of the C-allele when compared to the G-allele [27] , [28]; confirmed by others reports on cancer [61] , [62] . In our analysis , miR-146a expression in nerve biopsies from leprosy patients revealed a different pattern: C-allele carriers are related with the high levels of the mature miR-146a . In agreement with our findings , it was shown by Kogo and colleagues that miR-146a was highly expressed in carriers of CC genotype than GG in both healthy and tumor tissues from patients with gastric cancer [63] . Also , in lupus a study showed that the presence of the C-allele correlates with increased expression of the mature miR-146a [64] . Nevertheless , we did not observe differences in skin biopsies from PB and MB patients . Perhaps , in this case , it seems that rs2910164 G>C SNP might impact miR-146a expression very early in the progression from latent infection to active disease , since pure neural form could be considered an earlier stage of the leprosy development . We could hypothesize that in the early stages of progression towards active disease , miR-146a expression in the macrophages/dendritic cells may be differentially regulating cytokine secretion and the emergence of T cell specific subpopulations precipitating disease outcome [65] . This controversy in the literature suggests that the presence of this SNP G>C ( rs2910164 ) , and maybe others , may govern the expression of mature miRNAs . Recently it has been shown that miR-21 targeting CYP27b1 , an enzyme that convert the vitamin D pro-hormone to its active form , inhibits the microbicidal vitamin D dependent-pathway [66] . Also , the same study showed that miR-146a was the second most differentially expressed miRNA in lepromatous leprosy skin biopsies [66] , although in our hands , with a high number of samples we detected no difference between the clinical forms of the disease . In summary , we demonstrated the genetic association between miR-146a C-allele with leprosy susceptibility . Our data also suggest that miR-146a was overexpressed in leprosy biopsies and also produced by mTHP-1 infected with live M . leprae . Subjects carrying the risk allele also express high levels of miR-146a which correlates with lowest levels of TNF as readout of the inflammatory responses . | In spite of the successful drug therapy , leprosy is still affecting people worldwide . It is well known that host genetic background influences leprosy development and that genetic variants have been associated with the disease . Therefore we conducted a study to evaluate the role of microRNAs ( miRNAs ) polymorphisms in leprosy . We observed that a polymorphism in miR-146a is associated with the risk to develop leprosy in Brazilians . Based on the analysis of clinical specimens , we found that the genetic variant was correlated with elevated levels of miR-146a and it is also a negative regulator of tumor necrosis factor ( TNF ) , an important inflammatory mediator in the leprosy context . These findings provide tenable evidences that miR-146a is important in the control of gene expression during M . leprae infection and also may contribute with leprosy development by controlling TNF levels . | [
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... | 2014 | Pre-miR-146a (rs2910164 G>C) Single Nucleotide Polymorphism Is Genetically and Functionally Associated with Leprosy |
Experimental visceral leishmaniasis ( VL ) represents an exquisite model to study CD8+ T cell responses in a context of chronic inflammation and antigen persistence , since it is characterized by chronic infection in the spleen and CD8+ T cells are required for the development of protective immunity . However , antigen-specific CD8+ T cell responses in VL have so far not been studied , due to the absence of any defined Leishmania-specific CD8+ T cell epitopes . In this study , transgenic Leishmania donovani parasites expressing ovalbumin were used to characterize the development , function , and fate of Leishmania-specific CD8+ T cell responses . Here we show that L . donovani parasites evade CD8+ T cell responses by limiting their expansion and inducing functional exhaustion and cell death . Dysfunctional CD8+ T cells could be partially rescued by in vivo B7-H1 blockade , which increased CD8+ T cell survival but failed to restore cytokine production . Nevertheless , B7-H1 blockade significantly reduced the splenic parasite burden . These findings could be exploited for the design of new strategies for immunotherapeutic interventions against VL .
Antigen-specific CD8+ T cell responses are essential for protection and clearance of many microbial pathogens . CD8+ T cells recognize peptides which are presented in the context of major histocompatibility complex ( MHC ) class I via T cell receptor ( TCR ) . Rare naïve CD8+ T cells are activated in secondary lymphoid tissues following encounter with dendritic cells expressing peptide/MHCI complexes [1] . Once activated , antigen-specific T cells typically undergo massive expansion , differentiate into effector cells , and acquire the capacity to kill and produce cytokines [2]–[5] . The magnitude of expansion largely depends on the amount of antigen and/or the number of the naïve precursors [6] , [7] . This robust proliferation is then followed by a programmed contraction , which occurs independently of duration of infection , magnitude of expansion or antigen dose [7] . Only 5–10% of the cells present during the peak phase survive the contraction , becoming long-lived memory cells [8] . Memory cells show increased responsiveness and undergo dramatic clonal expansion after reencounter with the same antigen , and thereby confer protection [4] , [9] . This paradigm of T cell differentiation and memory formation has been mainly derived from models of acute viral and bacterial infections , such as Lymphocytic Choriomeningitis Virus ( LCMV; Armstrong strain ) , Vaccinia Virus and Listeria monocytogenes [2] , [7] , [10]–[12] . Yet it may not apply to CD8+ T cell responses generated in the presence of persistent antigen stimulation . Indeed , several degrees of dysfunction , such as delays in expansion and contraction , anergy , and suppression and exhaustion of effector responses , have been observed during chronic diseases [13]–[18] . The inhibitory receptor PD-1 and its ligand B7-H1 have been shown to play an important role in the regulation of CD8+ T cell function in anti-tumour and anti-microbial immunity , and also in the early CD8+ T cell fate decisions [19]–[22] . This pathway appears to induce T cell apoptosis and inhibits proliferation and cytokine production upon TCR engagement in vitro [23] , [24] . In vivo , B7-H1/PD-1 interaction was shown to control the initiation and reversion of anergy , to inhibit T cell functions , and to be the key pathway in the induction of exhaustion [21] , [25] , [26] . This functionally inactivated phenotype has also been described in humans , and shown to be reverted by treatment with blocking antibodies to B7-H1 , thereby restoring the capacity of CD8+ T cells to control disease and decrease viral load [21] . Experimental visceral leishmaniasis ( VL ) represents an exquisite model to study CD8+ T cell responses in a context of chronic inflammation and antigen persistence . In mice , the two main target organs of this disease are the liver and the spleen [27] . While in the liver the infection is self-resolving due to the development of a TH1-dominated granulomatous response , spleens infected with Leishmania donovani , the causative agent of visceral leishmaniasis ( VL ) , stay chronically infected . Together with CD4+ T cells , CD8+ T cells have been shown to be essential for the control of primary infections in various experimental models of Leishmaniasis [28]–[31] . They also appear to be the main mediators of resistance to rechallenge and the major correlates of protection in vaccine-induced immunity against several Leishmania species [30] , [32]–[35] . However , the onset of these responses seems to be delayed: polyclonal CD8+ T cell responses are only detectable 3–4 weeks into the infection in both L . major and L . donovani infected mice [29] , [30] . Due to a lack of knowledge of Leishmania-specific CD8+ T cell epitopes , antigen-specific CD8+ T cell responses in VL have thus far not been studied . In this study , transgenic L . donovani parasites expressing ovalbumin [36] were used to characterize the development , function and fate of Leishmania-specific CD8+ T cell responses during the course of infection . We show that L . donovani parasites evade CD8+ T cell responses by limiting their expansion and inducing functional exhaustion and cell death .
To determine the extent and significance of bystander activation and distinguish it from antigen-specific responses , we first compared the expansion of adoptively transferred OT-I CD8+ T cells in mice infected with wild type ( LV9 ) and Ovalbumin-transgenic ( PINK ) Leishmania donovani parasites . In order to visualize and analyze OT-I CD8+ T cell responses in LV9 infected mice , it was necessary to transfer 105 OT-I CD8+ T cells per mouse . Although at day 4 after infection there were twice as many OT-I CD8+ T cells in the spleen of LV9 infected compared to naive mice , these cells were 20 times fewer than those detected in PINK infected mice ( Figure 1A ) , despite similar splenic parasite burdens ( Figure S1 ) . By day 14 the number of OT-I CD8+ T cells in PINK infected mice was still 10 times higher then in LV9 infected mice , which had returned to baseline levels . At day 3 p . i . , about 30% of the OT-I CD8+ T cells in LV9 infected mice had undergone 3–4 rounds of division , downregulated CD62L and expressed high levels of CD44 . The percentage of dividing and activated cells remained unchanged throughout the course of infection ( data not shown ) . These data indicate that the proliferative response of OT-I CD8+ T cells observed following PINK infection results mainly from antigen-specific stimulation and expansion rather then bystander activation . We next compared the onset , expansion and dynamic of adoptively transferred OT-I CD8+ T cells in PINK infected mice to OT-I CD8+ T cell responses induced after infection with recombinant Vaccinia Virus expressing SIINFEKL ( rVV-SIINFEKL ) . Whereas L . donovani mounts chronic infections in the spleen ( Figure 1B ) , by contrast , VV is an excellent model for acute viral infections and is mainly cleared by prototypic CD8+ T cell response [10] . Adoptive transfer of 104 OT-I CD8+ T cells in rVV-SIINFEKL infected mice , resulted in peak expansion at day 6 , with 9×106 OT-I CD8+ T cells found on average in the spleen . The cells then underwent clonal contraction and by day 21 only about 7% of the cell numbers present during the peak phase were detected ( Figure 1C ) . Following transfer of 104 OT-I CD8+ T cells to PINK infected mice , maximum expansion was reached at day 9 , and the expansion was 100–200 times lower compared to rVV-SIINFEKL infections . By day 14 , the cell number was reduced by 70% . Similar numbers of OT-I CD8+ T cells were found in the spleen until day 28 ( on average 1 . 4×104–1 . 6 104 cells per spleen ) , after which time the number of OT-I CD8+ T cells further decreased , so that by day 41 only 50% of the cells had survived ( Figure 1D ) . We next characterized the phenotype of PINK induced OT-I CD8+ T cell responses based on the expression of CD62L , CD127 , CD44 , CD122 , and CD69 , and compared it to OT-I CD8+ T cell responses induced by rVV-SIINFEKL . As shown in Figure 2A , about 40–60% of the OT-I CD8+ T cells in mice infected with PINK acquired an effector phenotype by downregulating CD62L during the peak expansion , compared to 90% in rVV-SIINFEKL infected mice ( Figure 2B ) . In the latter group , the majority of the cells remained CD62Llo/int and started to slowly upregulate CD62L only after day 14 , suggesting that central memory cells were gradually generated . In contrast , in PINK infected mice 85% of the cells expressed high levels of CD62L at day 14 , and at day 21 , 92% of the OT-I CD8+ T- cells was CD62Lhi . Between day 21 and 28 , cells started down regulating CD62L again and by day 37 , 64% of the cells were CD62Llo/int , thereby re-acquiring characteristics of an effector phenotype . A similar biphasic pattern of expression was observed with the IL-7R ( Figure 2A , central panel ) . CD127 was down regulated early during the response ( day 3–6 ) , but by day 9 about 75% of the cells were CD127+ . After day 21 , cells started down regulating CD127 again and by day 37 , more then the half of the cells were CD127 negative . The timing of this shift in the phenotype of the OT-I CD8+ T cells corresponds to the expansion in splenic parasite load ( Figure 1B ) . CD44 was upregulated from day 3 on ( Figure S2A ) . The vast majority of the cells were CD44hi during the first 41 days of infection . Similarly , cells started upregulating CD122 at day 3 and remained CD122+ until day 21 ( Figure 2A ) . However , after day 21 about 50% of the cells had downregulated CD122 . CD69 has been reported to be transiently expressed during T cell activation and differentiation following antigen-presentation by dendritic cells; however , this molecule has also been shown to be persistently expressed by human and murine T cells in a context of chronic inflammation [37] . CD69 was transiently expressed by all cells present in the spleen of rVV-SIINFEKL infected mice at day 2 , while only 3–9% of the cells were CD69+ between day 6 and 21 ( Figure 2C , right panel ) . In contrast , in PINK infected mice about 40% of the OT-I CD8+ T cells expressed CD69 at day 3 . This percentage slightly increased during the course of infection with the exception of day 14 , when only 20% of the cells were CD69+ ( Figure 2C , left panel ) . We also monitored the proliferation of OT-1 CD8+ T cells by assessing the CFSE dilution over the course of the infection ( Figure S3 ) . Between day 2 and 6 after infection , the cells had undergone several rounds of division , resulting in a complete dilution of the CFSE staining ( Figure S3 ) . All OT-I CD8+ T cells present in the spleen were CFSE− until day 21 . In mice infected with PINK , OT-I CD8+ T cells had already undergone 4–5 rounds of division at day 3 ( Figure S3A ) , with maximal CFSE dilution observed at day 6 . Interestingly , CFSE dilution at day 6 was higher then at days 9 , 14 , 21 , and 28 , indicating that the cells present at these later time points had undergone fewer rounds of division then those present at day 6 . One possible explanation is that effector cells present in the spleen at day 6 have migrated to the liver , the other site of infection . To test this hypothesis , we enumerated the OT-I CD8+ T cells present in the liver during the course of infection ( Figure S3B ) . A maximum of 1500 cells was detected during peak expansion , suggesting that migration of effector cells to the liver was not responsible for the disappearance of these cells from the spleen . This suggests that effector cells that had expanded at day 6 had possibly died and were replaced by newly recruited and activated cells . Notably , these cells did not undergo more then 5–6 rounds of division , even during peak expansion . Taken together , these results show that OT-I CD8+ T cells following PINK infection display a biphasic activation pattern . During the first 9 days of infection , before OT-I CD8+ T cells undergo clonal contraction , they exhibit an effector phenotype; this activation results in limited expansion . After the first wave of activation , the majority of the cells that survived clonal contraction were CD62Lhi CD44hi CD127+ CD122+ and KLRG1− ( data not shown ) . This phenotype is similar to that displayed by central memory cells . By week 3 of infection , cells are reactivated , they downregulate CD62L and CD127 , but this time they start loosing CD122 expression and their numbers begin to wane . Given the unique alternation of surface phenotype of OT-I CD8+ T cell responses during L . donovani infections , we were intrigued to investigate the effector function of those cells . After a brief in vitro restimulation with the SIINFEKL peptide , cells were stained for INFγ , IL-2 , TNFα , Granzyme B , and CD107a . Surprisingly , L . donovani induced a very strong CD8+ T cell effector response , characterized by a high percentage of cells producing cytokines , of which more then the half were coproducing multiple cytokines ( Figure S4A and Figure 3 , left panels ) . 70–90% of OT-I CD8+ T cells expressed IFNγ between day 3 and day 28pi in PINK infected mice ( Figure 3A , left panel ) ; 45–55% of those cells were concomitantly producing TNFα; and 18–22% were co-producing IL-2 ( Figure S4A ) . These percentages were much greater then those observed following rVV-SIINFEKL infection ( Figure 3 , right panels ) . IL-10 was not detected at any time point during infection ( data not shown ) . In mice infected with rVV-SIINFEKL , we could observe a gradual increase over time in the percentage of polyfunctional cells and in the amount of IFNγ produced per cell ( Figure 3 , right panels and Figure S4B ) . This was not the case in PINK infected mice , where CD8+ T cell responses became less functional after the first 3–4 weeks of infection . After day 28pi , most of the cells stopped producing IFNγ , and those that did showed a decrease in the mean fluorescence intensity of the staining ( Figure S4A ) . A similar loss of production was observed for TNFα ( Figure 3B , left panel ) and IL-2 ( Figure 3C , left panel ) : cells secreting these cytokines became progressively less functional from day 14 on . In rVV-SIINFEKL infected mice , 40–60% of the OT-I CD8+ T cells were positive for Granzyme B during the first 2 weeks of infection ( Figure 3D ) . This percentage gradually decreased with the generation of memory cells ( Figure 3D , right panel ) . In contrast , in PINK infected mice the cells displayed a biphasic production pattern: 28–48% of the cells stained positive for Granzyme B during the first 9 days of infection; between day 14 and day 21pi , only 1–2% of the cells were positive ( notably , during this period cells display a central memory phenotype ) ; and by day 28pi , cells had reacquired the capacity to produce Granzyme B . We next measured degranulation by cell surface modulation of CD107a ( LAMP-1 ) . A pattern of expression similar to that seen for Granzyme B was observed for CD107a ( Figure S5 ) . Thus , following PINK infections OT-I CD8+ T cells appear to become dysfunctional over time and express CD69+CD44hiCD62lo/intCD122 lo/neg , however , they maintained degranulation and cytotoxic capacity . These characteristics are very similar to those described for exhausted cells [12] , [38] . Interestingly , functional exhaustion of OT-I CD8+ T cell was not observed in the liver . In this organ , OT-I CD8+ T cells were still producing high levels of IFNγ even at day 41pi ( data not shown ) , suggesting that there might be an organ-specific regulation of CD8+ T cell responses and that exhaustion is most likely the consequence of the suppressive splenic environment . In order to understand this biphasic activation pattern , we decided to asses whether the antigen presenting capacity of splenic DC changes during the course of infection . An in vitro proliferation assay using conventional splenic CD11chi DC purified from infected mice was carried out at different time points of infection . DCs were coincubated with labelled naïve OT-I CD8+ T cells for 72 h at 37°C . T cell proliferation was used as a read out for antigen presentation . Maximal proliferation was observed at day 6 after infection: at this time point on average 11 . 8% of OT-I CD8+ T cells had undergone cell division ( Figure 4B ) . In contrast , DC purified at day 9 , 14 and 21 induced a poor proliferation of OT-I CD8+ T cells ( 2–3 . 7% ) . Their capacity to present antigen increased then again at day 28 ( Figure 4B ) . However , it is important to note that chronic L . donovani infections result in splenomegaly . Spleens start to visibly enlarge between day 14 and day 21 . As the percentage of DC in the spleen remains the same that means that the capacity of DC to present antigen on a population level at day 21 and 28 is greater then appears from our proliferation assay . Thus it appears that , despite the constant presence of the parasite , antigen presentation by DC during the course of infection follows a biphasic pattern , with peaks at day 6 and day 28pi . As adoptively transferred OT-I CD8+ T cells appeared to acquire characteristics of an exhausted phenotype during chronic infection , we proceeded to examine the B7-H1 expression on dendritic cells from the spleen of infected mice . Conventional CD11chi dendritic cells significantly and increasingly upregulated B7-H1 during the course of infection ( Figure 5A ) . This upregulation was concomitant with increased PD-1 expression by OT-I CD8+ T cells ( Figure 5B ) . These data suggest the potential involvement of the PD-1/B7-H1 pathway in the induction of CD8+ T cell exhaustion observed in the spleen of chronically infected mice . We also investigated the expression of the costimulatory molecules CD40 , CD86 and CD80 . CD40 and CD86 were increasingly expressed during the course of infection ( data not shown ) ; in contrast , CD80 failed to be upregulated ( Figure 5A ) . To asses whether the PD-1/B7-H1 pathway was involved in the inhibition of cytokine production and/or cell death of adoptively transferred OT-I CD8+ T cells , we treated chronically infected mice with anti- B7-H1 blocking antibodies . Treatment started at day 15 , when the expression of B7-H1 began to increase; T cell responses and parasite load were monitored . The B7-H1 blockade prevented the dramatic reduction in cell numbers that was observed in untreated mice ( Figure 6A ) . The analysis of cell surface markers of OT-I CD8+ T cells in treated versus isotype control group revealed that cells expressing low/intermediate levels of CD62L may be surviving better in treated mice compared to the isotype control group ( Figure S6A ) . In order to investigate whether the PD-1/B7-H1 pathway could be involved in inhibiting the activation of newly migrated naïve OT-I CD8+ T cells in the spleen during chronic infection , we tested the capacity of ex-vivo purified dendritic cells to induce proliferation of naïve OT-I CD8+ T cells in vitro in the presence or absence of the anti-B7-H1 antibody . B7-H1 blockade did not increase the proliferation of OT-I CD8+ T cells ( Figure 6B ) . Taken together , these results imply that the PD-1/B7-H1 pathway could be either involved in the inhibition of the proliferative capacity or in the induction of cell death of effector CD8+ T cells . Further investigations are needed in order to clarify this mechanism of action . Surprisingly , the in vivo blockade only partially restored cytokine production . In fact , IFNγ production was only transiently restored , but OT-I CD8+ T cells were still increasingly loosing their capacity to produce IL-2 and TNFα ( Figure S6B ) . No differences were observed in the percentage of cells positive for Granzyme B or in the mean fluorescence intensity of the granzyme B staining . Despite this functional impairment , mice treated with anti-B7-H1 antibodies were able to control parasite growth , with the splenic parasite burden reduced by 70% at day 21 , 85% at day 28 , and 87% at day 35 ( Figure 6C ) . These results demonstrate that the PD-1/B7-H1 pathway plays a very important role in the suppression of CD8+ T cell responses during chronic L . donovani infections . We next assessed whether B7-H1 blockade would confer protection against infection with the L . donovani wild type strain LV9 in a non-transgenic model . We therefore treated C57BL/6 mice chronically infected with LV9 with the anti B7-H1 antibody as previously described and monitored the parasite burden at day 21 , 28 and 35 after infection ( Figure 6D ) . B7-H1 blockade significantly reduced the splenic parasite burden at day 21 ( 65% reduction ) , 28 ( 57 . 5% ) , and 35 ( 71 . 4% ) , suggesting that endogenous CD8+ T cell responses were most likely rescued by the blockade . Interestingly , B7-H1 blockade also conferred protection in the liver , but only at day 21 ( 53 . 2% reduction in the parasite burden ) and 28 ( 48 . 8% reduction ) , and was ineffective at day 35 , when parasite growth in this organ was under control and infection had already significantly decreased ( Figure S6C ) . To determine whether CD8+ T cells were the main mediators of protection following B7-H1 blockade , we induced OT-I CD8+ T cell responses by superinfecting chronically infected mice with rVV-SIINFEKL at a distant site . Mice were challenged subcutaneously at day 32 pi with wild type vaccinia virus ( VV ) or rVV-SIINFEKL , and euthanized 2 , 6 , and 9 days later . As expected , rVV-SIINFEKL induced a strong proliferation of OT-I CD8+ T cells ( Figure 7A and Figure S7 ) , whose numbers increased about 40-fold compared to the unchallenged , PINK infected group . Challenge with rVV-SIINFEKL not only induced a proliferative response of OT-I CD8+ T cells , but also restored cytokine production ( Figure 7C and 7D ) . Infection with VV did not have any effect on either proliferation or cytokine production , suggesting that the OT-I CD8+ T cell response was strictly antigen-specific . We next compared the splenic parasite burden of mice infected with PINK to that of mice superinfected with VV or rVV-SIINFEKL ( Figure 7B ) . Challenging mice with wild type VV did not alter the course of infection in the spleen . In contrast , infection with rVV-SIINFEKL reduced the parasite burden by 80% . This suggests that reviving CD8+ T cell responses during chronic L . donovani infections could be a successful strategy for immunotherapeutic interventions .
Our main findings demonstrate that L . donovani is able to evade the attack from CD8+ T cells by suppressing their expansion and effector function . The data show that despite the constant presence of parasites in the spleen , CD8+ T cells responses exhibited a biphasic activation pattern . The first wave of activation led to limited expansion . The second wave resulted in cell death and exhaustion of CD8+ T cells . B7-H1 blockade rescued CD8+ T cell responses from cell death , but failed to completely restore cytokine production . In spite of this , the parasite burden was considerably reduced after treatment , suggesting that maintenance of effector CD8+ T cell responses is crucial for the control of L . donovani infections in the spleen . The adoptive transfer experiments demonstrate for the first time that L . donovani induces CD8+ T cell responses early during infection . We were able to visualize this early response only because we transferred 104 OT-I CD8+ T cells . Physiologically , the number of naturally occurring naïve precursors for a determined epitope is estimated to range between 50 and 1000 cells per mouse [6] , [39]–[42] . However , due to the limited expansion capacity of CD8+ T cells in this model , transfer of such a low number of cells did not allow us to perform an accurate analysis of endogenous CD8+ T cell responses . This might explain why in previous studies the onset of polyclonal responses has been reported to be substantially delayed and could only be detected , mainly in the liver , after 3–4 weeks of infection [30] . Although we transferred the same number of cells with the same epitope specifcity , OT-I CD8+ T cells increased in numbers by only 5 fold in Leishmania infected mice , compared to 900 fold in mice infected with rVV-SIINFEKL . In other infection models , expansions up to 50 , 000-fold were observed [2] , [5] , [39] , [40] , [43] , suggesting that Leishmania induces a very poor CD8+ T cell expansion . When we assessed the antigen-presenting capacity of DC purified from infected animals , we found that even during the early stages of infection , DC were capable of inducing only a weak proliferative response of naïve OT-I CD8+ T cells . This is not surprising , since processing of Leishmania antigens for MHCI presentation has been shown to be TAP-and proteosome-independent [44] , a pathway that is much less efficient then the conventional ER-based , TAP-dependent pathway for Class I presentation . We also noted that OT-I CD8+ T cells present in the spleen at day 9 p . i . had undergone fewer rounds of division then those detected at day 6 p . i . , implying that between day 6 and 9 effector cells had died and were replaced with newly activated CD8+ T cells . This suggests that expansion could also be limited by cell death of effector cells . We also cannot rule out the possibility of defective recruitment of CD8+ T cells into the spleen . Leishmania infections are known to interfere with chemokine expression [45]–[47] , including CCL3 [48] , a chemokine that was recently shown to be involved in guiding CD8+ T cells to sites of CD4+ T cell-dendritic cell interaction [49] . Hence , the limited expansion of OT-I CD8+ T cells in L . donovani infected mice might be due to a combination of several factors , including low antigen load , poor recruitment of CD8+ T cells and/or increased cell death of effector cells . Mechanisms responsible for this poor expansion are currently under investigation . In agreement with the previous literature , OT-I CD8+ T cell expansion was followed by contraction at day 14 despite antigen persistence [7] . This contraction was much steeper then in rVV-SIINFEKL infected mice , suggesting that cells were dying more rapidly . One of the most striking findings was that about 80% of the cells that had survived contraction showed a central memory-like phenotype , by expressing CD62Lhi , CD44hi , CD127+ , CD122+ , CD69− . These cells produced high amounts of IFNγ upon restimulation and the majority were polyfunctional . Additionally , they did not produce Granzyme B . The remaining 20% displayed an effector phenotype ( CD62Llo , CD127− ) , suggesting that effector memory cells were not generated . A similar population of central memory-like cells has been recently observed in mice infected with Trypanosoma cruzi [50] . Despite being capable of antigen-independent survival , this population was shown to be maintained for over a year in the presence of antigen persistence . A recent report suggested a crucial role of T-bet as a molecular switch between central- and effector memory cells [51] , [52] . T-bet deficiency was shown to enhance generation of central memory cells . As T-bet is also involved in the induction of enhanced CD122 expression [53] , and CD122 expression by OT-I CD8+ T cells is gradually decreased during the course of VL , it is possible that this molecule might not be properly induced in Leishmania-specific CD8+ T cells . Another interesting observation was the biphasic activation pattern of OT-I CD8+ T cells , which reflects the variation in the capacity of DC to present antigen during the course of infection . This biphasic pattern can be in part explained by the biology of the Leishmania infections . These protozoan parasites are obligate intracellular pathogens that preferentially reside in macrophages , but they can also be found in other cell types [54] , [55] , including DC [56] . During the first wave of expansion , the majority of the cells capable of cross-presenting Leishmania antigen via MHCI is most likely killed by CTLs so that at the end of contraction very few DC presenting antigen survive and most of the parasites reside in cells that are unable to cross-present antigen . For the second wave of expansion , parasites will have first to be released from those cells in order to be phagocytosed by DC and then killed and processed for antigen presentation to CD8+ T cells . This explains why between d14 and d21 p . i . DC showed a very poor antigen presenting capacity . However , the amount of antigen presented during this period , although little , could still be enough to restimulate a memory response . Thus , OT-I CD8+ T cell responses might be already impaired at this early stage of infection . Indeed , the second wave of activation did not result in expansion , but in functional exhaustion and cell death of the OT-I CD8+ T cells . This dysfunctional response could be a consequence of an intrinsic problem following defective priming and/or could result from a suppressive splenic environment . Although we can not rule out that OT-I CD8+ T cell responses in L . donovani infected mice might also have some intrinsic defects , our data support the second scenario . Indeed conventional CD11chi splenic DC seemed to increasingly express the inhibitory molecule B7-H1 and failed to upregulate the costimulatory molecule CD80 . B7-H1 is constitutively expressed on subsets of macrophages , B-cells and thymocytes , and can be induced on dendritic cells , endothelial and epithelial cells [19] , [57] . Upregulation of B7-H1 on DC has been observed during several chronic infections and in a wide range of tumors [20] , [26] , [58] , [59] . Our results show that in vivo blockade of B7-H1 during chronic L . donovani infection increased the survival of OT-I CD8+ T cells . B7-H1 is thought to inhibit T cell proliferation and cytokine production by ligation with the PD-1 receptor [23] . Through ligation with a yet unknown receptor , B7-H1 can also induce programmed cell death of effector T cells [60] . Increased survival of OT-I CD8+ T cells after B7-H1 blockade could therefore result from restoration of the proliferative capacity or inhibition of induced cell death of effector CD8+ T cells . In contrast to what has been recently reported in the literature [21] , [26] , in vivo blockade of B7-H1 during chronic VL did not completely restore the functional capacity of exhausted OT-I CD8+ T cells . This suggests that suppression of cytokine production by CD8+ T cells during L . donovani infection might be induced by mechanisms other than through the B7-H1/PD-1 pathway . A recent report has demonstrated a synergistic effect between TGFβ and the B7-H1/PD-1 axis in suppressing CD8+ T cell responses [61] . As TGFβ does not seem to play an important role during chronic L . donovani infections [62] , the possibility that IL-10 , which is elevated in both mouse and human VL [63]–[69] , could synergistically act with the B7-H1/PD-1 axis , needs to be investigated . To our surprise , B7-H1 blockade resulted in significant decrease in the parasite burden even if it failed to fully restore IFNγ production . While CD4+ T cells are clearly an important source of IFNγ in VL , recently we have shown that therapeutic intervention with antigen-specific CD8+ T cells in chronically infected mice dramatically reduced the parasite burden [36] , indicating that CD8+ T cells might play a much more important role than previously thought . The current data reinforce these findings by showing that OT-I CD8+ T cells rescued from cell death by blocking B7-H1 or by superinfecting mice with rVV-SIINFEKL resulted in host protection . The mechanism of protection is not clear and might not merely rely on IFNγ production , as only 20% of the OT-I CD8+ T cells were producing low amounts of IFNγ at d35 pi . Nonetheless , most of the cells were granzyme B positive and were degranulating upon restimulation , suggesting that they have retained their cytotoxic capacity . To date there is no evidence that CD8+ T cells can mediate protection against L . donovani through their cytotoxic activity . In summary , this study shows that restoration of dysfunctional CD8+ T cell responses induced by chronic L . donovani infections results in disease control and host protection . This implies that targeting CD8+ T cell responses by therapeutic vaccination could be beneficial against chronic L . donovani infections . Moreover , these findings might provide insights into the development of novel strategies for therapeutic vaccination or other interventions aimed at inducing CD8+ T cell responses , which might circumvent and/or neutralize the immunosuppressive environment of the spleen .
C57BL/6-Tg ( OT-I ) -RAG1tm1Mom mice were purchased from Taconic; B6-Ly5 . 2 congenic mice were obtained from The National Cancer Institute ( Frederick , MD , USA ) , and B6 . 129S7-Rag1tm1Mom/J from The Jackson Laboratory . All mice were housed in the Johns Hopkins University animal facilities ( Baltimore , MD ) under specific pathogen-free conditions and used at 6–8 weeks of age . All experiments were approved by the Animal Care and Use Committee of the Johns Hopkins University School of Medicine . Ovalbumin-transgenic parasites were a gift from P . Kaye and D . F . Smith ( University of York , UK ) and were generated as previously described[36] . Wild type and ovalbumin transgenic Leishmania donovani ( strain LV9 ) parasites were maintained by serial passage in B6 . 129S7-Rag1tm1Mom/J mice , and amastigotes were isolated from the spleens of infected animals . Mice were infected by injecting 2×107 amastigotes intravenously via the lateral tail vein . Hepatic and splenic parasite burdens were determined either by limiting dilutions [31]or by examining methanol-fixed , Giemsa stained tissue impression smears[70] . Data are presented as number of parasites per spleen or as Leishman Donovan Units ( LDU ) . The recombinant vaccinia virus ( rVV ) encoding SIINFEKL ( chicken ovalbumin 257–264 ) was a gift from F . Zavala ( School of Public Health , JHU , Baltimore ) [71] . Mice were infected intravenously or subcutaneously with 2×106 pfu . OT-I/RAG1 mice , transgenic for a T cell receptor specific for chicken ovalbumin 257–264 presented by the MHC class I molecule H-2 Kb , were used as T cell donors . CD8+ T cells were enriched from splenocytes of naïve OT-I/RAG1 animals using magnetic cell sorting ( MACS ) , following manufacturers instructions ( Miltenyi Biotech ) . Naïve CD8+ T cells were then sorted to >98% purity using FACSVantage ( Becton Dickinson ) based on their expression of CD44 and CD62L . After sorting , cells were labelled with CFSE . Briefly , cells were resuspended at 5×107/ml in PBS and incubated with 2 . 5 µg/ml CFSE ( Molecular Probes , USA ) for 10 min . at 37°C . The reaction was stopped by addition of ice cold RPMI . Samples were then analyzed using a FACSDiva ( Becton Dickinson ) for CFSE uptake prior to adoptive transfer . Depending on the experiment , 1×104 or 5×104 cells were injected into the lateral tail vein of B6-Ly5 . 2 congenic mice . Animals were infected the day after with rVV-SIINFEKL and/or with wild type or ovalbumin-transgenic Leishmania donovani . 1×104 sorted naïve OT-I CD8+ T cells were adoptively transferred into B6-Ly5 . 2 congenic mice prior to infection with 2×107 ovalbumin expressing L . donovani amastigotes . At day 32pi mice were superinfected subcutaneously at the base of the tail with 2×106 PFU of Vaccinia Virus ( VV ) or with recombinant VV expressing the SIINFEKL peptide ( rVV-SIINFEKL ) . Animals were sacrificed at d2 , d6 and d9 after infection with rVV-SIINFEKL . OT-I CD8+ T cells were identified by staining splenocytes , lymphnode cells and hepatic mononuclear cells with biotinylated anti-CD45 . 2 antibody followed by PerCP-streptavidin ( BD Biosciences ) . The following antibodies were used to further characterize the OT-I response: APC-conjugated anti-CD44 and anti-CD8 , PE-conjugated anti-CD62L , anti-CD69 , anti-CD122 , anti-CD127 ( all obtained from BD Biosciences ) , and anti-PD-1 ( eBioscience ) . Splenocytes were also stained with APC-conjugated anti-CD11c , FITC-conjugated anti-MHCII , PE-conjugated anti-CD86 , PE-Cy5 . 5 conjugated anti-CD80 , and biotinylated anti-B7H1 and anti-CD40 , followed by PerCP-conjugated streptavidin ( all purchased by BD Biosciences ) . For all surface markers , cells were directly stained following standard protocols . For intracellular staining , splenocytes were stimulated with the SIINFEKL peptide for 4 hours in the presence of Brefeldin A and then stained with biotinylated anti-CD45 . 2 , followed by PerCP-conjugated strepavidin . After fixation , cells were permeabilized and stained with anti-Granzyme B ( Invitrogen ) or APC-conjugated anti-INFγ ( BD Biosciences ) , PE-conjugated anti IL-2 ( BD Biosciences ) , and PE-Cy7-conjugated anti-TNFα ( eBioscience ) . Cells were also stained with PE-conjugated anti-CD107 ( eBioscience ) following the protocol described by Betts et al . [72] . Flowcytometric analysis was performed with a LSRII ( Becton Dickinson ) . One to two millions cells per sample were acquired and analysed with the FACSDiva or with CellQuest software . Spleen of naïve and ovalbumin-transgenic L . donovani infected mice were digested with 0 . 4 mg/ml collagenase D for 30 minutes at room temperature . Conventional CD11chi dendritic cells were then enriched by MACS using CD11c microbeads ( purity 80–85% ) . Dendritic cells were seeded at a concentration of 2×104 cells/well in a 96 wells plate . After negative selection with anti-CD11c microbeads , CD8+ T cells were purified from the spleen of naïve OT-I/RAG1 mice using magnetic cell sorting ( Miltenyi Biotech ) ( 85–90% purity ) . CD8+ OT-I T cells were then labelled using a red fluorescent cell linker PKH26 ( Sigma ) in order to track proliferation . They were then added to the ex-vivo purified dendritic cells at a concentration of 105/well . 1 ng/ml of recombinant human IL-2 was also added to the wells . The proliferation of OT-I T cells was assessed 72 h later by flowcytometry using FACSDiva ( BD Biosciences ) and analysed with the FACSDiva software . Results are expressed as percentage of OT-I CD8+ T cells that have undergone one or more rounds of division . The percentage of cells that entered division when incubated with DCs from a naive animal was subtracted from this value . Antagonistic mouse B7-H1 monoclonal antibody ( clone 10B5 ) was purified on a protein G column from the supernatant of the hybridoma cell line . The hybridoma cell line was a gift from L . Chen ( Johns Hopkins University , School of Medicine , Baltimore ) . Hamster IgG ( Sigma ) was used as isotype control . Mice were treated every 4 days with 100 µg of antibody i . p . The first treatment started at day 15 p . i . Before treatment , antibodies were tested for functionally relevant LPS contamination , by assaying their ability to synergize with IFNγ for the induction of inducible NO synthase [73] . No activity was detectable in such assays ( sensitivity , 1 ng/ml LPS; data not shown ) . Results were analyzed using an unpaired Student t-test . P<0 . 05 was considered significant . All experiments were repeated at least twice . | The protozoan parasite Leishmania donovani is the cause of visceral leishmaniasis , a chronic disease that currently affects 12 million people worldwide . We are interested in understanding the immune mechanisms that can control infection . Preliminary studies suggested that CD8+ T cells can kill parasites and limit disease; however , studying these important killer cells has been hindered , because we do not know what parasite molecules they recognize . To overcome this , we engineered parasites to express ovalbumin . Since many tools exist to track and measure immune cells targeted at ovalbumin , we can now track the specific CD8+ T cell responses that develop upon infection with Leishmania . We found that Leishmania initially induced CD8+ T cells to divide and produce molecules such as IFN-gamma that may help them to kill parasites . However , the CD8+ T cells rapidly lost their effector function and died off as infection progressed . More encouragingly , though , we were able to recover some CD8+ T cell function by blocking immune inhibitory molecules that are induced by parasite infection . The recovered T cells killed parasites and controlled infection . These results are important as they could be exploited for the design of new therapeutic vaccine strategies aimed at inducing protective CD8+ T cells . | [
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] | 2009 | B7-H1 Blockade Increases Survival of Dysfunctional CD8+ T Cells and Confers Protection against Leishmania donovani Infections |
A fundamental observation of comparative genomics is that the distribution of evolution rates across the complete sets of orthologous genes in pairs of related genomes remains virtually unchanged throughout the evolution of life , from bacteria to mammals . The most straightforward explanation for the conservation of this distribution appears to be that the relative evolution rates of all genes remain nearly constant , or in other words , that evolutionary rates of different genes are strongly correlated within each evolving genome . This correlation could be explained by a model that we denoted Universal PaceMaker ( UPM ) of genome evolution . The UPM model posits that the rate of evolution changes synchronously across genome-wide sets of genes in all evolving lineages . Alternatively , however , the correlation between the evolutionary rates of genes could be a simple consequence of molecular clock ( MC ) . We sought to differentiate between the MC and UPM models by fitting thousands of phylogenetic trees for bacterial and archaeal genes to supertrees that reflect the dominant trend of vertical descent in the evolution of archaea and bacteria and that were constrained according to the two models . The goodness of fit for the UPM model was better than the fit for the MC model , with overwhelming statistical significance , although similarly to the MC , the UPM is strongly overdispersed . Thus , the results of this analysis reveal a universal , genome-wide pacemaker of evolution that could have been in operation throughout the history of life .
Genome-wide analysis of distances between orthologous genes in pairs of organisms from a broad range of taxa belonging to all three domains of life ( bacteria , archaea and eukaryotes ) revealed striking similarity between the distributions of these distances . All these distributions are approximately lognormal , span a range of three to four order of magnitude and are nearly identical in shape , up to a scaling factor [1]–[3] . Although many different explanations are possible of this remarkable conservation of evolutionary rate distribution across the entire spectrum of life , the simplest underlying model is that all genes evolve at approximately constant rates relative to each other , i . e . the changes in the gene-specific rates of evolution are strongly correlated genome-wide . This general model of evolution can be denoted Universal PaceMaker ( UPM ) of genome evolution: all genes in evolving genomes , in each evolving lineage , change their evolutionary rate ( approximately ) in unison although the pacemakers of different lineages need not to be synchronized . The existence of UPM is compatible with the considerable amount of available data on fast-evolving and slow-evolving organismal lineages , primarily different groups of mammals [4] , [5] . Conceivably , lineage-specific accelerations and decelerations of evolution can be caused by changes in the effective population size , and such rate changes are indeed expected to equally affect all genes in evolving genomes . The evolutionary rate has also been linked with other biological features of animals that are collectively denoted life history [5] . For instance , a genome-wide comparison of the evolutionary rates in the human and mouse lineages has shown that the number of fixed mutations per unit time is about twofold greater in rodents than it is in primates , with the implication that a lineage-specific , genome-wide change of evolutionary rate occurred after the separation of these lineages [6] . In the same vein , a genome-wide analysis of ratios between the evolutionary rates of orthologous genes in triplets of related bacterial , archaeal and mammalian species revealed near constancy of these ratios , with only a small percentage of gene-specific deviations that were attributed to functional diversification of individual genes [7] . A systematic study of densely populated phylogenetic trees for 44 mammalian genes has demonstrated clade-specific slowdown of evolution occurring independently in several orders including primates and whales [8] . Multiple studies of mitochondrial DNA evolution that used extensive samples from numerous taxa also detected consistent lineage-specific rates that differed by as much as an order of magnitude between animal taxa [9] , [10] . However , in other analyses , striking differences between lineages in the relative rates of evolution of different genes have been discovered , casting doubt on the universality of lineage-specific rates , leading to the idea of ‘erratic evolution’ [11] , [12] . The plausibility of the UPM notwithstanding , the genome-wide correlations between the evolutionary rates of individual genes also could be explained within the concept of molecular clock which is one of the central tenets of molecular evolution . In 1962 Zuckerkandl and Pauling discovered that the number of differences between homologous proteins is roughly proportional to the divergence time separating the corresponding species [13] , [14] . This phenomenon became known as Molecular Clock ( MC ) and has been validated by multiple independent observations [15]–[18] . The MC is the basis of molecular dating whereby the age of an evolutionary event , usually the split between lineages ( such as for example humans and chimpanzee ) , is estimated from the sequence divergence using calibration with dates known from fossil record [19]–[22] . From the phylogenetic point of view , when genes evolve along a rooted tree under the MC , branch lengths are proportional to the time between speciation ( or duplication ) events and the distances from each internal tree node to all descendant leaves are the same ( ultrametric tree ) up to the precision of the estimation ( the latter being determined by sampling error which is inevitable in comparison of finite-length sequences ) . Over the 50 years that elapsed since the seminal finding of Zuckerkandl and Pauling , the MC has been shown to be substantially overdispersed , i . e . the differences between the root to tip distances in many or most subtrees of a given tree usually greatly exceed the expectation from sampling error , under the assumption of a Poisson mutational process [23]–[26] . Notably , the overdispersion of the MC has been shown to be lineage-specific: the MC in lineages with large effective population sizes is overdispersed to a greater extent than the MC in lineages with small populations implying that deviations from the MC are controlled by selection [27] . The demonstration of the overdispersion of the MC inspired the relaxed MC model which is a compromise between an unconstrained tree with arbitrary branch lengths and an MC tree [28] , [29] . Under the relaxed MC , the evolutionary rate is allowed to change from branch to branch but this change is presumed to be gradual so that related lineages evolve at similar rates . The relaxed MC model underlies most of the modern methods of molecular dating . The strict MC implies that all orthologous genes present in a group of organisms and sharing the same evolutionary history evolve in a fully coherent manner even if at different rates . Indeed , if the divergence between gene sequences is solely determined by the divergence time and gene-specific evolution rate , phylogenetic trees reconstructed from different genes will have the same topology and nearly identical branch lengths up to a scaling factor which is equal to the relative evolution rate . Under the MC model , the differences between the corresponding branch lengths in different gene trees are due solely to the sampling error which arises from stochastic factors and is expected to be uncorrelated between trees . The relaxed MC model allows greater , non-random deviations in the lengths of corresponding branches but to our knowledge , the possibility that these evolution rate changes are correlated between genes has not been explicitly considered . The MC implies the constancy of gene-specific relative evolution rates , with deviations caused by overdispersion . However , the inverse is not true: the deviations of the absolute evolution rates from the clock could be arbitrarily high ( hence no MC ) but , if they apply to all genes in the genome to the same degree , the relative evolutionary rates would remain approximately the same throughout the entire course of evolution and in all lineages . In other words , the conservation of the evolutionary rate distribution follows from a model of evolution that is more general and less constrained than the MC , namely the UPM model . Here we sought to determine which of the two models of gene evolution , the MC and or the UPM , better fits the empirical data . To this end , we performed comparative analysis of phylogenetic trees for a genome-wide set of prokaryotic gene families and compared the goodness of fit for the two models . The results show that the UPM model is a better fit than the MC model for the evolution of prokaryotes . These findings are compatible with the previously observed accelerations and decelerations of evolution in individual evolving lineages . However , we show that synchronous , genome-wide change of evolutionary rates is a universal trend of genome evolution that appears to pervade the entire history of life .
Our data set consisted of the “forest” of phylogenetic trees reconstructed for 6901 orthologous gene families representing 41 archaeal and 59 bacterial genomes [30] ( see Supporting Text S1 ) . Although horizontal gene transfer is widespread in the evolution of prokaryotes [31] , [32] , the tree-like statistical trend is detectable in the genome-wide data set and moreover dominates the evolution of ( nearly ) ubiquitous gene families [30] , [33] . We encapsulate this trend in a rooted supertree ( ST ) that reflects the prevalent vertical descent in the evolution of archaea and bacteria ( see Supporting Text S1 ) . Each individual original gene tree ( GT ) is compared to the ST and reduced to the maximum agreement subtree ( MAST ) , i . e . the largest set of leaves whose phylogeny fits the ST topology . Removal of discordant nodes and edges leads to collapse of several edges of the original GT into a single edge ( Figure 1 ) ; then , the length of the newly created GT edge is the sum of the original contributing GT edges . Likewise , when a GT is mapped to the ST , several adjacent ST edges could correspond to a single edge in the reduced GT , forming a composite edge . Under both the MC and the UPM models , we assume that the lengths of the ST edges determine the expected lengths of the corresponding GT edges . For the MC model , edge lengths correspond to time intervals between speciation events , the ST is strictly ultrametric , and gene-specific evolutionary rates are measured in substitutions per site per time unit . Under the UPM model , edge lengths represent arbitrarily defined “ticks” of the universal pacemaker ( internal time ) , and gene-specific evolutionary rates are measured in substitutions per site per pacemaker unit of internal time . Formally:where li , k is the length of the i-th edge of the k-th GT , tj is length of the j-th ( possibly composite ) ST edge corresponding to the i-th edge of the k-th GT , rk is the gene-specific evolution rate , and εi , k is the multiplicative error factor for the given edge . We further assume that the error is random , independent for branches both within and between GTs , and comes from a lognormal distribution with the mean of 1 and an arbitrary variance , translating to a model with an additive normally distributed deviation in the logarithmic scale . Because the distributions of evolutionary rates tend to follow symmetric bell-shaped curves in log scale [3] , [34] , the assumption of a multiplicative , log-normally distributed deviation seems natural . First , we seek to find the set of ST edge lengths t and gene rates r that provides the best fit to the entire set of GTs . Under the assumption of a normally distributed deviation , the likelihood function for the set of GTs given t and r iswhere n is the total number of edges in the set of GTs and E2 is the sum of squares of deviations between the expected and observed edge lengths in the logarithmic scale:where the summation for i is done over the edges of a given GT and the summation for k is done over all GTs ( see Supporting Text S2 ) . Thus , finding the maximum likelihood solution for {t , r} is equivalent to finding the minimum of E2 . For the MC model , the ST edge lengths t are constrained by the ultrametricity requirement , whereas for the UPM model , ST edge lengths are unconstrained . For the analyzed set of 100 genomes , there is a choice of several possible ST topologies , produced using different methods ( see Methods and Supporting Figure S1 ) . We mapped all original GTs onto each of these STs and obtained reduced GTs that corresponded to the respective MASTs . The GTs that yielded MASTs with fewer than 10 leaves were discarded . The ST topology derived from the concatenated alignments of ribosomal proteins provided the maximum total number of leaves in the resulting set of reduced GTs and accordingly was chosen for further analysis . Altogether , we obtained 2294 reduced GTs with MAST size greater or equal to 10 species including 44 , 889 leaves and 82 , 896 edges . This set of trees was fit to an ultrametricity-constrained ST ( MC model ) and an unconstrained ST ( UPM model ) ( Table 1 , see Supporting Text S3 for details ) . We then compared the MC and UPM models in terms of the goodness of fit to the data . Obviously , the residual sum of squares is lower for the UPM model because it involves independent optimization of all 198 ST edge lengths , whereas under the MC model the edge lengths are subject to 99 ultrametricity constraints . To account for the difference in the numbers of degrees of freedom , we employed the Akaike Information Criterion ( AIC ) and the Bayesian Information Criterion ( BIC ) to compare the MC and UPM models . Under the assumption of normally distributed deviations:andwhere E2MC and E2UPM are the residual sums of squares for the MC and UPM models , respectively , n is the total number of GT edges and Δd is the difference in the number of parameters optimized in the process of fitting ( in our case Δd = −99 ) . Because lower AIC values correspond to better quality of fit , negative ΔAIC would indicate preference for the MC model whereas a positive ΔAIC would indicate support for the UPM model . The relative likelihood weight of the suboptimal model can be estimated as 1/exp ( |ΔAIC|/2 ) . The same calculations were repeated for smaller , more conservative subsets of gene families with MAST>20 and MAST>30 and also using BIC to compare the fit to the UPM and MC models ( Table 1 ) . Overall , the results presented in Table 1 reveal overwhelming support of the UPM model over the MC model . The only exception is the ΔBIC value for MAST>30 that weakly supports the MC model . This outcome is predictable given the much larger number of parameters in the UPM model , the small number of trees in this subset and the heavier penalty that BIC imposes on parameter-rich models [35] . Thus , the results show that the evolutionary rates tend to change synchronously for the majority ( if not all ) of the genes in evolving genomes although the rate of the UPM relative to the astronomical time differs for different lineages . The results of this analysis show that the apparent genome-wide constancy of the relative rates of gene evolution across vast spans of life's history ( Figure 2A ) is not a trivial consequence of MC but at least in part results from a distinct , fundamental evolutionary phenomenon , the UPM ( Figure 2B ) . The difference between the UPM and MC models is highly significant but small in magnitude . Root mean square deviation ( r . m . s . d . ) of GT edges from the expectations derived from UMP ST is large ( a factor of 2 . 45 ) and only slightly less that the r . m . s . d for the MC ST ( a factor of 2 . 48 ) . Thus , similar to MC , the UPM appears to be substantially overdispersed . To assess the robustness of the finding that UPM fits the GTs better than MC , we isolated the contributions of individual trees to the E2MC and E2UPM ( E2MC , k and E2UPM , k respectively ) , took 1000 bootstrap samples of the set of GTs and computed ΔAIC values for each sample . All 1000 ΔAIC values obtained for the resampled sets were positive ( in the range of 1511 to 2147 ) , providing 100% support to the superiority of the UPM model and ensuring that this result is consistent for the majority of the GTs and is not determined by a small number of strongly biased trees ( see Supporting Text S3 and Supporting Figure S2 for details ) . The distribution of the E2MC , k/E2UPM , k ratios ( Figure 3 ) shows a strong bias toward values greater than unity ( 73% of the GTs ) , supporting the robustness of this result . The E2MC , k/E2UPM , k ratio characterizes the degree to which the k-th GT favors the UPM model . Linear model analysis shows that this value is significantly and independently influenced by the average goodness of fit to the ST ( p-value ≪0 . 001; Figure 4 ) , the fraction of the original GT leaves remaining in the MAST with ST ( p-value ≪0 . 001; Supporting Figure S3 ) and the number of the original GT leaves ( p-value ≪0 . 001; Supporting Figure S3 ) . Thus , the GTs that retain a greater number of leaves in the MAST , fit the ST better and are wider distributed among prokaryotes , typically show the strongest preference for the UPM model over the MC model . These three factors together explain ∼9% of the variance in ln ( E2MC , k/E2UPM , k ) . Neither the relative evolution rate nor the functional class of the gene significantly impact the degree of preference of UPM over MC ( see Supporting Text S3 and Supporting Figure S3 for details ) . Interpreting these findings in terms closer to biology , widely-distributed genes that are subject to relatively little horizontal transfer or sporadic changes of evolution rate that reduce the fit to ST appear to make the greatest contribution to the UPM . These observations imply that the UPM is indeed a fundamental feature of genome evolution , at least in prokaryotes . The distribution of estimated relative evolution rates ( Figure 5 ) spans values within a range slightly greater than an order of magnitude ( 0 . 26 to 4 . 58 ) . This range is considerably more narrow than the range of rates measured over short evolutionary distances [3] , [34] . Accelerations and decelerations of the UPM are likely to average out over long intervals of evolution , reducing the observed differences between genes . A logical extension of the UPM is a Multiple PaceMakers ( MPM ) whereby a number of uncorrelated pacemakers ‘guide’ their own sets of trees . In the extreme case , the number of PMs is equal to the number of GTs so that the individual GTs would be completely uncorrelated . We sought to explore this case in order to determine how well such a degenerate MPM ( dMPM ) model fits the data compared to the UPM and MC . Formally , under the basic assumptions of this work , the log likelihood of dMPM is infinite because the E2 value is estimated as the sum of squared differences between the observed and the expected edge lengths . Under dMPM , each edge is equal to its own expectation sothat E2 = 0 . However , this logic assumes that the tree edge length is measured precisely and is not subject to any error , whereas the E2 value is dominated by deviations of individual GTs from the universal standard ( MC or UPM ) . This assumption is obviously unrealistic , so to assess the likelihood of the dMPM , one needs to introduce the edge length estimate error explicitly . To obtain the lower limit on the E2 value induced by the inherent sampling fluctuations , one should note that the sum of the lengths of the 49 , 981 edges in 967 trees ( MAST size ≥20 ) is 13 , 018 . 5 ( substitutions per site ) , on average 0 . 26 per edge . With the typical prokaryotic protein length being ∼200 amino acids [36] , this translates into the average of ∼52 substitutions per tree branch . Assuming that substitutions are generated by a Poisson-type random process , one expects the standard deviation of approximately and the “mean” error of the observed value on the order of ( 52+ ) /52 = 1 . 14 or 0 . 13 log units per branch . Multiplying the square of this value by 49 , 981 edges , we obtain the E2 value estimate of 843 . 0 , much lower than 35065 . 0 for UPM . It should be noted that the use of the average gene length and the average number of substitutions per branch comprises the ‘best-case scenario’ because variations in both would necessarily introduce larger deviations which would increase the E2 value . To calculate the ΔAIC value , one needs to obtain the difference in the degrees of freedom between the UPM and dMPM models . The UPM model uses the estimates of 198 individual edge lengths in one UPM tree plus 967 GT rates; the dMPM model requires 967±198 edge length estimates and no GT rates , yielding Δd = −190 , 301 . Plugging these values into the equation for ΔAIC , one gets the difference of −194 , 269 in the UPM-dMPM comparison . Thus , the dMPM model is less likely than the UPM model by 83 , 370 orders of magnitude , an obvious indication that the assumption of completely uncorrelated rate changes does not fit the data . More specifically , the data would support no more than 476 pacemakers for 967 GTs under ideal conditions ( each GT follows its PM perfectly , so the E2 value remains to be solely determined by sampling fluctuations ) . Thus , the actual number of distinct pacemakers is expected to be much lower . The results of the genome-wide comparison of phylogenetic trees of prokaryote genes described here show that the UPM model fits the data substantially better than the MC model . These findings have no bearing on the validity of the MC but show that a more general conservation principle ( the UPM ) is sufficient to explain the observed correlations between gene-specific evolutionary rates . It seems a natural possibility that UPM is instigated by shifts in population dynamics of evolving lineages , with changes affecting all genes in the same direction and to a similar degree . In principle , UPM reflects the well-known phenomenon of lineage-specific acceleration-deceleration of evolution . However , to our knowledge , the previous studies on this phenomenon have focused primarily on mammals and to a lesser extent other vertebrates [4] , [5] . Here we show that the UPM can explain the correlations between the evolutionary rates of prokaryote genes on the whole genome scale and over time intervals that span effectively the entire history of life on earth . The discovery of the UPM opens up several areas of further inquiry . We show here that an unconstrained model of evolution ( dMPM ) does not fit the data but it remains to be determined whether or not distinct pacemakers govern the evolution of different classes of genes . The biological connotations of the UPM are of major interest . Mapping UPM shifts to specific stages of the evolution of life , changes in the life style and population structure of organisms as well as to the geological record could become an important direction of future research .
Three distinct supertrees ( STs ) were tested for the purpose of representing the vertical inheritance trend in the analyzed set of GTs . The first supertree ( ST1 ) was from [30] ( originally computed using the CLANN program [37]; the second supertree ( ST2 ) was computed using the quartet supertree method [38] for all species quartets in the complete set of GTs the third supertree ( ST3 ) was derived from a tree of concatenated sequences of ( nearly ) universal ribosomal proteins [39] . Maximum Agreement Subtrees ( MAST ) between the supertree ( ST ) and any given gene tree ( GT ) were computed using the agree program of the PAUP* package [40] . The set of MASTs with the analyzed GTs was computed for each of these STs , yielding a total of 43 , 068 MAST leaves for ST1 , 43 , 411 MAST leaves for ST2 and 44 , 889 MAST leaves for ST3 ( MAST ≥10 for each ST ) . Accordingly , ST3 was used for all further analyses as the topology that best represented the entire set of GTs . To perform the LS optimization of the ST edge lengths and the GT relative evolution rates , we used the function fmin_slsqp ( ) that is part of the scipy . optimize package of Python which minimizes a function using sequential least squares programming . The function also adopts a set of constraints that are necessary for the calculation . In both the MC and the UPM models , both the ST edges and the GT rates were constrained to positive values . For the UPM model , the distances from a node to any leaf in a subtree under that node were set equal for all subtrees . It can be shown by induction that this constraint implies an ultrametric tree . Thus , we have a constraint for every internal node; in a rooted binary tree with m leaves , there are m−1 such nodes . Consider a rooted supertree ( ST ) with a fixed topology . The ST encompasses a set of edges e defined by the ST topology and a set of unknown edge lengths t . Consider a set of unrooted GTs reduced to MAST with the given ST . Each GT encompasses a set of edges with known edge lengths and an unknown gene-specific evolution rate ( bk , lk and rk for the k-th GT , respectively ) . Each edge of each GT uniquely maps to an ST path ej , that is a subset of adjacent edges in the ST ( bk , i≡ej where ej⊆e for the i-th edge of the k-th GT ) . Let be the length of the path ej . We assume that the length of the i-th edge of the k-th GT is related to the length of the corresponding ST path ej:where εi , k is the multiplicative deviation factor for the given edge . We further assume that the deviation is random , independent for branches both within and between GTs , and comes from a lognormal distribution with the mean of 1 and an arbitrary variance , translating to a model with an additive normally distributed deviation in the logarithmic scale ( i . e . ln εi , k∼N ( 0 , σ2 ) ) . Given t and r , the expectation for the logarithm of the length of the i-th edge of the k-th GT is:and the likelihood of observing the length li , k is:where E2i , k = ( ln li , k−ln tj−ln rk ) 2 . For all observed edge lengths in all GTs ( l ) , the likelihood function isIn the logarithmic scale:where n is the total number of GT edges ( ) . Designating the residual sum of squares and substituting the estimate for σ2for large n , we obtain:Because n is constant for a given data set , finding the maximum of L ( l | t , r ) is equivalent to finding the minimum of E2 . Least Squares ( LS ) is called linear if the residuals are linear for all unknowns . Linear LS can be represented in a matrix format which has a closed form solution ( given that the columns of the matrix are linearly independent ) . However , our formulation requires taking logs over sums of unknowns in the case where a GT edge corresponds to a path in ST ( ) . Then , the problem becomes non-linear with respect to LS and can be solved only using numerical algorithms where the solution is obtained by iteratively refining the parameter values . This approach requires supplying initial values for the parameters . The goodness of the initial value estimation is critical for the convergence time of the iterative method and the risk of being trapped in local maximum points . We employed the following strategy for determining the initial values: For each ST edge , we computed the mean value of the sum over all GT edges that uniquely correspond to the given edge . Therefore , if we assign one gene a specific rate value ( e . g . the length of some edge ) , we obtain initial rate values for all genes . It can be easily shown that , if there are no errors in rates ( i . e . σ2 = 0 ) , the above procedure yields the accurate ( ML ) values for all unknowns . | A central concept of evolution is Molecular Clock according to which each gene evolves at a characteristic , near constant rate . Numerous studies support the Molecular Clock hypothesis in principle but also show that the clock is indeed very approximate . Genome-wide comparative analysis of phylogenetic trees described here reveals a distinct , more general feature of genome evolution that we called Universal Pacemaker . Under this model , when the rate of evolution changes , the change occurs synchronously in many if not all genes in the evolving genome . In other words , the relative rates of gene evolution remain constant across long evolutionary spans: if a gene is slow relative to the rest of the genes in the given lineage , it is always slow , and if it evolves fast , it is always fast . We show here that the Universal Pacemaker model fits the available data much better than the traditional Molecular Clock model . These findings are compatible with the previously observed accelerations and decelerations of evolution in individual lineages but we show that synchronous , genome-wide change of evolutionary rates is a global feature of genome evolution that appears to pervade the entire history of life . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] | [
"biology",
"computational",
"biology",
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"biology"
] | 2012 | Universal Pacemaker of Genome Evolution |
The protein-folding chaperone Hsp90 has been proposed to buffer the phenotypic effects of mutations . The potential for Hsp90 and other putative buffers to increase robustness to mutation has had major impact on disease models , quantitative genetics , and evolutionary theory . But Hsp90 sometimes contradicts expectations for a buffer by potentiating rapid phenotypic changes that would otherwise not occur . Here , we quantify Hsp90’s ability to buffer or potentiate ( i . e . , diminish or enhance ) the effects of genetic variation on single-cell morphological features in budding yeast . We corroborate reports that Hsp90 tends to buffer the effects of standing genetic variation in natural populations . However , we demonstrate that Hsp90 tends to have the opposite effect on genetic variation that has experienced reduced selection pressure . Specifically , Hsp90 tends to enhance , rather than diminish , the effects of spontaneous mutations and recombinations . This result implies that Hsp90 does not make phenotypes more robust to the effects of genetic perturbation . Instead , natural selection preferentially allows buffered alleles to persist and thereby creates the false impression that Hsp90 confers greater robustness .
Previous work in diverse eukaryotes has demonstrated that inhibition of the protein-folding chaperone Hsp90 reveals previously hidden phenotypic effects of standing genetic variation ( see “buffering” in Fig 1A ) [1–6] . This result prompts an important question [7–10]: does Hsp90 increase an organism’s robustness to genetic perturbation ? That is , by helping mutant proteins to fold , does Hsp90 buffer the phenotypic consequences of many new mutations that would otherwise have effects ? An alternative possibility is that Hsp90-buffered mutations are rare , yet appear more prevalent in nature because stabilizing selection does not purge such mutations , whereas it efficiently purges other mutations that have immediate ( non-buffered ) effects on phenotype [7] . Understanding robustness is crucial for understanding evolutionary processes [10–12] as well as complex disease ( i . e . , why certain mutations are associated with disease states in some genetic backgrounds but not others ) [13] . Furthermore , the potential to modulate robustness by inhibiting Hsp90 presents an intriguing strategy to treat cancer [14–16]; because tumors have increased mutation rates , they might be particularly reliant on proteins that buffer the effects of mutation [17 , 18] . Although discussions of mutational robustness ( sometimes termed genetic canalization ) often feature Hsp90 [5 , 15 , 19–24] , the aforementioned hypothesis that Hsp90-buffered variation is rare but accumulates in natural populations due to selection [7–10] has not been excluded . There is evidence that Hsp90 sensitizes cells to , rather than buffers , the effects of some genetic perturbations ( see “potentiation” in Fig 1B ) [6 , 25 , 26] . Genetic variants with effects that are more pronounced when Hsp90 is present are particularly common in studies of populations that recently underwent adaptive evolution [25 , 26] , reinforcing the hypothesis that natural selection filters which types of Hsp90-by-genotype interactions exist in nature . To distinguish the “robustness” hypothesis from the “selection” hypothesis , we study how Hsp90 modifies the phenotypic effects not only of standing genetic variation but also of new mutations that have experienced highly reduced selection pressure .
To study new mutations , we utilized 94 Saccharomyces cerevisiae mutation accumulation ( MA ) lines created previously [27] . These yeast lines were all founded by the same ancestral diploid strain . To produce each MA line , independent derivatives of this ancestral strain were bottlenecked 100 times each through single colonies that were randomly chosen—irrespective of colony size—in order to relax selection on growth rate and other fitness-related traits [27] . Because the lines were propagated asexually , recessive mutations were shielded as heterozygotes , further relaxing selection . During the repeated bottlenecking , which was carried out for an estimated 2 , 062 generations [27] , each line accumulated on average ~4 single-nucleotide mutations per haploid genome as well as a greater number of mutations in simple-sequence repeats [28 , 29] . The single-nucleotide mutations do not show a signature of selection; for example , the ratio of synonymous to non-synonymous substitutions does not deviate from random expectation [28] . A previous study demonstrated that the MA lines contain mutations that affect the morphologies of individual cells [9] . As in the previous study , we used haploid derivatives of the MA lines to assay the effects of mutations absent any influence of dominance [9] . Our expectation was that many of these lines would contain mutations with Hsp90-dependent effects on single-cell morphology . Although Hsp90 mediates the folding of only a subset of “client” proteins [30] , Hsp90 interactions may be indirect , mediated through other proteins connected to Hsp90 via a protein-protein network . We used high-throughput microscopy and automated image analysis [31] to quantify how Hsp90 inhibition changes variation in single-cell morphology between MA lines . We quantified differences in morphological variation using a statistical approach [9] that detects significant increases or decreases in phenotypic variance upon Hsp90 inhibition ( corresponding , respectively , to buffering or potentiating roles of Hsp90 ) . Detecting changes in phenotypic variance that are specifically due to Hsp90 inhibition requires an experimental design ( Fig 2 ) that accounts for the other factors that might affect phenotypic variation . Measuring phenotypic variance is far less common than studying phenotypic averages and presents a unique set of challenges [32] . Therefore , we describe our approach in some detail . We inhibited Hsp90 using geldanamycin ( GdA ) , a small-molecule inhibitor that binds the ATP-binding site of Hsp90 , rendering it unable to perform its cellular function [33] . This drug is commonly used to inhibit Hsp90 in diverse species [1–4] , including yeast [6 , 34]; GdA and its analogs have also been administered in human clinical trials to treat cancers [14 , 35 , 36] . Previous studies of Hsp90-mediated buffering in yeast used GdA concentrations ranging from 5 μM [6] to 200 μM [34] . We performed the majority of our experiments at 8 . 5 μM GdA because this concentration has minimal effects on the growth rate and lag duration of the yeast strains we study ( S1 Fig ) . In each step ( cell growth , cell staining , microscopy ) of each replicate experiment in this study , cells grown in Hsp90-inhibited ( 8 . 5 μM GdA ) and control conditions were studied side by side ( Fig 2A ) . The effects of GdA are therefore not confounded with replicate effects , which we estimated and removed using linear modeling . In each replicate , cells were grown to saturation followed by exponential growth in 96-well plates for 6 h to a density of approximately 1 x 106 cells/mL . Then cells were fixed in 4% paraformaldehyde and stained with dyes that label cell-wall components and nuclear DNA . Stained cells grown in control or inhibited conditions were mounted side by side , in duplicate , on 384-well microscopy plates . High-throughput microscopy and morphometric analysis [31] typically yielded between 100 and 500 quantified cell images from each cell-cycle stage in each strain in each replicate experiment ( S1 Fig ) . In addition to variation between replicate experiments , there is another source of variation that must be considered when estimating phenotypic variance between yeast strains: cell-to-cell variation within each yeast strain . Hsp90 inhibition has been shown to increase nongenetic heterogeneity for some single-cell morphological features [34] , so the common statistical assumption of homogeneous within-group variances is likely to be violated . Therefore , for each phenotype , we partitioned the within-strain variation and between-strain variation , as in previous work [9] , with separate within-strain variance terms for Hsp90-inhibited and control conditions by using a Bayesian mixed-model approach based on Markov chain Monte Carlo ( MCMC ) sampling , implemented using the R package MCMCglmm [37] . Using the approach outlined above ( and in Fig 2 ) , we quantified between-strain variation in 29 principal components ( PCs ) of MA-line morphology that capture most of the variance of 132 single-cell traits . These 132 traits represent a high-quality group of morphometric features identified in a previous study as having lower variation across replicate experiments [38] . Each trait is specific to cells from a particular phase of cell growth , such that 6 PCs represent 83% of the variance in morphology among yeast cells without a bud , 9 PCs represent 85% of the variance among cells with a small bud , and 14 PCs represent 86% of the variance among cells with a large bud ( S1 Table ) . Our ultimate goal was , for each of these PCs ( hereafter “phenotypes” ) , to assess whether Hsp90 inhibition significantly increases or decreases the amount of phenotypic variation between MA lines ( as schematized in Fig 1 ) . But first , we asked more generally whether the effects of the mutations in the MA lines are influenced by Hsp90 ( without asking whether such mutations are buffered or potentiated ) . Several observations suggest that the MA lines possess mutations with effects on morphology that differ when cells are grown in the control versus the 8 . 5 μM GdA condition ( Fig 3 ) . Linear modeling detects significant condition-by-line interactions contributing to variation in 27/29 phenotypes ( and 108/132 of the original traits ) at p < 0 . 001 ( S1 Table ) . These statistically detected interactions are borne out by inspection of cell images ( Fig 3A and 3B ) . For example , the second PC in unbudded cells is strongly influenced by traits that concern the position of the nucleus ( see loadings in S1 Table ) . The average distance from the nucleus to the cell wall in the MA line ancestor does not significantly change upon GdA treatment ( magenta line in Fig 3A ) . However , in some MA lines , the average distance from the nucleus to the cell wall decreases upon GdA treatment ( e . g . , yellow line and cell images in Fig 3A ) ; in other MA lines , the average distance from the nucleus to the cell wall increases upon GdA treatment ( e . g . , orange line and cell images in Fig 3A ) . A similar example is presented by the third PC in large-budded cells , which is strongly influenced by the bud angle . Whereas the average bud angle in the MA line ancestor ( magenta line in Fig 3B ) and in several MA lines ( e . g . , olive line in Fig 3B ) does not change significantly upon GdA treatment , the average bud angle in other MA lines does ( e . g . , cyan line in Fig 3B ) . Both examples make clear that mutations in the MA lines have effects that differ between the control and GdA treatments; results from other phenotypes are consistent with this conclusion as well ( S2 Fig ) . The significant interactions are not all driven by a handful of MA lines possessing mutations with particularly strong GdA-dependent effects . Rather , multiple MA lines demonstrate GdA-induced phenotypic changes that diverge from those observed in the MA ancestor ( Fig 3A and 3B; S3 Fig ) , so much so that there are 17 distinct most-divergent MA lines across 29 PCs ( Fig 3C ) . Our finding that many MA lines undergo phenotypic changes upon GdA treatment that diverge from those of the MA ancestor suggests that the phenotypic effects of many mutations are influenced by Hsp90 . It is unlikely that the few mutations per each MA line mostly fall in the coding sequences of Hsp90 clients . Further study of the mutations in MA lines with GdA responses that diverge from those of the ancestor could reveal whether Hsp90 often interacts directly with mutant versions of non-client proteins , or whether Hsp90’s influence on mutant phenotypes often manifests through indirect interactions percolating through protein networks [39 , 40] . Because most MA lines possess multiple mutations , we do not know which ones are responsible for the divergent GdA responses . Therefore , to gain insights about what types of mutations have GdA-dependent phenotypic effects , we focused on a subset of 20 MA lines for which whole genome sequencing identified only one single-nucleotide mutation each ( S2 Table ) . Of these MA lines , those that possess mutations predicted to have severe effects on protein function ( S2 Table ) [28 , 41] tend to have responses to GdA treatment that diverge more from those of the MA ancestor ( Fig 3D ) . GdA-by-genotype interactions can be partitioned [9] into a component that captures changes in the amount of phenotypic variance between MA lines ( Fig 4A; line spreading ) and a component that captures changes in rank order of MA lines ( Fig 4A; line crossing ) . Such partitioning quantifies the importance of buffering or potentiation ( line spreading ) relative to genetic interactions that do not alter the phenotypic variance among lines ( line crossing ) . For all 27 PCs with a significant condition-by-line interaction term in linear models , line spreading comprises only a relatively small percentage of Hsp90’s effect on phenotypic variation between strains; instead , line crossing dominates ( Fig 4B; for a PC demonstrating the typical ratio of line crossing to line spreading , see Fig 4C ) . The strong prevalence of line crossing across all PCs suggests that Hsp90 rarely acts specifically as a buffer or a potentiator , but often its relationship with spontaneous mutations can be described more accurately ( and simply ) as epistasis [10] . These observations , by suggesting that Hsp90 does not act exclusively—or even usually—as a buffer of spontaneous mutations , do not support the hypothesis that Hsp90 increases an organism’s robustness to genetic perturbation . Assuming that GdA treatment is equivalent to inhibition of Hsp90 , the above observations suggest there is epistasis between Hsp90 and many mutations that occur spontaneously in nature . This conclusion is consistent with Hsp90’s position as a hub of protein-protein interaction ( because hub proteins , either directly or indirectly , likely influence the phenotypic effects of mutations in many other proteins ) . However , there might be other reasons that GdA treatment affects phenotypic variation , including reduction of growth . Although previous studies [1 , 2 , 6] concluded that the effects of low-level GdA on phenotypic variation result from Hsp90 inhibition and not a nonspecific effect of GdA on growth , and although 8 . 5 μM GdA has minimal effects on growth of the strains we study ( S1 Fig ) , we sought to test this assumption as well . We performed additional experiments that indeed support the conclusion that GdA treatment affects phenotypic variation through its inhibition of Hsp90 . We find that the MA line responses to GdA and to 5 . 0 μM radicicol ( another Hsp90 inhibitor that is structurally unrelated to GdA [42] ) are highly correlated; the median Pearson correlation across 29 PCs ( r ) is 0 . 95 ( Fig 4D; for all PCs , see S4 Fig ) . MA line morphological responses to altering the duration of exponential growth are not as correlated ( median r = 0 . 76 ) ( Fig 4D; S4 Fig ) . Furthermore , although altering exponential growth duration often has a significant effect on the parameter we are most interested in—the amount of between-strain variation ( S5A Fig ) —neither the magnitude nor the direction of this effect predicts which phenotypes have between-strain variances that are significantly influenced by GdA ( S5B Fig ) . Finally , as noted below , restricting our dataset to traits for which variation is not significantly affected by growth duration does not alter the conclusions reported for the analyses of between-strain variation in the MA lines and other strain collections . These results suggest that GdA’s influence on between-strain phenotypic variation does not result generally from growth perturbation but rather is a specific consequence of Hsp90 inhibition . Although GdA treatment impacts the phenotypes of individual MA lines more than it changes the overall amount of variance between lines ( Fig 4B ) , we can still quantify whether such variance changes tend more toward buffering or potentiation . If yeast cells are robust to the effects of spontaneous mutations in a way that is compromised by Hsp90 inhibition , we would expect GdA treatment to increase phenotypic diversity between MA lines by revealing the previously buffered effects of mutation ( as schematized in Fig 1A ) . Instead , for many phenotypes , GdA treatment decreases morphological variation between MA lines ( compare ranges of blue and red lines in Fig 3A and 3B ) . To determine if these differences in variance are significant , we asked whether a 95% credible interval around each difference overlaps zero . Using this cutoff , several phenotypes ( 7 of 29 PCs; Fig 5A; S1 Table ) demonstrate significant decreases in variance upon GdA treatment . Using the same cutoff , no phenotype displays a significant increase in between-strain variance . A 95% confidence interval surrounding the median of the variance differences across all 29 PCs ( Fig 5A ) falls entirely below zero , suggesting that inhibiting Hsp90 with GdA generally tends to decrease the phenotypic effects of mutations on MA-line morphologies . These observations imply that Hsp90 , when not inhibited , tends to potentiate rather than buffer the phenotypic effects of new mutations . This conclusion is inconsistent with the “robustness” hypothesis described above . The idea that Hsp90 increases robustness against the effects of new mutations has persisted [5 , 14 , 15 , 19–24] despite alternate models for the prevalence of buffered variation in natural populations [7 , 8] , despite an absence of studies focusing on new mutations rather than standing genetic variation [7 , 43] , and despite the previous observations that in some cases Hsp90 potentiates , rather than buffers , the effects of genetic perturbation [6 , 25 , 26] . Our finding that Hsp90 tends to potentiate the effects of new mutations more than to buffer them ( and that neither potentiation nor buffering is nearly as salient as line-crossing epistasis ) must be reconciled with the observation , made in species spanning eukaryotic diversity , that impairing Hsp90 reveals cryptic genetic variation [1–6] . We investigated the hypothesis that , although buffered mutations are less likely to arise than potentiated mutations , buffered mutations preferentially accumulate in populations over evolutionary time [7] . To test this selection hypothesis , we repeated all experiments on several additional collections of yeast strains isolated from natural environments ( S3 Table ) . These four yeast collections , hereafter called the “Ale , ” “Diverse , ” “SPD , ” and “SPH” strains , consist respectively of ( 1 ) 36 strains used in ale production; ( 2 ) 24 strains from a worldwide collection from diverse habitats [44 , 45]; ( 3 ) 18 strains of the closely related , nondomesticated species Saccharomyces paradoxus , isolated from one soil environment in England [44 , 45]; and ( 4 ) 18 haploid derivatives of the SPD strains [45] . Unlike the mutations in the MA lines , which accumulated under artificially reduced selection pressure , the standing genetic variation among natural yeast isolates has survived natural selection . Patterns of genetic variation in the collections of natural isolates that have been sequenced ( Diverse , SPH , and SPD ) reveal evidence of selection [44 , 46] . These studies also suggest that fermentation processes , like those used to cultivate strains in the Ale collection , impose selective pressures [44 , 46] . Further studies demonstrate that stabilizing selection has acted specifically on the morphological traits that we study , constraining morphological variation between strains in the Diverse collection [47] . Consistent with strong stabilizing selection , we find that levels of morphological variation in each of the four collections of natural isolates do not far exceed those observed in the MA lines ( S6 Fig ) , even though their levels of genetic diversity are vastly greater [44] . These observations , plus the strong relationship between yeast morphology and critical processes such as growth and reproduction [47] , provide compelling evidence that genetic variation contributing to single-cell morphology in the Ale , Diverse , SPD , and SPH strain collections has been constrained by natural selection . The selection hypothesis predicts that the single-cell morphologies in yeast collections representing natural isolates will tend to become more divergent upon Hsp90 inhibition . Consistent with the selection hypothesis , Hsp90 inhibition tends to increase morphological diversity in all four such yeast collections ( Fig 5B ) . The qualitatively different responses to GdA observed in the natural isolates versus the MA lines persist when the MA lines are subsampled ( S5A Fig ) , when we eliminate phenotypes for which between-strain variation is influenced by growth ( S5C Fig ) , and when we study separately those phenotypes specific to cells in different phases of the cell cycle ( S5D Fig ) . The tendency toward potentiation in the MA lines also persists when we restrict our analysis to the 20 MA lines that each possess a single coding mutation ( S5E Fig ) , arguing against the possibility that this tendency is somehow caused by the combined effects of multiple spontaneous mutations per line . The distinction between the MA lines and natural isolates might support the hypothesis that natural selection acts as a filter of new mutations with epistatic interactions [7 , 8] , skewing the relationship between Hsp90 and the rest of the genotype from one that tends toward potentiation to one that tends toward buffering . However , another possibility is that the ancestor of the MA lines possesses a specific genetic make-up that causes it to interact differently with GdA . Perhaps MA-line growth is uniquely responsive to GdA in a way we did not control for . Or perhaps the MA line ancestor possessed a faulty copy of a gene involved in protein homeostasis that causes Hsp90 inhibition to have unexpected effects . To rule out such possibilities , we repeated our experiments on an additional yeast strain collection that consists of 78 recombinant ( Rec ) progeny from a cross between two divergent yeast strains from the “Diverse” collection [48] . Although each individual mutation in these Rec lines has been exposed to selection , the unique combination of mutations in each line has not , because the mutations are largely private to one or the other lineage [44] . Therefore , studying these lines served two purposes: ( 1 ) testing an additional collection of lines each representing a unique genotype that has not experienced selection and ( 2 ) testing if Hsp90 buffers the effects of recombination , as it has been suggested that recombination exerts a selective force favoring genetic buffering [49 , 50] . Previous studies of Rec lines have produced mixed results . A study in yeast found roughly equal frequencies of buffering and potentiation [6] . In contrast , studies in plants concluded that genetic buffering by Hsp90 is common [2 , 4 , 20] . However , in the plant Rec inbred lines , line-specific phenotypes revealed by Hsp90 inhibition were also seen in those lines without Hsp90 inhibition , albeit less frequently and in more mild form [2 , 20] . The apparent increase in variance among lines can therefore be explained by Hsp90 buffering within-line rather than between-line variation . Our study avoids this problem by using statistical procedures that partition multiple contributions to variance . Similarly to the MA lines , we found that GdA treatment tends to decrease phenotypic diversity among the Rec lines ( Fig 5C ) . These differences between strain collections suggest that Hsp90’s influence on the amount of phenotypic variance between strains tends toward potentiation , but that natural selection tends to skew this genetic interaction toward one of buffering ( Fig 5A–5D ) . To further characterize how the relationship between Hsp90 and genetic variation differs in the MA and Rec lines versus the natural isolates , we partitioned genetic interactions into line spreading versus line crossing ( see definitions in Fig 4A ) for those phenotypes that had a significant interaction term in linear models ( p < 0 . 01; orange shading in S1 Table ) . In all collections of yeast studied here , line spreading comprises only a relatively small percentage of Hsp90’s effect on phenotypic variation between strains , and , instead , line crossing dominates; this is especially true in the MA and Rec lines ( Fig 5E ) . The selection hypothesis [7] explains how stabilizing selection can re-purpose epistatic proteins as buffers of standing genetic variation ( Fig 6 ) , transforming patterns dominated by line crossing ( Fig 5E: MA and Rec lines ) into patterns with more line spreading in the direction of buffering ( Fig 5E: Ale , Div , SPH , and SPD strains ) .
For years , discussions of robustness and genetic canalization have featured Hsp90 [5 , 15 , 19–24] . The evidence that Hsp90 impairment reveals cryptic genetic variation fit a narrative in which mutationally robust genetic systems accumulate genetic variation that can be expressed when robustness is compromised [19 , 51] . Although the logic of this narrative is sound ( decreasing robustness will reveal cryptic genetic variation ) , the converse ( revelation of cryptic genetic variation means robustness has decreased ) is not necessarily true . Indeed , as pointed out over a decade ago , revelation of cryptic genetic variation can occur even when robustness increases [7] . The persistence of the idea that Hsp90 is an agent of genetic canalization is especially unwarranted , because Hsp90 function has been shown in some cases to be required for new mutations to have effects [6 , 25 , 26] . As a result , two parallel descriptions of Hsp90 have existed: Hsp90 is a “capacitor” that buffers genetic variation until its function is impaired , and Hsp90 is a “potentiator” that enables new mutations to have effects [52 , 53] . When Hsp90 is viewed as both a capacitor and potentiator , the circularity of associating it with genetic canalization and mutational robustness should be even more apparent—Hsp90 buffers what it buffers ( and does not buffer what it does not buffer ) . Studies promoting the robustness hypothesis often reference Hsp90’s role as a protein-folding chaperone , painting a vivid picture of Hsp90 buffering mutational effects by helping proteins fold properly despite mutation [14 , 23] . But Hsp90 can influence mutant proteins indirectly , potentiating their effects by activating pathways that promote their expression [39] or activating stress response programs that allow mutant phenotypes to manifest without causing cell death [54] . Sometimes the contrasting roles of Hsp90 are not named as capacitance or potentiation , but rather are both attributed to Hsp90’s role in “buffering” mutant proteins , where the meaning of “buffering” is not limited to maintaining wild-type protein activity but also includes enabling new activity [26] . The confusion surrounding discussions of Hsp90's interactions with genetic variation could perhaps be cleared up by abandoning specialized terms such as "buffer" or "potentiator" in favor of descriptions of epistasis [10] . Our results provide direct evidence that Hsp90 does not tend to increase mutational robustness for the large set of cell-morphological traits we studied . Instead , the relationship between Hsp90 and new mutations is dominated by line-crossing epistasis . Therefore , on both theoretical and , now , empirical grounds , Hsp90 should not be described as an agent of genetic canalization . Using a very similar experimental design , except with deletion of the focal gene rather than pharmacological inhibition of its protein product , Richardson et al . ( 2013 ) reached the same conclusions about another candidate robustness-increasing factor , the histone variant H2A . Z . Together , the Hsp90 and H2A . Z results raise the question of whether any gene product increases robustness to mutation on a genome-wide scale [10] . Additionally , our results highlight that natural selection transforms the types of epistasis that are present in nature , leaving the false impression of robustness . Although it is appreciated that selection filters out certain types of new mutations ( e . g . , deleterious ones ) , the idea that natural selection also skews the types of epistasis that accumulate in genomes is much less appreciated . The false equality drawn between the genetic interactions common in nature and those that are common among new mutations has likely prevented a full and accurate understanding of natural variation in complex traits ( including human disease traits ) . To be clear , our results do not contradict the prevalence or importance of cryptic genetic variation in nature [55] . Our study confirms that natural genetic variation is enriched for buffered alleles . Previous work explains how the release of such variation , perhaps by stress that impairs Hsp90 function , may play a pivotal role in adaptation by revealing phenotypic diversity under novel or stressful conditions [1 , 2 , 5 , 56] . Revelation of cryptic genetic variation by environmental or genetic perturbation also likely contributes to complex human disease [13] . Our study contributes to previous work by clarifying why cryptic genetic variation exists , potentially shedding new light on the properties of cryptic alleles . For example , perhaps the biased subset of Hsp90-interacting variation that accumulates in nature tends to interact with Hsp90 via a different mechanism than most new mutations . The nature of Hsp90’s interaction with new mutations is of high practical importance , because Hsp90 inhibitors are considered promising anticancer drugs . One rationale for inhibiting Hsp90 has been that Hsp90 protects tumor cells from the deleterious effects of their elevated mutation rates by suppressing the effects of many mutations [14] . Our work suggests that Hsp90 does not , on balance , protect cells from mutations’ effects and therefore challenges the validity of this rationale . An alternative and more straightforward rationale for inhibiting Hsp90 is that some Hsp90 client proteins are known to promote tumor development; targeting the chaperone therefore might work as a broad strategy for targeting signaling proteins to which cancer cells are “addicted” [15] . Although the rationale for this strategy does not depend on whether or not Hsp90 tends to suppress the effects of mutations , unfortunately , the strategy has not in general succeeded . The culprit appears to be that high doses of Hsp90 inhibitor are necessary , and these doses activate the stress-response factor HSF1 , which itself promotes malignancy [15 , 26] . More recent work ( upon which ongoing clinical trials are based ) has focused on enhancing the efficacy of another anticancer drug by combining it with low-dose Hsp90 inhibition that does not activate the stress response [26] . The rationale in this case is that low-level inhibition is enough to prevent Hsp90 from potentiating the effects of adaptive mutations , thereby curtailing a tumor’s evolution of resistance to the other drug . A similar principle was proposed for the utility of Hsp90 inhibition in preventing resistance to antifungal drugs [25] . Results for estrogen receptor-positive breast cancer cells are promising [26] . It is possible that a bias toward potentiation of new mutations by Hsp90 , as we observed in yeast , contributes to the success of such an approach . However , one might expect the contribution to success to be small , given the prevalence of line-crossing epistasis that we observed . Further investigation of the spectrum of effects of mutations that arise during adaptive evolution with and without Hsp90 inhibition , and ultimately the mechanism by which Hsp90 modifies the phenotypic effects of mutations , is warranted .
Yeast growth , cell staining , and microscopy were performed the same way for all six strain collections ( for additional details about strain collections and sequencing of MA lines , see S1 Text as well as S3 Table ) . We prepared cells for microscopy using established protocols [9 , 38 , 57] , with modifications ( S1 Text ) . Briefly , yeast strains were grown from frozen stocks to saturation in rich media ( YPD ) in 96-well plates ( 48 h of growth ) . Each plate was used to inoculate a pair of new 96-well plates: a control plate containing synthetic complete media ( SC ) plus DMSO and an Hsp90-inhibited plate containing SC + 8 . 5 μM GdA solubilized in DMSO ( Fig 2 ) . Our goal was for everything about this pair of 96-well plates to be as similar as possible , including the concentration of DMSO , the identity of the yeast strain in each well , the orientation of plates during culturing , and the timing of all subsequent steps . This pair of 96-well plates ( GdA+ and GdA− ) was grown to saturation and then was used to inoculate a fresh pair of plates containing the same control or Hsp90-inhibited media ( Fig 2A ) . This freshly inoculated pair of plates was grown for 6 h ( to mid log ) , after which we removed growth media , added 4% paraformaldehyde , and fixed cells for 1 h . Cells were then stained overnight and mounted on 384-well glass bottom microscopy plates ( Fig 2A ) . We performed epifluorescence microscopy using a Nikon Eclipse Ti automated microscope using a 40× objective . For each strain , typically between 500 and 1 , 000 cells were imaged per condition ( S1 Fig ) ; at least two complete biological replicate experiments were performed per strain . Imaged cells were analyzed for quantitative morphological traits using CalMorph software ( Fig 2B ) [31] . All data analysis was performed using the open-source R statistical computing package ( http://www . r-project . org/ ) , and analysis generally followed that done previously for similar morphology datasets [9 , 38 , 57] , with modifications ( S1 Text ) . Briefly , for each strain collection , each morphological trait was normalized to have a mean of zero and a standard deviation of one following Box-Cox transformation . Critically , this normalization allows the variances of GdA+ and GdA− conditions to differ , but standardizes the amount of variation across strain collections and phenotypes for downstream statistical analysis and visualization . Linear modeling was used to normalize data from replicate plates ( S1 Text ) . As was done previously , we restricted our analysis to 132 high quality morphometric traits chosen for their lower replicate-to-replicate variation [38] . We eliminated redundancy among these traits by using PC analysis ( PCA ) . The loadings of morphological traits onto PCs are fairly similar between strain collections ( S1 Table ) . We used a linear model , fit using maximum likelihood in the R package lme4 [58] , to estimate the contribution of genotype and condition to variation in each phenotype ( PC ) . When a likelihood ratio test indicated that linear models including a genotype-by-condition interaction term fit the data significantly better than those without ( p < 0 . 01 ) , we reported a significant genotype-by-GdA interaction for that phenotype in that strain collection ( orange shading in S1 Table ) . Partitioning of this interaction variance into line-crossing and line-spreading components ( Figs 4B and 5E ) was only done for PCs with a significant genotype-by-GdA interaction and was performed as described previously [9] . For each PC , condition-specific strain means and between-strain variances were estimated from linear models using MCMC sampling with the R package MCMCglmm [37] , following methodology outlined in a previous study ( S1 Text ) [9] . For each PC , the difference between the control and Hsp90-inhibited variances was called significant when the 95% highest posterior density interval of the difference obtained from the MCMC samples did not overlap zero . Although this analysis does not explicitly control for multiple hypothesis testing , the strong directionality of our results ( i . e . , that we detect seven and six PCs with decreased variance in MA and Rec lines , respectively , but none with increased variance ) suggests that the trends we detect at this significance threshold are meaningful . We performed a control experiment on a subset of the MA lines , chosen randomly ( 21 MA lines; S2 Table ) , plus the MA line ancestor in order to directly compare the effects of 8 . 5 μM GdA , 5 . 0 μM radicicol , and a shortened exponential growth period . All experiments were performed using procedures described previously ( Fig 2 ) , except that all conditions ( GdA , Rad , less growth ) were present on the same 96-well plate . This plate was removed from the 30°C incubator once after 4 h to harvest cells in the “less growth” condition , and again after 6 h to harvest cells in other conditions; cells from all conditions were prepared for microscopy and imaged together ( see S1 Text ) . | Most biologists appreciate that natural selection filters new mutations ( e . g . , by eliminating deleterious ones ) , such that genetic variation in nature is biased . The idea that selection also skews the types of genetic interactions that exist in nature is less appreciated . For example , studies spanning diverse species have shown that the protein Hsp90 , which helps other proteins to fold properly , tends to diminish the observable effects of genetic variation . This observation has led to the assumption that Hsp90 also buffers the effects of new mutations . This untested assumption has served as a rationale for cancer-treatment strategies and shaped our understanding of variation in complex traits . We measured the effects of new mutations on the shapes and sizes of individual yeast cells and found that Hsp90 does not tend to buffer these effects . Instead , Hsp90 interacts with new mutations in diverse ways , sometimes buffering , but more often enhancing mutational effects on cell shape and size . We conclude that selection preferentially allows buffered mutations to persist in natural populations . This result alters common perceptions about why cryptic ( i . e . , buffered ) genetic variation exists and casts doubt on cancer-treatment strategies aiming to target presumed buffers of mutational effects . | [
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"evo... | 2016 | Selection Transforms the Landscape of Genetic Variation Interacting with Hsp90 |
In eukaryotes , histone H3 lysine 9 methylation ( H3K9me ) mediates silencing of invasive sequences to prevent deleterious consequences including the expression of aberrant gene products and mobilization of transposons . In Arabidopsis thaliana , H3K9me maintained by SUVH histone methyltransferases ( MTases ) is associated with cytosine methylation ( 5meC ) maintained by the CMT3 cytosine MTase . The SUVHs contain a 5meC binding domain and CMT3 contains an H3K9me binding domain , suggesting that the SUVH/CMT3 pathway involves an amplification loop between H3K9me and 5meC . However , at loci subject to read-through transcription , the stability of the H3K9me/5meC loop requires a mechanism to counteract transcription-coupled loss of H3K9me . Here we use the duplicated PAI genes , which stably maintain SUVH-dependent H3K9me and CMT3-dependent 5meC despite read-through transcription , to show that when PAI sRNAs are depleted by dicer ribonuclease mutations , PAI H3K9me and 5meC levels are reduced and remaining PAI 5meC is destabilized upon inbreeding . The dicer mutations confer weaker reductions in PAI 5meC levels but similar or stronger reductions in PAI H3K9me levels compared to a cmt3 mutation . This comparison indicates a connection between sRNAs and maintenance of H3K9me independent of CMT3 function . The dicer mutations reduce PAI H3K9me and 5meC levels through a distinct mechanism from the known role of dicer-dependent sRNAs in guiding the DRM2 cytosine MTase because the PAI genes maintain H3K9me and 5meC at levels similar to wild type in a drm2 mutant . Our results support a new role for sRNAs in plants to prevent transcription-coupled loss of H3K9me .
The eukaryotic cell is under constant threat from transposons and other invasive sequences . Transposons can drain cellular resources for RNA and protein synthesis and can damage the cell through expression of aberrant gene products or activation of transposon movement . A major mechanism to protect against these deleterious effects is to target transposons and other repetitive sequences for silencing mediated through chromatin modifications . In most eukaryotes , transposon chromatin is marked by methylation of histone H3 at the lysine 9 position ( H3K9me ) . In some eukaryotes including mammals and plants transposon chromatin is also marked by cytosine methylation ( 5meC ) . An important question is how H3K9me and 5meC are accurately maintained on transposons but not on host genes . A conserved strategy to maintain H3K9me and 5meC is to use the modifications as methyltransferase ( MTase ) binding recognition motifs . For example , in Arabidopsis thaliana , dimethylation of H3K9 ( H3K9me2 ) maintained by three partially redundant histone MTases—SUVH4 ( also known as KYP , At5g13960 ) , SUVH5 ( At2g35160 ) , and SUVH6 ( At2g22740 ) —is associated with 5meC maintained by the CMT3 cytosine MTase ( At1g69770 ) [1]–[4] . The SUVH MTases contain a 5meC binding domain and CMT3 contains an H3K9me binding domain , suggesting that the SUVH/CMT3 pathway involves an amplification loop that can perpetuate both H3K9me and 5meC [5] , [6] . Consistent with this model , mutations in the CMT3 or MET1 ( At5g49160 ) cytosine MTases , which act to maintain 5meC in non-CG and CG sequence contexts respectively , result in reduced H3K9me2 levels on transposons and repetitive sequences [3] , [7]–[10] . In addition , a suvh4 suvh5 suvh6 triple H3 K9 MTase mutant displays similar reduced non-CG methylation patterns to a cmt3 mutant [4] . Although the H3K9me/5meC amplification loop provides a mechanism to stably maintain both modifications in untranscribed regions of the genome , at junctions where modified sequences are transcribed through from nearby unmodified promoters , H3K9me can be removed by transcription-associated histone replacement or histone demethylation [7] , [11] . What prevents transcriptional destabilization of H3K9me patterns ? Duplicated Arabidopsis genes encoding the tryptophan synthesis enzyme phosphoribosylanthranilate isomerase ( PAI ) provide an ideal system to understand the balance between transcription and SUVH-mediated H3K9me2/CMT3-mediated 5meC . In most Arabidopsis strains there are three unlinked PAI gene duplications that lack 5meC [12] . However , in the Wassilewskija ( Ws ) strain one of the PAI loci is rearranged as a tail-to-tail inverted repeat ( IR ) of two genes PAI1–PAI4 ( At1g07780 ) , which triggers the recognition of PAI sequences as invaders . The PAI1–PAI4 IR as well as two unlinked singlet genes PAI2 ( At5g05590 ) and PAI3 ( At1g29410 ) are modified by H3K9me2 and 5meC , coextensive with their regions of shared sequence identity [3] , [13] ( see Figure S1 for PAI gene maps ) . The PAI1–PAI4 IR is fused to a heterologous promoter with a transcription start site approximately 500 base pairs ( bp ) upstream of the PAI1 5meC boundary , which drives constitutive expression of PAI1 transcripts [14] . The polyadenylated transcripts that accumulate from this locus consist of a majority class that terminates normally in the PAI1 3′ untranslated region at the center of the IR and a minority class that extends through PAI1 into palindromic PAI4 sequences to provide a source of fold-back double-stranded RNA ( dsRNA ) . Therefore the PAI1–PAI4 locus is able to stably maintain H3K9me2 and 5meC on the IR sequences even in the face of substantial read-through transcription . The PAI2 and PAI3 singlet genes also stably maintain H3K9me2 and 5meC even though they are likely to be only partially silenced by limited upstream modifications: at PAI2 5meC extends only 250 bp upstream of the predicted transcription start site , and at PAI3 5meC extends only as far as the predicted transcription start site [12] , [13] . Arabidopsis uses three cytosine MTase pathways to control 5meC: the CMT3 pathway maintains 5meC mainly in non-CG contexts in conjunction with the SUVH H3K9 MTases , the MET1 pathway maintains 5meC mainly in CG contexts , and the DRM2 ( At5g14620 ) pathway initiates 5meC on new invasive sequences under the guidance of small RNAs ( sRNAs ) , as well as contributing to maintenance of non-CG methylation at some loci [15] . In a cmt3 or a suvh4 suvh5 suvh6 mutant , the Ws PAI genes are depleted for 5meC in non-CG contexts [4] , [16] . In addition , in a cmt3 met1 double mutant the PAI genes are depleted for 5meC in all contexts [3] . Therefore , the DRM2 pathway plays a minimal role in the maintenance of PAI 5meC patterns . However , genetic or epigenetic changes that impair the production of transcripts that read through from PAI1 into palindromic PAI4 sequences at the PAI1–PAI4 IR cause reduced levels of PAI 5meC in non-CG contexts [13] , [14] , [17] , [18] . In light of these results , we hypothesized that sRNAs processed from dsRNAs might underlie a mechanism to prevent the loss of SUVH/CMT3-mediated modifications due to read-through transcription , independently of the role for sRNAs in guiding DRM2 . To test the hypothesis that sRNAs control the SUVH/CMT3 pathway , we used mutations in Arabidopsis dicer-like ( DCL ) ribonucleases to block processing of sRNAs from dsRNAs , and monitored the effects on Ws PAI gene H3K9me2 and 5meC levels . Arabidopsis encodes four DCLs ( reviewed in [19] ) . DCL1 ( At1g01040 ) is specialized for processing 21 nucleotide ( nt ) microRNAs ( miRNAs ) needed for developmental gene regulation , whereas DCL2 ( At3g03300 ) , DCL3 ( At3g43920 ) , and DCL4 ( At5g20320 ) have partially redundant roles in processing sRNAs used in other silencing pathways . For example , DCL3 processes 24 nt sRNAs used to guide DRM2 to matching target sequences such as transgene insertions and transposons [20] , [21] . In a dcl3 mutant DCL2 and DCL4 can partially compensate by processing 22 nt and 21 nt sRNAs respectively corresponding to the same genomic target sequences [22] , [23] . Here we show that the dcl2 dcl3 dcl4 mutant has reduced levels of H3K9me2 and non-CG methylation on PAI sequences relative to wild type , corresponding to loss of PAI sRNAs . We also show that a drm2 mutant maintains similar levels of PAI H3K9me2 and 5meC relative to wild type . Therefore the PAI genes illustrate that DCL-dependent sRNAs help maintain SUVH/CMT3-mediated modifications through a distinct mechanism from their role in guiding DRM2 . In the dcl mutant there is a weaker reduction in PAI 5meC levels but a similar or stronger reduction in PAI H3K9me2 levels compared to a cmt3 mutant , indicating a connection between sRNAs and maintenance of H3K9me2 patterns independent of CMT3 function . We also show that upon inbreeding in the absence of DCL function , remaining PAI 5meC is destabilized . Our results reveal a new pathway for sRNA control of H3K9me2 and associated 5meC patterns in plants . This pathway provides a homeostatic mechanism to use a product of read-through transcription—sRNAs—as a means to counteract transcription-coupled loss of H3K9me2 on transposons and repeats .
To test whether dicer-dependent sRNAs contribute to maintenance of PAI 5meC patterns , we generated strains where dicer mutations were combined with the three methylated PAI loci from Ws and assayed PAI 5meC patterns using both DNA gel blot and bisulfite sequencing assays . For PAI DNA gel blot analysis we cleaved genomic DNA with each of three 5meC-sensitive restriction enzymes that have cleavage sites within methylated PAI sequences: HincII ( sensitive to methylation of the outermost non-CG cytosines in 5′ atGTCAACag 3′ , where the recognition sequence is shown in uppercase ) , MspI ( sensitive to methylation of the outer non-CG cytosines in 5′ CCGG 3′ , and HpaII ( sensitive to methylation of either the inner CG or outer non-CG cytosines in 5′ CCGG 3′ ) . HincII cleaves at the translational start codons of PAI1 , PAI2 and PAI4 but not PAI3 , and the MspI/HpaII isoschizomers cleave in the second introns of PAI2 , PAI3 , and PAI4 but not PAI1 ( Figure S1 ) . We found that genomic DNA prepared from dcl2 , dcl3 , and dcl4 single insertional null mutants and the dcl2 dcl4 and dcl3 dcl4 double mutants had similar PAI cleavage patterns to wild type Ws genomic DNA when assessed by HincII , MspI , or HpaII DNA gel blot assays ( Figure 1 ) . In contrast , genomic DNA prepared from the dcl2 dcl3 and dcl2 dcl3 dcl4 mutants displayed increased cleavage with HincII at PAI1–PAI4 and PAI2 , and with MspI at PAI1–PAI4 , PAI2 , and PAI3 relative to wild type Ws , diagnostic of partially reduced non-CG methylation levels at all three PAI loci . Bisulfite sequencing of PAI1 and PAI2 proximal promoter/first exon regions in the dcl2 dcl3 dcl4 mutant compared to wild type Ws and Ws cmt3 showed that there was a partial loss of 5meC in CHG and CHH contexts . Therefore , the bisulfite sequencing data are consistent with the DNA gel blot assays . The results indicate that DCL2 and DCL3 act redundantly to maintain PAI non-CG methylation patterns . To determine whether the miRNA processing dicer DCL1 contributes to the remaining PAI non-CG methylation in the dcl2 dcl3 dcl4 triple mutant relative to cmt3 , we included a dcl1 dcl2 dcl3 dcl4 quadruple mutant strain in the DNA gel blot analysis ( Figure 1 ) . Because dcl1 null alleles are embryo-lethal we used a partial-function dcl1-9 allele that is viable but female-sterile [24] , [25] . The dcl1 dcl2 dcl3 dcl4 mutant displayed similar cleavage patterns to the dcl2 dcl3 dcl4 mutant , indicating that the dcl1-9 mutation does not enhance the partial loss of 5meC conferred by mutation of the other three DCL genes . In subsequent studies we focused on the dcl2 dcl3 dcl4 mutant , which has global depletion of sRNAs other than miRNAs [23] . For comparison to the dcl mutants we included genomic DNA prepared from cytosine MTase mutants in the Ws background ( Figure 1 ) . DRM2 is the major cytosine MTase controlling initiation of 5meC , but the related DRM1 MTase ( At5g15380 ) could also contribute to this pathway [26] . Therefore we used a drm1 drm2 double null insertional mutant . DNA from the Ws drm1 drm2 mutant displayed similar PAI cleavage patterns to wild type Ws in all three DNA gel blot assays , and similar 5meC patterns to wild type Ws in bisulfite sequencing analysis of PAI1 and PAI2 proximal promoter regions . DNA from a Ws met1 mutant displayed increased cleavage at all three PAI loci with HpaII , and partially increased cleavage at PAI1–PAI4 and PAI2 with MspI , but no difference from wild type PAI cleavage patterns with HincII in DNA gel blot assays , diagnostic of a partial loss of 5meC in CG and CCG contexts . DNA from the cmt3 mutant displayed nearly complete cleavage with HincII and MspI , and partially increased cleavage with HpaII in DNA gel blot assays , diagnostic of strong loss of 5meC mainly in CHG and CHH contexts . Compared to the cytosine MTase mutants across the three DNA gel blot assays , the dcl mutant PAI demethylation phenotypes are consistent with a partial defect in the SUVH/CMT3 pathway rather than a defect in the DRM or MET1 pathways . DCL3 and DRM2 are key factors in establishing new 5meC imprints . We used a previously developed genetic assay combining the Ws PAI IR dsRNA source locus with an unmethylated PAI2 target gene from another strain background to show that dcl3 and drm1 drm2 mutations impair the acquisition of new 5meC on PAI2 ( Text S1 , Figure S2 ) . Therefore the PAI genes use the same DCL3/DRM pathway for establishing 5meC imprints as other characterized loci . However , once PAI 5meC patterns are established , the DCL3/DRM pathway plays a minimal role in long-term maintenance ( Figure 1 ) . We used chromatin immunoprecipitation ( ChIP ) analysis with H3K9me2-specific antibodies on chromatin prepared from the dcl2 dcl3 dcl4 mutant compared to chromatin prepared from wild type , suvh4 suvh5 suvh6 , cmt3 , drm1 drm2 , or cmt3 drm1 drm2 strains to determine whether the dcl mutations affect levels of H3K9me2 as well as non-CG methylation on PAI sequences . Chromatin was analyzed by quantitative PCR with primer pairs specific for the PAI1 arm of the PAI1–PAI4 IR locus or the PAI2 singlet gene . At both PAI1–PAI4 and PAI2 the dcl2 dcl3 dcl4 mutant had reduced levels of H3K9me2 relative to wild type , although not as strongly as in the suvh4 suvh5 suvh6 H3K9 MTase mutant ( Figure 2 ) . Comparing the ChIP results to the assays for 5meC ( Figure 1 ) , the reduced PAI H3K9me2 levels in the dcl2 dcl3 dcl4 mutant are still sufficient to support substantial CMT3 activity . Therefore CMT3 might be able to use even sparsely distributed H3K9me2 as a localization signal . At both PAI loci the cmt3 mutant also had partially reduced levels of H3K9me2 , presumably because reduced 5meC levels impair SUVH localization to PAI sequences . At PAI2 increased transcription due to proximal promoter demethylation in a cmt3 mutant could also contribute to reduced H3K9me2 levels , perhaps accounting for a stronger relative reduction at PAI2 than at PAI1–PAI4 [3] , [16] . The dcl2 dcl3 dcl4 mutant had similar reduction in H3K9me2 levels to the cmt3 mutant at PAI2 , but a stronger reduction at PAI1–PAI4 . In contrast , the dcl2 dcl3 dcl4 mutant had weaker reductions in PAI2 and PAI1–PAI4 non-CG methylation levels compared to cmt3 ( Figure 1 ) . This comparison indicates that the dcl2 dcl3 dcl4 mutations impair maintenance of H3K9me2 independently of effects on CMT3 function . If the dcl mutations acted by impairing CMT3 to cause a partial reduction in PAI 5meC levels as the primary consequence , then the resulting reduction in H3K9me2 levels would be expected to be less than in the cmt3 mutant . At both PAI1–PAI4 and PAI2 , the drm1 drm2 mutant displayed similar levels of H3K9me2 to wild type , and the drm1 drm2 cmt3 mutant displayed similar levels of H3K9me2 to cmt3 ( Figure 2 ) . Therefore , the DRM cytosine MTases do not contribute to maintenance of PAI H3K9me2 patterns . To determine whether reduced PAI non-CG methylation and H3K9me2 levels in dcl2 dcl3 dcl4 correlate with loss of PAI sRNAs , we used RNA gel blot analysis to detect PAI sRNAs ( Figure 3 ) . As a negative control we used a mutant derivative of Ws , Δpai1–pai4 , where the PAI1–PAI4 IR source of dsRNA DCL substrates has been deleted by homologous recombination between flanking direct repeat sequences [17] . As a positive control we used the Δpai1–pai4 strain transformed with a PAIIR transgene consisting of an IR of approximately 700 bp of PAI cDNA sequences transcribed by the strong constitutive Cauliflower Mosaic Virus 35S promoter [14] . We previously determined that PAI sRNAs could be detected in the Δpai1–pai4 ( PAIIR ) transgenic strain but not in wild type Ws using RNA gel blot analysis with a PAI cDNA riboprobe . Furthermore , high-throughput sRNA sequencing in the C24 strain that has a similar PAI1–PAI4 IR to Ws detected PAI sRNAs at low levels [27] . We therefore optimized detection of rare PAI sRNAs by designing a high-affinity locked nucleic acid ( LNA ) probe corresponding to the sense strand of a 35 nt sequence in the PAI fifth exon . In wild type Ws the LNA probe detected low levels of PAI sRNA species of both shorter and longer sizes , between 21 and 24 nt relative to size markers , above the background signal in the Δpai1–pai4 negative control strain ( Figure 3 ) . This pattern is consistent with processing of PAI1–PAI4 palindromic transcripts into sRNAs by more than one dicer . Correspondingly , the C24 high-throughput sequencing analysis detected PAI sRNAs between 21 and 24 nt long covering the entire IR region [27] . The wild type Ws levels of endogenous PAI sRNAs were comparable to levels detected in a hundred-fold dilution of RNA prepared from the Δpai1–pai4 ( PAIIR ) positive control transgenic strain ( Figure 3 ) . The transgenic strain produced mostly smaller PAI sRNAs , as previously observed for this strain using a PAI cDNA riboprobe [14] , presumably due to differences in PAI IR expression patterns and structure . PAI sRNA species were depleted in the dcl2 dcl3 dcl4 strain to similar background levels as detected in Δpai1–pai4 ( Figure 3 ) . This result supports the hypothesis that loss of PAI sRNAs underlies the reduction in SUVH-dependent H3K9me2 and CMT3-dependent 5meC on PAI sequences . The lack of residual PAI sRNAs in dcl2 dcl3 dcl4 indicates a minimal contribution of DCL1 to generating these species , although DCL1 is functional in processing microRNAs such as miR167 . To determine whether loss of sRNAs causes destabilization of remaining PAI silencing modifications upon inbreeding by self-pollination , we introduced the dcl2 dcl3 dcl4 mutations into a Ws pai1 reporter background where silencing of the PAI2 singlet gene can be monitored by visual inspection . In wild type Ws , PAI1 expressed from the heterologous upstream promoter is the major source of PAI enzyme; expression of PAI2 is impaired by H3K9me2/5meC on proximal promoter sequences and PAI3 and PAI4 do not encode functional enzyme due to polymorphisms [12] . In the Ws pai1 missense mutant , the impairment of PAI2 expression is revealed through tryptophan deficiency phenotypes including reduced size and blue fluorescence under ultraviolet ( UV ) light caused by accumulation of the tryptophan precursor anthranilate [28] . The stable maintenance of PAI2 H3K9me2/5meC in pai1 is reflected in stable maintenance of blue fluorescence across generations of inbreeding . Mutations that decrease PAI2 H3K9me2 and/or 5meC levels in the Ws pai1 background , including cmt3 , met1 , and suvh4 , result in reduced fluorescence [2] , [16] , [28] . The initial pai1 dcl2 dcl3 dcl4 strain displayed partially reduced PAI 5meC patterns similar to the PAI1 dcl2 dcl3 dcl4 strain ( Figure 1 , Figure 4 ) . The pai1 dcl2 dcl3 dcl4 plants were larger and less fluorescent than pai1 plants , reflecting the partial reduction of non-CG methylation levels on PAI2 ( Figure 4 ) . Examination of pai1 dcl2 dcl3 dcl4 inbred populations revealed that blue fluorescence diagnostic of PAI2 silencing was not stably maintained . In a population of 191 pai1 dcl2 dcl3 dcl4 plants we found two non-fluorescent segregants ( 1 . 0% ) . In contrast , no non-fluorescent individuals were found in control populations of thousands of pai1 plants , consistent with our previous results . Each of the non-fluorescent pai1 dcl2 dcl3 dcl4 plants yielded approximately 75% non-fluorescent and 25% fluorescent second-generation progeny ( 54 non-fluorescent out of 70 total progeny plants [77%] for one line and 31 non-fluorescent out of 42 total progeny plants [74%] for another line ) . Approximately one third of the second-generation non-fluorescent plants lacked remaining PAI2 5meC in a HincII DNA gel blot assay , and these individuals yielded 100% non-fluorescent third-generation progeny , whereas the remaining second-generation non-fluorescent plants had partial levels of PAI2 5meC and yielded approximately 75% non-fluorescent third-generation progeny . For example , 12 out of 28 [43%] non-fluorescent second-generation progeny from one line had fully demethylated PAI2 phenotypes in the HincII assay and each of these individuals yielded 100% non-fluorescent progeny; the pai1 dcl NF line shown in Figure 4 is derived from one of these individuals . The segregation patterns are consistent with reduced PAI2 5meC levels and increased expression occurring on just one of the two chromosomes in the parental non-fluorescent plant and being inherited in a Mendelian fashion . DNA gel blot analysis of a non-fluorescent pai1 dcl2 dcl3 dcl4 line indicated a nearly complete loss of CCG methylation monitored by MspI cleavage and partially reduced CG methylation monitored by HpaII cleavage at PAI2 relative to the fluorescent parental line , consistent with the reversion of tryptophan deficiency phenotypes ( Figure 4 ) . However , PAI1–PAI4 and PAI3 maintained similar 5meC patterns to the fluorescent parental line . Therefore , in pai1 dcl2 dcl3 dcl4 the loss of PAI2 5meC and silencing detected by the blue fluorescence screen is not coupled to destabilization of 5meC at the other PAI loci . Both the initial loss of PAI non-CG methylation and the stochastic further loss of PAI2 silencing and 5meC in dcl2 dcl3 dcl4 are similar to patterns we previously observed in the Δpai1–pai4 mutant [17] . This comparison indicates that regardless of whether PAI sRNAs are depleted by loss of DCL function or by loss of the source of PAI dsRNA substrates for DCL cleavage ( Figure 3 ) , PAI2 silencing is similarly destabilized . The destabilization could be due to a combination of effects at PAI2 including impairment of H3K9me2 maintenance , increased transcription , and impairment of the DRM2/DCL3 pathway in resetting 5meC imprints ( Text S1 , Figure S2 ) . To determine whether other SUVH/CMT3 target loci besides the PAI genes have reduced 5meC levels in the dcl2 dcl3 dcl4 mutant , we used a survey approach with MspI DNA gel blot assays ( Figure 5 ) . We monitored representative sequences of three types: a degenerate ( 86% identical ) inverted repeat locus IR1074 [29] , highly repetitive 5S rDNA and 180 bp centromeric sequences ( CEN ) , or low-copy transposons Ta3 [7] and Mu1 [30] . At all of these sequences , there was no difference in MspI cleavage between wild type and the drm1 drm2 mutant , but greatly increased cleavage in the cmt3 mutant , indicating that CCG methylation at the monitored MspI sites is dependent on CMT3 with a minimal contribution from the DRM MTases . For each sequence , we tested both the pai1 dcl2 dcl3 dcl4 parental fluorescent strain and an isogenic non-fluorescent progeny line to determine whether these two strains had differences in 5meC patterns at loci other than PAI2 ( Figure 4 ) . The IR1074 , 5S rDNA , and CEN sequences showed partially increased MspI cleavage in both of the dcl mutant strains relative to wild type and cmt3 ( Figure 5 ) . For the highly repetitive sequences the increased cleavage was evident as a slight shift downwards in the peak intensity of the ladder of cleaved bands ( Figure 5B ) . Therefore , similarly to the PAI genes , these sequences require DCL function for maintenance of CMT3-dependent 5meC . The sequences had similar 5meC patterns in the fluorescent versus non-fluorescent pai1 dcl2 dlc3 dcl4 lines , indicating that the loss of PAI2 5meC in the non-florescent line is not coupled to more general destabilization of 5meC . In contrast , Ta3 and Mu1 showed no differences in MspI cleavage between the dcl mutant lines and wild type controls ( Figure 5 ) . The Mu1 transposon has a polymorphic arrangement between Ws and the Columbia ( Col ) strain in which the dcl alleles were originally isolated , and the dcl2 dcl3 dcl4 strain carries both Mu1 arrangements . Despite this complication , comparisons to wild type versus cmt3 controls in each strain background showed no evidence of demethylation in the dcl mutant lines . Therefore , not all SUVH/CMT3 targets display DCL-dependent maintenance of 5meC . In light of our hypothesis that sRNAs prevent loss of H3K9me2 due to read-through transcription , the different effects of the dcl mutations at different SUVH/CMT3 target loci could reflect the extent to which read-through transcription occurs across the modified sequences . For example , the dcl-sensitive locus IR1074 is likely to be transcribed across to make fold-back dsRNA because this locus produces sRNAs even in an RNA-dependent RNA polymerase rdr2 mutant background [29] . We also monitored H3K9me2 levels at the single-locus targets IR1074 and Ta3 by ChIP in the dcl2 dcl3 dcl4 mutant and the same control strains used for analysis of the PAI genes ( Figure 6 ) . At IR1074 H3K9me2 levels were reduced in the dcl2 dcl3 dcl4 mutant relative to wild type , although not as strongly as in the suvh4 suvh5 suvh6 mutant . In contrast , at Ta3 H3K9me2 was maintained at similar levels between the dcl2 dcl3 dcl4 mutant and wild type . The H3K9me2 ChIP results agree with the 5meC results indicating that full modification of IR1074 but not Ta3 depends on DCL function ( Figure 5A , Figure 5C ) . The cmt3 mutant had reduced levels of H3K9me2 at both IR1074 and Ta3 , presumably due to impaired SUVH localization and/or increased transcription caused by loss of non-CG methylation ( Figure 5 , Figure 6 ) . However , at both loci the drm1 drm2 mutant maintained H3K9me2 at similar levels to wild type , and the drm1 drm2 cmt3 mutant maintained H3K9me2 at similar levels to cmt3 ( Figure 6 ) . Therefore , CMT3 but not the DRM cytosine MTases contributes to maintenance of H3K9me2 patterns at IR1074 and Ta3 . At IR1074 , the dcl2 dcl3 dcl4 mutant and the cmt3 mutant displayed similar reductions in H3K9me2 levels relative to wild type ( Figure 6 ) . However , dcl2 dcl3 dcl4 had a weaker reduction in IR1074 non-CG methylation levels than cmt3 ( Figure 5 ) . This relationship is similar to that observed for PAI1–PAI4 and PAI2 ( Figure 1 , Figure 2 ) , and supports the view that loss of sRNAs in the dcl2 dcl3 dcl4 mutant impairs maintenance of H3K9me2 patterns independently of CMT3 function at loci subject to read-through transcription .
In Arabidopsis , H3K9me2 maintained by the SUVH4 , SUVH5 , and SUVH6 histone MTases is used to guide 5meC in non-CG contexts maintained by the CMT3 cytosine MTase [1]–[4] . The SUVH MTases contain 5meC-binding domains , and CMT3 contains an H3K9me-binding domain , leading to the model that the SUVH/CMT3 pathway involves an amplification loop between 5meC and H3K9me2 [5] , [6] . However , this amplification loop is not sufficient to maintain full levels of 5meC and H3K9me2 on the Ws PAI gene duplications , including a constitutively transcribed IR locus PAI1–PAI4 and partially silenced singlet genes PAI2 and PAI3 . In previous work we showed that production of palindromic transcripts from the PAI1–PAI4 IR is also required for maintenance of PAI non-CG methylation [13] , [14] , [17] , [18] . For example , in a Δpai1–pai4 mutant the PAI2 and PAI3 genes have reduced non-CG methylation , and the remaining 5meC on PAI2 is destabilized upon inbreeding [17] . Here we use mutations in the DCL dicer ribonucleases to show that PAI sRNAs processed from PAI dsRNAs are the key species that reinforce the SUVH/CMT3 amplification loop between H3K9me2 and non-CG methylation . Arabidopsis uses DCL-dependent sRNAs incorporated into argonaute ( AGO ) effector proteins as nucleic acid sequence-specificity guides in a variety of pathways including miRNA control of development , RNA interference , and guidance of 5meC mediated by the DRM2 cytosine MTase 19 , 23 . The DRM2 pathway contributes together with the SUVH/CMT3 pathway to maintenance of non-CG methylation at many 5meC target loci [15] , [31] . This overlap has obscured whether sRNAs have an independent role in the SUVH/CMT3 pathway . However , the Ws PAI genes maintain 5meC in non-CG contexts almost entirely through the SUVH/CMT3 pathway once initial 5meC is established ( Figure 1 , Text S1 , Figure S2 ) . The reduction in PAI non-CG methylation and H3K9me2 levels in dcl mutant backgrounds therefore indicates a direct connection between DCL-dependent sRNAs and the SUVH/CMT3 pathway ( Figure 1 , Figure 2 ) . DCL2 and DCL3 are the key dicers required for maintaining H3K9me2 and 5meC patterns on the PAI genes , suggesting a preference for longer 22 and 24 nt sRNAs in this pathway . ChIP analysis shows that the dcl2 dcl3 dcl4 mutant has reduced H3K9me2 levels at PAI loci similar to or stronger than in the cmt3 mutant ( Figure 2 ) . However , the dcl2 dcl3 dcl4 mutant has weaker reductions in PAI 5meC levels than the cmt3 mutant ( Figure 1 ) . Therefore reduced H3K9me2 levels in the dcl mutant cannot be accounted for as a secondary effect of impaired CMT3 function . Instead , the ChIP results support the view that the dcl mutations directly impair maintenance of H3K9me2 patterns . In this view , the partial loss of PAI H3K9me2 in dcl2 dcl3 dcl4 reduces CMT3 localization , resulting in reduced PAI non-CG methylation as a secondary effect . Similarly to the PAI genes , a subset of other SUVH/CMT3 target loci including highly repetitive 5S rDNA and CEN sequences have partially reduced non-CG methylation levels in the dcl2 dcl3 dcl4 mutant ( Figure 5 ) . However , some loci such as the low copy transposons Ta3 and Mu1 can maintain full 5meC levels relative to wild type despite the loss of DCL function . This variation could reflect the degree to which different SUVH/CMT3 target loci are transcribed across . This variation could also reflect which RNA polymerases are most active at different loci . Arabidopsis encodes five RNA polymerases: the conserved eukaryotic RNA polymerases POLI , POLII , and POLIII , and plant-specific POLIV and POLV implicated in targeting DRM2-dependent 5meC [32] . In particular , POLV is proposed to transcribe across target loci to make “scaffold” transcripts that recruit sRNA/AGO complexes and components of the DRM2 pathway [33] , [34] . Because of its specialized role in making silencing-associated transcripts , POLV might be less disruptive of H3K9me2 than other RNA polymerases designed to express host genes . In this case , protein-encoding loci like the PAI genes that are transcribed by RNA POLII , and 5meC targets that depend on RNA POLII for scaffold transcript synthesis [35] , might have a stronger dependence on an sRNA-based mechanism to maintain H3K9me2 than POLV-transcribed regions of the genome . Our previous studies with allelic variants of the PAI1–PAI4 IR locus support the hypothesis that the level of transcription across the locus determines the extent to which sRNAs are needed for maintenance of PAI 5meC levels . In one study we used transgene-expressed sRNAs to direct 5meC and transcriptional silencing to the upstream promoter that drives transcription through PAI1–PAI4 , thereby impairing production of PAI dsRNAs and sRNAs [14] . In this transgenic strain the PAI1–PAI4 locus was able to maintain full 5meC levels , whereas the PAI2 and PAI3 singlet genes had partially reduced non-CG methylation levels . These patterns are consistent with a model where the decreased transcription of PAI1–PAI4 specifically reduces its dependence on PAI sRNAs . In a second study we characterized a mutant derivative of Ws where a rearrangement in the center of the PAI1–PAI4 IR introduces a new polyadenylation site and reduces the levels of transcripts that extend into palindromic PAI4 sequences , without altering promoter sequences or the level of transcription across the locus [18] . In the rearrangement mutant the PAI1–PAI4 IR locus as well as the PAI2 and PAI3 singlet genes had partially reduced non-CG methylation levels . These patterns are consistent with a model where read-through transcription at all three loci together with reduced PAI sRNAs results in loss of H3K9me2 and 5meC at all three loci , similarly to the situation in the dcl2 dcl3 dcl4 mutant . Taken together , our results support a homeostatic mechanism where sRNAs produced from heterochromatic regions by read-through transcription feed back to counteract depletion of H3K9me2 and associated 5meC levels caused by read-through transcription . The mechanistic relationship between sRNAs and maintenance of H3K9me2 patterns remains to be determined . In the fission yeast Schizosaccharomyces pombe , an sRNA-loaded AGO protein in the RITS effector complex interacts with nascent transcripts at centromeric repeats to recruit the Clr4 H3K9 MTase ( reviewed in [36] ) . Plants could use an analogous effector complex interaction mechanism to target SUVH H3K9 MTases to specific regions of the genome . Consistent with this possibility , in a suvh4 suvh5 mutant background , the remaining SUVH6 MTase maintains levels of H3K9me2 and associated 5meC similar to wild type at the PAI1–PAI4 IR but not at the PAI2 singlet gene [4]; this locus-specific activity could reflect preferential interactions between SUVH6 and effector complexes that assemble near a site of dsRNA synthesis . Alternatively , sRNA-AGO complexes could recruit intermediate factors that then promote SUVH activity at specific targets . A third possibility is that sRNAs could guide a pathway that protects heterochromatic sequences from H3K9 demethylation . For example , the IBM1 JumonjiC domain H3K9 demethylase acts to prevent H3K9me2 and non-CG methylation from accumulating in transcribed genes 11 , 37 , 38 . IBM1 could be excluded from also acting at heterochromatic sequences through a mechanism that involves sRNA-AGO complexes . Furthermore , sRNA-dependent mechanisms that promote addition of H3K9me2 or prevent removal of H3K9me2 could operate in concert . Pathways where sRNA-AGO complexes guide H3K9me to appropriate regions of the genome have been identified in organisms ranging from fission yeast to the protozoan Tetrahymena thermophila to the insect Drosophila melanogaster , even though these organisms lack 5meC [39]–[41] . Our discovery that Arabidopsis also uses sRNAs to maintain H3K9me could represent a plant-specific variation on this fundamentally conserved strategy . In this case , the sRNA/SUVH/CMT3 pathway and the sRNA/DRM2 pathway could have both evolved from a basal mechanism involving sRNA-AGO guidance of H3K9 MTases . Consistent with this possibility , SUVH variants that lack catalytic activity but maintain methyl-DNA binding are required for DRM2-dependent 5meC [42] . The plant sRNA/H3K9me maintenance mechanism is interwoven with the SUVH/CMT3 chromatin binding amplification loop and partially redundant functions of the MET1 and DRM pathways to create a reinforced silencing network . However , loss of the sRNA/H3K9me maintenance mechanism cannot be completely buffered by the other pathways , and results in both immediate reductions and longer-term destabilization of H3K9me2 and 5meC . The unique properties of the PAI genes make them ideal reporters to further understand how sRNAs are harnessed to control maintenance of H3K9me2 on appropriate target sequences in plant genomes .
T-DNA insertional dcl alleles were obtained from the Arabidopsis Biological Resource Center ( ABRC ) or from the laboratory of James Carrington at Oregon State University . The dcl2-1 , dcl3-1 , and dcl4-2 mutations are likely null alleles originally isolated in the Col strain [21] , [23] . The dcl1-9 mutation is a partial function allele originally isolated in Ws , but then crossed five times to the Landsberg erecta ( Ler ) strain 24 , 25 . Each dcl mutant was crossed to Ws . PCR-based genotype markers were used to identify dcl mutant progeny homozygous for the three PAI loci from Ws ( Table S1 ) . Each dcl allele was crossed a second time with Ws to increase the proportion of the genome contributed by the Ws parent . The resulting dcl single mutant strains were then crossed with each other to generate double , triple , and quadruple mutant combinations . The dcl mutations were also crossed into the Ws pai1 reporter strain [28] . The Ws drm1 drm2 double T-DNA insertional null strain was obtained from the laboratory of Steven Jacobsen at UCLA [43] . The Col cmt3-11T T-DNA insertional null strain was obtained from the ABRC [44] . The Ws pai1 , Ws Δpai1–pai4 , Ws Δpai1–pai4 ( PAIIR ) , Ws cmt3i11a , Ws x met1-1 , and pai1 suvh4R302* suvh5-1 suvh6-1 strains were previously described [4] , [14] , [16] , [17] , [28] . Ws cmt3illa and Ws drm1 drm2 mutants were crossed to make the Ws drm1 drm2 cmt3 strain . Plant genomic DNA preparation and DNA gel blot assays for 5meC were performed as previously described [12] . Bisulfite sequencing of the top strands of PAI1 and PAI2 proximal promoter regions was performed as previously described [14] . PAI bisulfite sequencing primers are listed in Table S1 . Total RNA was extracted with TRIzol reagent ( Invitrogen ) using the manufacturer's protocol . Low molecular weight ( LMW ) RNA was enriched by precipitating high molecular weight RNA out of solution with 0 . 5 M NaCl , 10% polyethylene glycol ( MW 8000 ) . The remaining LMW RNA was precipitated with 100% ethanol and resuspended in water treated with diethyl pyrocarbonate . LMW RNA was fractionated on a 17% acrylamide 7 M urea gel and transferred to a Hybond-N membrane ( GE Healthcare ) . sRNA 5′ ends were chemically crosslinked to the membrane as previously described [45] . Membranes were hybridized in OligoHyb buffer ( Ambion ) overnight at 42°C with 32P 5′ end-labeled oligonucleotide probes . Probes were either an LNA modified PAI1 exon 5 sense 35-mer ( Exiqon ) or an miR167 antisense 21-mer ( Table S1 ) . Probed membranes were washed three times with a 2× SSC , 0 . 1% SDS solution . sRNA sizes were estimated from an ethidium bromide-stained low molecular weight DNA ladder ( USB ) , and by comparison to the PAI sRNA species observed in the Δpai1–pai4 ( PAIIR ) control strain [14] . Formaldehyde crosslinking and chromatin preparations were performed as previously described [46] starting with two grams of aerial tissue from three-week-old plants grown in soilless potting medium ( Fafard mix 2 ) under continuous illumination . Chromatin was immunoprecipitated with anti-H3K9me2 monoclonal antibody [47] or carried through the protocol with no antibody added as a control ( mock precipitation ) . Immunoprecipitations were performed as previously described [3] . Each ChIP assay was performed in at least three independent biological replicates . Quantitative PCR amplification of immunoprecipitated DNA was performed using the 7300 Real-Time PCR System ( ABI ) , with three replicate reactions for each sample . ChIP primer sequences are listed in Table S1 . | Methylation of histone H3 at the lysine 9 position ( H3K9me ) is a fundamental chromatin modification that suppresses expression from invasive and repetitive sequences such as transposons . In plant genomes , regions modified by H3K9me are maintained with precise boundaries . However , at junctions where H3K9me target regions are subject to read-through transcription from outside promoters , the stability of H3K9me patterns is jeopardized by transcription-coupled processes that remove this modification . We show that maintenance of H3K9me patterns at such vulnerable sites requires small RNAs corresponding to the H3K9me target region . We use a sensitive reporter system to show that , in the absence of small RNAs , target regions subject to read-through transcription undergo an immediate reduction in H3K9me levels , followed by further losses in progeny plants upon inbreeding . Our results support a new function for small RNAs in maintaining accurate H3K9me patterns in the plant genome . | [
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] | 2011 | Small RNAs Prevent Transcription-Coupled Loss of Histone H3 Lysine 9 Methylation in Arabidopsis thaliana |
The influence of lipid molecules on the aggregation of a highly amyloidogenic segment of human islet amyloid polypeptide , hIAPP20–29 , and the corresponding sequence from rat has been studied by all-atom replica exchange molecular dynamics ( REMD ) simulations with explicit solvent model . hIAPP20–29 fragments aggregate into partially ordered β-sheet oligomers and then undergo large conformational reorganization and convert into parallel/antiparallel β-sheet oligomers in mixed in-register and out-of-register patterns . The hydrophobic interaction between lipid tails and residues at positions 23–25 is found to stabilize the ordered β-sheet structure , indicating a catalysis role of lipid molecules in hIAPP20–29 self-assembly . The rat IAPP variants with three proline residues maintain unstructured micelle-like oligomers , which is consistent with non-amyloidogenic behavior observed in experimental studies . Our study provides the atomic resolution descriptions of the catalytic function of lipid molecules on the aggregation of IAPP peptides .
A range of human diseases including Alzheimer's disease , Parkinson's disease , the spongiform encephalopathy and type 2 diabetes mellitus ( T2DM ) is associated with amyloid deposits of normally soluble proteins or peptides [1]–[3] . In T2DM , the main protein component of fibrillar protein deposits in the pancreatic islets of langerhans has been identified as a 37-residue hormone referred to as islet amyloid polypeptide ( IAPP ) or amylin [4] , which is synthesized in β-cells of the pancreas and cosecreted with insulin [5] , [6] . There are convincing evidences that the toxicity of amyloid related diseases may be caused by the soluble intermediate oligomers instead of mature fibrils [7]–[9] , and the interaction between lipid bilayer and these soluble oligomer [10]–[14] . For example , channel-like annular structures of oligomers of several amyloidogenic peptides have been observed on the lipid membrane [15] , [16] , and have been studied by molecular dynamics simulations as well [17] , [18] . Moreover , up to 10% components in amyloid deposits from patient tissues were lipid molecules , indicating that the lipids can be uptaken from membranes and then wrapped into fibrillar amyloid [19]–[22] . Most studies so far treated the lipid bilayer as a template to exert its influences on the conformation and aggregation properties of peptides [23]–[26] . There is , however , missing information about how individual lipid molecule involving in the peptide aggregation process . It will then be beneficial to understand the molecular details of how single lipid molecule influences the assembly process of amyloidogenic peptides which is the main focus of the current study . Besides the external factors , such as lipid bilayer , pH value , the sequences of peptide themselves have great effects on the aggregation behaviors . Several other species such as non-human primates [27] , cats [28] , raccoons [28] , and rodent species ( rat [29] , mouse [30] , hamster [31] , etc . ) can produce IAPP , but the primary sequence of IAPP varies slightly among species . Importantly , IAPP from rodent species , such as rat/mouse IAPP ( rIAPP ) lose capacities of aggregating into amyloid fibrils [31] , but transgenic mouse models that express human IAPP ( hIAPP ) develop islet deposits [32] . The rIAPP differs from hIAPP in six amino acids and five of them are clustered in a short decapeptide ( residues 20–29 ) , which is considered to be strongly amyloidogenic and forms similar unbranched fibrils itself to the full-length hIAPP [33] , [34] . The three proline substitutions in rIAPP20–29 are believed to be highly responsible for the lacking of the amyloidogenic property of the segment or full-length peptide [34] . Although rIAPP has been intensively applied in experimental research acting as a potential peptide inhibitor for peptide aggregation [35] , [36] , the molecular mechanism of its resistance to amyloid is still not crystal clear . Here , the aggregation of rIAPP20–29 segments is subjected to the same simulation condition as hIAPP20–29 to explore the non-amyloidogenic properties of the peptide and meanwhile to evaluate the simulation results as a negative control . Due to the metastable and short-lived nature of soluble pre-fibril oligomers at the early steps of fibril formation , experimental data are usually difficult to obtain [37] , [38] . Thus , the computational approaches have been employed to complement experimental investigations to gain the insight into the aggregation mechanisms [39]–[44] . Considering multiple copies of peptides needed due to the self-assembly nature of amyloid formation , various simplified representations of molecular systems using implicit solvent models were preferred rather than all-atom models . Santini et al . performed ART-OPEP simulations on trimer of Aβ16–22 by treating side chains as a bead and solvent implicitly [39] . A novel mechanism for single β-strand to surmount unnatural registry without dissociation , referred to as “reptation” was proposed before experimental characterization [45] . Cheon et al . used ProFASi package to reduce the bonded potential energy to include torsional angles only and treated hydrogen bonds explicitly [46] . They were able to carry out two series of 100 Monte Carlo simulations on 20 copies of two fragments Aβ16–22 and Aβ25–35 . They observed early-stage events and obtained an atomic-detailed description of “nucleated conformational conversion” ( NCC ) [47] model for amyloid aggregation . In these studies , simulations were usually started with randomly oriented , extended or random-coiled peptides which underwent ab initio folding to form β-sheet oligomers . Albeit simplified models allow studying large-scale systems [46] or observing more events in limited simulation time [39] , all-atom explicit solvent models can reproduce amyloid aggregation in aqueous environment more accurately and supply more information on sidechain contacts [48] . Nguyen et al . prolonged a series of conventional MD simulations to 300 ns on Aβ16–22 of 3–6 oligomer size with explicit solvent [49] . The extensive simulations were able to probe the interpeptide sidechain contacts and large conformational fluctuations upon monomer addition to preformed β-sheet oligomers in a “dock-lock” mechanism . In our studies , an enhanced-sampling method , replica exchange molecular dynamics ( REMD ) [50] was implemented [51] , and all water and peptide atoms are treated explicitly by applying OPLS-AA force field [52] . The four copies of amyloidogenic segment hIAPP20–29 and an extra dioleoylphosphatidylcholine ( DOPC ) lipid molecule were initially set in extended conformation and dispersed in simulation boxes . The formation of β-sheet containing tetramers , was observed within 100 ns ab initio REMD folding simulations . The acquirement of abundant intermediate states suggested two possible β-sheet transition pathways . Simulation of four hIAPP peptides without lipid molecule was also performed . Nonamyloidogenic rat IAPP segments were studied as a negative control with the aim of understanding the inhibitory effect of three proline substitutions .
A large amount of experiments have well demonstrated that full-length ( 37-aa ) rIAPP and segment rIAPP20–29 do not form amyloid fibrils in vivo or in vitro [31] , [34] , [53] . Time evolution of percentage of residues that adopt β-sheet conformation is shown in Figure 1 . Consistent with experimental studies , rIAPP20–29 segments seldom exhibit β-sheet structures . Less than 5% residues in the disordered strands adopt β-sheet conformation . Meanwhile , more than 25% residues in hIAPP and hIAPP/lipid participate in β-sheet regions at the end of simulations . On free energy landscapes , the dominant minima are separated by small free energy barriers ( Figure 2 ) . The representative structures related to each local minimum are characterized by conformational features according to VMD coloring schemes [54] . Coils and turns are the predominant structure motifs for rIAPP oligomers with a small portion of helices . The whole aggregate is compact and single strand twists to form a coil without long extended β-structure portion . In contrast , quite ordered β-sheet dimers or trimers are the representative of hIAPP snapshots . In the eight hIAPP representative snapshots ( B1–B4 and C1–C4 ) , both parallel and antiparallel H-bonding patterns are observed . And the mixture of ordered and amorphous structures in the hIAPP ensemble illustrates the dynamical equilibrium between the two states . The ensemble statistics from dPCA results and time series of β-sheet percentages both suggest the fact that after 100 ns simulation , while rIAPP20–29 aggregates remain disordered , hIAPP oligomers are on divergent ways to form amyloid nuclei in the form of β-sheet dimer , trimer , or even tetramer . Of interest is that the decrease of Cα-atom radius of gyration ( Rg ) is much faster than the increase of length of β-sheet regions in four hIAPP20–29 strands . Within 5 ns , hIAPP20–29 Cα-Rg rapidly drops from initially ∼1 . 6 nm to ∼1 . 1 nm and continues to slowly decrease to ∼0 . 95 nm in the following 90 ns . Nevertheless , only 5% hIAPP residues are transformed into β-sheet structure and β-sheet composition reaches a relatively stable level ( ∼20% ) after 50 ns . These early stage ( 5–50 ns ) intermediate species are condensed ( small Rg ) but less structured ( low percentage of β-sheet regions ) , which may be the amorphous aggregates described in other's simulations [46] , [55] , [56] as well as experiments [45] , [47] . The rapid collapse of initially dispersed strands is followed by a slow structural reorganization to allow amorphous species to transform into ß-sheet oligomers which can act as potential nuclei on the way to higher-level aggregates . Several experimental studies have found that unlike full-length hIAPP , amyloid fibrils constituted by fragment hIAPP20–29 contain both antiparallel and parallel β-sheet structure by using FTIR ( Fourier transform infrared spectroscopic ) [57] and ssNMR ( solid-state NMR ) [58]–[60] techniques . Although both parallel and antiparallel β-sheets are observed in representative snapshots , the two opposite orientation patterns are found to have different occurrences by monitoring number of antiparallel ( ap-NB ) and parallel β-bridges ( p-NB ) during simulation course . From Figure 3 , ap-NB and p-NB increase at different rates and eventually ap-NB is more than two times favored than the parallel pattern . A β-bridge occurrence contact map which is constructed to disclose the detailed information of β-strand alignment patterns indicates the same orientation preference of the decapeptide ( Figure 4 ) . In principle , numbers of counts from left panels ( antiparallel β-bridges ) are overwhelmingly more than those from right panels ( parallel β-bridges ) indicating antiparallel β-sheets are much preferred over parallel sheets . Such observation was also found in a recent Monte Carlo simulation: when the aggregation size is small , the fraction of antiparallel β-sheets is dominant [48] . Furthermore , the registry patterns of interacting strands within one β-sheet layer are demonstrated clearly in contact maps . Both parallel and antiparallel β-sheets exhibit a mixture of in-register and various out-of-register patterns . Although the tetrameric oligomers are partially ordered in β-sheet conformation , no uniform alignment patterns are found to be more favorable than others . The in-register patterns are able to extend β-sheet to a longer length than that of out-of-register patterns . The out-of-register patterns are more often found in antiparallel orientation than in parallel pattern . For hIAPP and hIAPP/lipid regardless of parallel and antiparallel patterns , the C-terminal region contributes more in the β-bridge formation . It is not surprising to find that rIAPP has much less β-bridge contact counts considering its nonamyloidogenesis nature . To investigate the roles that residues play in aggregation , secondary structure propensity ( SSP ) for the ten residues are analyzed ( Figure 5 ) . It is obvious that three hydrophobic residues A25 , I26 , and L27 in hIAPP20–29 show high propensity for β-structures . The hydrophobic region ( residues 25–27 ) is considered to be the core part of β-sheets for fragment hIAPP20–29 by experiments [57] , [58] . The terminal residues ( S20 , N21 , and S29 ) are generally unstructured , as their preferences for any of the three sorts of secondary structures are very low . Residues N22 , F23 , and G24 show high propensity for turn/bend . This may be due to the higher backbone flexibility of G24 , and the side chain of F23 can be helpful for stabilizing turn/bend structures . The whole hIAPP20–29 sequence shows a rather low propensity for helical structures . In the presence of lipid ( Figure 5 C ) , the probabilities of residues 22–24 taking turn/bend structures are reduced by approximately 10% , and their probabilities for β-sheets are increased contrastively . Moreover , fewer occurrences of β-hairpin strands are found in the presence of lipid molecule . Consequently , a role of lipid molecule in the aggregation process is disclosed that it prevents peptide from the formation of monomeric hairpin structure and helps the peptide stay in extended conformation . Compared to hIAPP , rIAPP fragment shows a similar propensity for turn/bend in residues 22–24 , but β-structure possibilities of the whole sequence greatly decrease with only those of V27 and L28 remaining a relatively high level . Single mutation I27V slightly reduces ability of hIAPP20–29 [34] to form amyloid fibrils probably because that a valine residue has a nearly same hydrophobicity and SSP as an isoleucine residue does . Figure 6 presents a snapshot of the region on the C-terminals of two rIAPP strands from the simulation . The two rIAPP strands are in a perfect in-register alignment with only one parallel β-bridge between two Val26 amino acids . This alignment pattern makes a large contribution to rIAPP sheet alignments ( Figure 4B ) . The snapshot offers hints about influence of two prolines ( P25 , P28 ) on the interstrand hydrogen bonding network . As illustrated in the sketch plot , V26 residues form stable interstrand H-bonds but the extended H-bond ladder is disrupted by the missing hydrogen atoms on proline amide groups . The occurrence of β-bridges between V26 reaches a large number of over 10000 counts compared to around 2000 counts of other residue pairs in Figure 4 . The other pairs ( G24 , P25 , and L27 ) on the same alignment pattern show a zero β-bridge count . Similarly , the contact numbers around prolines in either parallel or antiparallel patterns are at a comparatively low level , indicating that prolines fail to form H-bonds in nearly all alignment patterns . Both the β-bridge contact maps of rIAPP and the parallel , in-register dimer snapshot describe the same story that the failure of proline to be H-bond donor prevents extension of β-bridges and therefore avoids formation of stable β-sheets . In addition to the disruption of continuity of H-bonds caused by proline , the uniform backbone structure within β-sheets is also perturbed . In Figure 6 , amide and carbonyl groups of the G24 which sits before P25 lose their appropriate positions for H-bond formation . The cyclic structure of proline side chain limits its φ backbone dihedral angles at a small range between −90° to −60° , which brings an extra conformational rigidity to its structure and makes proline a structural disruptor in secondary structure elements such as α-helices and β-sheets . We find that proline dihedral angle φ is restrained to a narrow range which cannot accommodate β-sheet structure ( Figure S2 ) . The distributions of φ dihedral angle of the three prolines ( P25 , 28 , 29 ) on rIAPP as well as their counterpart residues on hIAPP and hIAPP/lipid show a clear difference . As a rule , the φ angle for a β-sheet structure is about −120° to −140° . The counterpart residues on hIAPP all have a considerable probability for φ angle in the range between −120° to −140° . However , φ angles of three prolines locate in an extremely narrow range ( −90° to −60° ) with little overlapping region with β-sheet structure . Thus backbone structure around prolines would induce considerably unfavorable high energy if it adopted a β-sheet conformation . To examine how the disordered rIAPP aggregates lacking of backbone H-bonds can be stabilized , an ensemble of 100 structure snapshots has been extracted from the region with the lowest free energy ( free energy = 0 on the free energy landscape ) for calculating binding energy . The binding energy was calculated by MM/GBSA method and is specified in method section . The binding energy for hIAPP was also estimated for comparison . The breakdown of binding energy components is listed in Table 1 . It is shown that the amorphous rIAPP oligomer configuration can be stabilized at a comparable level to hIAPP ( with similar ΔEtotal ) . For both IAPP segments , the inter-peptide interaction ( with negative ΔEvdw and ΔEelec ) contributes the oligomerization favorably , while the polar solvation energy is unfavorable ( positive ΔEgb ) . The difference of ΔEelec between rIAPP and hIAPP , 122 . 5 kJ/mol , and the difference of ΔEgb , −96 . 5 kJ/mol correlates with the fact that there is less backbone H-bonding interaction within rIAPP oligomer , and relatively favorable solvation energy for rIAPP . Overall , aggregation of both peptides is driven by nonpolar interaction . The nucleation process of four hIAPP20–29 strands in this study involves complex structural transition from initial amorphous oligomer states to highly ordered β-sheets . The fundamental element of structural transition is the backbone hydrogen bond formation . Thus the number of extended β-bridges ( NB ) should be a suitable reaction coordinate for describing the conversion process . The ordered oligomer state , namely β-sheet dimer , trimer and tetramer , can also be used to describe the degree of order of an ensemble of structures . The evolution of different β-sheet oligomerization state as a function of NB is elaborated based on the ensemble trajectories at low temperatures . Besides , following a replica trajectory that contains information of a continuous structural evolution , the transition between an amorphous state and an ordered state can be vividly demonstrated . The number of NB is depicted by bar chart in Figure 7 for both hIAPP and hIAPP/lipid systems . The percentages of different β-sheet oligomer states in an ensemble with a fixed NB are plotted as symbols . The population of ensembles with NB value in the range of 2 to7 is large . Those ensembles with large NB ( more than 7 ) are highly ordered but have a relatively small population . The dominant β-sheet oligomer size changes gradually with the increase of NB: unstructured→dimer→trimer→tetramer . The increase of β-sheet oligomer sizes also indicates the transformation from amorphous aggregates to a more ordered state . Such transformation is realized by monomer addition . The ensembles which have two separate dimers are found to be hardly populated . It is surprising to discover that even when NB is small , the percentage of trimer is larger than that of dimer ( e . g . NB = 6 for hIAPP , and NB = 5 for hIAPP/lipid ) . Similarly , the percentage of tetramer is larger than that of trimer when NB = 12 for hIAPP , and NB = 11 for hIAPP/lipid . For a clear description , at the transition point , four illustrative sketches for trimeric and tetrameric sheets are drawn , with short dashed lines denoting single β-bridges . Only three out of the ten residues in each strand forming hydrogen bonds are competent to stabilize a tetrameric sheet . This indicates that the β-sheet nucleation site is not necessary to have a long and perfect in-register pattern; a short β-sheet region is capable of being a template to invite free monomers to join the nucleus . The “template” hypothesis was inspired and supported by the work of Kameda and Takada [61] , as the hydrogen bond donors and acceptors on the template are in perfect positions for hydrogen bond forming with another monomer . An interesting difference between two hIAPP systems is that for hIAPP/lipid , it always needs less value of NB to develop structures with higher degree of order . And hIAPP/lipid system has more structures with large NB . Clearly the presence of lipid molecule helps to stabilize the ordered structures and therefore accelerates the emergence of higher order of β-sheet oligomer . We have examined several folded replica trajectories , and key intermediate states from two hIAPP trajectories are shown to demonstrate the detailed transitions from amorphous oligomer to ordered β-sheet oligomer in Figures 8 . Among all folded trajectories we have traced , some of the aggregation pathways are simple and straightforward , in which the increase of the β-sheet oligomer size is simply through monomer addition and sheet extension accomplished by forming more hydrogen bonds between the two β-strands . Other trajectories show much more complicated pathways and involve more reorganization process such as detachment/reattachment of the aggregates ( Figure 8A ) and conformational reorganization such as parallel to antiparallel transition ( Figure 8B ) . In Figure 8A , snapshot 2 is a β-sheet trimer with parallel in-register H-Bond pattern . It undergoes a complete detachment process . All the H-bonds are lost in snapshot 3 . The reattachment finishes in snapshot 4 where a new β-sheet trimer is formed . The three strands involved ( 1 , 2 and 4 ) are different from the previous ones ( 2 , 3 and 4 ) . The H-bond pattern is changed to antiparallel . When the structure evolves to snapshot 5 , a new strand is added to the trimer and a tetramer is formed . The transition from parallel sheet to antiparallel sheet has also been captured in replica trajectory B . Such transition does not need a complete detachment process as in trajectory A . It involves only internal reorganization as trajectory B shows . In this case , one single hydrogen bond in the parallel pattern remains and the whole strand rotates by 180° around the hydrogen bond . Afterwards the newly generated antiparallel hydrogen bonds will form near the place of the original hydrogen bond . The lipid-associated peptide toxicity and aggregation enhancement has been widely established under a variety of lipid models such as micellar [62] and bilayer membranes [25] , even free fatty acids and lipids [63] , [64] . The mature amyloid fibrils are found to contain a portion of lipids which are supposed to be taken up from membranes and wrapped together with peptide while aggregation goes on [25] . Based on results discussed previously , the presence of a lipid molecule has clear effects on the aggregation process of hIAPP peptides: The propensities for β-sheet structure of residues 20–23 in hIAPP/lipid system are increased and their propensities for turns and bends are correspondingly reduced ( Figure 5 ) ; the value of NB needed for the formation of β-sheet oligomers is consistently reduced by one in the presence of the lipid molecule ( Figure 7 ) . To probe the lipid-peptide binding manners in a statistical way , the occurrences of atomic contacts ( NC ) between heavy atoms on lipid head/tail groups and Cα atoms on hIAPP fragment are calculated ( Figure 9A ) . All residues have similar probabilities of contacting with head group . The general pattern of such contact is the H-bonds formed between head group and polar side chains . In contrast , the probabilities of contacting the tail group for different residues are quite distinct . Nonpolar residues show obviously higher inclination , especially for residues F23 , G24 , and A25 , indicating a specific lipid-binding site . The binding site is exactly the region which has high SSP for turns and bends in the absence of lipid ( Figure 5 ) . The increased propensity for β-sheet of this region in hIAPP/lipid ( Figure 5C ) is due to the specific binding of nonpolar lipid tails . The binding of lipid molecule functions in another critical way that helps to stabilize the ordered conformation of β-sheet oligomers . Figure 9B describes temperature dependence of average number of β-bridges , NB . NB decreases slowly to zero when the temperature increases to over 500 K . In nearly all temperature range the average NB of hIAPP/lipid is more than that of hIAPP . The melting temperature ( where NB = 3 . 5 ) is 380 K without lipid and increases to 400 K in the presence of lipid . It is well known that amyloid β-sheet structure is stabilized not only by backbone hydrogen bonds network and also by close side chain packing [65] , [66] . In Figure 10 , three structures with the largest number of extended β-bridges from two hIAPP simulations are shown . Nonpolar surfaces are coalescent into a patch because the hydrophobic residues are prone to pack with each other ( Figure 10 A , in the absence of lipid molecule ) . With the presence of lipid molecule , the lipid molecule is selectively docked onto the hydrophobic patches . The β-sheet region is undoubtedly stabilized through lipid binding on such hydrophobic patch ( Figure 10 B , C ) which makes the hydrophobic clusters dissociate much more difficult . This also explains why less value of NB is needed to maintain β-sheet oligomer in hIAPP/lipid system ( Figure 7 ) .
The difference of five residues between hIAPP and rIAPP in the core region exerts evident effects on aggregation characteristics , among which three proline substitutions have the strongest influences . Proline is commonly found in turns exposed to solvent , which may benefit from its rigidity that costs less entropy penalty upon folding . The cyclic structure of side chain makes proline not compatible to any secondary structures , but it is occasionally found as the first residue of α helices and in the edge strands of β sheets to prevent protein self-assembly . In an atomic detailed level , we have studied how the special structure features of proline , including lacking of amide hydrogen atom and φ dihedral angle which is not overlapping with β-structure , influence the aggregation ability of IAPP20–29 . The missing hydrogen atoms on proline backbones disrupt the H-bonding network and therefore the β-sheet stability is weakened . Besides , the rigid backbone of proline induces unfavorably high energy to β-conformation . The two reasons explain the loss of amyloid aggregation ability of rIAPP20–29 brought by proline mutation . Comparatively , effects of the other two residue mutation ( F23L and I26V ) are indifferent . Their SSP greatly resemble that of counterparts on hIAPP and a certain amount of backbone H-bonds are formed among the two variant residues , indicating a less important function in abolishing rIAPP20–29 aggregation . Recent NMR measurements have provided several constraints on hIAPP protofilaments with striated ribbon morphologies [65] . The basic structural unit of the model contains two layers in a C2 rotational symmetry about fibril axis , and the peptide forms parallel H-bonds to adjacent β-strand within each sheet . Unlike 37-aa hIAPP , fragment 20–29 shows obvious antiparallel H-bonding preference without a clear and uniform strand alignment configuration which may arise from structural heterogeneity or polymorphism in amyloid fibrils [57]–[60] . A most recent ssNMR[60] study suggested an antiparallel pattern with the central FGAI region in registration ( F23 H-bonded to I26 ) . We also observed the same alignment pattern mixed with in-register and other out-of-register patterns . It is reasonable that no uniform or dominant alignment pattern was observed if merely peptide tetramer was studied in 100 ns simulation , as the oligomer size is inadequate to form a stable nucleus for β-sheet elongation . It can be predicted that , similar to the ways of parallel-to-antiparallel transitions that occurred in our simulation , the pre-nucleus tetramer will undergo conformational reorganization and adopt a uniform alignment pattern so long as both the number of oligomers and simulation time exceed a critical values . The fragment 20–29 was thought to form a highly ordered hydrophobic core in fibrils . Nevertheless , recent studies by ssNMR [65] and X-ray [67] indicate an obvious bend around residue G24 in mature fibrils derived from full-length hIAPP , which most probably arises from small-sized Gly and aromatic ring of Phe23 nearby . A similarly conformational preference of the segment in membrane-mimicking environments was also found by solute state NMR[68] and MD simulation study[69] . We also found that the SSP of F23 , G24 , and A25 for bend and turn indeed is higher than that for β-sheet structure . Moreover , we have observed significant occurrence of hairpin conformation in a monomeric form . Due to the limited size of the peptide segment and the lacking of other stabilizing factors , the hairpin monomer is likely to be only a transient form in aqueous environment . NMR observation also supports a linear β-strand for fragment hIAPP20–29[60] . We found that the probability of hairpin emergence can be reduced by lipid interaction at a specific binding site at positions 23–25 . The lacking side chain on G24 and the large nonpolar side chains of neighboring F23 and A25 comprise a perfect hydrophobic cavity on peptide surface for lipid tail embedding inside . The embedded lipid reduces the backbone flexibility of G24 and renders the segment in linear β-strands , which accounts for the increased propensities for β-structure of only residues 23 , 24 , and leaving SSP of other residues mainly unchanged in the presence of lipid molecule . The experimentally observed sigmoidal profile of fibrillogenesis kinetics is normally interpreted by a nucleated growth mechanism [47] , [70] . The self-assembly kinetics is characterized by an initial lag phase ( nucleation ) which is assumed to be the time required for a “nucleus” of critical size to form . This is followed by an exponential growth phase ( elongation ) where fibril growth proceeds rapidly by association of monomers or oligomers to the nucleus . By probing the aggregation behavior of Sup35 , Serio et al . has proposed a revised nucleated growth mechanism NCC model which depicts that nuclei form through conformational rearrangements within micelle-like , structurally dynamic oligomers [47] . The condensed but disordered pre-nucleus species were probed in our and others' simulations/experiments [46] , [47] , [55] , [56] , [70] . These amorphous oligomers formation are mainly driven by hydrophobic effects . The competition between hydrophobicity and backbone H-bonding is believed to be a major determinant of aggregation process [46] . In our simulations , β-sheet dimers were generated under the help of the hydrophobic residues . As a β-sheet template of minimum size , dimers facilitate isolated monomeric peptide in solution to participate in the nucleus . Majority of the disordered-to-ordered conversions occurs without fully dissociation of the early-stage molten oligomers . Thus the aggregates sustain a low Rg ( radius of gyration ) throughout the conversion processes . These indicate that the conformational rearrangements from amorphous to nucleus-competent oligomers involve mainly internal reorganization which is consistent with the “reptation” mechanism [39] , [45] . Although the appearance of β-sheet dimers can perform as the starting point of peptide aggregation , monomer addition is unfavorable until the nucleus reaches a critical size according to nucleated growth mechanism [71] . This brings a question on how high-energy pre-nucleus β-sheet oligomers can be stabilized in aqueous environment . In our 100 ns simulations , the final tetramers are partially ordered with only 25% residues in β-sheet conformation . The terminal residues , as indicated by SSP , hardly join in the β-sheet core . They interact with intrastrand or interstrand residues through polar contacts on side chains . The formation of backbone H-bonds constitutes less than 50% of overall hydrogen bonds . The hydrophobic side chains tend to cluster into patches in order to minimize the exposed nonpolar surface area . In the presence of a lipid molecule , the hydrophobic tails additionally help to stabilize the unstable short β-sheet dimers and trimers by specific binding to nonpolar patch . In conclusion , pre-nucleus species prefer a partially ordered structures rather than a perfect extended β-sheet conformation . These partially ordered short β-sheet oligomers comes from the process of repeated detachment/reattachment or internal reorganization to search for the most preferred orientation and alignment patterns . This explains why aggregation process can be promoted by free lipids without a membrane or micellar surface for peptide to concentrate on [63] , [64] . In summary , the present all-atomic REMD simulations suggest an explanation on how the proline substitutions influence the amyloid aggregation capacity of rIAPP20–29 . Preference for antiparallel interstrand orientation and the lack of uniform registration alignment are the two characteristics of early-stage per-nucleus oligomers . The rapid-collapsed amorphous aggregates can evolve to partially ordered β-sheets through conformational rearrangements and two pathways of parallel-antiparallel transitions are traced . Meanwhile , key residues which are responsible for either β strand formation ( A25 , I26 , and L27 ) or lipid binding ( F23 , G24 , A25 ) are recognized . The specific interaction between lipid tails and hydrophobic residues is found to stabilize the β-sheet region , indicating a catalysis role of lipid molecule in hIAPP peptide self-assembly . These findings are applicable to other types of amyloidogenic peptides and indicate a general pattern of interaction between lipid and amyloidogenic peptides [72] , [73] . Interestingly , a similar specific lipid-hydrophobic residues interaction has also been resolved for explaining the toxicity action of antimicrobial peptides [74] , [75] .
The peptide segments r/hIAPP20–29 and dioleoylphosphatidylcholine ( DOPC ) molecule were represented by all-atom OPLS-AA force field [76] , [77] and solvated by explicit SPC water molecules . Totally three REMD simulations were performed . For abbreviation , rIAPP , hIAPP , and hIAPP/lipid will be used to represent the simulation systems with 4 rIAPP20–29 , 4 hIAPP20–29 , and 4 hIAPP20–29 together with DOPC lipid molecule , respectively . The peptides capped by ACE and NME groups in N and C terminals were initially constructed in a fully extended conformation and separated by at least 2 nm from each other to avoid interaction bias . The four identical peptides ( rIAPP20–29 or hIAPP20–29 ) in each system were arranged in parallel or mixed parallel/antiparallel patterns to include all four possible arrangements: ( i ) N-terminals of all four peptides were placed upwards; ( ii ) N-terminals of three out of four peptides were placed upwards; ( iii ) N-terminals of two peptides on one side were placed upwards , and ( iv ) N-terminals of two peptides on the diagonal directions were placed upwards . The four starting structures in different arrangements were alternately used as the initial frames of 36 replicas to avoid bias in favor of parallel or antiparallel β-sheet alignments during REMD simulations . The initial configurations are shown in Support Information , Figure S1 . The DOPC lipid molecule in an extended state was aligned along the center axis of the box , parallel to the linear peptides . The peptides in each system were solvated in a 4*4*4 nm cubic box of SPC water , keeping a minimum distance of 1 nm between the solute and each face of the box . The final setup of each system contained 1833 SPC water molecules for rIAPP system , 1841 SPC water molecules for hIAPP system and 1782 water molecules for hIAPP/lipid . All systems were neutral and no extra counterions were added . The GROMACS program suite [78] and OPLS-AA force field [76] , [77] were used in all three systems . The parameters for bonded and non-bonded interactions of DOPC lipid molecules were derived from related OPLS force field . All bonds involving hydrogen atoms were constrained in length according to LINCS protocol [79] . Electrostatic interactions were treated with particle mesh Ewald method [80] with a cutoff of 0 . 9 nm , and a cutoff of 1 . 4 nm was used in the calculation of van der waals interactions . The integration time step of simulation was set to 0 . 002 ps . The protein and the water groups were separately coupled to an external heat bath with a relaxation time of 0 . 1 ps . Non-bonded pair lists were updated every 5 integration steps ( 0 . 01 ps ) . After 500 steps of steepest-descent minimization , the REMD simulations continued for 100 ns . The temperatures in the REMD simulations were ranged from 315 . 0 K to 516 . 7 K , and proper temperature intervals were selected to result in approximately 30% averaged exchange possibility for each replica . Exchanges between neighboring replicas were tried every 1000 steps ( 2 ps ) and the conformation coordinates were output every 500 steps ( 1 ps ) . After 100 ns REMD simulation , each system generated an ensemble of 100 , 000 structures at each temperature and total 3 , 600 , 000 structures at all temperatures . The DSSP algorithm written by Wolfgang Kabsch and Christian Sander was used to identify secondary structure conformation of β-sheet oligomers [81] . The algorithm is mainly based on identification of H-bonding ( hydrogen-bonding ) patterns . The identification of H-bonds is relied on calculating electrostatic interaction energy between H-bond accepter C , O and donor N , H atoms . A good H-bond has about −3 kcal/mol interaction energy . Here , a generous cutoff is chosen ( if ) and well tested to allow for an N-O distance up to 2 . 2Å . Depending on the H-bonding patterns , DSSP recognizes mainly seven types of secondary structures which can be grouped into three classes: helix ( α-helix , 310-helix , π-helix ) , β-strand ( isolated β-bridge , extended β-sheet ) and loop ( turn , bend ) . β structures are the dominant secondary structures in our aggregation simulation . β-bridge is the basic unit of β-sheet . Either a parallel or antiparallel β-bridge forms between residues i and j , if there are two H bonds between two nonoverlapping stretches of three residues each , i−1 , i , i+1 and j−1 , j , j+1 . Then β-sheet can be defined accordingly as a set of consecutive β-bridges of identical type ( parallel or antiparallel ) . In our study , the size of a β-sheet oligomer is defined more strictly as following: β-sheet dimer is formed only when two β-strands connected by a minimum of two β-bridges ( instead of one β-bridge according to DSSP default definition ) ; β-sheet trimer is defined as only one β-strand connected by two other β-strands in the same mode; similarly β-sheet tetramer is identified if two β-strands are connected to two other β-strands respectively . A modified PCA version , referred to as dihedral angle PCA or dPCA , was used to represent the conformational distribution on the free energy landscape [82] . In dPCA measurement , only backbone dihedral angles are considered; other internal fluctuations ( such as bond lengths , bond angles , etc . ) and overall motions are efficiently removed because they contribute comparatively little to the fold of peptide . The method is more appealing than traditional PCA specifically for amyloid aggregation . The reason is that conformational transition into β-sheet during this process can be reflected by variation of backbone dihedral angles , instead of sidechain configuration . After free energy landscapes are plotted , the representative structure of individual local minimum is chosen as following: the structures with their V1 and V2 components close to the local minimum are selected; then a clustering method based on pair-wise RMSD is applied; usually a group with a dominant population emerges; the structure which is the center of the group is assigned to the representative structure . The RMSD cutoff is 0 . 2 nm for peptide backbone atoms . Here the combination of dPCA and clustering has overcome the limitation of each method: the heterogeneous ensemble in local minima of dPCA is screened by clustering method; the structural ensemble with a large population which cannot afford to be grouped by clustering method is easily analyzed by dPCA . The binding energy of tetrameric oligomers was estimated by equation: and are the energies of tetrameric oligomer and individual monomer , respectively , both are consisting of two terms: one is peptide vacuum potential energy calculated by GROMACS package and the other is solvation energy estimated by using generalized Born ( GB ) model in the sander module of AMBER 9 [83] . The source code of the tleap program was modified to allow the use of OPLS-AA force field . The modified GB model used was developed by A . Onufriev , D . Bashford and D . A . Case [84] . | People diagnosed with diabetes have increased from 30 million to 246 million over the last two decades . One hallmark of type 2 diabetes is the formation of amyloid in the pancreatic islet , which is composed of human islet amyloid polypeptide ( 90% ) and lipid molecules ( 10% ) . In the long-lasting endeavors against the disease , it is important to understand , at the atomic level , the interaction between peptide aggregation and lipid molecules . In this study , we use molecular dynamics simulations to explore the influence of lipid molecules on the self-assembly process of toxic peptide segments . Moreover , a negative control simulation , employing the non-amyloidogenic rodent sequence , is also performed to evaluate the robustness of the simulation protocol . Our study provides a generic picture of the catalytic role of lipid molecules in the process of amyloidogenesis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biophysics/biomacromolecule-ligand",
"interactions",
"biophysics/theory",
"and",
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] | 2009 | Amyloidogenesis Abolished by Proline Substitutions but Enhanced by Lipid Binding |
Histone modifications regulate gene expression and chromosomal events , yet how histone-modifying enzymes are targeted is poorly understood . Here we report that a conserved DNA repair protein , SMRC-1 , associates with MET-2 , the C . elegans histone methyltransferase responsible for H3K9me1 and me2 deposition . We used molecular , genetic , and biochemical methods to investigate the biological role of SMRC-1 and to explore its relationship with MET-2 . SMRC-1 , like its mammalian ortholog SMARCAL1 , provides protection from DNA replication stress . SMRC-1 limits accumulation of DNA damage and promotes germline and embryonic viability . MET-2 and SMRC-1 localize to mitotic and meiotic germline nuclei , and SMRC-1 promotes an increase in MET-2 abundance in mitotic germline nuclei upon replication stress . In the absence of SMRC-1 , germline H3K9me2 generally decreases after multiple generations at high culture temperature . Genetic data are consistent with MET-2 and SMRC-1 functioning together to limit replication stress in the germ line and in parallel to promote other germline processes . We hypothesize that loss of SMRC-1 activity causes chronic replication stress , in part because of insufficient recruitment of MET-2 to nuclei .
Repetitive sequences pose challenges to genome integrity during DNA replication , DNA repair , and transcription . In eukaryotes , repetitive genomic regions typically adopt a condensed chromatin structure that is thought to limit potentially harmful consequences of repetitive sequences by limiting transcription , stabilizing DNA to promote correct repair of DNA breaks , and limiting formation of secondary structures that would otherwise impede DNA replication [1–4] . Inappropriate transcription of repetitive regions leads to DNA:RNA hybrids ( R-loops ) , which can prevent replication fork progression . DNA break repair is particularly important at repetitive regions because homologous recombination between non-allelic repetitive sequences causes duplication/deletion of genomic regions [5 , 6] . Replication of heterochromatic regions also requires modification of histones within newly incorporated nucleosomes; histone chaperones and some DNA replication factors recruit histone methyltransferases for this purpose [7 , 8] . Beyond regulation at repetitive sequences , replication and chromatin state are interdependent throughout the genome , e . g . , chromatin compaction influences replication fork progression [9] , and conversely , impaired replication can affect chromatin modification status and reduce the accuracy of histone incorporation at sites across the genome [10 , 11] . Thus , the interplay among histone modifications , DNA replication , and repetitive sequences is complex . H3K9 methylation is a histone modification widely associated with heterochromatin [12 , 13] . In C . elegans , different repetitive sequences accumulate H3K9me2 and/or H3K9me3 [14–18] , and loss of these marks correlates with increased susceptibility to DNA replication stress [17] . H3K9me1 and me2 are deposited primarily by MET-2 ( methyltransferase-2 ) , the sole C . elegans member of the SETDB1 family important for heterochromatin establishment and maintenance in numerous species [19–22] . MET-2 also promotes H3K9me3 formation , perhaps indicating that H3K9me1/me2 are substrates for H3K9 trimethylation [22] . SET-25 ( SET domain proteins ) is responsible for H3K9me3 at other sites [21 , 22] , and SET-32 is required for H3K9me3 in the initiation of heritable RNA-based transcriptional silencing [23–25] . Genetic studies indicate that MET-2 , alone or together with SET-25 , promotes germline viability and is critical for fertility in strains maintained at elevated culture temperatures over numerous generations [17 , 18 , 21 , 26] . Moreover , during meiosis , H3K9me2 is enriched on non-synapsed chromosomes , e . g . , the male X chromosome , characteristic of a process termed meiotic silencing [27 , 28] . Overall , H3K9 methylation at repetitive sequences appears to ensure long-term stability of the genome and production of viable gametes and offspring . Known SETDB1 interactors include co-factors as well as proteins required for stable interaction with chromatin or for re-establishing H3K9 methylation following DNA replication . Members of the ATF7IP ( activating transcription factor 7-interacting protein; also called mAM/MCAF1 , Mbd-1 chromatin associated factor ) protein family are SETDB1 co-factors in vertebrates [29 , 30] and Drosophila [31] . C . elegans LIN-65 is a structurally related ( but not orthologous ) protein necessary for H3K9me2 deposition [32 , 33] and for MET-2 nuclear import in the embryo [33] . C . elegans ARLE-14 promotes MET-2 association with chromatin [33] as do vertebrate KAP1 ( KRAB-associated protein 1; also called TRIM28 ) and hnRNP K ( heterogeneous nuclear ribonucleoprotein K ) [20 , 21 , 34] . SETDB1 also associates with a member of the SWI/SNF ATPase family , BAF155/SMARCC1 [34] , and inactivation of BAF155 , or any of several other BAFs , impairs SETDB1 activity at retroviral elements [35 , 36] . During replication , CAF-1 ( chromatin-associated factor–1 ) and MBD1 ( methyl-CpG binding domain protein ) recruit SETDB1 to re-establish H3K9 methylation behind the replication fork [7 , 37 , 38] . Thus , numerous factors ensure H3K9 methylation in different contexts . To better understand how MET-2 activity is targeted in the C . elegans germ line , we sought to identify MET-2-interacting proteins . Here we describe SMRC-1 , the sole C . elegans ortholog of vertebrate SMARCAL1 ( SWI/SNF-related , matrix associated , actin-dependent regulator of chromatin , subfamily A-like 1 ) . SMARCAL1-related proteins comprise a distinct subfamily of SWI/SNF ATPases and are thought to protect genome integrity by promoting the repair and restart of stalled DNA replication forks [39–41] . In vitro , SMARCAL1 proteins bind single stranded ( ss ) DNA and can rewind DNA substrates , e . g . , replication forks and D-loops at Holliday junctions , and RNA:DNA substrates , meaning they might act on R-loops [39 , 42–44] . Studies in human cultured cells showed that telomere maintenance , an endogenous source of replication stress , requires SMARCAL1 activity [45] . The MET-2—SMRC-1 association is interesting given that MET-2 provides some protection against lethality caused by DNA replication stress [17 , 18] . We demonstrate that SMRC-1 protects against DNA replication stress , limits accumulation of DNA breaks and mutations , and promotes germline and embryonic viability and development . Moreover , SMRC-1 promotes H3K9me2 deposition and an increase in nuclear MET-2 abundance under conditions of replication stress . Genetic data suggest MET-2 and SMRC-1 function in a common mechanism in the germ line to limit DNA damage caused by replication stress and in parallel to promote other germline processes . Our data suggest SET-25 does not promote SMRC-1-mediated processes and has a minimal role in limiting replication stress . Taken together , our data suggest that SMRC-1 recruits MET-2 to limit the adverse effects of replication stress .
We evaluated the smrc-1 phenotype in order to determine the importance of SMRC-1 during development . smrc-1 mutants had reduced fertility , an increased frequency of male offspring , and reduced embryonic viability ( Table 1 ) . These phenotypes resulted primarily from the loss of maternal smrc-1 ( + ) product and were more severe at elevated culture temperature . At 25°C , smrc-1 ( om136 ) M+Z- F1 hermaphrodites ( the progeny of smrc-1 ( +/- ) mothers ) were viable and fertile , although they produced fewer embryos than did wildtype controls ( S1 Table , Table 1 ) . Some smrc-1 ( om136 ) M-Z- F2 individuals died as embryos , and survivors included a high proportion of males ( a Him phenotype , typically due to X chromosome nondisjunction ) ( Table 1 ) . Most viable smrc-1 ( om136 ) M-Z- F2 hermaphrodites were fertile but produced fewer embryos than the F1 generation ( Tables 1 and 2 ) . Most F3 embryos were non-viable ( Table 1 ) . We investigated the developmental defects underlying impaired smrc-1 fertility by DAPI staining smrc-1 ( om136 ) M-Z- F2 adult hermaphrodites and evaluating their germ lines . Fertile F2 adults typically had normal germline organization , whereas sterile F2 adults had obvious germline defects such as abnormal nuclear morphology , reduced numbers of germ cells compared with wildtype , and/or failure to produce sperm , oocytes , or both gamete types ( S2 Fig ) . In rare cases , germ cells were not at all visible in the adult gonad . When both sperm and oocytes were present , one or both gamete types were presumably fertilization-defective . A subset of sterile hermaphrodites had polyploid ( endomitotic ) oocytes in the oviduct , a phenotype that most commonly arises due to impaired ovulation [49 , 50] . We conclude that SMRC-1 function promotes multiple aspects of germline development . We addressed the sensitivity of smrc-1 mutants to DNA replication stress by exposing them to hydroxyurea ( HU ) , a treatment that causes replication fork stalling . We initially examined treated smrc-1 ( om136 ) M-Z- F2 individuals , and later examined M+Z- F1 individuals for comparison with low-fertility genotypes . We treated larvae with HU beginning at L1 stage and monitored their survival and fertility ( see Methods ) . Survival of L1 larvae post-HU exposure reflects their ability to resolve DNA lesions and resume development; fertility of surviving adults specifically reflects the ability of mitotic germ cells to resolve DNA lesions . HU treatment had a significantly more severe effect on viability and fertility of smrc-1 M-Z- F2 mutants than wildtype ( Fig 1A ) . The replication stress hypersensitivity of the smrc-1 mutant suggests a prominent role for SMRC-1 in limiting replication-associated DNA damage . We hypothesized that smrc-1 mutants might accumulate mutations over successive generations which would reduce survival and fertility as a result of errors due to replication stress , and possibly other sources of DNA damage [42 , 51–54] . We evaluated this possibility by serially passaging 16 smrc-1 ( ea8 ) mutant lines at 25°C and recording brood sizes in each generation ( see Methods ) . To eliminate bias , we passaged the first L4 larva at each generation; if that animal developed as a sterile adult , we rescued the line by passaging a fertile sibling . We observed a broad range of brood sizes at each generation among the 16 serial lines ( 0 to >100 offspring ) ( Fig 2 ) . Eleven lines had to be rescued by siblings at least once in the course of 30 generations . Overall , there was a trend toward reduced fecundity in successive generations and populations appeared to become sicker . Germ cell apoptosis is elevated in many C . elegans DNA damage response mutants [55] , and thus we considered that elevated apoptosis might contribute to the reduced smrc-1 fertility . We evaluated apoptosis by monitoring expression of CED-1::GFP , a protein expressed on the surface of phagocytic cells as they engulf apoptotic cells [56] , and by staining with the vital dye , acridine orange . Here , CED-1 is expressed by sheath cells , components of the somatic gonad that engulf apoptotic germ cells . Both assays revealed elevated levels of apoptosis in smrc-1 germ lines compared to controls ( Fig 3A , S2 Table ) . In the C . elegans hermaphrodite gonad , CED-1 is expressed by somatic sheath cells adjacent to the germ line [56] . As expected based on the literature , we observed rare CED-1::GFP -positive cells at the loop region of wildtype gonad ( Fig 3A ) . smrc-1 mutants contained significantly more CED-1-positive cells , often present throughout the germ line ( Fig 3A ) . smrc-1 apoptosis was significantly reduced in the absence of CEP-1/p53 ( Fig 3A , S2 Table ) , an essential component of the DNA damage checkpoint machinery active at the late pachytene stage [57] . In contrast , apoptosis was not significantly suppressed by inactivation of PCH-2 ( S2 Table ) , a component of the machinery that monitors chromosome pairing [58] . We conclude that smrc-1 mutants accumulate unrepaired DNA damage that , in turn , triggers the DNA damage checkpoint and results in elevated germline apoptosis . C . elegans mutations that cause DNA damage to accumulate , due to either an increased number of DNA lesions or impaired DNA damage repair machinery , are classified as “mutators” [59] . We hypothesized that SMRC-1 might limit accumulation of mutations . We investigated this possibility in two ways . First , we screened for reversion of the dominant unc-58 ( e665 ) phenotype; this assay allows detection of intragenic and extragenic suppressors and is commonly used to quantify mutator activity [60] . We observed a 3- to 5-fold increase in unc-58 reversion in the smrc-1 mutant background compared to wildtype at both 20°C and 25°C ( Fig 3B ) . For comparison , reversion increased 8- to 15-fold in DNA damage response mutants clk-2 , hus-1 , and mrt-2 [60] . Second , we assayed for enhancement of the dog-1 phenotype . DOG-1 ( deletions of G-rich DNA ) helicase is related to human FANCJ and essential for proper replication of poly G/C tracts in C . elegans [61] . Poly G/C tracts can assume a DNA secondary structure that is a natural source of replication stress [62] . Proteins that function in the DNA damage checkpoint or homologous recombination , e . g . , CEP-1 or XPF-1 and SWS-1 , respectively , are implicated in maintaining poly G/C tract integrity in the absence of dog-1 activity [63–65] . We evaluated poly G/C tract integrity by assessing the deletion rate within G/C-rich exon 5 of the vab-1 gene . vab-1 microsatellite deletions or insertions did not accumulate in smrc-1 ( ea8 ) or smrc-1 ( ea46 ) single mutants ( Fig 3C ) . In contrast , deletion frequency was two-fold higher in dog-1;smrc-1 double mutants compared to dog-1 single mutants at both 20°C and 25°C ( Fig 3C ) . For comparison , deletion frequency was increased 2 . 7-fold in cep-1 , 3 . 2-fold in xpf/him-9 , and 1 . 5-fold in cep-1 mutants compared to wildtype [63 , 65] . We conclude that SMRC-1 limits the accumulation of deletions within poly G/C regions when DOG-1 activity is absent . In vitro studies suggest that mammalian and Drosophila SMARCAL1 may act on DNA:RNA hybrids ( 43 ) , and R-loops cause DNA replication stress in vivo ( 5 ) . We were interested in determining if SMRC-1 might limit accumulation of R-loops . We evaluated R-loop abundance in wildtype and smrc-1 ( om138 ) M-Z- F2 mutants by immunolabeling with a DNA:RNA hybrid-specific antibody , S9 . 6 [66] . We quantified the proportion of nuclei with S9 . 6 foci ( the S9 . 6 labeling index ) in the proliferative , leptotene/zygotene , and pachytene regions of the germ line . smrc-1 germ lines had a significantly greater S9 . 6 labeling index than wild type in each of these regions ( Fig 3D ) . Moreover , the S9 . 6 -positive smrc-1 germ line nuclei had more S9 . 6 foci on average than the positive wildtype nuclei ( Fig 3D ) . We interpret these data to indicate that SMRC-1 activity limits R-loop abundance . We considered whether the loss of SMRC-1 function in proliferating germ cells might contribute to formation of DSBs that could impact genome integrity . In early C . elegans meiosis , SPO-11 endonuclease initiates DSB formation at multiple sites along each chromosome; in most nuclei , only one DSB per chromosome is repaired as a crossover ( CO ) and others are repaired as noncrossovers ( NCOs ) [67 , 68] . COs do not occur and homologs prematurely dissociate at diakinesis if SPO-11 is absent [67 , 68] . Introduction of DSBs from exogenous sources , such as ionizing radiation ( IR ) , can partially rescue COs in spo-11 mutants [67 , 68] . We took advantage of the spo-11 univalent phenotype to test whether unregulated ( non-SPO-11-mediated ) DSBs might arise in smrc-1 mutants and contribute to the loss of germline viability . First , we generated smrc-1; spo-11 double mutants and evaluated diakinesis chromosomes in the most proximal oocyte ( at the -1 position ) in each gonad arm . In smrc-1 ( ea8 ) and smrc-1 ( om138 ) single mutants , we observed 4–7 DAPI-bright bodies in the -1 oocyte ( Fig 3E ) . Faint links occasionally were visible between what appeared to be distinct chromosomes in smrc-1 mutant oocytes , suggesting aberrant connections between non-homologous chromosomes ( Fig 3E ) . The presence of 7 DAPI-bright bodies in some nuclei is consistent with five synapsed autosomal pairs and two non-synapsed X chromosomes , which would lead to nullo-X gametes and subsequent production of male offspring , as observed . In smrc-1 ( om138 ) ;spo-11 ( ok79 ) double mutants raised at 25°C , we observed striking evidence of additional DNA damage . We observed 5–12 DAPI-bright bodies , and more frequent faint links between chromosomes ( at least one linkage observed in 22% of nuclei , N = 41 ) ( Fig 3E ) . Therefore , SMRC-1 appears to limit production of aberrant DNA damage that could be carried into meiosis and allow inappropriate connections between chromosomes . RAD-51 is a single-strand DNA binding protein that associates with ssDNA adjacent to DSBs and facilitates the homology search and strand engagement in homologous recombination [69] . We performed anti-RAD-51 labeling to evaluate the distribution of DSBs in the smrc-1 ( om138 ) M-Z- F2 germ line . We observed RAD-51 foci primarily in meiotic nuclei and more rarely in mitotic nuclei , similar to wild-type controls ( S4A Fig ) . Several differences were noted , however . Specifically , while DSBs occurred in leptotene stage in both wild type and smrc-1 mutants , RAD-51 foci formation was delayed in smrc-1 . One possibility is that SPO-11-induced breaks occurred over a more protracted period of time in smrc-1 mutants . Also , a large number of RAD-51 foci persisted into late pachytene ( zone 6 ) in smrc-1 , suggesting that DSB repair is delayed , as might be expected for the non-SPO-11 mediated DNA damage ( described above ) . Finally , the total number of RAD-51 foci was elevated in smrc-1 mutants compared with wild type . This increase is consistent with the presence of both SPO-11-mediated and pre-meiotic DNA damage-associated DSBs in the smrc-1 germ line . We next evaluated RAD-51 foci in smrc-1;spo-11 meiotic nuclei as a means of visualizing aberrant DSBs . Few RAD-51 foci were observed in the spo-11 ( ok79 ) negative control , as expected [70] . RAD-51 foci were substantially more abundant in smrc-1 ( om138 ) ;spo-11 ( ok79 ) and smrc-1 ( om136 ) ;spo-11 ( me44 ) germ lines , particularly in leptotene-pachytene nuclei ( S3A Fig ) . These foci may represent DSBs that formed due to DNA damage during mitosis or pre-meiotic S phase . We note that RAD-51 foci were more abundant at late pachytene nuclei in smrc-1 single mutants , which presumably contain both SPO-11-induced and aberrant DSBs . This result raises the possibility that smrc-1 is required for the normal processing/repair of meiotic DSBs . The presence of elevated RAD-51 foci prompted us to ask whether meiotic CO events might have an altered distribution in smrc-1 mutants . For C . elegans , CO frequency is significantly greater on the autosomal arms than in the chromosome centers [71] . We first assayed CO frequency in control and smrc-1 animals in two small intervals within the central region of chromosome I . Our data indicated a several-fold increase in recombination between visible marker mutations in these intervals in smrc-1 mutants compared to controls ( S3B Fig ) . We next mapped CO distribution along the length of chromosome I by assaying single nucleotide polymorphisms ( SNPs ) . We generated a smrc-1 allele in the polymorphic CB4256 strain background and evaluated SNPs distributed across chromosome I ( S1A Fig; see Materials and methods ) . This strategy allowed us to measure recombination within five large intervals along the chromosome ( S3C and S3D Fig ) . The recombination frequency in these large intervals was not statistically different from controls except for a significant decrease in recombination frequency within interval 4 ( P<0 . 03 ) . Strikingly , we observed a >7-fold increase in the frequency of double CO events in the smrc-1 background relative to the control , which indicates impaired CO homeostasis . The differences obtained with the two mapping strategies could be explained by the size of the regions assayed; the domains with the genetic markers are both contained within the -8 . 5cM– 5cM region assayed by the chromosome-wide analysis . By interrogating a large domain with out cytological assay , fluctuations in recombination at the local level may be buffered by compensatory changes in nearby regions . Alternatively , the locally elevated CO rates between the visible markers might be explained by the presence of sequences that are prone to breakage , e . g . , microsatellite repeats , are located between the pairs of visible markers . Indeed , numerous microsatellite repeat sequences are located between unc-11 and dpy-5 , including a ~14 . 8 kb cluster ( at chromosomal position 4280037–4294876 ) ; several smaller microsatellite repeat clusters are located between dpy-5 and unc-13 ( www . wormbase . org ) . Such sequences could also account for the increased frequency of double COs . Our attention was originally drawn to SMRC-1 as a consequence of our co-immunoprecipitation ( co-IP ) studies designed to identify MET-2-associated proteins . In these studies , we performed IPs using anti-MET-2 polyclonal antibody ( described in Mutlu et al . , 2018 ) and consistently recovered a protein of the expected size , ~150 kD , that was absent from met-2 ( n4256 ) negative controls ( Fig 4A ) . SMRC-1 was recovered in these assays . To validate the association , we 3xflag-tagged the endogenous smrc-1 gene using CRISPR-Cas9 genome editing ( S1A Fig ) and performed anti-FLAG co-IP . We consistently recovered MET-2 in the 3xFLAG::SMRC-1 co-IP ( Fig 4B ) . We investigated SMRC-1 and MET-2 distribution in the germ line to identify where they are co-expressed . We visualized SMRC-1 by immunolabeling dissected 3xflag::smrc-1 gonads . We note that 3xflag::smrc-1 animals developed normally and had brood sizes similar to controls , suggesting the epitope tag did not substantially impact SMRC-1 function ( Table 1 ) . We detected 3xFLAG::SMRC-1 in proliferative and meiotic germ cell nuclei in XO males and XX hermaphrodites ( Fig 5A and 5B ) . In males and hermaphrodites , labeling intensity decreased as nuclei transitioned from the proliferative region into early meiosis ( leptotene-zygotene stages ) and then increased again as nuclei moved through pachytene and diplotene stages . In males , signal decreased again during the condensation phase of spermatogenesis and moved to the nuclear periphery , well apart from chromatin ( Fig 5A ) . In hermaphrodites , signal intensity was strongest in diakinesis stage oocytes , consistent with embryos inheriting substantial SMRC-1 protein ( Fig 5B ) . We visualized MET-2 with several reagents , including anti-MET-2 antibody , epitope-tagged transgene generated by mosI-mediated single copy insertion ( mosSCI ) , and endogenously-tagged MET-2 generated by CRISPR-Cas9 editing ( S1A Fig; Materials and methods ) [33] These reagents detected nuclear MET-2 throughout the germ line ( Fig 6 , S1B–S1D Fig ) , consistent with both our previous observation of nuclear MET-2 in embryonic nuclei using the same reagents [33] and also examination of adult somatic tissue [72] . We observed MET-2 puncta superimposed on a more diffuse signal in germline nuclei and , to a lesser extent , cytoplasm in both male ( Fig 6A ) and hermaphrodite ( Fig 6B ) germ lines . The MET-2 distribution appeared to shift as germ cells moved from the proliferative region into and through meiosis; nuclear puncta were more obvious in mitotic and leptotene-zygotene nuclei , and the signal became more evenly distributed as nuclei entered and progressed through pachytene stage ( Fig 6 ) . We conclude that germline MET-2 comprises nuclear and cytoplasmic pools . Nuclear puncta resemble the nuclear hubs observed in embryos which are thought to be sites of methyltransferase activity [33] . We co-visualized SMRC-1 and MET-2 using a 3xmyc::smrc-1 3xflag::met-2 strain generated by CRISPR-Cas9 genome editing ( S1A Fig ) . Labeling this strain verified that SMRC-1 and MET-2 are expressed in the same nuclei ( Fig 6C , S4 Fig ) . We observed partial overlap between 3xMYC::SMRC-1 and 3xFLAG::MET-2 signals within nuclei as would be expected for proteins that physically associate ( Fig 6C , S4 Fig ) . Human SMARCAL1 localizes to stalled replication forks and forms foci in response to HU treatment of cultured cells [41] . We tested the impact of stalled replication on SMRC-1 distribution by comparing the 3xFLAG::SMRC-1 signal in germ cells with and without HU treatment . For this assay , we treated L4 larvae with 25mM HU for 24 hours at 22°C and then dissected and immunolabeled the adult gonads . Germ cell nuclei located distal to the leptotene/zygotene region were enlarged and appeared to have ceased mitosis , consistent with robust activation of the replication checkpoint ( Fig 7A ) . We quantified the anti-FLAG signal and normalized it relative to ( i ) DAPI , ( ii ) mCherry-tagged histone H2B included in the strain background , and ( iii ) histone H3 . We consistently observed elevated SMRC-1 abundance in distal nuclei of HU-treated animals ( Fig 7A , S5 Fig ) , suggesting that DNA replication stress triggered an increased SMRC-1 abundance during mitosis . Zeller et al . ( 2016 ) reported that met-2 set-25 double mutants have reduced viability following HU treatment , suggesting that H3K9 methylation offers protection from replication stress [17] . DNA damage has been shown in other systems to increase H3K9me2 levels in other systems [3] . Regulated nuclear import of MET-2 is one way in which its activity is controlled in the embryo [33] . Given these observations , we hypothesized that nuclear MET-2 abundance in the proliferative germ line might increase under conditions of replication stress . We tested this idea by treating 3xflag::met-2 L4 larva with HU ( as described above for 3xflag::smrc-1 , see Methods ) and visualizing 3xFLAG::MET-2 by immunolabeling . We reproducibly observed elevated MET-2 levels in mitotic germ cell nuclei of HU-treated animals compared to untreated controls ( Fig 7B ) . We conclude that replication stress triggers an increased MET-2 accumulation in nuclei of proliferative germ cells . We performed anti-H3K9me2 labeling to determine if the increase in nuclear MET-2 correlates with increased activity . As previously reported , we observed weak or no H3K9me2 signal in the proliferative germ line and any signal that was present tended to be punctate and located near the nuclear periphery ( [21 , 27 , 73]; this study ) . In HU-treated germ lines , we observed weak , diffuse labeling that tended to be located more centrally ( Fig 7D ) . To compare the H3K9me2 signal in these two sets of nuclei , we modified the Corrected Total Cell Fluorescence calculation previously developed to compare cellular immunolabeling signal to calculate specifically a Corrected Total Nuclear Fluorescence ( CTNF ) value ( Fig 7D ) ( see Materials and methods ) . The CTNF was significantly greater for distal nuclei that had received the HU treatment , suggesting that replication stress led to an increase in H3K9me2 levels . Since nuclear SMRC-1 and MET-2 levels increase in the distal germline upon replication stress , we asked if SMRC-1 promotes the MET-2 increase . For this purpose , we generated a strain carrying the smrc-1 ( om138 ) mutation in a 3xflag::met-2 background and assayed the impact of HU treatment . We observed a significant increase in nuclear MET-2 abundance , however the increase was less pronounced and more variable than in smrc-1 ( + ) controls ( Fig 7C ) . These results are consistent with SMRC-1-dependent and -independent regulation of nuclear MET-2 accumulation during replication stress . In the course of these experiments , we noted that smrc-1 and wildtype germ cells responded differently to HU treatment . In wildtype , distal germline nuclei became notably enlarged and decreased in number , as reported in the literature ( e . g . , [74] ) . The size increase and number reduction were less pronounced in smrc-1 nuclei , although still significant ( Fig 8A ) . To investigate if smrc-1 mutants were resistant to mitotic arrest , we repeated the HU treatment and performed anti-H3S10phos ( histone H3 phosphorylated on serine 10 ) labeling to detect mitotic nuclei [75] . Untreated wildtype and smrc-1 ( om138 ) mutants had the same mitotic index , whereas HU-treated smrc-1 mutants had a significantly higher mitotic index than HU-treated wildtype controls ( Fig 8B ) . Hence , smrc-1 germ cells appear to be resistant to mitotic arrest . We note that the failure to elicit a cell cycle arrest is not due to an inability to respond to HU , as there was a decrease in mitotic nuclei numbers of HU exposure in smrc-1 mutants . Failure of mitotic arrest may explain why MET-2 abundance does not increase as much in the distal germ line of HU-treated smrc-1 mutants as it does in HU-treated wildtype . Mitotic arrest occurs when the mitotic DNA damage checkpoint has been tripped; this checkpoint is HUS-1-dependent and distinct from the later DNA damage checkpoint that triggers apoptosis [76] . Perhaps SMRC-1 promotes the mitotic DNA damage checkpoint , and hence the resistance to mitotic arrest observed in smrc-1 mutants . We asked whether SMRC-1 promotes germline H3K9me2 by immunolabeling smrc-1 mutants passaged for either two or 30 generations at 25°C . We evaluated smrc-1 ( om136 ) XX hermaphrodite and XO male germlines in the M-Z- F2 generation and in five smrc-1 ( ea8 ) lines in the F30 generation . In M-Z- F2 animals , the H3K9me2 labeling pattern appeared comparable to wild type in both smrc-1 XO and XX germ cells ( Fig 9A ) . Among F30 germ lines , the average H3K9me2 signal was weaker than wildtype in a majority of gonads evaluated ( Fig 9B ) . In a small subset , H3K9me2 signal was comparable to or greater than controls ( Fig 9B ) . H3K9me2 signal was similar among nuclei within individual germ lines , suggesting a systemic change in H3K9me2 regulation throughout the tissue . To investigate the genetic relationship between smrc-1 and met-2 , we generated a smrc-1 met-2 double knockout strain ( S1A Fig ) and assayed the smrc-1 met-2 phenotype in parallel with met-2 and smrc-1 single mutants ( Tables 1 and 2 ) . At 25°C , met-2 and smrc-1 homozygotes remained fertile for numerous generations . In contrast , smrc-1 met-2 double mutant fertility dropped to near zero by the F2 generation . The clutch size of smrc-1 met-2 M+Z- F1 double mutants was similar to smrc-1 M+Z- F1 single mutants , but a lower proportion of offspring were viable ( Table 1 ) . Only 8% of the viable smrc-1 met-2 M-Z- offspring were fertile ( Table 2 ) , and they produced very few embryos , only ~6% of which were viable ( Table 1 ) . We also observed a protruding vulva ( Pvl ) phenotype in ~43% of smrc-1 met-2 M-Z- animals ( Table 2 ) that may reflect DNA damage in the vulval precursor cells [77] . Overall , the smrc-1 met-2 phenotype is consistent with SMRC-1 and MET-2 acting redundantly to promote one or more essential germline process ( es ) . Given that MET-2 and SET-25 modify some common regions of the genome , we considered that SET-25 activity might contribute to SMRC-1-related processes . To address this question , we first investigated the impact of SET-25 loss on the smrc-1 developmental phenotype at 25°C . 100% of set-25 single mutants were fertile . ~8% of smrc-1 set-25 double mutants were sterile , similar to smrc-1 single mutants , and the two genotypes had similar developmental defects ( Table 2 , S2B Fig ) . We next investigated the impact of SET-25 loss on HU sensitivity . The response set-25 single mutants to HU resembled wild type and response of smrc-1 set-25 double mutants resembled smrc-1 single mutants ( Fig 1 ) . We conclude that SET-25 activity is not essential for protection from DNA replication stress , and loss of SET-25 activity does not impact the smrc-1 developmental phenotype . The met-2 ( n4256 ) set-25 ( tm5021 ) double mutant was previously described as slow growing with substantial embryonic lethality , elevated HU sensitivity , and increased CEP-1-dependent germline apoptosis at 25°C [17] . met-2 set-25 double mutants also produce some abnormal oocytes and have elevated apoptosis [18] . We regenerated the met-2 ( n4256 ) set-25 ( tm5021 ) double mutant and grew it in parallel with smrc-1 met-2 and smrc-1 set-25 to compare germline development of animals grown together under the same conditions . At 25°C , embryonic lethality was very high in met-2 set-25 double mutants and all adult escapers were fertile , as reported ( Table 2 ) . We also evaluated HU sensitivity in the met-2 smrc-1 set-25 triple mutant . Since sterility was high in the met-2 smrc-1 set-25 M-Z- F2 individuals , we analyzed the responsiveness of M+Z- F1 individuals to HU exposure ( Fig 1B ) . At the highest doses of HU , met-2 smrc-1 set-25 M+Z- sensitivity was significantly elevated compared to either smrc-1 met-2 or smrc-1 set-25 M+Z- double mutants and the effect on fertility ( i . e . , the effect in the germ line ) was particularly striking ( Fig 1B ) . Hence , H3K9 methylation per se combined with SMRC-1 together provide substantial protection from replication stress .
SMRC-1 promotes fertility , likely as a consequence of its roles in DNA repair and limiting DNA damage . We hypothesize that the increased sensitivity to replication stress and reduced ability to repair DNA lesions contribute to the smrc-1 developmental phenotypes . SMRC-1 is particularly important at elevated culture temperatures , and we note that the loss-of-function phenotypes of Drosophila Marcal1 and mouse SMARCAL are also more severe at elevated culture temperature [78] . SMRC-1 buffers against replication stress , limits R-loop accumulation , limits SPO-11-independent DSBs , promotes MET-2 accumulation in the nucleus under conditions of replication stress , and affects the distribution of meiotic crossovers . At stressful temperatures , SMRC-1 activity affects H3K9me2 accumulation throughout the germ line . The association with SMRC-1 may recruit MET-2 to the nucleus where they may function at the replication fork . SMARCAL1 family proteins are hypothesized to function outside of S phase to promote DSB repair [79 , 80] . and SMRC-1 may recruit MET-2 to help stabilize chromatin for repair in this context . Based on genetic data , SMRC-1 and MET-2 appear to both shared and distinct functions in the germ line . We hypothesize that SMRC-1 and MET-2 act together to limit germline sensitivity to replication stress . In contrast , the met-2 smrc-1 synthetic sterility may be the cumulative effect of severely reduced H3K9 methylation in combination with DNA damage beyond that at replication forks . We consider two non-mutually exclusive models for how the MET-2 –SMRC-1 association may promote genome integrity . First , SMARCAL1 family proteins associate with ssDNA at the replication fork , hence SMRC-1 may be well-positioned to recruit MET-2 for re-establishment of H3K9me2 marks on nascent chromatin ( Fig 10A ) . Reestablishing heterochromatin at repetitive sequences after DNA replication is important for maintaining genome stability [81–83] . Second , SMRC-1 may recruit MET-2 to DNA breaks , thereby stabilizing DNA and facilitating repair . The interaction could be important when breaks arise during replication and/or at another point in the cell cycle ( Fig 10B ) . SETDB1 is recruited to DNA damage sites directly and specifically in mammalian systems and SETDB1 enrichment is essential for proper repair of the DNA lesions [84] . Consistent with this finding , Checchi et al . ( 2011 ) observed elevated germline apoptosis and sensitivity to cep-1 loss in animals treated with met-2 RNAi , which may indicate a role for MET-2 in mediating the DNA damage response [85] . At repetitive regions , MET-2-mediated H3K9me2 deposition may have an additional beneficial effect of reducing the DNA replication rate to assure the complete and accurate replication of error-prone repetitive sequences . In this scenario , MET-2 may function in a positive feed-back loop to attract more SMRC-1 , thus reinforcing replication fidelity . It has been proposed that MET-2-mediated H3K9me2 deposition limits transcription of repetitive regions and thereby limits RNA:DNA hybrid formation at those sites [17] . Perhaps one way in which MET-2 limits RNA:DNA hybrid stability is by recruiting SMRC-1 , which may have a role in resolving the hybrids . Little is known about possible meiotic functions for SMARCAL1 family proteins as functional studies have only been performed in mitotic cells . Mammalian SMARCAL1 associates with the ssDNA binding protein , RPA , during DNA replication and catalyzes replication fork regression , ultimately promoting branch migration [38 , 43 , 86 , 87] . Holliday junctions , which resemble replication forks , are present during meiotic recombination , and C . elegans RPA ( RPA-1 ) is present in pachytene nuclei and promotes meiotic DSB repair [88 , 89] . The meiotic recombination pattern observed in smrc-1 mutants may therefore have multiple underlying causes . C . elegans meiotic DSBs are enriched on chromosomal arms where they inversely correlate with repetitive sequences and H3K9me2 enrichment [71 , 90] . These data fit with observations from a number of species that DSBs–and therefore COs–tend not to occur at repetitive sequences , perhaps in part due to H3K9me2 [2] . Our RAD-51 labeling data and diakinesis chromosome analyses indicate that SMRC-1 protects the genome from aberrant DSBs and inaccurate repair . In smrc-1 mutants , RAD-51 foci persisted late into pachytene , consistent with delayed DSB repair in the absence of SMRC-1 . In C . elegans , the process of CO homeostasis ensures that most DSBs are repaired via a non-crossover ( NCO ) mechanism and only one DSB per chromosome is resolved via CO [67] . Our mapping data indicate that SMRC-1 activity promotes CO homeostasis . One explanation for the loss of CO homeostasis in smrc-1 mutants may be that aberrant DSBs in the smrc-1 proliferative germ line are not subject to the same strict regulatory controls as SPO-11-induced breaks . An alternative hypothesis is that SMRC-1 activity limits CO frequency . Human SMARCAL1 promotes DSB repair via non-homologous end joining ( NHEJ ) in cultured cells [80] and Drosophila Marcal1 mediates the synthesis-dependent strand annealing ( SDSA ) step in DSB DNA repair [79] . SMRC-1 activity may limit meiotic recombination by promoting NCO repair , perhaps by recruiting/stabilizing MET-2 at repetitive regions . The association between MET-2 and SMRC-1 could serve as a surveillance system to prevent DSB formation at repetitive regions , thus limiting the occurrence of CO at these sequences .
Syracuse University issued an IACUC number to E . M . M . for the custom anti-MET-2 antibody generation , which was performed by Yenzym Antibodies LLC . The Syracuse University IACUC number is #09 = 021 . C . elegans were maintained according to standard methods [91] . Details of nematode strains , mutant construction by CRISPR , and epitope tagging can be found in S1 Text . Protein blots and immunohistochemistry were performed using standard methods . Detailed procedures , including antibodies used and quantification methods , can be found in S1 Text . MET-2 IP was performed with nuclear extract prepared from him-8 ( e1489 ) adults . 3xFLAG::SMRC-1 IP was performed with whole extract from endogenously-tagged 3xflag::smrc-1 adults . Detailed procedures can be found in S1 Text . Assays were carried put as previously described [92] . L1 larvae of different genotypes were treated with HU for a pulse of 16 hr at 25°C and then cultured using standard conditions . L4 larvae were treated with HU for 16 hr at room temperature ( ~22°C ) until adulthood , and then immunolabeled . Detailed HU treatment protocols can be found in S1 Text . We assayed suppression/reversion of the unc-58 ( e665 ) phenotype as described [59] in unc-58 control and smrc-1 ( ea8 ) ;unc-58 mutants raised at 20°C . To detect dog-1 enhancement , we assayed for deletions in vab-3 exon 5 as described [62] . Details are included in S1 Text . Six lines of balanced smrc-1 ( ea8 ) /qC1 were maintained at 25°C for three generations and then expanded to 16 unbalanced founders . Strains were maintained by serial passaging as described in the S1 Text . | Post-translation modifications to histone proteins are known to regulate gene expression and chromosomal events such as recombination . Histone modifications are highly dynamic and are deposited by large number of histone-modifying enzymes . Little is known about how these enzymes are regulated . Using a model system , the nematode Caenorhabditis elegans , we show that a conserved histone-modifying enzyme , MET-2 , associates with a conserved DNA repair protein , SMRC-1 . In mammals , the SMRC-1 homolog , SMARCAL1 , participates in repairing DNA that is damaged during replication . Focusing on the tissue responsible for production of sperm and eggs , the germ line , we find that SMRC-1 protects cells from DNA replication stress and promotes the accumulation of nuclear MET-2 . Moreover , SMRC-1 affects MET-2 germline activity ( as measured by histone modification state ) in populations grown for multiple generations at stressful culture temperatures . Genetic analysis indicates that MET-2 and SMRC-1 participate in a common mechanism to limit DNA damage in the germ line . We propose that histone modifications are regulated to promote DNA replication and DNA repair . | [
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"d... | 2019 | A DNA repair protein and histone methyltransferase interact to promote genome stability in the Caenorhabditis elegans germ line |
Circadian clocks are molecular timekeeping mechanisms that allow organisms to anticipate daily changes in their environment . The fundamental cellular basis of these clocks is delayed negative feedback gene regulation with PERIOD and CRYPTOCHROME containing protein complexes as main inhibitory elements . For a correct circadian period , it is essential that such clock protein complexes accumulate in the nucleus in a precisely timed manner , a mechanism that is poorly understood . We performed a systematic RNAi-mediated screen in human cells and identified 15 genes associated with the nucleo-cytoplasmic translocation machinery , whose expression is important for circadian clock dynamics . Among them was Transportin 1 ( TNPO1 ) , a non-classical nuclear import carrier , whose knockdown and knockout led to short circadian periods . TNPO1 was found in endogenous clock protein complexes and particularly binds to PER1 regulating its ( but not PER2’s ) nuclear localization . While PER1 is also transported to the nucleus by the classical , Importin β-mediated pathway , TNPO1 depletion slowed down PER1 nuclear import rate as revealed by fluorescence recovery after photobleaching ( FRAP ) experiments . In addition , we found that TNPO1-mediated nuclear import may constitute a novel input pathway of how cellular redox state signals to the clock , since redox stress increases binding of TNPO1 to PER1 and decreases its nuclear localization . Together , our RNAi screen knocking down import carriers ( but also export carriers ) results in short and long circadian periods indicating that the regulatory pathways that control the timing of clock protein subcellular localization are far more complex than previously assumed . TNPO1 is one of the novel players essential for normal circadian periods and potentially for redox regulation of the clock .
Circadian clocks are endogenous oscillators that have evolved in almost all eukaryotes to anticipate daily rhythms in their environment . In mammals , circadian rhythm generation is a cell-autonomous process with transcriptional-translational feedback loops as fundamental mechanism [1] . A key step is the rhythmic inhibition of the CLOCK-BMAL1 transactivation activity by a negative feedback complex that contains PER and CRY proteins [2] , which thereby inhibit their own expression . Circadian oscillations only occur because this negative feedback is delayed , i . e . after complex maturation , modification and nuclear translocation of PER and CRY proteins . While it is widely accepted that the regulation of subcellular localization of negative feedback components is a critical step for the generation of a normal near-24-hour period , our understanding of the mechanisms of nucleo-cytoplasmic translocation of mammalian clock proteins is limited . Several studies highlighted the importance of posttranslational modifications ( PTMs ) of clock proteins for the regulation of subcellular localization . For example , oscillations in nuclear abundance of CLOCK-BMAL1 are largely dominated by rhythmicity of BMAL1 protein and its PTMs , since overall CLOCK levels are barely rhythmic and the absolute abundance of BMAL1 protein seems to be much lower than of CLOCK [3] . Thus , BMAL1 levels are likely rate limiting for CLOCK-BMAL1 nuclear abundance . Indeed , BMAL1 is required for nuclear localization of CLOCK [4] , and nucleo-cytoplasmatic shuttling of BMAL1 seems to play an essential role for it [5] . A critical signal in this context is the phosphorylation of BMAL1 at Ser90 mediated by CK2α [6] , which promotes CLOCK-BMAL1 heterodimerization and—probably thereby—nuclear accumulation of both proteins . Circadian inhibition of CLOCK-BMAL1 transcriptional activity is fundamentally determined by the precisely timed activity of the PER/CRY complex . This nuclear complex has been found to be bigger than 1 MDa [7 , 2] and consists–in addition of PER and CRY proteins–of several additional proteins that are likely recruited to the CLOCK/BMAL1 heterodimer to contribute to transcriptional shutdown . Thus , the timing of nuclear localization and activity of this complex as well as its inactivation is of critical importance for circadian period . PTMs of PER and CRY proteins play dominant roles in this context . PER and CRY proteins are heavily phosphorylated in a circadian manner [3] regulating their subcellular localization as well as stability , which ultimately impacts in the nuclear activity of the PER/CRY complex [8–13] . As it is the case for CLOCK and BMAL1 , also PER and CRY proteins support each other’s nuclear localization [3 , 14 , 15] , thus anything affecting PER or CRY protein stability will likely also influence the activity of the inhibitory complex as a whole . PER proteins are the rate limiting component of the PER/CRY complex [3 , 16] and have been suggested to be primarily responsible for the timely nuclear accumulation of the dominant CLOCK/BMAL1 repressors–the CRY proteins [17–19] . Thus , PER stability as well as nuclear accumulation dynamics is of utmost importance for circadian rhythm generation . These studies demonstrate the critical role of timely nuclear localization of clock proteins for circadian rhythm generation . The precise molecular mechanism , by which nuclear localization of clock proteins occurs , however , is very little understood . Because of their size and the fact that they form various complexes already in the cytosol [2] , clock proteins cannot passively diffuse into the nucleus but have to be actively transported through the nuclear pore via nuclear import carriers . The predominant , “classical” nuclear import pathway involving Importin α and β , which recognize so-called classical nuclear localization signals ( cNLS ) , seems to play an important role in this context . Within the last two decades several functional cNLS were identified in clock proteins [20–23] . Indeed , misregulation of Importin α2 ( KPNA2 ) impairs clock development and alters the localization of PER proteins [24] . Also RNAi-mediated downregulation of Importin β ( KNPB1 ) affects circadian dynamics probably by decreasing nuclear localization of PER and CRY proteins [25] . While these studies focused on specific components of the classical nuclear import pathway , the fact that ~60 different proteins comprise the nucleo-cytoplasmic translocation machinery , suggests a much more complex regulation . Here , we present a systematic approach to test the impact of 62 genes involved in nuclear-cytosolic translocation for circadian rhythm generation . Using RNAi-mediated knockdown and live cell bioluminescence recording of circadian rhythms in reporter cells , we identify fifteen of those genes whose expression is essential for normal circadian dynamics . Among them was Transportin 1 ( TNPO1 ) , a non-classical nuclear import carrier , whose knockdown or knockout led to short circadian periods . One cargo of TNPO1 is PER1; it interacts with TNPO1 , and its ( but not PER2’s ) nuclear accumulation as well as nuclear import kinetics is reduced upon Tnpo1 knockdown . Interestingly , oxidative stress conditions increased binding between TNPO1 and PER1 and impaired nuclear PER1 import suggesting that TNPO1-mediated PER1 nuclear localization is a redox-sensitive input into the circadian clock .
To identify nucleo-cytoplasmic translocation associated genes essential for circadian dynamics we RNAi-knocked down the expression of 62 genes of the nucleo-cytoplasmic translocation machinery . These included 29 nuclear pore complex components as well as 27 nuclear import and export carriers . Human osteosarcoma ( U-2 OS ) reporter cells—an established and robust cellular clock model—expressing luciferase from a Bmal1 promoter fragment were transduced with up to three shRNA constructs per target gene , and circadian luciferase activity rhythms were monitored over a period of one week . Fifteen genes turned out to be essential for normal ~24 hour period length . The depletion of nine of them led to period lengthening and six of them to period shortening ( Fig 1 and S1 Table ) . Interestingly , while the genes whose knockdown led to longer periods included the classical import carrier Kpnb1 ( also known as Importin β ) , depletion of the alternative nuclear import carrier Tnpo1 ( also known as Karyopherin β2 ) led to an opposite period phenotype shortening the circadian period by 1–1 . 5 hours ( Fig 2A ) . In addition , knockdown of Tnpo1 altered clock gene expression resulting in early phases of circadian rhythms and–in particular for the Period genes–also in reduced overall expression levels ( S1 Fig ) . Together , these data suggest the existence of a yet unknown nuclear import pathway important for circadian oscillations . To validate these results , we created a total Tnpo1 knockout cell line using CRISPR/Cas9 genome editing technology . We lentivirally transduced U-2 OS reporter cells with CRISPR/Cas9 vectors harboring different guide RNAs ( gRNA1 and gRNA2 ) , selected for positively transduced cells and tested circadian dynamics . One of the two tested guide RNAs ( gRNA1 ) caused a significant period shortening of the cell population by ~1 hour ( gRNA1; p < 0 . 001 ) as well as an efficient depletion of TNPO1 protein , while gRNA2 was ineffective with regard to both period shortening and protein depletion ( Fig 2B ) . To test , whether we obtained a total knockout of the Tnpo1 gene in the gRNA1 targeted cells , we performed limited dilution and sequenced the single cell clones to detect insertions or deletions ( indels ) causing shifts of the open reading frame . From the identified six single cell clones with indels on both Tnpo1 alleles , only one had indels causing frame shifts that lead to premature STOP codons in the open reading frame of Tnpo1 . As expected from the cell population , this total knockout clone also displayed ~1–1 . 5 hour shorter circadian rhythms as well as no detectable TNPO1 protein expression ( S2 Fig ) . In contrast to classical nuclear localization signals , most TNPO1 cargoes contain non-classical motifs , so called M9 NLSs . While M9 NLSs are less well defined compared to classical NLSs , most M9 NLSs contain a PY motif ( 23 out of 24 known TNPO1 cargos; [26] ) . In addition , it has been shown that TNPO1 interaction to e . g . FOXO4 is independent of M9 NLSs and rather mediated by covalent intermolecular disulfide bridges [27] . If the period shortening upon Tnpo1 depletion is a direct effect on the core circadian oscillator , canonical clock proteins might be direct targets of TNPO1 and thus might contain recognition motifs in their primary structure . To identify potential TNPO1-binding sites in circadian clock proteins , we searched for putative M9 NLSs in the protein sequences of BMAL1 , CLOCK , CRY1 , CRY2 , PER1 and PER2 . In addition to classical NLSs , five of the investigated clock proteins contain at least one evolutionary conserved PY motif ( CRY1 , CRY2 , PER1 , PER2 and BMAL1; S2 Table ) . Due to the lack of structural knowledge , for many of these motifs it is unknown , whether they are surface-exposed in the native protein . Nevertheless , we tested , whether these sequences ( 40–50 amino acids ) are in principle able to promote nuclear localization in a TNPO1-dependent manner . To this end , we expressed them as fusion proteins with cyan or yellow fluorescent protein ( CFP or YFP ) and analyzed the subcellular localization of these fusion proteins in U-2 OS and HEK293 cells either with or without endogenous TNPO1 . While six of the ten investigated clock protein-derived PY-peptides could not drive CFP in the nucleus , four PER and CRY peptides promoted CFP nuclear localization similar to a positive control peptide derived from the known TNPO1 cargo hnRNP A1 [28] . These effects were at least in part TNPO1-dependent , since nuclear localization of the fusion proteins was diminished when endogenous Tnpo1 was downregulated ( S3 Fig ) . In addition , mutation analyses showed that the TNPO1-promoted nuclear localization of the YFP fusion protein does not only depend on the presence of the PY-motif , but also of upstream basic residues that have been suggested to contribute to TNPO1 recognition [29] ( S4 Fig ) . Together , these data are compatible with a role for TNPO1 in regulating the nuclear localization of the negative limb circadian clock proteins . To test whether TNPO1 interacts with endogenous circadian clock proteins , we analyzed nuclear circadian clock protein complexes in unsynchronized U-2 OS cells using immunoprecipitation with two different anti-CLOCK or control IgG antibodies followed by mass spectrometry . Indeed , endogenous TNPO1 was found to be part of such complexes together with known circadian clock proteins such as CLOCK , BMAL1 , PER1 , CRY1 and others ( Fig 3A ) . To test for binding of TNPO1 to PER and CRY proteins , we performed co-immunoprecipitation experiments in HEK293 cells with epitope-tagged clock proteins but only detected very weak if any interaction signals ( not shown ) . To increase the sensitivity of detecting also transient interactions , we performed a co-immunoprecipitation experiment using a luciferase-based readout . Since the putative TNPO1 recognition motifs of CRY proteins are not surface-accessible in the native protein [30 , 31] we focused on PER proteins as established regulators of the negative feedback complex’s subcellular localization . MYC-tagged TNPO1 was immunoprecipitated from HEK293 cell lysates also containing PER1 or PER2 fused to full-length firefly luciferase . In addition , switching the orientation of the assay , we immunoprecipitated V5-tagged PER1 and PER2 and tested for TNPO1-luciferase binding . Only for PER1 a consistent and significant interaction with TNPO1 was detected in both orientations of the co-immunoprecipitation experiment ( Fig 3B ) , while PER2 was only weakly detected in TNPO1-complexes ( and vice versa ) . These results are further supported by luciferase complementation experiments where full-length PER proteins and TNPO1 were expressed as fusion proteins with firefly C- and N-terminal luciferase fragments in HEK293 cells [32] . Upon binding of PERs with TNPO1 , a functional luciferase is reconstituted whose activity was measured in cell lysates . In this assay , PER1 and to a lesser extent ( not significant ) also PER2 but not the negative control βGAL promoted luciferase complementation ( S5 Fig ) . Taken together , in both types of binding assays PER1 showed a robust interaction with TNPO1 , while TNPO1 binding to PER2 was less reliably detectable suggesting that PER1 is a bona fide cargo of TNPO1 . This is further supported by results from immunoprecipitation experiments using U-2 OS cells stably expressing a PER1-luciferase fusion protein , where we detected specific interaction with PER1 upon immunoprecipitation of endogenous TNPO1 ( Fig 3C ) . To investigate , which region of the PER1 sequence is responsible for TNPO1 interaction , we generated truncated versions of PER1 , which lack either one or two C-terminal PY-motifs ( Fig 3D top ) . While the shorter version ( amino acids 1–706 ) did not specifically precipitate with TNPO1 , the longer version ( amino acids 1–924 ) was still significantly detectable in the TNPO1 precipitate , albeit with less intensity ( Fig 3D ) indicating that the C-terminal region of PER1 is required for TNPO1 binding . To test , whether these C-terminal PY-motifs and/or C-terminal cysteine residues ( in analogy to the FOXO4-TNPO1 interaction [27] ) are required for PER1-TNPO1 interaction , we generated a full-length , but mutant form of PER1 , in which both PY-motifs and all seven cysteine residues that are conserved between mouse and human are exchanged by alanine residues ( Fig 3D top ) . Although the expression level of this mutant PER1 fusion protein was similarly high as wild-type ( as estimated by the luciferase counts in the cell lysates ) , it did not specifically precipitate together with TNPO1 ( Fig 3D ) indicating that the C-terminal PY motifs and/or cysteine residues are required for PER1-TNPO1 interaction . It has been shown that the classical Importin α/β pathway has a dominant role for nuclear localization of PER proteins [20]; yet we still detected substantial nuclear staining of a PER1-Venus fusion protein , whose classical NLS has been mutated , which is further increased when nuclear export is pharmacologically inhibited ( Fig 4A ) . While it is formally possible that nuclear localization of this mutant PER1 is mediated by endogenous interacting proteins that are shuttled via the Importin α/β-dependent pathway , we suggest that alternative nuclear transport pathways contribute to PER1 nuclear localization . To study whether TNPO1 acts as nuclear carrier for PER proteins , we analyzed the subcellular distribution of PER-Venus fusion proteins in the presence or absence of endogenous TNPO1 in unsynchronized U-2 OS cells . While the localization of PER2 is not altered upon Tnpo1 depletion using RNAi , PER1’s localization is significantly less nuclear when TNPO1 is absent ( Fig 4B , S6 Fig ) suggesting that TNPO1 promotes PER1 but not PER2 nuclear translocation . If TNPO1 is a nuclear import carrier for PER1 , also the nuclear import kinetics should be decreased upon Tnpo1 depletion . To test this , we performed fluorescence recovery after photobleaching ( FRAP ) experiments again with or without TNPO1 . We bleached nuclear PER1-Venus in U-2 OS cells and imaged the recovery of fluorescence thereafter every 2 . 5 minutes , which largely corresponds to the nuclear import of PER1-Venus . Upon Tnpo1 depletion ( S6 Fig ) , the nuclear recovery of the PER1-Venus fluorescence is substantially slowed down with a mean 25% recovery time of 33 minutes in contrast to 13 minutes in controls ( Fig 4C , S1 and S2 Movies ) . A less efficient Tnpo1 knockdown construct resulted in an intermediate 25% recovery time of 22 minutes suggesting a dependence of PER1 nuclear import kinetics on TNPO1 expression levels . Such a correlation was not seen for PER2 , where nuclear import kinetics was not significantly different when Tnpo1 was depleted ( Fig 4C ) . TNPO1-mediated nuclear transport has been described to be modulated by reactive oxygen species [27 , 33] . For example , the nuclear import of the transcription factor FOXO4 is mediated via heterodimerization with TNPO1 through an intermolecular disulfide bond [27] . To test whether the TNPO1-PER1 interaction also responds to oxidative stress , we performed co-immunoprecipitation experiments after briefly treating cells or cell lysates with hydrogen peroxide ( H2O2 ) . Indeed , H2O2 treatment resulted in significantly higher interaction signals of PER1 ( but not PER2 ) and TNPO1 ( Fig 5 , S7 Fig ) . This increase in interaction was H2O2 dose-dependent and also occurred with an alternative oxidizing reagent ( diamide ) but binding is abolished under reducing conditions ( S8 Fig ) . Interestingly , increased binding upon H2O2 treatment did not lead to an increased nuclear import–on the contrary . When we measured subcellular localization of PER1-Venus in H2O2-treated cells , we observed significantly less nuclear PER1 ( Fig 6A ) . This effect was TNPO1-dependent and specific for PER1 , since H2O2-treatment neither affected PER1 localization in Tnpo1-depleted cells ( Fig 6A , S6 Fig ) nor did it alter PER2-Venus subcellular localization ( Fig 6A ) . In addition , H2O2-treatment also affected the subcellular localization of the truncated version PER11-924-Venus , but not the shorter PER11-706-Venus version ( Fig 6B , S9 Fig ) . Since the short PER11-706 lacks the cNLS in addition to the two C-terminal PY-motifs and seven conserved cysteine residues ( Fig 3D top ) , it is formally possible that any H2O2-mediated effect is undetectable due to the overall cytosolic localization of the fragment . Nevertheless , since PER11-706 did not precipitate specifically with TNPO1 ( see Fig 3D ) these data may suggest that cysteine residues between amino acids 706 and 924 contribute to the H2O2-mediated alteration in subcellular localization of PER1 . Furthermore , H2O2 decelerated the rate of nuclear PER1-Venus import measured in FRAP experiments but it had no effect on cells where Tnpo1 is downregulated ( Fig 6C , S3 and S4 Movies ) . Together , this indicates that reactive oxygen species ( ROS ) modulate the nuclear import of PER1 by strengthening its interaction to TNPO1 , yet in an unexpected direction–ROS slows down nuclear import of PER1 .
Nuclear localization of circadian clock proteins at the right time of day–in particular of the negative limb–is crucial for circadian dynamics , since it defines the delay in negative feedback that determines the endogenous period . For example , in the nucleus PER and CRY proteins are thought to be almost exclusively present in a large 1 . 9 MDa complex . In the cytoplasm , however , PER and CRY proteins are incorporated into at least four ( putative precursor ) complexes of ∼0 . 9–1 . 1 MDa , two of which include PER1 but not PER2 [2] . Given the sizes of these complexes , active nuclear transport involving specialized import carriers is required for a directed and timely nuclear localization . Here , we show that nuclear shuttling of clock proteins ( specifically PER1 ) is far more complex than previously assumed . Using systematic genetic perturbation of more than 60 components involved in nuclear-cytosolic translocation , we identified Transportin 1 ( among others ) —a non-classical nuclear import carrier—to be essential for a normal circadian period . TNPO1 interacts with PER1’s C-terminal region and is required for its timely nuclear localization , while TNPO1 depletion has no effect on PER2 localization . Yet , both PER proteins also contain recognition motifs for the classical import mediated by the Importinα/β pathway that has been shown to be also critical for PER protein localization [20 , 34 , 35] as well as correct circadian period [25] . This implies that at least PER1 ( and maybe also associated proteins ) is transported to the nucleus by the classical pathway as well as the non-classical pathway via TNPO1 . In fact , upon mutation of the classical NLS , the nuclear localization of PER1 is not completely abolished . It is not uncommon that one protein can be transported in the nucleus by more than one carrier [26] . Importantly , we do not exclude that TNPO1 has additional cargoes , whose timely transport is important for circadian dynamics . For example , although the PY-motifs found in CRY proteins are not surface-exposed in the native proteins , it is formally possible that CRYs bind to TNPO1 via their ( reactive ) cysteine residues that can occur in an oxidized state in cells [36] . It is intriguing that the effect on the circadian period upon knockdown of Importin β or TNPO1 goes in the opposite direction–long for Importin β depletion and short for TNPO1 depletion—although the depletion of both carriers leads to attenuated nuclear localization of PER1 . This may be explained by the higher specificity of TNPO1 for primarily PER1-containing cytosolic sub-complexes [2] , while Importin β probably contributes to shuttling of all PER/CRY complexes in the nucleus [25] . While the short period upon TNPO1 depletion is in agreement with the short period in wheel running behavior of Per1 knockout mice [37] , a mechanistic explanation is still difficult , since we do not fully understand , whether PER1 is the only TNPO1 cargo relevant for circadian dynamics , whether PER1-containing complexes are shuttled at different circadian times and with a different kinetics and whether ( compared to PER2 ) PER1 has a fundamentally different role in the nucleus . We speculate that Tnpo1 knockdown blocks an efficient nuclear transport of PER1 leading to an altered composition of the nuclear PER/CRY complex with more PER2 occupying the “PER slots” in the complex . We hypothesize that a complex with more PER2 has a higher repressive potential ( which may explain the lower transcript levels of Per genes upon Tnpo1 knockdown ) . This is in agreement with recent data from the Weitz lab [2] showing that loss of PER1 or PER2 differentially affects the actions of CK1δ within the nuclear PER complex , which might have an effect on its repressive power . An alternative hypothesis may be that Tnpo1 knockdown allows PER1 to incorporate into the PER/CRY complexes more rapidly leading to a faster “maturation” and shuttling of the nuclear complex , since the competition between Importin β-mediated and TNPO1-mediated nuclear translocation is shifted towards the presumably faster Importin β-mediated shuttling . This might lead to both a more efficient transcriptional repression ( reflected by lower transcript levels of Per genes ) and a shorter period . In recent years , it became increasingly obvious that cellular redox state and the canonical transcriptional-translational circadian clock are intimately linked ( for a review , see [38] ) . Yet , while it is well accepted that redox state is under circadian regulation and feeds back to the clock ( also as possible adaptation to redox stress ) the underlying molecular mechanisms are very poorly understood . Here , we identify an additional link between redox state and circadian clock by showing that TNPO1-PER1 interaction is strengthened upon oxidative stress—very similar to the effects observed for other TNPO1 cargoes , i . e . FOXO4 [27] and DJ-1 [33] . Whether this increased binding occurs via disulfide bonds ( as for the FOXO-TNPO1 interaction ) is currently unclear , but it is conceivable given the reactive cysteine residues described for PER proteins [36] . In fact , both truncation of the C-terminal ~500 amino acids of PER1 that contain two PY-motifs and seven conserved cysteine residues as well as mutation of all these residues abolished TNPO1 binding . Since for PER2 C-terminal cysteine residues ( amino acids 1210 and 1213 of mPER2 ) have been implicated in CRY1 binding [36] , it is possible that also for PER1 such C-terminal cysteine residues are relevant for CRY binding . Further work is needed to pinpoint the exact location and characterize a potentially differential role of the putative reactive cysteine residues in PER1 . Surprisingly , however , increased binding to TNPO1 upon oxidative stress did not lead to accelerated nuclear import–on the contrary , import was slowed down . This was very specific for PER1 and TNPO1 , since firstly PER2 import rate was unaffected by redox stress and secondly redox stress had not effect on PER1 import kinetics when TNPO1 was depleted . We can only speculate , why increased binding of PER1-TNPO1 leads to slower import ( similar to Tnpo1 knockdown ) : We probably observe in FRAP experiments the combined effect of Importin β and TNPO1 on PER1 import rate . Assuming that Importin β-mediated transport is faster than TNPO1-mediated transport , a stronger binding to TNPO1 might shift the relative contribution of the two carriers towards the slower one . Preventing PER1 nuclear localization in oxidative conditions might be a mechanism to boost the expression of the antioxidant defense master regulator Nrf2 , a CLOCK/BMAL1 target gene that can it is inhibited by PER/CRY complex in the nucleus [39] . In addition , PER1 has been assigned an anti-apoptotic role [40 , 41] , thus a reduced nuclear localization of PER1 in oxidative conditions might be protective through promoting apoptosis . Future experiments are required to unravel the mechanisms and impact of TNPO1-mediated redox crosstalk to the clock . Apart from TNPO1 , we identified several additional players associated with nuclear-cytoplasmic translocation to be important for circadian dynamics . Again , knockdown of such components resulted in period changes with opposite directions even if they seem to be involved in similar processes . For example , knockdown of nuclear pore complex proteins NUP160 , NUP153 or NUP85 led to long and knockdown of NUP54 to short periods for yet unknown reasons . Similarly , depletion of nuclear export carrier XPO1 ( also known as Exportin 1 ) resulted in long period , while knockdown of RanBP16 ( also known as Exportin 7 ) in short periods . The genes kpnb1 , xpo1 , sec13 , which show a circadian phenotype upon knockdown in our screen , have been previously suggested to alter circadian dynamics upon knockdown or inhibition . KPNB1 mediates PER/CRY nuclear translocation and is required for a normal circadian clock function [25] . Pharmacological inhibition of XPO1 has been shown to lengthen the circadian period in an inhibitor dose-dependent manner [14] . In addition , the nucleo-cytoplasmic translocation protein SEC13 was identified as an essential component for normal circadian rhythm generation [42] . Different members of the kapα-family , KPNA1 , KPNA3 and KPNA7 have been shown to bind to wild type CRY2 but not a cNLS mutant of the circadian clock protein [21] . In line with those findings , altered PER1/PER2 localization upon misregulation of kpna2 expression was reported [24] . However , we did not find alterations upon knockdown of individual members of the kapα family members , which may be due to redundancy or compensation effects by other members of this gene family . In general , in screening endeavors , negative results need to be carefully interpreted , because of such effects and because knockdown efficiency cannot be evaluated for every single shRNA construct . Together , these data emphasize our lack in knowledge about how the cell achieves timely subcellular localization of clock protein complexes and its associated consequences for circadian dynamics . We predict several levels of complexity: ( i ) clock proteins have more than one carrier , e . g . PER1 ( and associated proteins ) is transported by Importin β and TNPO1; ( ii ) transport processes might be regulated in a time-dependent manner; e . g . Tnpo1 transcript levels are rhythmic ( almost in-phase with Per1 ) and anti-phasic to Importin β transcript rhythms [43 , 44]; ( iii ) signaling may have an impact on the relative usage of the alternative carriers; e . g . redox stress increases interaction of PER1 with TNPO1 and slows down its nuclear import . Considering a similar complexity for nuclear export processes of both clock proteins and clock mRNAs , it becomes obvious that much more work is needed to unravel such regulatory mechanisms . Therefore , the work presented here identifying TNPO1 as carrier for PER1 , its role for circadian dynamics as well as its regulation by cellular redox state is a first step in this direction .
RNAi constructs were purchased from Open Biosystems . Lentiviruses were produced in HEK293T cells in a 96-well plate format essentially as described [45] . Virus-containing supernatants were filtered and U-2 OS ( human , American Type Culture Collection [ATCC] # HTB-96 ) reporter cells were transduced with 100 μl of virus filtrate plus 8 ng/μl protamine sulfate . After 1 d , medium was exchanged to puromycin-containing ( 10 μg/ml ) medium prior to bioluminescence recording . U-2 OS cells ( human , ATCC HTB-96 ) stably expressing firefly luciferase from a Bmal1 promoter fragment [11] were seeded either onto a white 96-well plate ( 2×104 cells/well ) or in 30 mm NUNC dishes ( 2x105cells/well ) . After 72 hours , cells were synchronized with dexamethasone ( 1 μM ) for 30 minutes , washed with PBS and cultured in Phenol-Red-free DMEM containing 10% fetal bovine serum , antibiotics ( 100 U/ml penicillin and 100 μg/ml streptomycin ) and 250 μM D-luciferin ( Biothema , Darmstadt , Germany ) . Bioluminescence recordings were performed at 35–37°C in a 96-well plate luminometers ( TopCount , PerkinElmer , Rodgau , Germany ) or LumiCycle ( Actimetrics , Düsseldorf , Germany ) . Data were analyzed using ChronoStar software as described previously [11] . The generation and validation of U-2 OS TNPO1 knockout cells was performed as previously reported for FBXL3 knockout U-2 OS cells [46] . Briefly: Oligonucleotides specific for the target site of Tnpo1 were designed using the Optimized CRISPR Design tool ( http://crispr . mit . edu/ ) and ligated into the lentiCRISPR v2 plasmid ( Addgene #52961 ) [47] using a BsmBI restriction site . PCR-products for sequencing were phosphorylated and ligated into a pUC19 vector . Single clones were sequenced using the M13 forward primer . Total RNA was prepared using Pure Link RNA Mini Kit ( Life Technologies ) according to the manufacturer’s protocol and then reversely transcribed to cDNA using M-MLV Reverse Transcriptase ( Life Technologies ) . Quantitative PCR was performed with SYBRGreen fluorescence assays and analyzed in a CFX96 machine ( Bio-Rad , Munich , Germany ) . For quantitative PCR , QuantiTect primers ( Qiagen ) were used except for Gapdh ( hGAPDH_fwd: TGCACCACCAACTGCTTAGC , hGAPDH_rev: ACAGTCTTCTGGGTGGCAGTG ) . The transcript levels were normalized to Gapdh and evaluated according to the 2-ddCt method . Western blotting was performed essentially as described [11] . Briefly , cells were harvested in RIPA lysis buffer containing 1:100 protease inhibitor cocktail ( Sigma , Munich , Germany ) or PKB lysis buffer [27] without protease inhibitors . Equal amounts of protein were separated by SDS-PAGE using 4% to 12% Bis-Tris gels ( Life Technologies ) , transferred to nitrocellulose membrane , and incubated over night with anti-TNPO1 antibody ( 1:1000 , ab10303 , Abcam ) , anti-βACTIN ( 1:100 , 000 , A3853 , Sigma ) anti-V5 ( R960-25 , Invitrogen ) or anti MYC-antibody ( sc40 , Santa Cruz Technologies ) . Next day , membranes were probed with HRP-conjugated secondary antibodies ( donkey anti rabbit ( sc2305 , Santa Cruz Technologies ) or goat anti mouse ( sc2005 , Santa Cruz Technologies ) , 1:1000 in TBST ) , and a chemiluminescence assay was performed using Super Signal West Pico substrate ( Pierce , Rockford , IL ) followed by protein detection . For subcellular distribution assay of PY-peptides , corresponding sequences were PCR-amplified from U-2 OS wild type cDNA and ligated into pEYFP or pECFP ( Clontech ) using BglII and SalI restriction sites . RNAi constructs , harboring a GFP , were mutated to generate an out-of-frame shift and thus non-fluorescent GFP expression vectors . Fluorescence imaging was performed using either the Leica DMIL LED Fluo fluorescence microscope , the LeicaDM6000 confocal microscope Sp5 or the Olympus IX81 confocal microscope and the Leica Application Suite software , V3 . 7 or the Olympus Fluoview software . Image analysis were performed using ImageJ 1 . 44p . We performed a modified ( unpublished ) standard chromatin immunoprecipitation ( ChIP ) in triplicates using 1 mg of formaldehyde cross-linked protein extracts from unsynchronized U-2 OS with the following antibodies: rabbit anti-CLOCK ( Abcam ) , rabbit anti-CLOCK ( Cell Signaling ) and rabbit anti-IgG ( Cell Signaling ) . After the final wash of the ChIP , proteins bound to ChIP-grade agarose beads ( Cell Signaling ) were digested into peptides as follows . First , 50 μl of digestion buffer ( 2 M urea in 50 mM Tris , pH 7 . 5 , 2 mM DTT and 1 μg of trypsin ) was added and incubated for 30 min at 37°C . Subsequently , beads were spin down and the supernatants collected and saved . Then , another 50 μl of 2 M urea in 50 mM Tris , pH 7 . 5 , and 10 mM chloroacetamide was added to the beads prior to incubation at 37°C for 5 min . Beads were then spin down and the supernatants were collected and combined with those saved from the previous step . The supernatant mixture was then incubated over-night at 25°C to complete protein digestion that was stopped in the morning by adding 1 μl trifluoroacetic acid . Peptides were then cleaned for MS measurement using SDB-RPS stage tips as described [48] . Half of the peptide volume was used for the analysis using an LC 1200 ultra-high-pressure system ( Thermo Fisher Scientific ) coupled via a nano-electrospray ion source ( Thermo Fisher Scientific ) to a Q Exactive HF Orbitrap ( Thermo Fisher Scientific ) . Prior to MS , the peptides were separated on a 50 cm reversed-phase column ( diameter of 75 mm packed in-house with ReproSil-Pur C18-AQ 1 . 9 mm resin [Dr . Maisch GmbH] ) over a 120 min gradient of 5%-60% buffer B ( 0 . 1% formic acid and 80% ACN ) . Full MS scans were acquired in the 300–1 , 650 m/z range ( R = 60 , 000 at 200 m/z ) at a target of 3e6 ions . The fifteen most intense ions were isolated , fragmented with higher-energy collisional dissociation ( HCD ) ( target 1e5 ions , maximum injection time 120 ms , isolation window 1 . 4 m/z , NCE 27% , and underfill ratio of 20% ) , and finally detected in the Orbitrap ( R = 15 , 000 at 200 m/z ) . The raw MS files were processed using MaxQuant ( version 1 . 5 . 5 . 2 ) with the integrated Andromeda search engine using FDR < 0 . 01 [49] . Variable modifications for oxidized methionine ( M ) and acetylation ( protein N-term ) as well as a fixed modification for carbamidomethyl ( C ) were included in the search . The standard “match between runs” option was enabled . For the identification of peptides and proteins the UniProt human FASTA database ( from September 2014 ) was used . Bioinformatics analyses were performed with the Perseus software ( version 1 . 5 . 4 . 1 ) [50] . After removing potential contaminants as well as reverse sequences , label free intensities were transformed to logarithm with base 2 and entries that contained less than 2 values in at least one group ( IgG control , CLOCK ( Abcam ) or CLOCK ( Cell Signaling ) ) were filtered out . Missing values from the remaining proteins were imputed using random values from the normal intensity distribution with a down shift ( 2 . 3 ) and a width 0 . 3 ( as described in [51] . We then performed a Welch’s t-test using the triplicate label free intensities of each protein in the IgG ( control ) versus either CLOCK precipitates . For CoIP assays full-length coding sequences ( or mutant/truncated versions ) of PER1 , PER2 and TNPO1 were cloned into Gateway destination vectors ( pcDNADest40-V5 , pEFDest51-Luc , pcMYC-CMV-D12 ) according to the manufacturer’s protocol ( Invitrogen , Darmstadt , Germany ) . HEK293 cells were grown to 60% confluence , transfected with equal amounts of DNA with Lipofectamine2000 ( Thermo Fisher Scientific ) according to the manufacturer's protocol . 48 hours after transfection , cells coexpressing either PER1/2-V5 and TNPO1-Luc or PER1/2-Luc and MYC-TNPO1 were harvested in PKB buffer [27] without any protease and phosphatase inhibitor cocktails . To generate a U-2 OS cell line stably expressing PER1-LUCIFERASE , the full-length coding sequences of PER1 was cloned in to a pLenti6 vector with the CDS of firefly luciferase at the C-terminus . Also for these cells , harvest of cell lysate was performed using PKB buffer [27] without any protease and phosphatase inhibitor cocktails . About 500 μl of whole cell lysates ( corresponding to 1 to 4 mg total protein ) were pre-cleared with 30 μl of agarose G+ beads at 4°C for one hour on a rotating wheel . Lysates were centrifuged at 3500 rpm at 4°C for 5 minutes . Supernatant was transferred to low-binding tubes and 2 μg of either anti-TNPO1 ( ab10303 , Abcam ) , anti-MYC ( sc-40 , Santa Cruz Technologies ) , anti-V5 ( R960-25 , Invitrogen ) or normal mouse IgG ( sc-2025 , Santa Cruz Technologies ) antibody were added and incubated for one hour on a rotating wheel prior to washing and luminescence measurement . For determination of bioluminescence , the CoIPs were washed two to four times with ice cold 1 x PBS and dried with a 27G x 0 . 75” syringe . Repeated measurements using the β-scout device ( PerkinElmer ) were performed up to 10 minutes . Luminescence counts were analyzed relative to the counts of the normal mouse IgG control CoIPs . For the expression of Venus-fusion proteins , either full-length or mutant/truncated versions of mPER2 CDS or mPER1 CDS were shuttled into a pLenti6 vector with the CDS of the fluorophore at the C-terminus [32] . Confocal microscopy of live cells was performed with an Olympus IX81 microscope ( Olympus , Tokyo , Japan ) with a ×60 ( 1 . 35 numerical aperture ) water-immersion objective in a climate chamber at 37°C under 5% CO2 . Dynamics of nuclear import were measured by bleaching nuclear fluorescence of cells expressing Venus-tagged versions of PER1 or PER2 . Recovery of fluorescence was observed by taking pictures every 2 . 5 minutes . Mean nuclear and cytoplasmic fluorescence was calculated and mean background fluorescence was subtracted . Initial nuclear fluorescence was set to 1 . 0 and the bleached fraction was set to 100% [52] . Nuclear recovery was normalized to changes in cytoplasmic fluorescence to compensate for overall bleaching due to repeated measurements . The nuclear export inhibitor leptomycin B ( LMB ) was added 60 min before imaging at a final concentration of 10 ng/ml . Unless indicated , induction of oxidative stress was performed using 200 μM hydrogen peroxide ( or 200 μM diamide ) for 30 minutes prior to measurements and imaging . For the luciferase-based CoIPs H2O2 was added approximately every hour from the time point of cell lysis till measurement of luminescence . To induce reducing conditions , Tris ( 2-carboxyethyl ) phosphine ( TCEP ) was added to PKB lysis buffer at a final concentration of 1 mM . Notably , the H2O2 concentration used here is much higher than measured under physiological or pathophysiological conditions , in which concentrations usually do not exceed the low-micromolar range . However , exogenously added H2O2 rapidly degrades by cellular catalase and peroxidases , thus it is very common in bolus-based experiments that H2O2 concentrations in the upper micromolar or even millimolar range are used to evoke a cellular response [for a review see [53]] . The luciferase complementation assay was performed as described in Kucera et al . , 2012 [32] . Briefly: CRY1 , βGal , PER1/2 and TNPO1 CDS were cloned into pcdnaDest40-Luc or Luc-pEFDest51 using the Gateway Cloning system . HEK293 cells were transiently transfected with a pair of split firefly luciferase reporter construct ( 400 ng each transfection ) . For normalization , the renilla luciferase vector pRL-SV40 ( 4 ng; Promega , Mannheim , Germany ) was cotransfected . 48 hours after transfection cells were lysed in 200 μL passive lysis buffer ( Promega , ) and frozen for at least one hour at −80°C . The Dual-Luciferase Reporter Assay System ( Promega ) and a multisample plate-reading luminometer ( Orion II , Berthold Detection Systems ) was used to measure luciferase activity of the cell lysates . Statistics was performed using GraphPad Prism version 5 . 00 for Windows ( GraphPad Software , La Jolla California USA ) . | Circadian clocks are endogenous timekeeping mechanisms allowing organisms to anticipate daily changes in their environment . In mammals , the fundamental mechanism of these clocks is a delayed negative feedback loop , in which timely auto-repression of clock components is essential . This repression occurs at a transcriptional level and requires clock proteins to enter the nucleus in a precisely timed manner , a regulation that is little understood . We performed a systematic genetic screen for factors modulating subcellular localization in oscillating human cells and identified Transportin 1 ( TNPO1 ) as a non-classical carrier protein required for a normal circadian period . The primary target of TNPO1 within the circadian clockwork is PERIOD1 , whose nuclear shuttling is modulated by TNPO1 . In addition , TNPO1-mediated nuclear import may constitute a novel input pathway of how cellular redox state signals to the clock , since redox stress increases binding of TNPO1 to PER1 and decreases its nuclear localization . | [
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"organell... | 2018 | The non-classical nuclear import carrier Transportin 1 modulates circadian rhythms through its effect on PER1 nuclear localization |
Understanding African Trypanosomiasis ( AT ) host-pathogen interaction is the key to an “anti-disease vaccine” , a novel strategy to control AT . Here we provide a better insight into this poorly described interaction by characterizing the activation of a panel of endothelial cells by bloodstream forms of four African trypanosome species , known to interact with host endothelium . T . congolense , T . vivax , and T . b . gambiense activated the endothelial NF-κB pathway , but interestingly , not T . b . brucei . The parasitic TS ( trans-sialidases ) mediated this NF-κB activation , remarkably via their lectin-like domain and induced production of pro-inflammatory molecules not only in vitro but also in vivo , suggesting a considerable impact on pathogenesis . For the first time , TS activity was identified in T . b . gambiense BSF which distinguishes it from the subspecies T . b . brucei . The corresponding TS were characterized and shown to activate endothelial cells , suggesting that TS represent a common mediator of endothelium activation among trypanosome species with divergent physiopathologies .
Animal African trypanosomiasis ( AAT ) is a severe disease affecting livestock in sub-Saharan Africa throughout an area of approximately 10 million km2 , and causing annual economic losses of several billion dollars [1] , [2] . The disease is characterized by severe anaemia , weight loss and immunosupression , leading to the death of the animal if not treated . It is caused by the parasites Trypanosoma congolense , Trypanosoma vivax and to a lesser extent , Trypanosoma brucei . Human African trypanosomiasis ( HAT ) , also known as sleeping sickness , affects mostly poor populations living in rural areas of Africa with ∼10 000 new cases reported per year according to World Health Organization reports . Patients suffer from progressive neurological dysfunction that culminates in death . Almost 90% of the reported HAT cases are caused by Trypanosoma brucei gambiense . During the course of infection , African trypanosomes remain exclusively extracellular and are found intermittently in the blood of the mammalian host as bloodstream forms ( BSF ) . Therefore , these parasites are necessarily in contact , either directly or indirectly , with the endothelial cells . At a certain stage of infection , T . b . gambiense and T . vivax invade internal organs , including the central nervous system , which requires direct contact with the endothelial cells of blood brain barrier ( BBB ) [3] , [4] . On the contrary , T . congolense remains exclusively intravascular , but binds to the walls of capillaries of infected cattle and to bovine aortic endothelial cells ( BAE ) in vitro [5] , [6] . The vascular endothelium is not only a permeability barrier but also a multifunctional “organ” that , among other functions , plays a critical role in regulating the immune response [7] . Endothelial cell activation is a central pathophysiological process allowing the endothelium to participate in an inflammatory response by triggering activation pathways of the cell leading to a rapid up-regulation of gene transcription . Furthermore , endothelial cells from different locations present a phenotypic variation and can generate different responses to the same stimulus . NF-κB is an essential and well characterized family of transcription factors , conserved from human to Drosophila , and known to be a central mediator for numerous cell functions including the immune response [8] , [9] , [10] . Under normal conditions , NF-κB is bound to IκB ( NF-κB inhibitor ) and retained in the cytoplasm . In an infection context , a cascade of kinases is activated by the pathogen , leading to IκB phosphorylation and NF-κB translocation to the nucleus , where it induces the expression of NF-κB-responsive genes . Most products of these genes are crucial for the immune system and specifically for the establishment of an inflammatory response , such as pro-inflammatory cytokines , nitric oxide synthase and leukocyte adhesion molecules . The NF-κB pathway plays a prominent role in regulating the immune response to parasite infections [11] . NF-κB-dependent response of endothelial cells has been described for Trypanosoma cruzi , contributing to cell invasion and inflammation [12] , [13] , which are important components in the development of congestive heart failure observed during Chagas' disease [14] . Likewise , the host's inflammatory response is a major pathophysiological factor in disease progression during African trypanosomiasis , hence the outcomes of this activation on the host pathogen relationship could be considerable . However , to our knowledge , no studies have yet examined the activation of endothelium by African trypanosomes , with the exception of one study on endothelial activation by T . b . gambiense [15] , which was limited to a single endothelial cell model with no detailed characterization and did not further investigate the molecular mediators of activation . Such studies are of major interest as they contribute to the understanding of the host-pathogen relationship , which is the key to the “anti-disease strategy” to fight African trypanosomiasis based on neutralizing the pathological effects rather than eliminating the parasites . This concept was proposed as an alternative to control trypanosomiasis after the failure of classical chemotherapy and vaccine [16] . Sialidases ( SA ) and trans-sialidases ( TS ) are proposed as pathogenic factors in AAT [17] and their involvement in virulence has been extensively described in T . cruzi [18] . In African trypanosomes , their role has been well established in the insect stages , where they mediate a trans-sialylation process ( transfer of carbohydrate-linked sialic acids to an acceptor sugar on the parasite surface ) of their major glycoproteins to form a protective coat that is essential for parasite survival in the fly gut [19] , [20] . However , expression of TS and SA active enzymes was not clearly demonstrated in BSF of T . vivax and T . congolense and their role in mammalian hosts was not elucidated until recently [21] , [22] . In fact , SA/TS activities result from active secretion with a correlation with parasite load in the blood but also from passive release after immune-mediated lysis of the parasite , and fluctuate throughout the course of infection in the mammalian hosts . During this stage , SA and TS play a crucial role in the infection process , most critically in anaemia , via erythrocyte desialylation [22] , [23] , [24] . Moreover , TS were shown to induce endothelial cell and B lymphocyte activation by T . cruzi [13] , [25] , and given its prominent role as virulence factor in AAT , the newly identified TS of African trypanosomes BSF seemed to be appropriate candidates to mediate endothelial cell activation . However , the literature agrees on the absence of TS and SA activities in BSF of T . b . brucei , and to our knowledge no studies regarding SA and TS activities have been performed on T . b . gambiense BSF . Here we compared the capacity for endothelial activation of four different species of African trypanosomes , using primary cultures of BAE and both human and murine endothelial cell lines . These African trypanosome species , known to cause different physiopathologies , had distinct activation capacities , interestingly in correlation with the presence of SA/TS activity . Specifically , T . b . brucei BSF did not activate the endothelial cells , whereas T . vivax , T . congolense and T . b . gambiense BSF were capable of endothelial cell activation via the NF-κB pathway . The different endothelial cell models we used allowed identification of organ and species specificities while characterizing the endothelial cell activation process . Kinetic patterns of activation and clear quantification of activated cells were established for the first time regarding an African trypanosomes/endothelial cell interaction study . We clearly demonstrated the presence of enzymatic TS and SA activity in T . b . gambiense BSF and showed involvement of TS , most likely through their lectin-like domain , in endothelial cell activation , and subsequently in inflammation . Most importantly , our findings pointed towards the TS as a common mediator of activation among different species of African trypanosomes . These data reinforced the role of these enzymes as virulence factors involved , not only in anaemia , via their catalytic properties but also in inflammation development via their lectin domain . Moreover , these findings render TS a potential vaccine target , and given the established role of TS in the interaction of T . cruzi with its host [26] , [27] , this vaccine could ideally serve against both American and African trypanosomes . Lastly , this study offered new insights into the host-pathogen interaction in African trypanosomiasis , which is essential for any attempt to control this disease .
To determine whether African trypanosomes can activate endothelial cells , primary cultures of BAE were first cocultivated with four species of African trypanosomes: BSF of T . congolense ( two isolates IL3000 and STIB910 ) , T . vivax ( Y486 ) , T . b . gambiense ( 1135 and LiTat ) and T . b . brucei ( AnTat 1 . 1 and 427 ) . BAE were previously cultivated for 24 h in culture medium without serum to avoid the interference of serum components with activation of NF-κB . NF-κB translocation to the nucleus was studied by indirect immunofluorescence as an indicator of cell activation . For BAE in culture medium alone ( control cells ) , NF-κB staining was mainly limited to the cytoplasm ( data not shown ) . In the presence of T . congolense , T . vivax and T . b . gambiense , the nucleus of the majority of BAE became stained ( Fig . 1A ) . Interestingly , staining was limited to the cytoplasm in the presence of T . b . brucei . The percentage of activated cells ( with stained nuclei ) was determined by counting approximately 500 cells for each condition and at various time points , in order to establish the activation kinetics . Data was then normalized to the control by retrieving the percentage of cells activated in culture medium alone ( control ) representing the background activation ( Fig . 1B ) . 78±2 . 96% of BAE were activated in the presence of LPS ( positive control ) . Activation by both T . congolense and T . b . gambiense started after 6 h of coculture , with 45±3 . 2% and 48 . 5±10 . 9% of cells activated respectively , whereas T . vivax activated BAE only after 16 h of coculture , with 35 . 9±3 . 5% of BAE activated . However , the number of parasites used in T . vivax activation assays was 10 times lower than for the other species , due to experimental limitations , which could explain the delay observed in activation kinetics . Finally , T . b . brucei did not activate BAE at all . Note that after 24 h of coculture , an increased rate of activation by serum components was observed in control cells and used to normalize the quantification , therefore results at this particular time point are probably underestimated . Since heterogeneity of endothelial cells from different organs and species is well known [28] , [29] , [30] , it was imperative to analyse a large panel of endothelial cells . Here , a primary culture of BAE was studied as a preferred model , since cattle are a natural host for animal African trypanosomes . In addition , we tested 13 different organ-specific endothelial cell lines , from either human ( H ) or murine ( M ) origins: they are microvascular endothelial cells , with the exception of HUVEC and H and M Peripheral Lymph Nodes ( PLN ) . H and M PLN also represent the high endothelial cells ( HEC ) phenotype . These cells have conserved endothelial cells properties , more specifically morphological characteristics ( microscope observation ) , endothelial cell markers [31] , and a functional TNFα-induced NF-κB cell signaling pathway ( Fig . S1A and B ) . Results showed that T . b . brucei did not activate the tested endothelial cells , regardless of their origin ( Fig . S1C and D ) , whereas T . b . gambiense did ( Fig . 2 ) , thus confirming the results seen with the BAE . Interestingly , T . congolense activated murine but not human endothelial cells , with the exception of H PLN , which could be explained by its HEC phenotype ( Fig . 2 and S2 , Table S1 ) . This suggests that activation by T . congolense is species-specific . Furthermore , T . congolense seemed to activate murine cells with preference for some tissues: M Brain , M Lung and M bone marrow ( BM ) were the most activated , 51% , 49 . 8% , and 40% of cells activated respectively , while M PLN , M Spleen and M Thymus were less activated , 25% , 29% , 24 . 6% of cells activated , respectively . Results with T . vivax also showed organ specificity: all endothelial cell lines were activated but neither M Thymus nor H Skin cell lines . The kinetics of activation showed an additional scale of variability between the 13 endothelial cell lines used ( Fig . 2 ) . For example , while 2 h of coculture were sufficient for T . congolense to activate M Brain , M Lung and M BM; M PLN and M Spleen required 24 h for activation . Not only did the minimum coculture time for activation vary between these endothelial cells , but also the peak of activation , which occurred at different time points depending on cell origin . For instance , M Lung were most activated after 24 h of coculture with T . congolense , while M BM were most activated after 16 h of coculture . To summarize , our results showed that the African trypanosomes T . congolense , T . vivax and T . b . gambiense did activate endothelial cells and , interestingly , T . b . brucei did not . Activation of endothelial cells by African trypanosomes was not uniform: specificity for species and for organs was observed , and a proper activation profile for each endothelial cell line was defined according to the activation kinetics . In order to determine the consequences of NF-κB activation on the endothelial cell , three different markers , potentially regulated by the NF-κB factor , were studied: i ) production of pro-inflammatory cytokines IL-1β and IL-6; ii ) modification of the expression pattern of adhesion molecules ICAM-1 and VCAM-1; iii ) and nitric oxide production by NO synthase . These assays were performed on M BM and Lung , the two cell lines most activated by T . congolense and for which commercial antibodies are extensively developed . IL-1β and IL-6 produced in the coculture media were measured at different time points ( Fig . 3A ) . As controls , endothelial cell lines were stimulated by 5 ng/ml of TNFα for 4 h ( data not shown ) , or unstimulated to determine basal level of IL-1β and IL-6 ( data not shown ) . In the presence of T . congolense , significant amounts of IL 1β were detected after 6 h whereas for IL-6 , longer incubation periods were required: 24 h for M Lung and 48 h for M BM . This seemed to reflect the known autocrine effect of IL-1 β on the production of IL-6 . M Lung activated with T . congolense produced high amounts of IL-1β and IL-6 that reached 52 . 9±4 . 56 pg/ml after 6 h of coculture and 102±8 . 3 pg/ml after 24 h of coculture , respectively . M BM activated with T . congolense produced smaller amounts of cytokines: 18 . 7±6 pg/ml of IL-1β at 6 h and 56 . 9±26 . 3 of IL-6 at 48 h of coculture . The expression of the adhesion molecules ICAM-1 and VCAM-1 on M Lung and M BM was examined by flow cytometry at 6 h , 16 h and 24 h of coculture with T . congolense ( Fig . 3B ) . The modification in the expression pattern is evaluated by the ratio of median fluorescence intensity ( MFI ) of cells in the presence of T . congolense , over MFI of unstimulated cells . Results showed that in presence of T . congolense ICAM-1 expression in M Lung increased 1 . 79±0 . 26 fold at 6 h , and reached 2 . 42±0 . 32 fold at 24 h ( Fig . 3B ) . The down regulation of ICAM-1 observed at 16 h is in harmony with the NF-κB activation kinetics of M lung and could be due to ICAM-1 shedding from membrane surface [32] . T . congolense had no influence on VCAM-1 expression in M Lung , which is consistent with previous results showing that VCAM-1 is not inducible in this endothelial cell type [29] . ICAM-1 and VCAM-1 expression in M BM increased 1 . 94±0 . 24 and 2±0 . 26 fold , respectively , after 24 h of coculture with T . congolense . Intracellular production of nitric oxide ( NO ) was measured by fluorometric assay ( Fig . 3C ) . In the presence of T . congolense , fluorescence intensity increase reached 32 . 9±9 . 2% , 30 . 4±9% and 20 . 9±3 . 1% in M Lung , M BM and BAE respectively . Addition of Nω-Nitro-L-arginine , a specific inhibitor of NO synthase reduced it to 4 . 5±1 . 8% , 5 . 4±4 . 8% and 4±3 . 4% respectively ( Fig . 3C ) . This indicates that the observed increase of NO production was a direct result of L-arginine-dependent NO synthase activity . Lastly , and as expected , T . b . brucei that did not activate the endothelial NF-κB pathway was unable to stimulate a pro-inflammatory response by neither M BM nor M Lung as shown by the lack of production of IL-1β and IL-6 and by a nearly absent up-regulation of ICAM-1/VCAM-1 on both endothelial cell types ( Fig . S3A and B ) . In conclusion , activation of NF-κB induced the production of pro-inflammatory molecules by the endothelial cells , marked by the production of the cytokines IL-1β and IL-6 , nitric oxide and expression of adhesion molecules . To determine which NF-κB pathway ( classical or alternative ) is responsible for the observed activation and subsequent pro-inflammation , we i ) performed all assays regarding the pro-inflammatory response using JSH-23 , a specific inhibitor of p65 subunit of NF-κB , ii ) examined the phosphorylation of IκB , the inhibitor of NF-κB . In presence of JSH-23 the increase of NO , cytokine production , and ICAM-1/VCAM-1 up-regulation were inhibited , indicating that this observed response was a specific consequence of NF-κB activation , and most importantly , via the classical pathway . Briefly , in the presence of JSH-23 , cytokine secretion by both M BM and M lung cocultivated with T . congolense did not exceed 10 pg for IL-1β and 20 pg for IL-6 ( Fig . 4A ) . ICAM-1 and VCAM-1 expression on M BM did not increase after 24 h of coculture with T . congolense ( Fig . 4B ) , whereas on M Lung up-regulation of ICAM-1 decreased to 1 . 36±0 . 19 fold . In the NO assay , fluorescence intensity increase after 16 h of coculture with T . congolense was reduced to 4 . 4±3 . 8% , 0 . 5±0 . 4% and 0 . 5% for BAE , M BM and M lung respectively ( Fig . 4C ) . To explore the molecules involved in upstream NF-κB translocation , we examined the presence of a phosphorylated form of IκB . In M Lung , IκB was not phosphorylated in the presence of T . b . brucei ( Fig . S3C ) whereas in the presence of T . congolense a phosphorylated form of IκB is detected after 2 h of coculture ( Fig . 4D ) , in synergy with the NF-κB activation kinetics of M Lung . Taken together , these data indicate that T . congolense activates the endothelial cells via the classical pathway precisely , and that the subsequent pro-inflammatory response is dependent on this specific NF-κB pathway . In order to determine if this activation was mediated by soluble factors released by the trypanosomes , we incubated BAE with i ) T . congolense or T . b . gambiense culture supernatant , ii ) T . congolense cultured in a transwell: this permeable support insert prevents the physical contact between the two cell types , yet allows the passage of the secreted soluble molecules through a permeable membrane . Results showed that trypanosomes culture supernatant was sufficient to activate BAE ( data not shown ) , and that physical contact was not essential for activation since BAE separated from T . congolense by a transwell were still activated ( data not shown ) , which suggests that soluble factors were involved in cell activation . TS are well-known pathogenicity factors in African trypanosomiasis [21] , [22] , [23] and are involved in endothelial activation by the American trypanosome T . cruzi [13] . We previously showed that TS were released from T . congolense and T . vivax and accumulated in the culture medium during exponential growth [21] , [22] but unlike T . cruzi [33] , these enzymes were released as soluble forms and not into vesicules ( data not shown ) . Consequently , trypanosomal TS could potentially act as soluble mediators of NF-κB activation . Therefore , BAE were incubated for 16 h , separately with three recombinant trypanosomal TS produced in the yeast Pichia pastoris as already described [21] , [22]: TcoTS-A1 , TcoTS-D2 ( from T . congolense ) and TvTS2 ( from T . vivax ) ( Fig . 5A ) . TcoTS-A1 , TcoTS-D2 and TvTS2 activated the BAE in a dose dependent manner , while BAE incubated with the recombinant cathepsin CB1 produced and purified according to the same protocol as the TS ( control ) [34] were not activated . Note that activation of the human and murine cell lines by these recombinant TS showed heterogeneity , similarly to the previous activation results by the African trypanosomes ( Table S1 ) . Moreover , silencing or knock out of TS genes impaired activation capacity of T . congolense . Four previously obtained mutant cell lines of T . congolense BSF [21] were tested ( Fig . 5B ) . These mutants were less virulent in mice and the induced anaemia is lower [21] . The ΔA1-C cell line contained RNAi constructs designed to be relatively specific to only one subfamily of TS . The ΔA2-C and ΔB2-C cell lines contained constructs with highly conserved sequences shared by all TcoTS genes intending to silence active SA and TS . Finally , the knock-out KOL2 cell line was specifically designed to silence TcoTS-Like2 encoding for an enzymatically inactive TS with a supposedly functional lectin domain . Although activity was not eliminated , an expected decrease in SA activity was observed in the mutant cell lines . The KOL2 cell line displayed 100% SA activity as expected since L2 is an inactive TS . Residual activity in mutant cell lines was expressed as a percentage of total SA activity displayed in the ΔGFP control cell line . Compared to ΔGFP , ΔA1-C , ΔA2-C and ΔB2-C mutant cell lines retained only 40±6 . 2% , 38±3 . 3% and 21 . 5±14 . 5% of their activation capacity respectively which is consistent with their residual SA activity: 45% , 35% and 25% respectively . Interestingly , the KOL2 cell line also displayed an impaired activation capacity ( only 45 . 9±9 . 6% of activation capacity retained ) ( Fig . 5B ) . This result suggests that catalytic activity may not be required for the activation process . Furthermore , since no SA nor TS activity was detected in BSF of T . b . brucei [21] , we overexpressed heterologous TS of T . congolense ( TcoTS-A1 ) or T . vivax ( TvTS2 ) in T . b . brucei BSF to determine the effect of this expression on the activation by this species . Expression of the heterologous TS after induction by tetracycline was confirmed by measuring SA activity in the modified cell lines ( Fig . S4 ) . Tetracycline induced T . b . brucei TcoTS-A1 and T . b . brucei TvTS2 cell lines activated 56 . 8±13 . 9% and 79±11 . 9% of BAE respectively , compared with 1% of BAE activated by non-transfected T . b . brucei cell line ( Fig . 5C ) . Taken together , these results reflect the prominent role played by trypanosomal TS family in endothelial cell activation . To assess the direct effect of TS on inflammation , we injected mice with recombinant TcoTS-A1 or TvTS2 and evaluated the inflammatory response by measuring levels of IL-1β , IL-6 and total NO ( nitrate and nitrite ) in the sera of mice at days 0 to 7 of the experiment ( mice were injected four times on days 0 , 1 , 2 , 4 ) . Injection of TcoTS-A1 resulted in a strong increase of IL-1β , IL-6 and NO levels , most significantly at day 4 ( Fig . 6 ) . Compared to pre-injection , IL-1β , IL-6 and NO levels increased approximately 20 fold , 9 fold and 6 fold respectively . Results with TvTS2 were comparable . When myricetin , a specific inhibitor of trypanosomal TS [22] , [35] , was injected simultaneously with TS , the inflammation observed in mice was inhibited ( Fig . 6 ) . For example , at day 4 after injection , IL-1β levels were 116±60 as opposed to 632±60 pg/ml in the absence of myricetin . This inhibitory effect of myricetin on inflammation is consistent with previous results showing that myricetin impaired TS-induced haematocrit decrease in mice [22] . These results strongly reinforce a direct effect of TS on pathology , and precisely on development of inflammation . Previous studies showed presence of SA activity in BSF of African trypanosomes T . vivax and T . congolense but not T . b . brucei [21] , [22] . Interestingly , activation results shown here are consistent with presence of SA activity . Although very close genetically ( sequence identity averages 99 . 2% in coding regions ) [36] , T . b . gambiense and T . b . brucei displayed distinct activation profiles . Since no data was available on SA and TS in T . b . gambiense BSF , we tested this species for the presence of enzymatic activities . Fluorometric assays revealed both SA and TS activities: 7 . 82±2 . 07 µU/109 cells and 1281±137 µU/109 cells of the 1135 strain respectively , and 7 . 1±1 . 04 µU/109 cells and 940±21 µU/109 cells of the LiTat strain respectively , compared with almost undetectable activities in T . b . brucei BSF ( Fig . 7A and B ) . In order to identify which enzyme ( s ) was responsible for this activity in T . b . gambiense , and since SA/TS activity was predominantly in the insoluble fraction of T . congolense and T . vivax [21] , mass spectrometry was performed on membrane preparations of T . b . gambiense ( 1135 and LiTat ) in comparison with T . b . brucei ( 427 and AnTat 1 . 1 ) . In both species , peptides belonging to SA genes were identified ( Table S2 ) . These genes are well conserved between these two species ( Fig . S5 ) . For T . b . gambiense , some corresponded to the ortholog of TbTS-like D1 encoded by Tbg972 . 2 . 3310 , others to the ortholog of TbSA B2 encoded by Tbg . 7 . 8790 , and the rest corresponded commonly to the orthologs of TbSA B encoded by Tbg972 . 5 . 850 and TbSA B2 . For T . b . brucei , the identified peptides were specific to TbTS-like D1 , TbSA B and TbSA B2 , encoded by Tb927 . 2 . 5280 , Tb927 . 5 . 640 and Tb927 . 7 . 7480 , respectively . Similarly to T . congolense , TS-like enzymes are degenerated and probably not functional . SA B and/or B2 could be active in T . b . gambiense BSF but not in T . b . brucei BSF , which is consistent with previous results [37] where no enzymatic activity was detected on purified SA B and B2 of T . b . brucei . To test this hypothesis , we overexpressed both identified heterologous SA of T . b . gambiense in T . b . brucei BSF . SA and TS activities of the induced mutant cell lines were verified by fluorometric assays ( Fig . S6 ) and were lower than those of the T . b . gambiense BSF WT . As expected , tetracycline induced T . b . brucei TbgSA B and T . b . brucei TbgSA B2 cell lines activated 48 . 8±6 and 41 . 2±15 . 2% of BAE respectively , compared with 3 . 4±3 and 4 . 2±3% of BAE activated by the non-transfected and the transfection control T . b . brucei cell lines respectively ( Fig . 7C ) . Note that attempts to express T . b . gambiense SA B and B2 were not successful in either E . coli ( solubility problems ) or Pichia pastoris ( absence of expression ) . Finally , addition of myricetin , a TS inhibitor , shown to impair TS enzymatic activity and also pathogenicity of T . vivax [22] , [35] , reduced the activation capacity of T . b . gambiense 1135 in a dose dependent manner: compared to 100% of activation capacity in absence of myricetin , only 40±1 . 6% of activation capacity remained in the presence of 1 µg/ml of myricetin ( Fig . 7D ) . Myricetin-induced inhibition of BAE activation by the recombinant TS and by T . congolense was also verified ( data not shown ) . Taken together , these results showed a direct involvement of SA of T . b . gambiense in endothelial cell activation , thus making these enzymes a common activation mediator of African trypanosomes . To further investigate involvement of the lectin domain of TS family members in NFκB activation , we produced a recombinant catalytically inactive mutant of TcoTS-A1 bearing a Tyr438: His438 substitution with an intact lectin domain , based on the construction of a mutant TS of T . cruzi , described previously [13] , [38] . Absence of SA and TS activities of the recombinant mutant TS was verified by fluorometric assay ( data not shown ) . Inactivation of the catalytic site had minor effects on the activation capacity of TcoTS-A1 , with 58 . 6±6 . 3% of BAE activated , compared with 68 . 6±4% by the active form TcoTS-A1 ( Fig . 8A ) . BAE cells were also incubated with different lectins . WGA , Mal , and SNA , which recognize sialic acid residues , were able to activate BAE , whereas ConA and succinylated WGA , which have no binding specificity for sialic acid , were not ( Fig . 8B and C ) . Additionally , desialylation of BAE cell surface by mild periodate treatment inhibited BAE activation by T . congolense IL3000 by 47 . 8±0 . 1% ( data not shown ) . Finally , addition of TcoTS-A1 reduced binding of Mal to its α-2 , 3 sialic acid ligands on BAE to 50% ( Fig . 8D ) , while SNA binding to α-2 , 6 sialic acids , which is not a substrate for TcoTS-A1 ( data not shown ) was not affected . Pre-incubation with myricetin abrogated the effect of TcoTS-A1 as a competitor to Mal binding suggesting that myricetin could impair the lectin activity of TcoTS-A1 . Myricetin alone had almost no effect on Mal or SNA binding , indicating that its inhibition of TcoTS-A1 lectin binding and subsequently activation of endothelial cells is indirect , possibly due to a steric hindrance after binding to the catalytic domain . These results indicated that the process of endothelial cell activation involves the lectin domain of trypanosomal TS and most likely α-2 , 3 linked sialic acid on endothelial cell surface .
Host-pathogen interaction in AAT and HAT is poorly understood . As parasite strains resistant to chemotherapy arise [17] , the needs for accurate knowledge of this interaction are increasing , mainly to move forward to an anti-disease strategy , a promising method of controlling trypanosomiasis . During infection by African trypanosomes , an intimate interaction with the host endothelium occurs , and exploring it can provide considerable insights on the host-pathogen interaction . Using indirect immunofluorescence , we showed that T . congolense , T . vivax and T . b . gambiense were capable of activating endothelial cells through the NF-κB pathway , and more specifically the classical NF-κB pathway . In fact , immunofluorescent studies were conducted using anti-p65 antibodies , and inhibition assays using JSH-23 specific inhibitor of p65 subunit of NF-κB , a subunit implicated in the classical NF-κB pathway only . We also established that the Ser 32/36 of IκBα is phosphorylated , which is another characteristic of the classical NF-κB activation pathway [9] , and further implies an involvement of IKKβ , a kinase known to play a critical role in this specific phosphorylation . Endothelial cell activation by the African trypanosomes was species and tissue specific , and each endothelial cell line displayed a specific pattern of activation kinetics . These findings are consistent with the well-known phenotypic variation between endothelial cells from different species and , moreover , from different locations within the same species [7] . Interestingly , the tissue specificity observed with T . congolense activation is consistent with the tropism to lung , bone marrow and peripheral lymph nodes observed in vivo [5] . Nevertheless , the observed species-specific activation of endothelial cells by T . congolense remains to be elucidated . It may possibly be linked to a TS binding specificity given that endothelial glycocalyx presents vast differences among microvascular endothelial cells of different species [30] . On the other hand , some cell lines were activated by the parasites but not the recombinant TS nor lectins . This can be explained by a default of enzyme's accessibility to substrate , compared to a parasite that releases numerous molecules , such as proteases , which can expose specific substrates , making them more accessible . Using species and organ specific endothelial cell lines seemed an effective strategy to avoid overlooking specific features of the trypanosome/endothelial cell interaction due to this phenotypic variation . This should be taken into consideration when selecting endothelial cell lines adapted for trypanosomiasis studies . Several arguments supported a potential role of TS in endothelial cell activation , in addition to their key role in infection and anaemia in AAT [21] , [22] . The fact that T . congolense and T . vivax BSF release these enzymes into the blood in vivo and into the culture media in vitro [23] , makes TS potential soluble mediators of the interaction process . We consequently demonstrated this role by showing that recombinant trypanosomal TS activated the BAE , and that inhibition of expression of TS impaired the activation capacity of T . congolense mutant cell lines . Significantly , trypanosome species that activated the endothelial cells possess SA activity in their BSF whereas T . b . brucei , which is incapable of endothelial activation , do not . Furthermore , over-expression of heterologous TS of T . congolense and T . vivax enabled T . b . brucei BSF to activate BAE . The controversial finding that T . b . brucei did not activate any of the endothelial cell lines , even though the closely related subspecies T . b . gambiense did , could be explained by the fact that only T . b . gambiense BSF expressed active TS . Here we detected SA and TS activities in T . b . gambiense BSF and used mass spectrometry to identify peptides corresponding to orthologs of TbTS like-D1 , TbSA B2 and TbSA B . However we cannot entirely exclude the possibility of presence of TbTS ortholog in T . b . gambiense even though no corresponding peptides were found . Note that this protein was detected by the same technique in the procyclic forms of the parasite , as expected . Surprisingly , peptides corresponding to these same three enzymes were identified in T . b . brucei BSF . Even though expressed in both species , these SA seem to be enzymatically active only in T . b . gambiense . Only four amino acids differ between TbSA B2 and TbgSA B2 ( Fig . S4 ) , and three between TbTS-like D1 and its ortholog whereas there are significantly more differences between TbSA B and TbgSA B . These substitutions are mostly in the lectin domain which could , therefore , affect sugar binding , and subsequently impair the SA/TS enzymatic activity and the activation capacity of T . b . brucei . This is consistent with previous studies describing punctual substitutions with major impact on TS activity [38] , [39] . This raises the question about the suitability of T . b . brucei as a model for the study of T . b . gambiense- caused HAT . These results strongly confirm the involvement of TS in endothelial cell activation , but above all , they suggest that TS represent a common mediator of endothelial cell activation among trypanosome species that induce divergent physiopathologies . More specifically , the lectin-like domain of the TS appeared to be predominantly responsible for the endothelial cell activation . Firstly , the knockout of TcoTS-Like2 impaired the activation of BAE by T . congolense and secondly the loss of TS enzymatic activity by inactivating the catalytic site had no effect on the BAE activation . Additionally , commercial lectins recognizing sialic acid residues on the endothelial cell surface activated the BAE , which not only reinforces our results on lectin domain involvement but also , and together with the finding that desialylation of BAE surface inhibits their activation , points towards an implication of sialic acid in this trypanosome/endothelial cell interaction . TcoTS-A1 selectively inhibited the binding of MAL on BAE indicating that α-2 , 3 sialylated molecules act as potential receptors of TS on the endothelial cell surface . In conclusion , the endothelial cell activation is apparently mediated by α-2 , 3 sialylated receptors on the endothelial cell surface , and TS of the trypanosome , most likely through their lectin domain . This result is similar in T . cruzi-mediated endothelial cells activation [13] but as in T . cruzi , molecules bearing such sialic acids residues have not been yet identified . To our knowledge , this would be the first report showing a direct evidence of NFκB activation by lectins . NFκB activation by neuraminidase through sialidase activity is well known . It is mostly described for endogenous mammalian SA such as Neu1 desialylation of TLR4 [40] . It was also demonstrated with pathogen SA such as NanA of Streptococcus pneumoniae [41] . However activation by pathogen lectins is very poorly described . Only one study has suggested that streptococci stimulation of endothelial cells and subsequent release of cytokines involved lectin interactions [42] . Interestingly , our evidence correlates with the role of inactive TS of T . cruzi in endothelial cell activation [13] , which could suggest lectin involvement , thus promoting a novel role of lectin binding domains of trypanosomal TS in host/pathogen interactions . NF-κB activation is known to impact on host/pathogen relationship in parasitic infections , in particular by regulating immunity and inflammation [11] . Knowing that SA/TS silencing correlates with impairment of virulence in experimental infection with T . congolense [21] , and that recombinant SA/TS directly contribute to anaemia by erythrocyte desialylation and subsequent erythrophagocytosis , it was crucial to elucidate their role in inflammation . Here , we showed that NF-κB activation of endothelial cells induced a pro-inflammatory response in vitro shown by the production of nitric oxide via the NO synthase , of the pro-inflammatory cytokines IL-1β and IL-6 , and the expression of the adhesion molecules ICAM-1 and VCAM-1 , known to intervene in leukocyte recruitment during infections . Most importantly , our results demonstrate that TS alone can induce a pro-inflammatory response in mice . This production of pro-inflammatory molecules could have significant consequences because it participates in the cascade of events leading to inflammation , a phenomenon that is closely related to AAT clinical symptoms and more specifically cachexia and anaemia , known as the major lethal features of AAT contributing to mortality . For instance , it has been demonstrated that NO can be implicated in host pathology by inhibiting hematopoiesis [43] . Anaemia can also be partially linked to inflammation via non-specific erythrophagocytosis by macrophages hyperactivated by chronic inflammation [44] , and also via the IL-6 induced production of hepcidin , an iron regulatory hormone [45] . Additionally , cachexia is mainly a consequence of excessive production of TNFα [46] which is a major inflammatory cytokine . On the other hand , the late stages of HAT are characterized by parasites crossing the BBB and invasion of the CNS [3] , [4] , [47] by mechanisms that are still not fully understood . One proposed model is that parasites could use leukocytes alteration to disrupt the integrity of the endothelial barrier to penetrate the nervous system [48] . Here we show a NF-κB dependent induction of ICAM1 and VCAM1 expression . These are crucial adhesion molecules in the process of leukocyte transmigration , therefore , NFκB activation by T . b . gambiense may potentially play a role in crossing the BBB , by increasing leukocyte migration and subsequently , parasite penetration into the CNS . However , TS presence does not necessarily imply BBB crossing , which probably involves other factors . In summary , this work reinforces the role of TS as virulence factors in AT , and upholds the hypothesis that TS-induced NF-κB activation may contribute to inflammation , an important component in the development of pathogenesis during AT . Eventually , the already established anti-disease effect of immunization by TS in mice [21] may be partially explained by its effect on the endothelial activation and the consequent inflammatory response . Here , we proposed a dual role for trypanosomal SA/TS in both inflammation and anemia ( Fig . 9 ) . In fact , SA/TS are released very early in the infection course and cause erythrocyte desialylation , strongly contributing to the development of anaemia at least in the acute phase of the infection; at the same time , SA/TS activate endothelial cells leading to a pro-inflammatory response with outcomes on the immune system , anaemia development and potentially on BBB crossing [49] . SA/TS might also participate in immune system activation as described in T . cruzi [50] . Other virulence factors are well known to intervene in this host/pathogen interaction , such as cathepsins and PAMPs ( Pathogen-Associated Molecular Patterns ) . Cathepsins cause host injuries , anaemia and could participate in BBB crossing [51] . PAMPS , like CpG DNA , GPI-anchor of VSG , etc . , interact with specific receptors , called PRR ( Pattern Recognition Receptors ) and trigger a host immune response leading to production of cytokines and other mediators that participate in inflammation , anaemia and host injuries [52] , [53] . Finally , all these factors sustain a balance between parasite growth and its control , on which the host survival depends . Here we propose a model of the host/pathogen molecular cross talk showing the central role of SA/TS . In such a model , parasites use different strategies to trigger anaemia and inflammation . This configuration is consistent with previous results showing that mice models respond differently to infections depending on the trypanosome species [54] . It also explains why in field infections , the severity of the disease and its physiopathological features are widely dependent on the trypanosome species and its specificities , such as the ability to invade tissues , the genetic variability between isolates ( especially from distinct geographical regions ) and above all the host range [55] . This study provides a considerable background to identify the cellular components responsible for the interaction of African trypanosomes with endothelial cells , which represents a central part of the understanding of the host/pathogen molecular cross talk . The functional importance of the endothelial cell activation needs to be further explored , mainly by in vivo studies in field infections comparing the inflammatory responses of mammalian hosts to African trypanosomes species with different capacities for endothelial cell activation in vitro . Lastly , this work defines TS as a promising new target for an anti-disease strategy to control trypanosomiasis in Africa , especially as TS represent a common interaction molecule for both human and animal African trypanosomes .
All animal procedures were carried out in strict accordance with the French legislation ( Rural Code articles L 214-1 to L 214-122 and associated penal consequences ) and European Economic Community guidelines ( 86-6091 EEC ) for the care of laboratory animals and were approved by the Ethical Committee of Centre National de la Recherche Scientifique ( CEEA50 ) , Région Aquitaine and by the University of Bordeaux 2 animal care and use committee ( Permit number: A33-063-916 ) . All efforts were made to minimize animal suffering . Eight-week-old female BALB/c mice were injected intraperitoneally with 100 µg of recombinant trypanosomal TS , named TcoTS-A1 and TvTS2 and previously described [21] , [22] , for 3 consecutive days ( days 0 , 1 and 2 ) and a fourth time on day 4 , either alone or with myricetin ( Sigma , 5 mg kg−1 ) . Blood samples were collected on day 0 , 2 , 4 , and 7 by tail bleed in 100 µl capillary tubes coated with Na-heparin and serum was prepared for further analyses . Bloodstream forms ( BSF ) of the T . congolense IL3000 or STIB910 strain ( kindly provided by the International Livestock Research Institute , Nairobi , Kenya ) and genetically modified derivatives were cultivated as previously described [56] . BSF of the T . vivax Y486 strain ( isolated in Nigeria and kindly provided by the International Livestock Research Institute , Nairobi , Kenya ) were collected from infected mice blood as described [22] and maintained in Tc BSF medium supplemented with 20% GS and 2 mM glutamine . BSF of the T . b . gambiense 1135 or LiTat strain [57] and of the T . b . brucei AnTat 1 . 1 or 427 strain were maintained in IMDM ( Fisher ) supplemented with 10% FCS and 150 mM L-cystein ( Sigma ) . Primary cultures of bovine aortic endothelial cells ( BAE ) ( kindly provided by E . Genot , Bordeaux ) were cultured in Tc BSF specific medium [56] supplemented with 20% GS ( goat serum , Invitrogen ) and 2 mM glutamine ( Sigma ) , and used between passages 5 and 15 . Human endothelial cell lines from umbilical vein , brain , peripheral lymph nodes , lung , intestine , skin , appendix , and murine endothelial cell line from brain , lung , peripheral lymph nodes , spleen , thymus , and bone marrow were kindly provided by C . Kieda ( Orléans , France ) and were cultured in Opti MEM I medium ( Invitrogen ) supplemented with 2% FCS ( fetal calf serum ) , 0 . 4% penicillin/streptomycin ( Invitrogen ) , and 0 . 2% fungizone , in 24 well plates in a 5% CO2 humidified atmosphere at 37°C . For coculture experiments , the seeded endothelial cells were washed in PBS ( phosphate-buffered saline: 137 mM NaCl , 10 mM Phosphate , 2 . 7 mM KCL , pH 7 . 4 ) , maintained in Tc BSF without serum for 24 hours , then cocultured with T . congolense , T . b . brucei , T . b . gambiense ( 106/ml ) or T . vivax ( 105/ml ) in Tc BSF medium supplemented with 10% GS . Culture with recombinant proteins or lectins was performed in DMEM supplemented with 1% Serum + ( Invitrogen ) , 1 mM sodium pyruvate ( Invitrogen ) , and 50 UI/ml/50 µg/ml penicillin/streptomycin . T . congolense mutant cell lines ΔA1-C , ΔB2-C , ΔA2-C ( knock down of TS by RNAi ) , ΔGFP ( control cell line ) and KOL2 ( knock out of TcoTS-Like2 gene ) were previously obtained in the laboratory [21] . Briefly , RNAi transfections were performed in IL3000 for constitutive expression of the transgene . Transfected cell lines containing the three constructs p2T7Ti-A1-C , p2T7Ti-B2-C and pLew100-A2-C were named ΔA1-C , ΔB2-C and ΔA2-C respectively . As a control of transfection and experimental infection , the p2T7Ti/GFP vector ( kindly provided by J . E . Donelson ) [58] was inserted into the IL3000 strain . For the knockout cell line , IL3000 was transfected with pGLneo-TcoTS-Like2 and subsequently with pGLbla-TcoTS-Like2 to obtain the double knockout cell line named KOL2 . For expression of heterologous sialidases in BSF of T . b . brucei , genes encoding TcoTS-A1 , TvTS2 , TbgSA B and TbgSA B2 were cloned in the plew100 vector then transfected in the T b . brucei 427:90-13 BSF strain using Amaxa system with the X001 program as described by the manufacturer . Transfectants were selected by addition of phleomycine ( 2 . 5 µg/ml ) in the culture medium , and checked by PCR on genomic DNA . Expression of the transgene was induced by addition of tetracycline ( 1 µg/ml ) in the culture medium . The vector plew100 containing GFP was used as transfection control . Mutant inactive recombinant TcoTS-A1 of T . congolense was constructed by site-directed mutagenesis ( using QuikChange II; Stratagene ) using specific mutant primers bearing a Tyr438 :His438 substitution and expressed in the EasySelect Pichia system ( Invitrogen ) according to the manufacturer's instructions . Expression in the Pichia pastoris strain X-33 and subsequent purification using ion-exchange chromatography were performed as described previously [21] . The recombinant proteins TcoTS-A1 , TcoTS-D2 , TvTS2 and CB1 were previously obtained in the laboratory [21] , [22] , [34] Endothelial cells were grown on glass slides into 24 wells plate and incubated with BSF of the wild type and mutant cell lines of T . congolense IL3000 and STIB910 strains , T . b . brucei AnTat 1 . 1 and 427 strains , T . b . gambiense 1135 and LiTat strains , T . vivax Y486 strain , and different amounts of the recombinant sialidases or the commercial lectins SNA , Mal II , WGA , ConA ( GaLab ) and succinylated WGA ( Vector Laboratories ) . At different time points , cells were washed in PBS , fixed with 360 µl paraformaldehyde ( 1% in PBS ) for 10 min , permeabilized with 40 µl Triton X-100 ( 1% in PBS ) for 5 min , and PFA was neutralized by adding 40 µl glycin ( 1 M in PBS ) for 5 min . Cells were then washed twice in PBS and slides were incubated with rabbit polyclonal anti-NF-κB antibody ( Cell Signaling ) ( 1∶100 in PBS 0 . 1% triton X-100 0 . 1% BSA ) for 40 min , washed , then incubated for 20 min with Alexa Fluor 488 mouse anti-rabbit IgG antibody ( Molecular Probes ) ( 1∶100 in PBS 0 . 1% Triton X-100 0 . 1% BSA ) . DNA was stained by adding 1 µg/ml 4′ , 6′-diamidino-2-phenylindle ( DAPI ) for 5 min , and slides were mounted in Vectashield ( Vector Laboratories ) . Cells were observed with a Zeiss UV microscope and images were captured using a MicroMax-1300Y/HS camera ( Princeton Instruments ) and Metamorph software ( Universal Imaging Corporation ) . Interleukins IL-1β and IL-6 were measured in 100 µl of mice sera for in vivo assays , or 100 µl of supernatant from a 2 h , 6 h , 16 h , 24 h and 48 h in vitro coculture of M Lung or M Bone Marrow endothelial cell lines with T . congolense IL3000 strain or T . b . brucei AnTat 1∶1 strain . Assays were performed using the ELISA Ready-SET-Go kits ( eBioscience ) according to the manufacturer's instructions . For in vitro assays , M Lung and M Bone Marrow cell lines , or BAE were incubated for 6 h , 16 h and 24 h with T . congolense IL3000 strain and washed in PBS . Generation of intracellular nitrite was determined by adding 5 µM of 5 , 6 diaminofluorescein diacetate ( DAF-2DA , Sigma ) for 40 min at 37°C in the dark . Medium was removed , cells were washed in PBS for 15 min , and fluorescence intensity was measured with an Optima plate reader ( BMG Labtech , Germany ) ( Excitation/Emission: 485/520 ) . This assay was also performed after addition of 1 mM Nω-Nitro-L-arginine ( Sigma ) , a competitive inhibitor of NO synthase . For in vivo assays , total nitrate and nitrite levels in mice sera were measured using a fluorometric kit ( Calbiochem ) according to the manufacturer's instructions . Sera were deproteinized by ultrafiltration through Vivaspin columns ( Sartorius ) prior to assay . Fluorescence intensity was measured with an Optima plate reader ( BMG Labtech , Germany ) ( Excitation/Emission: 350/420 ) . M Lung and M Bone Marrow endothelial cell lines were cocultured with T . congolense for 6 , 16 and 24 h . Culture medium was removed and cells washed then detached by trypsin treatment . Endothelial cells Fc receptors were blocked by adding mouse serum ( 1∶10 ) for 30 min . Cells were then incubated with rat monoclonal antibody anti-CD31 APC conjugated ( eBioscience ) and rat mAb anti-ICAM1 or anti-VCAM1 FITC conjugated ( Biolegend ) for 30 min on ice , then washed in PBS . Data were collected on a FACS Canto ( Becton Dickinson ) flow cytometer and analysed using the FACSDiva software . Cells were gated for forward and side-angle scatters and 10 000 fluorescent particles of each gated population were analysed . The analytical method was described in the results . BAE layers were detached with a cell scraper , washed with PBS and incubated with FITC conjugated SNA ( GaLab ) ( 10 µg/ml in PBS 1% BSA ) or fluorescein conjugated MAL ( Vector Laboratories ) ( 10 µg/ml in PBS 1% BSA ) for 30 min on ice . For the lectin inhibition binding assay , BAE were incubated with 10 µg TcoTS-A1 , 0 . 5 µg Myricetin ( Sigma ) or TcoTS-A1 pre-incubated with Myricetin ( 10 µg∶0 . 5 µg ) . Total protein preparations of M Lung ( one confluent well of a 6 wells plate per condition ) were obtained by adding 100 µl of 1% SDS with loading buffer and heating at 100°C in the presence of a protease inhibitor cocktail ( complete Mini , EDTA-free; Roche Diagnostics , GmbH ) . 20 µl were loaded per well and separated by SDS-PAGE ( 10% ) before transfer onto polyvinyl difluoride ( PVDF ) membranes ( Immobilon-P , Millipore ) . Membranes were incubated with mouse monoclonal anti-phospho-IκB-α ( Ser32/36 ) or anti-IκB-α ( Cell Signaling ) 1∶1000 in TBS ( Tris-buffered saline: 50 mM Tris , 150 mM NaCl , pH 7 . 6 ) 0 . 05% tween20 , 5% BSA followed by horseradish peroxidase -conjugated anti-mouse immunoglobulin G ( IgG ) ( Sigma; 1∶10 000 ) . Antigen-antibody interactions were developed using Immobilon Western chemiluminescent HRP substrate ( Millipore ) . Sialidase ( SA ) and trans-sialidase ( TS ) activities were determined as previously described [59] . Details are provided in Text S1 . To desialylate BAE , cells were treated with mild periodate , which specifically destroys the glycerol side chain ( C7–C9 ) of sialic acids , as previously described [60] , [61] . BAE were incubated with 2 mM NaIO4 solution for 30 min at 37°C . BAE viability was not affected by this treatment . Membrane preparations of T . b . gambiense 1135 or LiTat BSF or T . b . brucei 427 BSF , SDS-PAGE and mass spectrometry analysis were performed as described previously [21] . Details are provided in Text S1 . | African trypanosomiasis remains by far the most devastating parasitic disease in Africa affecting both humans and livestock . The current control strategies are not efficient because of the increasing resistance to trypanocidal drugs , and the antigenic variation that impedes vaccine development . An alternative strategy aiming to neutralize the pathological effects of the parasite rather than eliminate it was proposed . Therefore , it is essential to understand the development of pathogenesis and characterize the pathogenic factors . In this context , we wanted to elucidate the host-pathogen interaction between the African trypanosomes and the mammalian host endothelium . For the first time , we clearly demonstrated that animal African trypanosomes activate the endothelial cells via the NF-κB pathway and cause a pro-inflammatory response in vitro and in vivo via their TS . By comparing four different trypanosomes species , we showed that they displayed distinct capacities for activation . For the first time , we identified sialidase activity in the human parasite T . brucei gambiense and showed that sialidases are the mediators of this endothelial activation , in both human and animal trypanosomes . Interestingly , the lectin-like domain of this enzyme was responsible for the activation rather than the catalytic site . This study brings considerable insights into the host-pathogen relationship and designates the sialidases as a perfect target for an anti-disease strategy . | [
"Abstract",
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"Methods"
] | [] | 2013 | Identification of Trans-Sialidases as a Common Mediator of Endothelial Cell Activation by African Trypanosomes |
Many bacterial pathogens promote infection and cause disease by directly injecting into host cells proteins that manipulate eukaryotic cellular processes . Identification of these translocated proteins is essential to understanding pathogenesis . Yet , their identification remains limited . This , in part , is due to their general sequence uniqueness , which confounds homology-based identification by comparative genomic methods . In addition , their absence often does not result in phenotypes in virulence assays limiting functional genetic screens . Translocated proteins have been observed to confer toxic phenotypes when expressed in the yeast Saccharomyces cerevisiae . This observation suggests that yeast growth inhibition can be used as an indicator of protein translocation in functional genomic screens . However , limited information is available regarding the behavior of non-translocated proteins in yeast . We developed a semi-automated quantitative assay to monitor the growth of hundreds of yeast strains in parallel . We observed that expression of half of the 19 Shigella translocated proteins tested but almost none of the 20 non-translocated Shigella proteins nor ∼1 , 000 Francisella tularensis proteins significantly inhibited yeast growth . Not only does this study establish that yeast growth inhibition is a sensitive and specific indicator of translocated proteins , but we also identified a new substrate of the Shigella type III secretion system ( TTSS ) , IpaJ , previously missed by other experimental approaches . In those cases where the mechanisms of action of the translocated proteins are known , significant yeast growth inhibition correlated with the targeting of conserved cellular processes . By providing positive rather than negative indication of activity our assay complements existing approaches for identification of translocated proteins . In addition , because this assay only requires genomic DNA it is particularly valuable for studying pathogens that are difficult to genetically manipulate or dangerous to culture .
The complete genomes of over 150 bacteria including many pathogens have been sequenced . Comparative genomics can predict functions for up to 75% of the annotated open reading frames ( ORFs ) , the majority of which encode housekeeping proteins . Nevertheless , the functions of hundreds of proteins typically defy prediction . Included within this set are bacterial proteins that play a role in the development of infections . For example , both intra- and extra-cellular bacterial pathogens , like their viral counterparts , affect intracellular processes by directly delivering proteins into the cytoplasm of host cells by processes such as type III , type IV and type VI secretion [1–3] . The components of the complex protein “machines” that mediate translocation are relatively well conserved , but their substrates , the translocated proteins , are poorly conserved and rarely share detectable translocation signal sequences . Numerous genome-wide experimental approaches have been undertaken to identify bacterial proteins involved in pathogenesis . These include screening banks of transposon-generated mutants for alterations in pathogenesis [4] as well as identifying proteins selectively expressed during infections [5] . Although these screens are effective they are limited to bacteria that are genetically manipulable and have susceptible animal or cell culture models . In addition , since translocated bacterial proteins are often functionally redundant they are often missed in genetic screens for mutants defective in specific steps of pathogenesis [6] . Alternative complementary experimental approaches are needed to facilitate rapid screening of genomes to identify potential pathogenic proteins on which to focus subsequent studies . Although yeast cannot serve as a physiologic model of human infection , S . cerevisiae is an established organism for characterizing translocated bacterial proteins that target conserved eukaryotic physiology . For example , translocated proteins that alter the mammalian actin cytoskeleton as well as the secretory and signal transduction pathways have been demonstrated to target analogous processes in yeast [7–13] . Perhaps unsurprisingly , such targeting is often detrimental to yeast and results in measurable growth inhibition . This suggests that yeast growth inhibition is a sensitive indicator of translocated proteins . Indeed , expression of three of the five known Yersinia pseudotuberculosis effector proteins and four of the eight LEE ( locus of enterocyte effacement ) encoded enteropathogenic Escherichia coli effector proteins inhibit yeast growth [7 , 14] . Of note , in each of these cases , the effector proteins were expressed at relatively high levels in yeast as fusion proteins . There is limited information available on the specificity of yeast growth inhibition as an indicator of translocated proteins . A recent genome-wide screen for Legionella pneumophilia proteins that inhibit yeast growth suggests that growth inhibition due to expression of non-translocated proteins is rare . In this screen that covered ∼60% of the Legionella proteome , fragments of only nine Legionella proteins , three translocated and six non-translocated proteins , inhibited yeast growth [8] . However , this study was likely limited because each Legionella protein fragment was fused to the classic SV40 nuclear localization signal , a hemagglutinin epitope tag and the acidic transcription activation domain of B42 . Since fusion to the SV40 NLS is often sufficient to direct nuclear localization of normally cytoplasmic proteins , altered targeting could interfere with the activity of Legionella proteins . Similarly , the acidic activation domain could disrupt function . Furthermore , the Legionella proteins were expressed at relatively high levels in yeast . The observations summarized above suggested that yeast growth inhibition could be used as a functional genomic screen to identify previously unknown virulence proteins that target host cells . In order to determine if this is indeed the case as well as to optimize such screens , we systematically characterized the behavior in yeast of known translocated and non-translocated bacterial proteins when expressed at either relatively high or low-levels on their own or fused to GFP . We developed an economical high throughput growth assay to facilitate such studies on a genome-wide scale . We observed that expression of half of the 19 Shigella translocated proteins tested but almost none of the 20 non-translocated Shigella proteins nor >1 , 000 F . tularensis proteins significantly inhibit yeast growth . Not only does this study establish that yeast growth inhibition is a sensitive and specific indicator of translocated proteins , but we also identified a new substrate of the Shigella type III secretion system ( TTSS ) , IpaJ , previously missed by other experimental approaches . These observations demonstrate that yeast growth inhibition can be used as a functional genomic screen to identify pathogenic proteins .
Shigella , the causative agent of bacterial dysentery , is an intracellular pathogen that mediates its own uptake and subsequent dissemination between host cells in part by the action of translocated effector proteins ( for review [15] ) . All pathogenic Shigella species contain a virulence plasmid that encodes ∼106 full-length ORFs including one of the largest known sets of proteins translocated by a TTSS [16 , 17] . This plasmid encodes at least 14 known substrates of the TTSS [18–23] as well as seven additional proteins have been proposed to be TTSS substrates based on homology [16] . A third of these ORFs are organized within two operons required for entry of Shigella into host cells . These operons encode components and regulators of a TTSS as well as a few translocated proteins [16 , 17] . The GC-content of all 34 genes present in these two operons is ∼34% while the GC-content of Shigella chromosomal genes is generally >50% . Twenty-two additional low GC-content ( <40% ) genes are present throughout the virulence plasmid . The low GC-content of these genes suggests that many also encode proteins involved in virulence . Indeed many of these genes encode translocated proteins [16] . The virulence plasmid also encodes two autotransporters , one of which , IcsA , is delivered directly into host cells once the Shigella are internalized [24] . Because it contains a combination of known translocated and non-translocated proteins the virulence plasmid was initially treated as a “mini genome” to determine the relative sensitivity and specificity of yeast growth inhibition as an indicator of translocated pathogenic proteins . We expressed three groups of proteins from the Shigella flexneri virulence plasmid in yeast . The first group was composed of 18 proteins translocated by Shigella into host cells ( Table 1 ) . The second group was composed of 20 proteins that are confined to the bacterium during infection ( non-translocated ) . This group includes proteins involved in plasmid segregation , transcriptional regulation and TTSS chaperones ( Table 2 ) . The third group was composed of three candidate translocated proteins based on the low GC-content of their corresponding genes ( orf13 , orf212 and ipaJ ) . Shigella proteins that interact with the bacterial outer membrane as well as the components of the TTSS apparatus were not expressed in yeast because of concern that such membrane associated proteins would not correctly fold when expressed de novo in yeast . Several considerations determined the design of our quantitative yeast growth assay . First , heterologous proteins can be introduced and maintained in yeast by integrating their genes into the yeast chromosome , a labor-intensive strategy , or by carrying the genes on a plasmid . Two such plasmids that differ in their mechanism of plasmid maintenance and copy number are commonly used in yeast . The first class of plasmids referred to as 2μ plasmids are maintained at a copy number of 30–50 . The second class referred to as centromere ( cen ) containing plasmids are maintained at a copy number of 1–3 . Second , two methods are commonly use to confirm expression of heterologous proteins in yeast . Antibodies can be raised against each protein or proteins can be tagged with a common epitope . Because raising antibodies would be prohibitively laborious in a large screen , we chose the simpler epitope approach . Thus , genes encoding each of the chosen Shigella proteins were cloned as GFP fusion proteins on both high and low copy plasmids under the control of an inducible promoter ( GAL10 ) . GFP was used as the epitope to enable future localization studies . Traditionally quantitative yeast growth assays involve generating growth curves based on frequent optical density ( OD ) readings of individual aerated cultures grown in test tubes . Recently it was demonstrated that yeast growth in microscale ( 350 microliter ) liquid cultures parallels that observed in larger cultures [25] . In this assay , a single 96-well plate is incubated at 30°C in a plate reader . The plates are shaken at regular intervals to promote aeration and OD readings are obtained every 20 minutes for 24 hours . This assay produces growth curves very similar to the larger volume methods , but it requires the dedication of a plate reader for 24 hours to a single set of 96 cultures . Alternatively , the commercially available Phenotypic MicroArray technology ( Biolog ) allows for simultaneous monitoring growth rates from numerous 96-well plates using proprietary technology [4] . However , this technology is relatively costly . We adapted the microscale method to develop a growth assay to measure the growth of thousands of cultures in parallel . In our assay yeast are incubated at 30°C without agitation in 96-well plates . The OD600 of each well is read at 24 , 30 , 36 , 48 and 52 hours , corrected for nonlinearity ( Figure S1 ) , and smooth growth curves are spline interpolated through the 5 time points . Although yeast grow more slowly in these non-aerated conditions , the growth curves exhibit the expected lag , exponential , and log phases ( Figure S2 ) . Moreover , because the area under the curves is highly correlated with the 48hr time points , a single 48hr measurement actually suffices to assess relative effects . Thus , all subsequent analyses presented herein are based exclusively on the 48hr time points . Figure 1 summarizes the yeast growth phenotypes due to expression of each of the Shigella proteins when expressed as a GFP fusion protein from either a low or high copy number plasmid . Under both conditions , expression of the translocated proteins resulted in more frequent and more severe growth inhibition than the non-translocated proteins . The growth phenotypes resulting from expression of the Shigella proteins fell into three classes: severe , moderate and minimal to no growth inhibition . Expression of six Shigella proteins completely inhibited yeast growth under all conditions tested . These include four translocated proteins ( VirA , IcsB , IpgB2 and IpgD ) , one non-translocated protein ( MvpT ) , and one protein of unknown secretory status ( IpaJ ) . Expression of four additional translocated proteins ( OspC1 , OspF , IpgB1 and OspD3 ) and one non-translocated protein ( ParA ) conferred intermediate inhibitory growth phenotypes when expressed at low-levels in yeast . Several additional translocated and non-translocated proteins moderately inhibited growth when expressed at high levels . Overall , low-level expression of the bacterial proteins increased the specificity while high-level expression increased the sensitivity of growth inhibition as an indicator of translocated proteins ( Figure 1A vs . 1B ) . Although , in general , under both conditions tested , expression of the translocated proteins resulted in greater growth inhibition than the non-translocated proteins . Thus , the specificity of the assay for detection of translocated proteins improves when focusing on proteins whose expression inhibits growth more severely . Expression of GFP-IpgB2 was so toxic to yeast that we were only able to obtain healthy yeast transformants that carry the fusion gene on a low but not a high-copy number plasmid even under conditions that repress the GAL10 promoter . This is presumably due to low-level basal expression from the GAL10 promotor in the presence of glucose [26] . In the few cases where the activities of the translocated Shigella proteins are known , the targets of the proteins that conferred moderate to severe yeast growth inhibition are conserved between yeast and humans . Specifically , VirA [21] , IpgD [18] , OspF [27–29] and IpgB1 [23] and IpgB2 [30] , target microtubules , inositol phosphate signaling , mitogen activated protein kinases and G-protein signaling , respectively . Conversely , the translocated protein IpaA [31] , which interacts with vinculin , a protein not present in S . cerevisiae , had no effect on yeast when grown expressed at low-levels and only weakly inhibited yeast growth when expressed at high-level . Expression of two non-translocated bacterial proteins , MvpT and ParA , also resulted in marked growth inhibition . MvpT is a bacterial toxin that kills bacteria in the absence of its antitoxin , MvpA [32] . Co-expression of MvpA suppressed MvpT toxicity in yeast as it does in bacteria ( data not shown ) . Other bacterial toxin-antitoxin systems have been previously observed to behave similarly in yeast [33 , 34] . ParA is involved in plasmid partitioning and its close homolog , Bacillus Soj , forms nucleoprotein filaments in vitro [35] . This protein localizes to the yeast nucleus ( unpublished data ) suggesting that it has a conserved interaction with DNA . Thus , growth inhibition due to MvpT and ParA expression also appear to be due to targeting of conserved cellular processes though in these cases the bacterial proteins likely target basic cellular processes conserved among yeast and prokaryotes . We were unable to clone mvpT onto a low-copy yeast expression plasmid in bacteria without generating mutations in the gene encoding MvpT . However , we were able to clone this gene on to a high-copy number plasmid . Of note the yeast low-copy number plasmid is a high copy number ( pUC based , copy number 500–700 ) bacterial plasmid while the yeast high-copy number plasmid is a low copy number ( pBR based , copy number 15–20 ) bacterial plasmid . Thus , we hypothesize that the low-level expression in bacteria of GFP-MvpT from the GAL10 promotor or a cryptic bacteria promotor from the high-copy number bacterial plasmid is lethal . Our pilot screen with proteins encoded on the Shigella virulence plasmid suggested that yeast growth inhibition is both a sensitive and specific indicator of translocated bacterial proteins . However , this screen only analyzed the effects due to expression of 20 non-translocated proteins . Since these non-translocated proteins are encoded on a virulence plasmid , there was an inherent bias in this screen towards bacterial proteins involved in plasmid maintenance/segregation and regulation of type III secretion . This bias might overestimate the specificity of yeast growth inhibition for detection of translocated proteins since it is conceivable that bacterial housekeeping proteins involved in cellular processes highly conserved among prokaryotes and eukaryotes might act as dominant negative alleles to inhibit yeast growth . For example , expression of Legionella sterol desaturase , a homolog of S . cerevisiae ERG25 an essential protein involved in ergosterol biosynthesis [36] , results in yeast growth inhibition [8] . Thus , we set out to systematically examined the effects on yeast growth due to a comprehensive collection of ORFs encoded within a bacterial genome by taking advantage of the availability of a library of annotated Francisella tularensis subspecies tularensis ( strain SCHU S4 ) ( F . tularensis ) ORFs cloned into a recombination-based cloning vector [37 , 38] . F . tularensis is the causative agent of tularemia . Like S . flexneri , F . tularensis is a gram-negative intracellular pathogen . Based on comparative genomic analyses and extensive pathway prediction modeling , a putative function has been assigned to approximately two-thirds of the ∼1 , 600 annotated F . tularensis ORFs [39] . These include ORFs involved in metabolism , transcription , and translation . Since Francisella encodes homologs of common bacterial housekeeping genes , a survey of the behavior of Francisella proteins in yeast should provide insight into how other bacterial genomes would behave when expressed in yeast . Based on the results of our pilot screen , we decided to transfer each Francisella ORF to a low-copy number yeast expression vector such that each ORF was expressed as a GFP fusion protein under the control of the GAL1 promotor . We constructed a yeast expression plasmid compatible with the Gateway recombination-based system to facilitate transfer of the sequence-verified Francisella ORFs . Each of the translocated Shigella proteins conferred similar effects on yeast growth when expressed from either the original “traditional” low-copy number yeast expression plasmid or the newly constructed recombination-based cloning vector ( Figure S3 ) . We successfully transferred approximately two-thirds of the annotated F . tularensis ORFs into a yeast expression plasmid . Each ORF was transformed into yeast and up to six biologic replicates were analyzed for growth inhibition under the same conditions used to analyze yeast expressing the Shigella proteins ( Table S1 ) . Expression of none of the Francisella proteins screened severely inhibited yeast growth and expression of only three Francisella proteins resulted in minimal but reproducible growth inhibition ( Figures 2 and S4 ) . These proteins include FTT1130c ( CphA , cyanophycin synthetase ) , FTT0396 ( ParC , topoisomerase IV subunit A ) and FTT0384c ( a phosphatidylserine decarboxylase ( psd ) ) . parC and psd are likely housekeeping genes and were recently identified as essential F . novicida genes in a transposon library screen [40] . Cyanophycin synthetase is involved in synthesis of cyanophycin , a non-ribosome amino acid polymer common to cyanobacteria . The significance of the presence of this compound in Francisella is currently unknown . We next examined the levels of expression of the bacterial GFP fusion proteins in yeast to determine whether the lack of growth inhibition was due to poor expression of the Francisella proteins . The majority of the 48 Francisella and 39 Shigella proteins examined were expressed at comparable levels ( Figure S5 ) . However , expression of approximately 33% of the Francisella proteins and 15% of the non-toxic Shigella proteins was undetectable ( expression of three of the five toxic translocated proteins was undetectable presumably due to their associated severe growth inhibition ) . The significance of the decreased level of expression of the Francisella proteins is unclear , but may reflect their relative decreased G-C content ( 32 . 6% ) as compared to the Shigella ( 39 . 8% ) and endogenous yeast proteins ( 38 . 3% ) . Alternatively , if we assume that all of the toxic proteins are expressed in yeast , then all but two of the translocated proteins ( 89% ) were expressed in yeast , which suggests that these proteins have been selected for stability in eukaryotic cells . Thus , the relative decreased stability of the Francisella proteins might be due to optimization of their stability in prokaryotic as compared to eukaryotic cells . In regards to the sensitivity of growth inhibition for detection of translocated Francisella proteins , the significance of the scarcity of yeast growth inhibition is unclear . There is currently no data to demonstrate that F . tularensis secrete proteins directly into host cells [41] . These bacteria do not appear to encode type III , type IV or type V secretion systems . However , some species of Francisella secrete proteins via type IV pili [42] ( although there is no evidence that this secretion system is functional in F . tularensis ) and F . tularensis does appear to encode a type VI secretion system [43–45] . Since we have only screened approximately 2/3 of the annotated Francisella ORFs in yeast it is possible that a more complete screen would identify potential translocated proteins . Similarly , it is possible that translocated Francisella proteins are expressed too poorly in yeast to interfere with yeast growth and that increased levels of expression from a high copy number plasmid might result the sensitivity of detection of translocated F . tularensis proteins . Nevertheless , the rarity of growth inhibitory phenotypes observed with expression of both F . tularensis and non-translocated S . flexneri proteins in yeast demonstrates that yeast growth inhibition is a rare event and not due to random overexpression of bacterial proteins . Furthermore , since ∼50% of Francisella ORFs have E . coli homologs [40] it seems likely that proteins from other gram-negative will behave similarly when expressed in yeast . Thus , growth inhibition appears to be a highly specific indicator of translocated proteins . Presumably , the targets of some translocated proteins are not conserved among yeast and mammals and thus expression of these proteins will not affect yeast growth . Alternatively , a translocated protein may target a pathway that is conserved but is not normally rate-limiting for yeast growth . To address the latter possibility , yeast expressing all 42 Shigella GFP fusion proteins at low-levels were grown in the presence of four well-characterized yeast stressors: salt , nocodazole , sorbitol and caffeine [46] . Each was administered at levels that minimally impact wild-type growth . High salt and sorbitol both increase osmotic stress while high salt also results in a disturbance in ion homeostasis , nocodazole , destabilizes microtubules and caffeine has pleiotropic affects on yeast including inhibition of MAP kinases and calcium channels . The addition of the stressors to the media elicited marked changes in the phenotypes of two translocated proteins ( Figure 3 ) . OspB weakly inhibited growth in standard conditions , but in the presence of caffeine low-level OspB expression severely inhibited yeast growth . The phenotype of IcsA was similarly amplified by the addition of nocodazole . Although these were the only strictly conditional phenotypes , yeast expressing proteins that conferred intermediate growth phenotypes when expressed at low-levels ( IpgB1 , OspC1 , OspD3 , OspF and ParA ) displayed different sensitivities to the four stressors , implying that the phenotypes conferred by each of these proteins is due to a specific activity of the protein . In summary , the stress conditions increased the sensitivity of the assay with no loss of specificity . We assayed for growth inhibition in the presence of four well-characterized yeast stressors; however , there are numerous other agents that could also be applied . The potential value of such assays goes beyond the identification of proteins whose targets are not limiting for growth . The patterns of sensitivities of yeast expressing the bacterial proteins to individual stressors might provide insights into cellular processes targeted by the proteins . For example , the sensitivities of the complete set of ∼4 , 700 viable yeast deletion strains to dozens of stressors have been measured [46 , 47] . Thus , it should be possible to compare sensitivity patterns of the yeast deletion strains and yeast expressing bacterial proteins . The yeast genome is one of the most exhaustively characterized of any organism , so similarities in the patterns of sensitivities to various stressors between yeast deletion strains and wild type yeast expressing a given bacterial protein can potentially provide insight into the activity of the bacterial proteins . Such strategies have been successfully applied to identify the cellular targets of therapeutic agents [47 , 48] . Another explanation for the lack of detection of yeast growth inhibition due to expression of bacterial proteins is that fusion to GFP could potentially interfere with the activity and/or folding of the proteins and thus mask phenotypes . To address this possibility we conducted growth assays on yeast that express the Shigella proteins in the absence of GFP . The absence of GFP decreased the overall toxicity of almost all the Shigella proteins when expressed from a high-copy number plasmid ( Figure 4 vs . Figure 1 ) . High-level expression of all the Shigella GFP fusion proteins inhibited growth more often than their non-GFP counterparts ( 75% vs . 38% ) . In general , proteins that conferred the weakest growth phenotypes when fused to GFP and expressed at high-levels , no longer resulted in significant growth inhibition when expressed on their own or when expressed as GFP fusion proteins at relatively low-levels in yeast . In the extreme case , IpgB1 , a strong growth inhibitor when fused to GFP , conferred no detectable inhibition when expressed without GFP . Nevertheless , fusion to GFP appears to interfere with the activity of StbB and OspF , though only in the case of StbB was the growth inhibition phenotype entirely masked . Overall , these observations suggest that , in general , fusion to GFP increased the stability of the fusion proteins [49] and/or the transcriptional efficiency of the genes encoding the Shigella fusion proteins without disrupting their function . Of the three proteins of unknown function we expressed in yeast , only one , IpaJ , resulted in yeast growth inhibition . To determine if we had identified a new substrate of the Shigella TTSS , a plasmid that encodes IpaJ fused to 3x FLAG tag was introduced into wild type Shigella strain as well as a Shigella strain defective in TTSS due to a mutation in mxiM1 [50] , a component of the secretion apparatus . As demonstrated in Figure 5A , IpaJ is only detected by western blot analyses in the supernatants of wild-type Shigella after the addition of Congo red , a TTSS inducer , to the media [51] . Similarly , as shown in Figure 5B , detectable levels of IpaJ within host cells during the course of an infection is also dependent on an intact TTSS . Secretion of IpaJ appeared to be ∼100 times lower than that of either IpgB1 or OspF ( data not shown ) . This difference in secretion levels may explain the lack of detection of IpaJ in prior analyses of native proteins in supernatant preparations of Shigella mutants that constitutively secrete effector proteins [16] . Thus , our growth inhibition screen led to the identification of a new effector protein that had been previously missed by other experimental approaches . We have developed a semi-automated quantitative yeast assay to measure the growth of thousands of strains in parallel . Using this system , we demonstrate that yeast growth inhibition is a relatively specific and sensitive indicator of translocated bacterial proteins that target host cells . The efficiency of our growth assay combined with the increasing availability of bacterial genomes in recombination-based cloning vectors [52] which are easily transferred to yeast expression vectors , makes this assay a compelling functional genomic screen for the identification of translocated proteins . In particular , because it only requires DNA , the yeast growth assay is invaluable for studying organisms difficult or dangerous to grow . Furthermore , once a library of yeast expressing bacterial proteins is generated , additional high throughput assays can be designed to screen for bacterial proteins that target specific conserved eukaryotic cellular pathways . For example , Shohdy and colleagues used a reporter plasmid to screen for Legionella pneumophila proteins that interfere with yeast membrane trafficking [10] . Similarly , Rhode and colleagues identified a Shigella effector , IpaH9 . 8 that specifically inactivates the yeast mating pathway , a highly conserved MAPK signaling pathway , by targeting a mitogen activated kinase kinase for degradation via ubiquination [53] . Lastly , prior work has established that detailed understanding of a protein's activity in yeast can provide insights into its activity in more complex organisms [7–13] . Thus , bacterial proteins that inhibit yeast growth are obvious candidates for future studies in yeast to determine the molecular mechanisms underlying growth inhibition .
The Shigella genes cloned into the yeast expression vectors are summarized in Table S2 . Each designated open reading frame was PCR amplified from a 2457T S . flexneri serotype 2a DNA preparation . Genes were subcloned into two high copy number ( 2μ ) plasmids ( copy number 30–50 ) that carry the LEU2 gene and the GAL10 promoter , pFUS-GFP and pFUS-HIII [7] . The genes were either cloned in-frame with the carboxy-terminus of GFP ( pFUS-GFP ) or in-frame with a short sequence between the pFUS-HIII start codon and polylinker . The primers used for amplification via the polymerase chain reaction ( PCR ) contained engineered restriction sites at their 5′ ( ApaI or XhoI ) and 3′ ( NheI ) ends . When the ApaI site was introduced , the bacterial gene ATG was converted to CTG , thus converting methionine to leucine . The icsB gene , which contains an NheI site , was cloned directly into the pFUS vectors by homologous recombination in yeast . Each of the PCR-amplified genes was analyzed by DNA sequencing . The sequences were compared to the sequence of the Shigella flexneri serotype 2a strain 2457T virulence plasmid ( generously provided by Dr . Val Burland ( University of Wisconsin ) ) . With the exception of two synonymous mutations all of the sequence changes had been previously observed . Sequence variations specific to the 2457T virulence plasmid as compared to the published Shigella virulence plasmids are summarized in Table S2 . Low-copy-number ( centromere-containing ) plasmid ( copy number 1–3 ) versions of the clones were constructed by homologous recombination gap repair into pRS313 that contains the GAL10 promotor/GFP and terminator regions of the pFUS plasmid . The sequence-verified Francisella genes present in the Gateway pDNR entry vector were transferred to pBY011-GFP-clonNAT by a Gateway LR reaction . pBY011-GFP-clonNAT is a derivative of pBY011-D123 . The gene encoding GFP was introduced downstream of the GAL1–10 promotor and upstream of the attR1 site at the XbaI site . The clonNAT gene was introducing at the site in pBY011-D123 . All yeast expression plasmids were transformed into wild-type yeast strain S288C ( BY4741 MATa ) using the PEG/Lithium acetate method . Growth phenotypes of yeast that conditionally express the Shigella proteins were assayed in 96-well plates . Individual yeast transformants were inoculated into each well of a 96-well plate containing non-inducing selective synthetic media supplemented with 4% glucose . Saturated cultures were transferred to non-inducing selective synthetic solid media , 4% glucose using a 3 . 18 mm diameter 96-pinner tool ( V&P Scientific , Inc . , San Diego , CA ) on a Biorobot 3000 ( Qiagen , Valencia , CA ) . After one to two days , the yeast spots on the solid plates were transferred to fresh liquid 96-well plates using a 1 . 58 mm diameter 96-pinner tool and incubated 16–18 hours to grow to OD600 between 0 . 3–0 . 4 . The liquid cultures were next transferred with 1 . 58mm diameter pinner to inducing selective synthetic liquid media containing 4% galactose in 96-well plates ( “induction plates” ) . All incubations were done at 30°C and in the absence of agitation . In the case of the stressors , selective synthetic liquid media containing 4% galactose was supplemented with 500 mM sodium chloride , 6 mM caffeine , 0 . 5 M Sorbitol , and 3 μg/ml nocodazole . Growth in the 96-well plates was monitored by OD600 readings with a Wallac Victor II multiplate reader ( Perkin Elmer , Shelton , CT ) at 24 , 32 , 40 , 48 and 52 hours . The plates are shaken prior to being read in plate reader to allow for even distribution of colonies in the media . Thirteen hundred and seven individual annotated F . tularensis Schu 4 sequence-verified open reading frames were transferred from a Gateway entry vector to pBY011-GFP-clonNAT , a yeast expression plasmid , using Gateway technology ( Invitrogen ) . Thirty-seven of these reactions failed to yield bacterial transformants and were dropped from the screen . Plasmids were subsequently isolated from the remaining 1270 transformants . On two separate occasions , DNA from these transformants was transferred into wild type yeast ( S288C ) using a yeast 96-well transformation protocol using a 3 . 18 mm diameter 96-pinner tool . In each case , the transformants were selected on three independent 96-well plates , thus a total of potentially six independent yeast transformations were examined for each clone . A 96-pinner device with 1 . 58 mm pins was then used to transfer a subset of the transformants to a second solid media SC-URA non-inducing tray to allow for “normalization” of the amount of yeast recovered from each transformation . Yeast containing each of the Francisella ORFs were subjected to the 96 well liquid growth assay described above . As summarized in Table S1 the majority of the plasmids ( 1116 ) were successfully transformed in each of the six biologic replicates . In the 165 cases where six transformants were not obtained , the plasmids used to transform the yeast were examined by gel electrophoresis . Of these 165 plasmids , 61 were observed to have undergone an incorrect recombination event during the Gateway reaction and thus were classified as additional Gateway failures . We then restriction digested 50 of the plasmids used in the successful yeast transformations . Five of these plasmids demonstrated incorrect restriction patterns suggesting that ∼10% of the remaining Gateway reactions were incorrect . Thus , we estimated that we have successfully screened for growth phenotypes due to expression of ∼1 , 000 F . tularensis ORFs . Several observations suggest that the lack of detection of Francisella proteins that inhibit yeast growth is not due to false negatives . First , we did initially identify four Francisella ORFs that appeared to significantly inhibit yeast growth . Thus , our screen was indeed capable of identifying yeast strains impaired in growth . However , upon further analysis , it was apparent that the plasmid introduced into these strains , also resulted from an incorrect recombination event such that the plasmid no longer encoded a Francisella ORF . Indeed this plasmid was smaller than expect and yeast carrying this plasmid were impaired for growth under both inducing ( galactose ) and non-inducing ( glucose ) conditions . Second , based on the behavior of the known blanks and gateway recombination failures there was a low-rate of cross-contamination . Only 12 . 9% of the expected blank spots showed any growth . Thus , it is extremely unlikely that >1 of the 6 transformants of each Francisella ORFs screened was contaminated . Furthermore , examination of the original transformation plates demonstrates that when there is a contaminant , the most likely source is at the time of transformation and the rate of contaminant is minimal . Yeast that conditionally expressed each of the Shigella translocated and non-translocated proteins studied as well as 48 Francisella proteins fused to GFP were grown overnight in selective media supplemented with 2% raffinose . In the AM , cultures were back-diluted to OD600 = 1 . 0 in selective media supplemented with 2% raffinose . After 2 hours , protein production was induced by the addition of galactose to a final concentration of 4% . After an additional 4 hours , the yeast were pelleted and quickly frozen in a dry-ice/ethanol bath . The yeast were subsequently lysed using a bead-beat protocol and equal volumes of samples were analyzed by SDS-PAGE and subjected to western blot analyses with anti-GFP polyclonal antibody ( Sigma ) . PSTAIRE antibody ( Santa Cruz Biotechnology ) was used to monitor levels of Cdc2 as a loading control . A “sewing” PCR reaction was used to fuse IpaJ plus its upstream 500 nucleotides to a triple FLAG tag at its carboxy terminus . This fusion gene was cloned into pAM238 ( pSC101 ori ) and the resulting plasmid was introduced into wild-type and BS547 ( MxiM::aphA-3 ) [50] 2457T Shigella flexneri serotype 2a . To determine if IpaJ-FLAG was secreted by the Shigella strains , overnight cultures grown in TCS ( trypticase soy ) broth were diluted 1:100 into 6 mls TCS broth . The diluted cultures were grown for 2 hours until they reached an OD600 of ∼0 . 5 . The bacteria were resuspended in 2 mls phosphate-buffered saline ( PBS ) containing 10 uM Congo red ( CR ) ( Sigma ) and incubated for an additional 30 minutes . All incubations were conducted at 37°C . After this incubation the bacteria were centrifuged and the pellets were saved for the whole-cell lysates . The supernatant was then subjected to a second centrifugation step to ensure that few Shigella were present in the supernatant fraction . Proteins present in the second supernatant were precipitated by the addition of TCA ( trichloroacetate ) . Proteins were analyzed by Western blotting with an anti-FLAG antibody ( Sigma ) . For translocation assays , HeLa cells were infected ( MOI of 1:100 ) for 1 hour with wild-type and BS547 ( MxiM::aphA-3 ) Shigella that express IpaJ-FLAG . To synchronize infections and increase the efficiency of invasion , a plasmid that expresses , the plasmid pIL22 that constitutively expresses the afimbrial adhesin from uropathogenic E . coli was introduced into the Shigella strains [54] . Cells were washed with ice cold PBS and then lysed with 300 ul RIPA buffer plus protease inhibitors . The cells were spun down and the pellet was designated the insoluble fraction . The supernatant was spun down a second time and the result supernatant was designated the soluble fraction . Equal volumes of soluble and insoluble fractions were subjected to SDS-PAGE . Gels were blotted to nitrocellulose and probed with the anti-FLAG antibody ( Sigma ) .
The GenBank accession number for the Shigella virulence plasmid that encodes both the translocated and non-translocated proteins is gi|31983523:106938 . | Many bacterial pathogens promote infection and ultimately cause disease , in part , through the actions of proteins that the bacteria directly inject into host cells . These proteins subvert host cell processes to favor survival of the pathogen . The identification of such proteins can be limited since many of the injected proteins lack homology with other virulence proteins and pathogens that no longer express the proteins are often unimpaired in conventional assays of pathogenesis . Many of these proteins target cellular processes conserved from mammals to yeast , and overexpression of these proteins in yeast results in growth inhibition . We have established a high throughput growth assay amenable to systematically screening open reading frames from bacterial pathogens for those that inhibit yeast growth . We observe that yeast growth inhibition is a sensitive and specific indicator of proteins that are injected into host cells . Expression of about half of the injected bacterial proteins but almost none of the bacteria-confined proteins results in yeast growth inhibition . Since this assay only requires genomic DNA it is particularly valuable for studying pathogens that are difficult to genetically manipulate or dangerous to grow in the laboratory . | [
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] | 2008 | A Functional Genomic Yeast Screen to Identify Pathogenic Bacterial Proteins |
From 1992 onwards , outbreaks of a previously unknown illness have been reported in Asian seabass ( Lates calcarifer ) kept in maricultures in Southeast Asia . The most striking symptom of this emerging disease is the loss of scales . It was referred to as scale drop syndrome , but the etiology remained enigmatic . By using a next-generation virus discovery technique , VIDISCA-454 , sequences of an unknown virus were detected in serum of diseased fish . The near complete genome sequence of the virus was determined , which shows a unique genome organization , and low levels of identity to known members of the Iridoviridae . Based on homology of a series of putatively encoded proteins , the virus is a novel member of the Megalocytivirus genus of the Iridoviridae family . The virus was isolated and propagated in cell culture , where it caused a cytopathogenic effect in infected Asian seabass kidney and brain cells . Electron microscopy revealed icosahedral virions of about 140 nm , characteristic for the Iridoviridae . In vitro cultured virus induced scale drop syndrome in Asian seabass in vivo and the virus could be reisolated from these infected fish . These findings show that the virus is the causative agent for the scale drop syndrome , as each of Koch’s postulates is fulfilled . We have named the virus Scale Drop Disease Virus . Vaccines prepared from BEI- and formalin inactivated virus , as well as from E . coli produced major capsid protein provide efficacious protection against scale drop disease .
Scale drop syndrome in Lates calcarifer , Asian seabass , was first reported in 1992 in Penang , Malaysia , and since then outbreaks have been seen in Indonesia and in the Strait of Malacca . The phenotypic symptoms and pathology of the syndrome were described in detail by Gibson-Kueh et al . [1] . Typically , affected fish are characterized by darkened bodies , scale loss , tail and fin erosion , pallor of gills , and sometimes exophthalmia . Furthermore , fish often show lethargic behavior and severely affected fish stop schooling , sometimes show spiral swimming and a large proportion of the fish eventually die . The pathological symptoms comprise vasculitis in all major organs , including the skin and the brain , tissue degeneration , hemorrhages and necrosis . The cumulative mortality is estimated around 40–50% . The syndrome is so far described to affect Asian seabass only , both juvenile and adult fish , and seems to follow a seasonal pattern: the south-west monsoon/inter-monsoon season starting around September may be a trigger . It is an illness with unknown etiology of which the incidence is on the rise in commercial fish farms . As Asian seabass is a large , valuable fish kept in maricultures , scale drop syndrome currently results in significant economic losses for affected farms . With the increasing aquaculture of L . calcarifer , from 11 , 000 tonnes in 1990 to 75 , 000 tonnes in 2012 [2] , the syndrome is expected to occur with increased frequency . The identification of the cause of the disease , and if possible , the development of a vaccine , are therefore highly desired . Scale drop syndrome spreads between cages , indicating that an infectious agent is involved . Initially , it was believed that scale drop syndrome was caused by infections with Tenacibaculum maritimum , but so far this or other known microorganisms could not be linked to scale drop syndrome [1] . Since antibiotic treatment does not work to prevent disease , we hypothesized that a yet unknown viral agent is involved . Detection of an unknown virus requires specialized techniques . Nowadays , next generation sequencing platforms combined with viral purification and library preparations provide and excellent method to identify viruses of which the genome composition is unknown . The VIDISCA method ( Virus discovery cDNA-AFLP ) is one of the library preparation methods which has been successfully used to identify several novel viruses [3–7] . Our aim was to detect a possible viral pathogen using this approach . In this study the VIDISCA library of sera of fish affected by scale drop syndrome was sequenced via Roche-454 next generation sequencing , enabling identification and characterization of a novel virus , which we named Scale Drop Disease Virus ( SDDV ) . Three candidate vaccines were designed and all Koch's postulates were fulfilled .
Four serum samples of scale drop syndrome-affected fish from Singapore were analyzed with VIDISCA-454 . A total of 42 , 918 sequence reads were obtained of which 15 showed limited identity to known viruses in GenBank , indicating that the samples may contain an unknown virus . At the nucleotide level , these sequence reads showed identity to conserved parts of the megalocytiviruses from the family Iridoviridae . A real time qPCR was developed on a VIDISCA fragment which appeared to be part of the gene encoding the putative DNA-dependent RNA polymerase of the virus . Three out of four serum samples of the affected fish were PCR positive . Organ material from spleen , heart and kidney of diseased fish was also PCR positive . All serum , spleen , and kidney control samples from healthy fish remained negative in the qPCR . Testing of 30 more sera from early and late stage scale drop syndrome affected fish collected at a mariculture farm in Indonesia revealed another 25 positive fish , whereas none of 6 healthy control fish samples were positive ( S1 Table ) . The near complete genome sequence of the novel virus was obtained via genome walking . The genome length is at least 124 , 244 bp , consists of dsDNA , and it contains no less than 129 ORFs . A Blastn search showed that the closest relatives are among the megalocytiviruses , however , the identity was low ( depending on the gene , at most 60% ) . Dot plot analyses confirmed that there are large differences with all known members of the Iridoviridae , including the megalocytiviruses ( S1 Fig ) . In addition , an unusual low % G+C is present in the viral genome: only 37% whereas this value ranges between 53% and 55% for the other full length sequenced megalocytiviruses [8] . At this point it was not clear whether the novel virus was the causative agent or an innocent bystander of scale drop syndrome , but as a working hypothesis we called this virus Scale Drop Disease Virus ( SDDV ) . In literature , a set of 26 conserved Iridoviridae genes is used for genome comparison [9] and all of these 26 are present in the near complete sequence of SDDV . The location of each gene was determined and compared to the locations in other known members of the Iridoviridae family for which the full genome has been determined . S2 Table shows that the position of the conserved genes is unique and distinctively different from the other viruses . Phylogenetic analysis based on the deduced amino acid sequences of the 26 conserved proteins revealed that the virus clusters with the megalocytiviruses , yet it does not cluster as tightly as the other members ( Fig 1 ) . Full genome sequences are however not available for all known megalocytiviruses , therefore an additional phylogenetic analysis was performed using the deduced amino acid sequences of the major capsid protein ( MCP ) , which allowed inclusion of all ( sequenced ) members of the Iridoviridae family . S2 Fig shows that there are no close relatives , none of the known viruses clusters close to SDDV . PCR-positive sera were pooled and used to inoculate seabass kidney ( SK ) SK21 cells . A negative control was composed of the pooled sera from three PCR-negative fish without clinical signs of scale drop syndrome . Enlarged cells were already observed two days after inoculation of the SK21 cell cultures , and after six days , a high level of cytopathogenic effect ( CPE ) was observed . Cultures were harvested by applying three freeze-thaw cycles , and replication of the virus was confirmed by qPCR on the harvest . Subsequently , the harvest was used for a second passage on SK21 cells using 10 DNA copies per cell as inoculum , which corresponds to a multiplicity of infection ( MOI ) of 0 . 01 TCID50/cell ( determined by back calculation ) . CPE was clearly visible in the second passage on day four and it was complete ( 100% ) on day five . A third passage was started using the day four harvest of the second passage , again using 10 DNA copies per cell ( MOI 0 . 01 TCID50/cell ) as inoculum . The CPE was evident in the infected cultures , defined initially by distinct morphological changes in the cells , and at later times this was coupled with an increase in detachment from the culture vessel ( shown in Fig 2B and 2D ) . Viral replication was monitored by determining the number of viral genome copies on day 1 to 4 and the infectious titer on day 2 , 3 and 4 . The genome copy number increased from 2 x 106 to 3 x 1010 genome copies/mL . The virus titer ( TCID50 ) , increased from 3 . 2 10log TCID50/mL to 8 . 1 10log TCID50/mL on day 4 ( Fig 2E ) . Similar results were obtained when the virus was inoculated on Asian seabass brain cells ( SBB , established at MSD AH , S . Koumans , D . Remmers , S3 Fig ) . Negative control inoculation did not induce CPE and remained qPCR-negative during three subsequent passages on SK21 and SBB cells . The possibility that CPE was caused by dilution of a toxic factor instead of a virus was excluded by conducting serial passages of the SK21 culture harvest . A total of 5 successive passages at MOI 0 . 01 TCID50/cell were conducted on SK21 cells in T25 flasks . Cells were harvested at the time of near-complete CPE , usually day 3–4 after inoculation . Harvests of the cultures were titrated and showed to be between 7 . 6–8 . 2 10Log TCID50/mL culture harvest . Contamination by viral or bacterial contaminants like mycoplasmas was excluded as well by using the tests as described in the European Pharmacopeia [10] . Furthermore VIDISCA-454 was performed on the culture supernatant of passage 3 . Besides the novel virus , no sequences with significant identity to any virus were detected . To confirm correlation between presence of the virus and the induction of CPE , a qPCR analysis of DNA samples isolated from CPE-positive and -negative wells in a representative plate of the titration assay was performed . In all wells that were CPE-positive a high concentration of SDDV DNA was detected ( > 108 copies/mL , shown in S3 Table ) . Members of the Iridoviridae have an outer protein capsid composed of capsomers , covering an inner lipid membrane bilayer that envelopes the genome . The lipid membrane adopts an icosahedral morphology that roughly follows the contour defined by the outer layer of capsomers [11] . Electron microscopy of SDDV shows virions with a diameter of about 140 nm ( Fig 3 ) . In the concentrated virus suspension two types of particles can be discerned: particles that have the outer membrane and capsid , where the core appears dark and particles that have lost the outer membrane and appear with a lighter-colored core ( Fig 3A ) . Most particles had no or had lost the outer membrane . The icosahedral symmetry , the inner core and capsid are typical for members of the Iridoviridae . An internal membrane is not visible ( Fig 3 ) . As Iridoviridae family members have been reported to be present as enveloped and non-enveloped viruses , we investigated if a lipid envelope was essential for infectivity of the virus in SK21 cells by applying chloroform treatment . Titrations of the virus harvest incubated with 0% , 10% , and 50% ( v/v ) chloroform yielded different TCID50 values . The 10log TCID50/mL of the 0% ( v/v ) control was 7 . 5 , compared to 5 . 5 of the sample treated with 10% ( v/v ) chloroform and 5 . 4 10log TCID50/mL of the 50% ( v/v ) chloroform-treated sample S4 Fig ) . This indicates that at least a subset of virions remained infectious after chloroform treatment . The 100-fold decrease in TCID50 could be the result of loss of the enveloped subset of virions that are sensitive to chloroform , but this could also be an effect of chloroform on naked virions . A lipid envelope , if present , does not seem essential for infectivity of the virus . To examine if the newly discovered virus was indeed the causative agent of scale drop syndrome , L . calcarifer were infected with virus culture harvest . Virus culture harvests ( passage 3 ) were applied via different routes of infection: intraperitoneal ( IP; 0 . 1 mL or 5 . 5 x 106 TCID50/fish ) , intramuscular ( IM; 0 . 01 mL or 5 . 5 x 105 TCID50/fish ) , and a combination ( IP 0 . 1 mL + IM 0 . 01 mL ) . A fourth group of fish was infected IP with 0 . 1 mL of a 1:10 dilution of culture harvest in virus dilution buffer ( PBS with increased salt concentration ( 1 . 5% NaCL ) ; IP 1:10 ) to match the infectivity dose in the IM infection . The fifth group contained control fish . Records of fish mortalities in the observation tanks between day 0 and day 28 are shown in Fig 4A ( 15 fish/group ) . Clinical symptoms characteristic of scale drop syndrome , including tail and fin erosion , broken fins , scale loss and exophthalmia , were clearly visible on day 7–14 after infection in the IP-challenged group ( Fig 4C; day 10 ) and the IP- + IM-challenged group . In the IP-challenged fish , mortality was observed from 5 days post-infection . Cumulative mortality reached 60% for the IP group and 47% for the IP + IM group . Fish that received an IP 1:10-dose only or an IM dose showed less severe clinical symptoms and a later onset of mortality and lower cumulative mortality: 20% ( IP 1:10 ) and 13% ( IM ) . No mortality was observed in control fish . Pooled serum samples collected from animals at 1 , 3 , 7 , 10 and 14 days after infection were analyzed by qPCR for the presence of viral DNA . Viral DNA was detected in all groups on day 3 , 7 , 10 and 14 , and not in the control group ( Fig 4D ) . The amount of viral DNA copies peaked around 10 days post-infection . Further fulfillment of Koch’s postulates [12] was achieved by confirming the presence of the infectious agent in the experimentally infected fish . Pooled sera of day 7 and 10 of the abovementioned infections were used to inoculate SK21 cells . All sera were tested at 1:100 and 1:1000 dilution . Sera from all infected groups gave CPE on day 3 with the exception of the 1:1000 IP 1:10 dose day 10 serum , which gave CPE on day 7 ( S4 Table ) . Control sera remained CPE negative in this assay . The presence of SDDV in the CPE positive cultures was confirmed by PCR and sequencing of the PCR products . A vaccination-challenge study was performed to investigate whether a vaccine could protect the fish from scale drop syndrome . The optimal SDDV challenge dose for IP injection in 67 g fish was determined to define the best virus concentration to infect fish following vaccination . Groups of 15 fish were injected with 2 . 0 x 108 TCID50/fish , 2 . 0 x 107 TCID50/fish , and 2 . 0 x 106 TCID50/fish using a 0 . 1 mL IP injection . Mortality reached 100% ( 15/15 ) in the 2 . 0 x 107 TCID50/fish and 2 . 0 x 106 TCID50/fish groups , but unexpectedly not in the group that received the highest viral dose ( 2 . 0 x 108 TCID50/fish: 11/15 or 73% ) at day 27 post inoculation . Based on these results a dose of 2 . 0 x 107 TCID50/fish was chosen to challenge the fish after vaccination . A formalin-inactivated virus vaccine , a BEI ( binary ethyleneimine ) -inactivated virus vaccine , and a recombinant MCP protein produced in E . coli ( recMCP ) vaccine were tested . An oil-adjuvanted vaccine made with dilution buffer only was used as control . Vaccines were administered as 0 . 1 mL IP dose at day 0 of the experiment when the fish averaged a weight of 60 g . Fish were challenged with an IP challenge of 2 . 0 x 107 TCID50/fish on day 28 post vaccination , when the average weight had reached 83 g . Survival after challenge was monitored for 28 days ( Fig 5 ) . Mortality in the control group was high with only 8% survival after 28 days ( 2/25 ) , whereas all three prototype vaccines provided protection with relative protection percentages of 74% ( formalin-inactivated ) , 70% ( BEI-inactivated ) and 91% ( recMCP ) . The virus concentrations of the formalin-inactivated vaccine and the BEI-inactivated vaccine differed 10-fold ( Table 1 and Fig 5 ) . To examine whether the formalin-inactivated vaccine could provide better protection at a higher dose , a formalin-inactivated vaccine was also made from the same virus stock ( 108 TCID50/mL ) that was used to make the BEI-inactivated vaccine . The relative protection percentage of this vaccine was however equal ( 70% ) . The presence of viral DNA was examined in sera of 5 surviving vaccinated-challenged fish on day 28 post infection , of each vaccine group , and the two surviving fish of the placebo vaccination . The two sera of the surviving placebo vaccinated fish were both positive for SDDV DNA . Also 3 of the 5 sera of the fish receiving the BEI-inactivated SDDV vaccine were positive for SDDV DNA , indicating that residual replication of the virus occurred . The sera of the formalin-inactivated and recMCP vaccinated fish were all negative for SDDV DNA ( S5 Table ) . The Kaplan Meier survival curve of fish vaccinated with placebo was significantly different from those of the vaccinated fish ( p<0 . 001 , Tarone-Ware test ) , thus all three vaccines provide protection against death caused by SDDV infection . The recMCP had the highest relative protection percentage ( 91% ) , which may suggest that this vaccine provides a better protection than inactivated virus , however statistical tests showed that the difference between the protection by recMCP and the inactivated virus vaccines was not significant . The commercial MSD vaccine Aquavac IridoV against red seabream iridovirus ( RSIV ) showed no cross protection against SDDV ( 47% survival in placebo-vaccinated and 47% survival in Aquavac IridoV-vaccinated fish ) .
This study describes the characterization of a virus which infects Lates calcarifer and causes scale drop syndrome . This previously unknown virus belongs to the Iridoviridae family and has only limited similarity with the known members . The pathogen was detected exclusively in sera and organs from fish with scale drop syndrome . To establish its role in scale drop syndrome , the virus was cultured and the culture harvests were used to infect Lates calcarifer , which resulted in the development of scale drop syndrome . The virus could subsequently be reisolated from the affected fish; hence all of Koch’s postulates were fulfilled . We propose to name the virus Scale Drop Disease virus ( SDDV ) , as we provide conclusive evidence that the virus is the single causative agent for the syndrome that can now be classified as scale drop disease . SDDV is a member of the Iridoviridae , a family of viruses that consists of five genera . The three genera that infect vertebrates ( fish , amphibians and reptiles ) are Ranavirus , Lymphocystivirus and Megalocytivirus . The two genera that infect invertebrates ( insects , crustaceans and possibly mollusks ) are Chloriridovirus and Iridovirus . Analyses of putatively encoded proteins of conserved genes of SDDV revealed most phylogenetic relatedness with the megalocytiviruses . SDDV clusters with the megalocytiviruses but forms a separate branch within this genus . The megalocytiviruses are well known pathogens in tropical fish in South East Asia and Japan . One of the most extensively investigated megalocytiviruses is RSIV . RSIV is the causative agent of a disease with high mortalities that was first found in red sea bream ( Pagrus major ) , which is a species of major importance for the tropical fish industry [13] . Later , two other megalocytiviruses that cause systemic diseases in fish , infectious spleen and kidney necrosis virus ( ISKNV ) and turbot reddish body iridovirus ( TRBIV ) were discovered [14] . Based on sequence analysis and serological studies , it has been concluded by the ICTV that the group of ISKNV/TRBIV/RSIV are strains of the same viral species . Because the full genome of ISKNV was the first to be determined , ISKNV is the type species of this group [15] . Only threespine stickleback virus and SDDV cluster apart from ISKNV ( see S2 Fig ) . The genome size and genome type of SDDV are characteristic for the Iridoviridae . Their genome is a single molecule of linear , double stranded DNA between 105 and 212 kbp , which is terminally redundant and circularly permuted [9 , 16] . Also based on its morphology , the classification of SDDV as a member of the Iridoviridae is justified ( Fig 3 ) . Irodovirids display icosahedral symmetry and are characterized by a central DNA-protein complex , an outer proteinaceous capsid and an intermediate lipid membrane associated with polypeptides that covers and protects the genetic material . The diameter of SDDV , 140 nm , is in the range of 120–200 nm that has been reported for other the Iridoviridae family [14] . The virions can be both enveloped and non-enveloped , depending on the mode of exit from the cells , i . e . through lysis or budding . The EM pictures revealed that only some virus particles contained an envelope ( Fig 3 ) , but one should keep in mind that loss of an envelope can also be caused by the experimental procedures used for EM . Nevertheless , the remaining infectivity after chloroform treatment shows that an envelope is not essential for SDDV infectivity . The EM pictures did not reveal whether SDDV has an internal lipid membrane . Advanced cryo-electron microscopy will be necessary to elucidate if such membrane is present or absent . An efficacious protection against SDDV would be very beneficial for the industry . Formalin-inactivated and BEI-inactivated SDDV whole virus vaccines established in this study show promising protection , although these vaccines have to be further developed . Previous studies on RSIV have shown that inactivated whole virus vaccines offer good protection against disease [17 , 18] . An efficacious commercial formalin-inactivated vaccine against RSIV is available from MSD ( Aquavac IridoV , an oil-adjuvanted vaccine ) . We tested if the Aquavac IridoV vaccine provides cross-protection against SDDV , but such cross-protection could not be shown . Most likely , SDDV and RSIV do not have sufficient antigenic epitopes in common , which is not unexpected based on the fact that the genetic differences and biological mechanisms of replication differ considerably between the viruses . The RSIV vaccine from Biken [19] , which is a formalin-inactivated RSIV culture supernatant of GF cells , was not tested . In addition to the inactivated whole virus vaccine , the vaccine based on a recMCP protein produced in E . coli showed highly efficacious protection against SDDV . It has already been suggested for other members of the Iridoviridae that MCP and other viral surface proteins are candidates for vaccine development , but so far no highly efficacious subunit vaccine has been described . Caipaing et al . [20] showed that only fish vaccinated with inactivated intact RSIV virus and not fish vaccinated with protein components such as MCP survived RSIV challenge . Fu et al . [21] showed that recombinant ISKNV MCP from prokaryotic origin emulsified with ISA 763 oil at a dose of 50 μg/fish gave a relative percent survival ( RPS ) of 64 . 3% . Our vaccination challenge study with 27 μg/mL ( 2 . 7 μg/fish ) SDDV recMCP from prokaryotic origin in ISA 763A VG oil provided a RPS of 91% , and thus the SDDV-MCP protein vaccine is a very promising candidate for vaccine development against SDDV . Further studies are necessary to investigate what factors determine the immune response against recombinant viral proteins , in particular MCP , in the different megalocytiviruses . Apart from the clear significance to all stakeholders involved in Asian mariculture , we provide data that contribute to the complete picture of the pathogenicity of the Iridoviridae . This is not only valuable to all researchers that investigate this family of viruses , but also the broader community , e . g . ecologists , oceanographers etc , that may encounter unsuspected and/or emerging diseases in fish .
L . calcarifer samples from Singapore were collected at a mariculture farm in December 2010 and July 2011 from fish with typical signs of scale drop syndrome . Serum was collected from blood that was allowed to clot for 2 hours at room temperature and subsequently stored overnight at 4°C . Blood was centrifuged and serum was transferred to tubes and stored at < -20°C . Various organs from diseased fish were collected ( heart , spleen , kidney ) . From healthy fish , two spleens , two anterior kidneys , two dorsal kidneys and two serum samples were obtained . Three groups of fish from an Indonesian farm were collected in June and November 2012: six control fish from a cage with no symptoms of scale drop syndrome , twenty-five fish from cages with early stage scale drop syndrome and five fish from a cage at the peak mortality stage of scale drop syndrome . The total list of samples is supplied in the S1 Table . All animal procedures were carried out in Singapore , and performed in strict accordance with the specific regulations that govern animal research in Singapore , following the Guidelines set forth by the National Advisory Committee for Laboratory Animal Research ( NACLAR ) on the Proper Care and Use of Animals for Scientific Purposes ( 2004 ) . Research on animals is regulated by the Agri-Food and Veterinary Authority of Singapore under the Animal and Birds Act , Animals and Birds ( Care and Use of Animals for Scientific Purposes ) Rules . The site carrying out the research is audited annually and licensed to perform animal research ( License No . VR001 ) . The MSD AH Innovation Ltd IACUC reviewed and approved the animal care and use protocol ( license number EXT-EXP/05082011 ) . Four serum samples of affected fish and two serum samples of healthy appearing fish from Singapore were analyzed by VIDISCA-454 . The VIDISCA-454 was performed as described by de Vries et al . [22] . In short , serum was centrifuged for 10 minutes at 10 , 000 x g and the supernatant was treated with TURBO DNase ( 2U/μl , Ambion ) . Subsequently , nucleic acids were extracted by the Boom extraction method [23] . A reverse transcription reaction with Superscript II ( Invitrogen ) was performed using non-ribosomal random hexamers [24] . Subsequently , second strand DNA synthesis was performed with 5 U of Klenow fragment ( New England Biolabs ) . Double-stranded DNA was purified by phenol/chloroform extraction and ethanol precipitation and digested with Mse I restriction enzyme ( New England Biolabs ) . Adaptors with different Multiplex Identifier sequences ( MIDs ) were ligated to the digested fragments of the different samples . Before PCR amplification , the fragments were purified with AMPure XP beads ( Agencourt AMPure XP PCR , Beckman Coulter ) . Next , a 28 cycles PCR with adaptor-annealing primers was performed . The program of the PCR-reaction was: 5 min 95°C , and cycles of 1 min 95°C , 1 min 55°C , and 2 min 72°C , followed by 10 min 72°C and 10 min 4°C . After purification with AMPure XP beads , the purified DNA was quantified with the Quant-it dsDNA HS Qubit kit ( Invitrogen ) and diluted to 107 copies/μl . Samples were pooled and Kapa PCR ( Kapa Biosystems ) was performed to determine the quantity of amplifiable DNA in each pool . Subsequently , the Bioanalyser ( hsDNA chip , Agencourt ) was used to determine the average nucleotide length of the libraries . The pools were diluted until 106 copies/μl , titrated with beads ( DNA:beads ratio of 0 . 5:1 , 1:1 , 2:1 and 4:1 ) and used in an emulsion PCR according to the supplier’s protocol ( LIB-A SV emPCR kit ) . Sequencing was done on a 2 region GS FLX Titanium PicoTiterPlate ( 70x75 ) with the GS FLX Titanium XLR 70 Sequencing kit ( Roche ) . Sequence reads were analyzed using the blastn and blastp algorithms ( National Center for Biotechnology Information ) . Tissue samples ( spleen , kidney and heart ) were homogenized to a 10% ( w/v ) homogenate in PBS using glass beads . DNA was isolated from homogenized tissue samples , serum and tissue culture virus harvest using the Qiagen DNeasy Blood & Tissue kit according to the manufacturer’s instructions with some adjustments: Fifty μL of tissue homogenate was digested by Proteinase K ( 20 μl , 600 mAU/ml , Qiagen ) , mixed with 130 μL solution ATL ( Qiagen ) , and incubated for 60 minutes at room temperature . Fifty μL of serum was mixed with 20 μl Proteinase K and 150 μL PBS , or 200 μl cell culture harvests was added to 20 μL Proteinase K . The mixtures were subsequently incubated with 20 μL RNase A ( 20 mg/mL ) left for 2 minutes at room temperature and from this step on the manufacturer’s instructions were followed . Sequences from the putative DNA dependent RNA polymerase gene ( beta subunit ) were used to design primers for PCR analysis . A primer set for red sea bream iridovirus ( RSIV ) was used as a control ( S6 Table ) . A quantitative PCR was set up based on a 164 bp amplicon of the putative DNA dependent RNA polymerase . The qPCR reactions were performed on a Bio-Rad CFX thermocycler and contained 1 U SuperTaq ( HT Biotechnology Ltd . ) , 1x qPCR buffer ( 0 . 5 M KCl , 0 . 1 M Tris-HCl ) , 300 nM dNTPs ( HT Biotechnology Ltd . ) , 200 nM forward primer , 200 nM reverse primer , 300 nM probe , 3 . 5 mM MgCl2 and 2 μl template DNA in a total volume of 25 μl . See S6 Table for detailed primer and probe information . Cycling conditions were 95°C for 4 min , followed by 35 cycles at 95°C for 30 sec , 50°C for 30 sec and 72°C for 30 sec . The rampspeed was set to 1 . 5°C/sec from 95°C to 50°C , and from 50°C to 72°C . Data were analyzed using Bio-Rad CFX Manager 2 . 0 software . A duplicate measurement of a dilution series of a cloned PCR product in pCR4-TOPO ( Invitrogen ) functioned as a standard curve . Positive or negative classification of the samples was based on the threshold cycle , as compared to the standard curve . The specificity of the qPCR was checked by gel electrophoresis of the amplified PCR product . Calibration curves with slope and y intercept were calculated by the CFX software , and PCR efficiency calculated from the slope was between 95% and 105% . The r2 of the calibration curve was >0 . 99 . The lower detection limit of the qPCR is 50 copies/μL . The viral sequences identified after performing VIDISCA-454 were used as template for primer design to perform gap-filling PCRs on DNA isolated from serum . Furthermore , DNA libraries digested with either Csp6I , CviAII or AseI were used to sequence fragments that overlap with sequences obtained with VIDISCA . The gap-filling PCR products and the overlapping PCR products were sequenced using BigDye terminator chemistry ( BigDye Terminator v1 . 1 Cycle Sequencing Kit , Applied Biosystems ) . Sequences were analyzed using Codoncode Aligner Software ( version 4 . 0 . 4 ) . Open reading frames ( ORFs ) were identified via ORF finder [25] . Only ORFs larger than 300 nt were scored , with the exception of the ORF putatively encoding the transcription elongation factor TFIIS which is smaller than 300 nt . The near full length sequence is deposited in GENBANK under accession number KR139659 . Blastn ( “somewhat familiar sequences” ) searches were performed to identify the closest relative of SDDV , and the similarity in genome organization via dot plot analysis . The following parameters were used: match/mismatch scores: 1 , -1 and gap costs: existence 2 , extension 1 [26] . Nucleotide and protein sequence alignments were generated using the multiple sequence alignment tool ClustalW . Phylogenetic trees were created with MEGA5 software using the neighbor-joining method , with partial deletion in case of gaps or insertions [27] . Only DNA sequences encoding continuous ORFs were included in an alignment . A bootstrap analysis of 500 replicates was performed to provide confidences to the clustering . The Seabass kidney SK21 cell line was established from Lates calcarifer kidney by the Schweitzer Biotech Company ( Taiwan ) . The cell line was cloned at MSD Animal Health in order to obtain a line that supports virus replication optimally ( S . Koumans , MSD Animal Health ) . SK21 cells were cultured in 899 mL Leibovitz's L15 medium ( Life Technologies ) supplemented with 100 mL ( 10% ( v/v ) ) FCS ( Biochrom AG ) and 1 mL ( 1% ( v/v ) ) of a Neomycin Polymyxin antibiotics solution ( 1000 x stock ) at 28°C in a humidified incubator at ambient CO2 levels . For virus culture , cells were seeded at 3 . 0 x 104 cells/cm2 and cultured for 24 hours prior to inoculation . The monolayer had reached a cell density of 3 . 5–4 . 0 x 104 cells/cm2 at the time of infection . Serum of early scale drop syndrome fish from Indonesia was used for virus culture . The initial inoculum consisted of a 1:10 ( v/v ) dilution of this pooled serum in culture medium . Culture medium was removed from the flask and the monolayer ( 3 . 5–4 . 0 x 104 cells/cm2 ) was covered with inoculum at 28°C and ambient CO2 levels for a minimum of 30 min . The inoculum was removed , fresh culture medium was added and cells were cultured until CPE was observed using an inverted light microscope ( Olympus CKX41 ) . Virus was harvested by freeze-thawing ( -70°C to 4°C; once to three times ) , and subsequently the harvest was cleared from cell debris by centrifugation at 1000 x g for 5 minutes at 4°C . The virus was passaged by inoculating subconfluent monolayers of cells ( 3 . 0 x 104 cells/cm2 ) , as described above , with a freeze-thawed and cleared harvest of a previous passage , which was diluted in culture medium to a concentration of 0 . 01 TCID50/cell . An SK21 cell suspension ( 6 . 0 x 104 cells/mL in cold ( 4°C ) 50% culture medium + 50% Leibovitz's L15 medium ( Life Technologies ) ) was seeded at 100 μL/well on a 96 wells tissue culture microtiter plate . The plates were incubated for 24 hours at 28°C in a humid atmosphere at ambient CO2 levels , where cells reached a density of 3 . 5–4 . 0 x 104 cells/cm2 at the time of inoculation . Ten-fold serial dilutions of cell culture harvest or serum were prepared up to 10−9 dilution and 100 μl per well was added to the SK21 cells . Per dilution , 10 wells were inoculated and negative controls were included in each experiment . The plates were incubated at 28°C for 9–10 days and screened for CPE with an inverted light microscope . TCID50 values were determined according to the method and calculations described by Reed and Muench [28] . A passage 3 virus harvest obtained 4 days after inoculation ( 12 mL ) was concentrated ( 60-fold ) in a Beckmann-Coulter ultracentrifuge at 30 , 000 x g for 16 hours at 4°C , suspended in 200μL L15 medium ( Life Technologies ) , and used for negative staining and cryo transmission electron microscopy ( EM ) . For negative staining EM , 10 μl of a 1:2 mixture of virus suspension and 3% ( w/v ) ammonium molybdate , pH 6 . 8 , was applied to a 100 mesh copper grid with carbon film . After 1 minute incubation excess was removed by blotting . Specimen were observed and photographed in a JEOL JEM2100 transmission electron microscope ( JEOL Ltd , Japan ) equipped with a Gatan US4000 camera . For electron tomography of negative stained virus particles , 10 nm gold particles were added as fiducials to the virus mixture to facilitate alignment . A series of ( two times binned ) images were recorded from virus particles at magnifications of 30 . 000 to 50 . 000 times by tilting the specimen from -65 to +65 with increments of 1 . The images were processed using a fiducial-bead based alignment procedure and back-projection algorithm , as implemented in the IMOD reconstruction package , to convert the information in the series of tilted projection images into a 3D tomogram [29] . For cryo-EM , 4 μl of the virus suspension was applied to a holey carbon grid . The specimen was then blotted and vitrified in liquid ethane using a Vitrobot ( FEI Company ) . Frozen specimen were observed at -180°C using a Gatan CT3500 cryoholder . Images were taken at 2–4 μm underfocus . Chloroform treatment of cultured virus harvest was applied to destroy a potential lipid membrane . A virus harvest of 7 . 5 10log TCID50/mL was mixed and incubated with 0% ( vol/vol ) , 10% ( vol/vol ) and 50% ( vol/vol ) chloroform for one hour at 4°C , and subsequently spun at 1000 x g for 10 minutes at 4°C . The water phase , and the 10−1 to 10−3 dilutions thereof , were titrated on SK21 cells as described above , and TCID50/mL was determined . An 83 kDa fusion protein construct with an N-terminal Glutathione-S-transferase and an internal histidine-tag for immobilized metal affinity chromatography purification was generated in E . coli . The major capsid protein ( MCP ) encoding sequence was synthesized ( Genscript ) and obtained in a plasmid backbone . An EcoRI-HindIII fragment was cloned in pET41A+ ( Novagen ) . The resulting plasmid ( pET41a+ GST-his-SDDV-MCP ) was transformed to E . coli Bl21star ( DE3 ) using standard transformation protocols and subsequently cultured on plates and in liquid culture at 37°C in Animal Component-free Luria Bertani medium ( LBACF ) containing antibiotics . A 500 mL baffled plastic Erlenmeyer flask with 100 mL Terrific Broth-medium Kan50 was inoculated with 1mL of the freshly grown overnight culture of transformed E . coli . The culture was incubated at 37°C and 175 rpm until it reached O . D . 600 nm ≈ 0 . 500 . At this point , IPTG was added to a final concentration of 1 mM and the culture was incubated for 2 . 5 hours . The culture was centrifuged at 5 , 000 rpm for 5 minutes at 4°C , and the pellet was stored at –20°C . The bacterial pellet was thawed on ice and resuspended in 2 mL PBS . Lysozyme ( 100μg ) , benzonase ( 1000U ) and MgCl2 ( until 10mM ) were added to improve lysis and reduce viscosity . The bacteria were sonicated on ice until a homogeneous suspension was obtained , mixed gently and incubated at RT for 1 . 5 hours . Thirteen mL denaturing lysis buffer ( 50 mM TRIS-HCl , 300 mM KCl , 6 M Urea pH 8 . 0 ) was added . The lysate was incubated at 4°C for 1 hour , sonicated again , and centrifuged at 9000 x g for 10 minutes at 4°C . The supernatant was transferred to a 15 mL tube and centrifuged for 30 minutes at 9000 x g and 4°C . After passage through a 0 . 45 μm filter , protein was purified from the supernatant using IMAC cartridges ( BioRAD laboratories cat . no . 732–4612 ) . The default denaturing IMAC procedure was carried out on a BioRAD Profinia apparatus . The GST-his-SDDV-MCP molecules that bound to the IMAC cartridge were eluted from the column using 15 mL of denaturing elution buffer ( 50 mM TRIS-HCl , 300mM KCl , 6 M Urea , 250 mM imidazole , pH 8 . 0 ) . Urea and imidazole were removed from the elution fraction by dialysis against 1 liter of PBS pH 7 . 4 . The concentration of the dialyzed GST-his-SDDV-MCP solution was determined by comparison with a BSA-dilution series on an SDS-page gel ( S5 Fig ) . The density of the signals was measured by GeneTools software from Syngene . SK21 cells were seeded at 3 . 0 x 104/cm2 and incubated for 24 hours at 28°C . The overnight culture medium was removed from the flask . Subconfluent monolayers at a density of 3 . 5–4 . 0 x 104 cells/cm2 were infected with an MOI of 0 . 01 TCID50 per cell in a reduced volume ( 0 . 5 mL ) . The cells were incubated at 28°C and ambient CO2 for 30 min . The inoculum was removed , fresh culture medium was added and cells were cultured until >50% CPE was observed at day 3 after inoculation . The virus was harvested by one freeze-thaw cycle at -70°C and 4°C , and subsequently cleared from cell debris by centrifugation at 1000 x g for 5 minutes at 4°C . The harvest was stored at -70°C until infection . The undiluted harvest was used for infection of fish ( 0 . 1 mL/fish of undiluted SDDV: 5 . 5 x 106 TCID50 per fish for intraperitoneal ( IP ) injection; 0 . 01 mL/fish of undiluted SDDV: 5 . 5 x 105 TCID50 per fish for intramuscular ( IM ) injection ) . A 1:10 dilution of culture harvest in dilution buffer ( PBS + 1 . 5% NaCl ) was used for IP infection of 5 . 5 x 105 TCID50 per fish to match the IM infection ( group 1–4 ) . Control fish ( group 5 ) were injected with dilution buffer only . Fish were infected at an average weight of 21 g . A total of 460 fish were available: four groups of 95 fish were kept in four separate tanks for infection , and 80 fish in a fifth tank served as controls . Tanks were filled with sea water ( 30 ppt ) of 28°C ± 2°C . Fish were starved for at least 36 hours prior to IP or IM infection to empty the gastro-intestinal tract in order to reduce the risk of damage to internal organs during injection . Immediately before the infection , 20 fish from each group were weighed together to obtain the average body weight for each group . All fish were anaesthetized using AQUI-S ( AQUI-S , New Zealand ) prior to the inoculation procedure . Fish were fed ad libitum from the day after infection . Infected fish ( groups 1–4 ) were kept in four separate 250L tanks . A vertical net was installed in each 250L tank to create partitions of 1/3 and 2/3 of the tank . The 1/3 partition held 15 fish for mortality observation . The other 2/3 ( 80 fish ) were used for harvesting of sera and organs over the time course of the experiment . Uninfected control fish ( group 5 ) were housed in one 250L partition of a separate 500L tank to align water temperature with the infected fish . From each of the 5 groups , sera of 15 fish were sampled at time point day 1 ( dissemination control ) , 3 , 7 , 10 and 14 post-infection ( total 75 fish ) . Excess fish ( 5 for each group , if without mortalities ) were culled at day 14 for collection of serum . The 15 fish in the observation tank were kept until day 21 to evaluate mortality of each infection method . At each sampling time point , the serum was pooled by group . Pooled sera of experimentally infected fish ( see above ) , harvested at day 7 and day 10 after infection , were screened for presence of infectious virus . Sera were diluted 1:100 ( v/v ) and 1:1000 ( v/v ) in culture medium for inoculation of SK21 cells , which was carried out as described above . If no CPE occurred in the first passage of the virus , a second passage was performed . Virus for vaccine production was cultured as described above . Cultures were harvested with one freeze-thaw cycle and stored at -70°C until inactivation . Formalin inactivation was performed by diluting formalin ( 37% ( w/w ) formaldehyde ) 10 times to 3 . 7% ( w/w ) by mixing with water . This diluted stock was diluted another 100 times with cool ( 4°C ) virus culture harvest to a final concentration of 0 . 037% ( w/w ) formaldehyde . The mixture was continuously stirred during an inactivation period of 10 days at 4°C . Binary ethyleneimine ( BEI ) inactivation was performed by activating 1 . 09 M bromoethylamine hydrobromide ( BEA ) with 1 . 91 M NaOH 1:1 ( v/v ) and subsequent incubation of 1 hour at 37°C to achieve a BEI concentration of 0 . 55 M . The culture harvest was inactivated at a final concentration of 0 . 01 M BEI . The pH of the mixtures was checked 2 and 21 hours after BEI addition and adjusted to be within the range of 7 . 4–7 . 65 by adding 1 . 0 M NaOH or 1 . 0 M HCl . The total Incubation time was 45 hours at 37°C . BEI was neutralized by adding sodiumthiosulphate to the mixture , and the pH was adjusted to be within the range of 7 . 4–7 . 65 as described above . The formalin- and BEI-inactivated culture harvests were titrated on SK21 cells to confirm successful inactivation of the virus . Three serial passages of the inactivated material confirmed absence of live virus in the inactivated harvest . The inactivated virus preparations were stored at 4°C until vaccine formulation . Vaccines were formulated in MONTANIDE ISA 763A VG oil ( Seppic ) , a water-in-oil emulsion based on non-mineral oils . The preparation of the emulsion was performed by mixing the water phase into the oil phase at 1000 rpm ( mixing velocity ) . The water phase consisted of inactivated virus harvest in culture medium , or purified recombinant protein in PBS . In Table 1 an overview of vaccines and antigen concentrations after formulation is provided . Five groups of 25 Asian seabass ( 60 g ) were randomly assigned to treatment groups ( vaccines see Table 1 ) . Fish were vaccinated with 0 . 1 mL of the prototype vaccines by intraperitoneal injection on day 0 . Negative control fish were injected with 0 . 1 mL of placebo vaccine ( vaccine dilution buffer PBS + 1 . 5% NaCl , formulated in ISA 763A VG oil as described above ) . Fish were starved for at least 36 hours prior to the vaccination . Immediately before the vaccination , fish from each group were weighed together to obtain the average body weight for each group . All fish were anaesthetized using AQUI-S ( AQUI-S , New Zealand ) prior to the vaccination procedure . Fish were fed ad libitum from the day after vaccination . Vaccinated fish were kept in 250 L partitions of 500 L tanks that were created by installing a vertical net in the tank . On day 28 post vaccination , all fish were challenged . The challenge dose was 2 . 0 x 107 TCID50/fish and the fish were subsequently kept in 125 L partitions of 250 L tanks that were created by installing a vertical net in the tank . Mortalities were recorded daily up to 28 days post challenge . Kaplan Meier survival curves were constructed using SPSS v22 ( IBM ) . Analysis of the similarity of the curves was performed using the Tarone-Ware test . The relative protection percentage ( RPS ) in the vaccination-challenge study was calculated as follows . The ‘percentage protected control’ was calculated as the number of survivors in the control group , divided by the total number of control fish , multiplied by 100% ( % protected control = [# survivors control] / [total # control fish] * 100% = x % ) . The ‘percentage protected in the vaccine group’ was calculated as the number of survivors in the vaccine group , divided by the total number of vaccinated fish , multiplied by 100% ( % protected vaccine group = [# survivors vaccine] / [total # vaccinated fish] * 100% = y % ) . The RPS is formulated as RPS = 1- ( % protection test / % protected control ) or in a mathematical formulation RPS = 1- ( x/y ) * 100% . A representative virus has been deposited with the Collection Nationale de Cultures de Microorganisms ( CNCM ) , Institut Pasteur , 25 Rue du Docteur Roux , F-75724 Paris Cedex 15 , France , under accession number CNCM I-4754 ) | Asian seabass or Lates calcarifer is a large , valuable fish kept in maricultures . Scale drop syndrome is an emerging disease in this species that currently results in significant economic losses for affected farms . Mortality rates can become as high as 50% , both in juvenile and adult fish . With the increasing mariculture of L . calcarifer , it is expected that economic losses due to the syndrome will increase as well . We provide conclusive evidence that the single causative agent for the syndrome is a newly identified member of the genus Megalocytivirus that has been designated scale drop disease virus . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | A Novel Virus Causes Scale Drop Disease in Lates calcarifer |
Although assays for detecting Yersinia pestis using TaqMan probe-based real-time PCR have been developed for years , little is reported on room-temperature-stable PCR reagents , which will be invaluable for field epidemic surveillance , immediate response to public health emergencies , counter-bioterrorism investigation , etc . In this work , a set of real-time PCR reagents for rapid detection of Y . pestis was developed with extraordinary stability at 37°C . TaqMan-based real-time PCR assays were developed using the primers and probes targeting the 3a sequence in the chromosome and the F1 antigen gene caf1 in the plasmid pMT1of Y . pestis , respectively . Then , carbohydrate mixtures were added to the PCR reagents , which were later vacuum-dried for stability evaluation . The vacuum-dried reagents were stable at 37°C for at least 49 days for a lower concentration of template DNA ( 10 copies/µl ) , and up to 79 days for higher concentrations ( ≥102 copies/µl ) . The reagents were used subsequently to detect soil samples spiked with Y . pestis vaccine strain EV76 , and 5×104 CFU per gram of soil could be detected by both 3a- and caf1-based PCR reagents . In addition , a simple and efficient method for soil sample processing is presented here . The vacuum-dried reagents for real-time PCR maintain accuracy and reproducibility for at least 49 days at 37°C , indicating that they can be easily transported at room temperature for field application if the machine for performing real-time PCR is available . This dry reagent is of great significance for routine plague surveillance .
Yersinia pestis , the causative pathogen of the plague , mainly resides in rodents and can be transmitted to humans by infected fleas [1] . As Y . pestis is highly virulent and infectious , it has always been recognized as one of the classical biological warfare agents [2] and was classified as a Category A pathogen by the U . S . Center for Disease Control and Prevention ( http://www . bt . cdc . gov/agent/agentlist-category . asp ) [3] . Y . pestis was traditionally identified by bacterial isolation and microscopy observation [4] , the phage lysis assay [5] and animal experiments , which was termed as a “four-step” protocol in China . Although it is time-consuming and laborious , this protocol is still a gold standard for laboratory confirmation of Y . pestis infections . Immunological methods were also developed for the detection of F1 antigen and antibodies against Y . pestis [6] , [7] , [8] . Immunological biosensors , based on fiber optics , magnetic and up-converting phosphor technology , were recently applied in detection of antigen and antibodies of Y . pestis as well [9] , [10] , [11] . These methods have played important roles in fighting plague , however , nucleic acid-based detection techniques could be an even powerful alternative for detecting Y . pestis . Conventional PCR-gel electrophoresis method has been developed for detecting Y . pestis in fleas and other specimens [12] , [13] , [14] . A handful of real-time quantitative PCR assays in various formats were also established for detecting and identifying Y . pestis [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22][23] . Real-time PCR assays provide greater specificity , and they require less time and labor to complete than conventional PCRs . The techniques applied include SYBR Green [24] , molecular beacon [25] , TaqMan probes [18] , [20] and minor groove binding ( MGB ) probes [22] ect . , targeting specific sequences on the chromosome and ( or ) plasmids . Although real-time PCR has been successfully used in detecting and identifying Y . pestis , the relevant reagents need to be transported under low temperature in dry ice in order to keep the activities of enzymes and labeled probes . In this report we developed a room-temperature stable reagent for real-time PCRs which targeted the 3a sequence [26] in chromosome and the caf1 gene [14] in the plasmid pMT1 . This reagent could be stable during transportation at room temperature and thus be reliably applied for on site detection of target microorganisms if the thermal cycler is available .
Genomic DNAs of four biovars ( Microtus , Orientalis , Antiqua and Mediaevalis ) of Y . pestis were stored in our lab . Closely related or other genomic DNAs used in this study include 9 species of Yersinia ( Y . enterocolitica , Y . intermedia , Y . aldovae , Y . bercovieri , Y . frederiksenii , Y . kristensenii , Y . mollaretii , Y . rohdei , Y . ruckeri ) , 16 different serotypes of Y . pseudotuberculosis , Brucella , Francisella tularensis , Bacillus anthracis , and E . coli DH5α; and DNAs from human blood and mouse . All bacterial strains used in this study were listed in Table 1 . The primers and probes for real-time PCR were designed based on the 3a sequence in the chromosome [26] and caf1 in plasmid pMT1 using Primer Express 2 . 0 ( PE Corporation , USA ) . Other primers were also designed for cloning the target 3a and calf1 sequences into pGEM-T Easy Vector ( Promega , USA ) . The primers and probes used in this study were listed in Table 2 . The PCR system ( 20µl ) contained 2µl of 10×buffer ( 500 mM KCl , 100 mM Tris-HCl , 25 mM MgCl2 , 1mg/ml glutin ) , 1µl of each forward ( F ) and reverse ( R ) primer ( 5µM ) , 1µl of TaqMan probe ( 5µM ) , 1 . 6 µl of dNTPs ( 2 . 5 mM ) , 0 . 2µl of Taq DNA polymerase ( 5U/µl ) , 5µl of DNA template , 5µl of enzyme stabilizer mixture ( 40% trehalose and 20% dextran ) , and 3 . 2µl of ddH2O . Real-time PCRs were all performed on the Roche LightCycler 1 . 0 with the optimized cycling parameters of pre-denaturation at 94°C for 5 min , 40 cycles of denaturation at 95°C for 5 s , annealing and extension at 60°C for 30 s , and finally cooled at 40°C for 10 s . Signal acquisition mode is “single” at each cycle end of amplification . DNA fragments flanking amplicons of 3a and caf1 were amplified using primers CF and CR ( Table 2 ) , respectively , from the 91001 genomic DNA for cloning into the pGEM-T Easy Vector according to the standard protocols reported elsewhere [18] , [27] . The ligation products were transformed into DH5α and positive clones were identified by PCR and sequencing . Plasmids containing target fragments were purified separately and linearized by Sal I digestion . The concentration of linearized plasmid solution was then determined by UV spectrophotometer for calculating the copy numbers of the target DNAs . The quantified plasmid solution was serially diluted by 10-fold to prepare the standard templates with known copy numbers of target DNAs . Therefore , the real-time PCR was performed using these templates for obtaining the standard curves of Ct-Log concentration for 3a and caf1 , respectively [18] , [27] . Ct , the cycle threshold , refers to the cycle at which the fluorescence from a sample crosses the threshold . Y . pestis live attenuated strain EV76 was cultivated in Luria-Bertani broth overnight at 26°C and then serially diluted by 10-fold . The number of viable cells was determined by counting colony forming unit ( CFU ) on the HBI ( Heart Brain Infusion ) agar plate . Five µl of each sample were directly applied to PCR amplification for evaluating the sensitivity of the PCR systems . All experiments were performed in duplicate . Genomic DNAs listed in Table 1 , human and mouse DNAs were employed to evaluate the specificity of the PCR systems . All reagents needed for real-time PCR were mixed up at appropriate proportion and dispensed into 200 µl microcentrifuge tubes with 15 µl each for a single reaction . The dispensed reaction mixture was then vacuum-dried . For stability evaluation , the dry reagents were kept away from lights at 37°C up to 79 days and the reagents were taken out at different time points to perform PCR by the following method . The reagents were reconstituted by adding 10 µl of ddH2O , and then , 5 µl of plasmid templates containing 101 , 103 , 105 and 107 copies/µl were separately added . The real-time PCRs were performed according to the protocol mentioned above . Only the real-time PCR system for 3a primers and probe was evaluated in this experiment . The stability of the real-time PCR reagent is assessed in terms of accuracy and reproducibility . A standard curve of the PCR system was made using the plasmid templates as mentioned above , and the “log10 concentration” of the freshly prepared reagents was chosen as reference values . Results of “log10 concentration” of the reagents kept at 37°C for different periods were compared with the reference ones for accuracy evaluation . Four plasmid templates ( 101 , 103 , 105 , and 107copies/µl ) were used to perform PCR in triplicate in parallel . The coefficient of variation ( CV ) of Ct values was used to evaluate the reproducibility of the 3a PCR systems . A culture of Y . pestis EV76 was serially diluted by 10-fold , and the number of viable cells was determined . For each of serial dilutions , 100 µl was added to 0 . 2 g soil samples , and the spiked samples were inoculated at 4°C over night for complete adsorption of the bacteria to soil . The soil samples were collected from the yard of our institute , and they were air dried at room temperature . The dried soil samples were directly spiked with dilution of Y . pestis cultures without any pretreatment for mimicking the real situation that is usually confronted in practice . The spiked soil samples were vortexed thoroughly and centrifuged at 2 , 000 rpm for 4 min and the supernatants were collected . Then , 1 ml of dH2O was added to the pellet , and the mixture was vortexed completely and centrifuged again at 2 , 000 rpm for 4 min . The supernatant was transferred to the tube containing the supernatants collected in the first step . This washing step was repeated once for collecting as many bacteria in the soil as possible . The supernatants collected above were mixed together for centrifuging at 8 , 000 rpm for 5 min to collect the target bacteria . The pellets were washed by dH2O once , and 100 µl of dH2O was added to each sample to resuspend the pellet for boiling in a water bath for 5 min . Five microlitres of this solution were used as templates for real-time PCR . We evaluated the sensitivity and specificity of the PCR system for detecting Y . pestis in the spiked soil samples and a blind experiment was performed for evaluating the feasibility of applying this technique in detection of Y . pestis directly from soil samples . The spiked soil samples with different concentrations of Y . pestis were prepared and processed following the procedures described above , and the washed supernatants were used for sensitivity evaluation . As for the specificity test , genomic DNA ( 100 µg each ) of the four biovars of Y . pestis , and other bacterial genomic DNAs listed in Table 1 ( 100 µg each ) were all spiked into soil samples separately , and the real-time PCR was performed to test the specificity of the primers and probes . All experiments were performed in triplicates . Soil samples spiked with different concentrations of EV76 were prepared by our colleague 1 , and disordered and renumbered by our colleague 2 , and then , the real-time PCR were performed by the first two authors of this article . After reporting the results by the first two authors , the three sides involved in this blind experiment sat together for evaluating agreement of the results to the original preparations . The blind test was performed in triplicates .
As shown in Fig . 1A and Fig . 1B , standard curves of both 3a and caf1 sequences , based on 10-fold serial dilutions of 10–108 copies/µl , have a linear relationship between the log10 concentration and the Cts , with slopes of −3 . 3687 and −3 . 5154 , respectively . The efficiency of each reaction is 98% and 93% , and Y-intercepts of 36 . 298 and 40 . 317 , respectively , indicating a good quantitative determination of the PCR system . The r2 ( coefficient of determination ) are 0 . 9996 and 0 . 9997 , for 3a- and caf1-based PCRs , respectively , implying the accuracy of serial dilution and the precision of sampling , and the ideal linearity of our PCR system as well . The sensitivity of real-time PCR system based on the 3a and the caf1 sequences is 1 . 5 and 15 CFU per system , respectively , using dilutions of EV76 as template ( Table 3 ) . For both 3a and caf1 primers and probes , all four biovars of Y . pestis gave positive signals , and the other genomic DNAs in Table 1 , human and mouse DNAs gave negative results , indicating the good specificity of these PCR systems . The vacuum-dried reagents were put in a 37°C incubator and taken out at different time points . We used the ratio of “log10 concentration” of them against the original “log10 concentration” of the standards as a criterion to evaluate the stability of the reagents . A value close to 1 indicates good stability of the reagents , and we set the ratio of 0 . 900∼1 . 100 as the standard of acceptable stability . As shown in Table 4 , only the results at the days 18 and 79 for lower concentration ( 10 copies/µl ) were beyond this threshold . However , for the days 23 , 32 and 49 at this concentration , the results were good enough to indicate the stability of the reagents . Therefore , we could reasonably attribute the biased results at day 18 for the lower concentration to the experimental errors . For concentration of template higher than 10 copies/µl , the vacuum-dried real-time PCR reagents could remain effective for at least 79 days at 37°C . For evaluating the reproducibility of the PCR reagents , four concentrations of standard templates ( 10 , 103 , 105 , 107copies/µl ) were chosen for performing real-time PCR in triplicates . As illustrated in Table 5 , the coefficients of variations ( CV ) of Ct value for all concentrations were less than 3% in the 79-day's storage at 37°C , indicating an excellent reproducibility . The results above suggest that the vacuum-dried real-time PCR reagent could be transported at room temperature by normal post or express mailing system . For the spiked soil with different concentrations of Y . pestis EV76 for evaluating sensitivity of our PCR system , the results demonstrated that 5×104 CFU per gram of soil could be detected by both 3a- and caf1-based PCR systems when Ct>35 was considered as a negative result . Genomic DNAs of four biovars of Y . pestis and its closely related and non-related bacterial genomic DNAs were all spiked separately into soil samples for evaluating the specificity of the PCR systems , and only the samples spiked with the four biovars of Y . pestis DNA showed positive results while all the samples spiked with other DNAs gave negative results . In the blind test , all the spiked samples with different amount of Y . pestis EV76 were detected correctly . As expected , the CFU values determined by the real-time PCR were 10 to 100 times lower than the ones spiked into the soil samples . These results implied that the real-time PCR systems could detect Y . pestis sensitively and specifically from soil samples .
In vitro amplification of nucleic acids by PCR has been widely used for both research and clinical diagnosis . Although quantitative real-time PCR of the TaqMan technology has been developed and applied in detection of pathogenic microorganisms from different clinical samples for many years , the reagents or diagnostic kits should be transported under dry ice for protecting enzymes from activity loss . The reverse-transcriptase PCR kit for detecting dengue virus was once evaluated by storing at room temperature , refrigerator and freezer , and the results indicated that the test kits could only be stored above its recommended storage temperature of −20°C for no more than 3 days [28] . Ramanujam et al . reported that the PCR reaction mixtures could be stabilized in carbohydrate polymers by forming glassy matrices that provide room-temperature stability [29] . This kind of stabilized reagent could be stable at 22°C for 6 weeks . Wolff et al . modified a nested PCR system using single-tube , preformulated mixes embedded in a trehalose matrix , which allowed the reagents to be stored for >6 months at ambient temperature [30] . Carbohydrates ( trehalose , inulin or dextran ) were also employed to stabilize influenza subunit vaccine in the dry state , making the vaccine stable for at least 26 weeks at room temperature [31] . It has been recognized that some yeast cells tend to accumulate high concentrations of trehalose when submitted to heat shock , which can lead to a more heat-stable conformation of the enzyme in yeast [32] . Trehalose has been used to stabilize methanol dehydrogenase for the detection of methanol [33] . In this study , we initially wanted to compare different kinds of carbohydrates in PCR mixture for their ability in stabilizing the PCR reagents . Fortunately , when we tested our first mixture of carbohydrates ( 10% trehalose and 5% dextran in PCR mixture , final concentration ) , we obtained very stable PCR reagents as mentioned in the result . Then , we gave up more comparisons between different kinds of carbohydrates and turned to evaluate how stability of this fortunate formulation . During the development of this ambient-stable PCR reagent , we once compared the dried reagents with or without stabilizers . The results showed that the dried reagents without stabilizers could give positive amplifications for higher concentrations of DNA template for the first two days at 37°C ( data not shown ) , however , the one with stabilizers , as shown in this report , could be stable for a much longer period at 37°C . This result indicated the stabilizing effect of the added carbohydrates on PCR reagents . As shown by our results , this set of reagents can be stable at 37°C for up to 79 days when the concentration of template is higher than 10 copies/µl . For lower concentration , it can be stable at least for 49 days at 37°C . These results reveal that the stabilized PCR reagent can be transported by normal post mailing or express mailing system without dry-ice protection . To test its feasibility of delivering the reagents to different places without cold preservation , we mailed this reagent to different labs in Xinjiang , Shanghai , Tibet , Sichuan , Liaoning , Guangdong , Hubei Provinces by domestic couriers . It usually took 2 to 4 days for the receivers to get the reagent . The receivers tested them and confirmed that the sensitivity of the reagent met the requirement ( data not shown ) . Plague is one of the most important natural focus-related zoonotic diseases . Routine surveillances are of paramount significance for disease case reporting and controlling . In some of the surveillance stations , dead animals without any information of its dead time and reason and their spoiled soil in the natural plague foci are common samples for detecting and isolating Y . pestis . It usually takes 10 to 20 days to get final results . If PCR could be used in routine surveillances , it would improve the surveillance efficiency [12] , [34] . However , the surveillance stations are usually set up in the field , some of which even do not have freezers or refrigerators . A room-temperature-stable PCR reagent is therefore urgently demanded . After developing such a kind of reagent and testing its stability at 37°C , we chose the spiked soils as clinical samples to develop a protocol for detecting Y . pestis because contaminated soils by dead animals are common samples for Y . pestis detection [35] and Y . pestis could be persisted in soils for a long time , up to 40 weeks [36]; and also because soils contain rich compounds that inhibit enzymes , including Taq polymerase used in PCR [37] , [38] , [39] , [40] . A protocol for soil processing can be readily modified to treat the animal specimens by adding a step of proteinase K digestion . During soil treatment , we once compared the different lysis methods of direct heating lysis and NaI lysis . Direct heating method gave a better sensitivity than NaI lysis and hence we employed the former in our final protocol . In the future application , we will distribute these reagents to different plague surveillance stations in China for their evaluation of its feasibility in field . | Plague , caused by Yersinia pestis , is one of the oldest and most dangerous diseases in human history , and has claimed millions of lives in the three major historical pandemics . Although panic caused by the Black Death is fading , the threat of the reemergence of plague pandemics still exists , with the additional potential of misuse in biowarfare or bioterrorism . Rapid on-site detection and identification of the pathogen is of paramount significance for timely implementation of effective countermeasures . TaqMan probe-based real-time PCR assays can give quick and accurate identification; however , the need for cold delivery and storage prevents its potential on-site application . The objective of this study was to develop a stable PCR system for easy delivery and storage under room temperature , which is vital for conventional plague surveillance and for preparedness in public health emergencies . We present a solution to this particular issue , hoping that it is helpful to future applications . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
"diseases/bacterial",
"infections",
"microbiology/medical",
"microbiology"
] | 2010 | Ambient Stable Quantitative PCR Reagents for the Detection of Yersinia pestis |
Working memory plays a key role in cognition , and yet its mechanisms remain much debated . Human performance on memory tasks is severely limited; however , the two major classes of theory explaining the limits leave open questions about key issues such as how multiple simultaneously-represented items can be distinguished . We propose a palimpsest model , with the occurrent activity of a single population of neurons coding for several multi-featured items . Using a probabilistic approach to storage and recall , we show how this model can account for many qualitative aspects of existing experimental data . In our account , the underlying nature of a memory item depends entirely on the characteristics of the population representation , and we provide analytical and numerical insights into critical issues such as multiplicity and binding . We consider representations in which information about individual feature values is partially separate from the information about binding that creates single items out of multiple features . An appropriate balance between these two types of information is required to capture fully the different types of error seen in human experimental data . Our model provides the first principled account of misbinding errors . We also suggest a specific set of stimuli designed to elucidate the representations that subjects actually employ .
The ability to store information about the world and use it at a later time is a critical aspect of human cognition , and comes in many different forms . One such is visual short term memory , which holds visual information for brief intervals , for example to make a decision or complete a task . Since it is important in many contexts , it has been the subject of a wealth of psychophysical and neurophysiological investigations , and offers constraints on coding and representation as well as on pure storage . Here , we consider a paradigmatic visual short-term memory experiment from [1] which is illustrated in Fig . 1A . Subjects were presented with an array of oriented coloured bars . After a short presentation time , the array was removed and one of the coloured bars was re-presented at a random orientation . The subjects had to rotate the bar back to its previously presented orientation ( the target orientation ) from memory . Thus multiple items must be stored , each having two features ( colour and orientation ) . One such feature is cued ( here colour ) , and the associated other feature ( orientation ) had to be recalled . As one might expect , the mean precision of recall ( typically defined as the inverse of the standard deviation of the errors ) decreases with the number of items , and does so smoothly . However , along with small deviations from the target orientation , subjects can sometimes make large errors . This effect has historically been explained by considering that memory can only store a small number of items , in a finite number of “slots” [2–5] . Items not allocated a slot cannot be recalled even approximately , and so are assumed to be guessed ( leading to large errors ) . The number of slots has been estimated to be fairly low for most individuals ( ∼4 items ) , although it can be expanded significantly by explicit training [6] . More recently , several groups have proposed alternative mechanisms for storage [1 , 7–11] based on the metaphor of a divisible , but limited resource . This resource is allocated amongst all the items that are stored , rather than only some being remembered at all . However , as more items are stored , each receives less of the resource , hence decreasing the precision of storage and/or recall . One key battleground for this debate has been the observation of characteristic , so-called misbinding errors [7 , 8 , 12] . These arise when subjects recall the orientation of another item with which they are presented ( that of a “non-target” ) instead of that of the target . Fig . 2 , reanalysed from [7] shows this for a task in which colour had to be recalled based on a cued location ( items did not have an orientation in this task ) . On the upper row , the distribution of errors around the correct target colour is shown; each plot is for a different number N of items in the array . The responses are distributed around the correct target colour , with a dispersion increasing with N . A characteristic baseline error level , increasing with set size , is also visible . This uniform baseline has been interpreted as the signature of guessing [5] . The lower row of graphs shows how this dispersion hides misbinding , by indicating the distributions of deviations between the response and all other non-target items . The presence of a significantly higher proportion of small such deviations is a sign of responses incorrectly reporting other non-target items . We measured the significance using a resampling procedure ( see Methods and the Misbinding errors section for details ) that ensure that the effect is not just due to the increased probability of being close to an item when their overall number increases; rather , it arises from biases in the recall process . Finite resource models provide a more natural account of misbinding than classical slot models . This is because all items are stored to some fidelity , making it possible that subjects recall the wrong item in some circumstances [1 , 7 , 13] . However , a formal theory of these circumstances is presently lacking . Further , although resource models have been successful in explaining psychophysical data , there is as yet no canonical implementation , or agreement about what exactly is the limited resource . One suggestion is that it is the total number of spikes available in a population of neurons [14 , 15] , using normalization [15 , 16] or by otherwise limiting the number of bits available to store the items [17] . However , accounts based on versions of this solve in a rather unusual way the problem of “multiplicity” , i . e . , when multiple items need to be represented simultaneously . That is , they typically employ distinct and separable storage for each possible item ( i . e . , effectively an unbounded number of slots ) , with the competition coming from restricting the total amount of activation across all storage units . This leaves unclear the mechanics of allocation of these distinct pools , which is key to misbinding . Here , we consider a different model in which a single set of storage units is employed , with different items being overlaid , as in a palimpsest [18–21] . A conventional palimpsest is a manuscript which has been partly scraped-off or cleaned before being written upon again , allowing past inscriptions to be recovered along with the most recent content . Similarly , we consider the case where multiple items are written on top of each other in the same neuronal population . For a paradigm in which items are presented sequentially , partial erasure would occur between each presentations . However , for the sake of simplicity , here we only consider paradigms in which all items are presented simultaneously , and so without erasure of the palimpsest in between . We will refer to this as a restricted palimpsest storage process . Depending on the representations used and how patterns decay and combine , the final memory state of the neuronal population will retain a trace of all items that have been written onto it . From this final memory state , we then consider a Bayesian probabilistic recall process starting from the cued feature , mimicking the experimental paradigm presented above . Recall performance in our model depends sensitively on the representation used to store different items in the memory . We consider two specific examples that we call “mixed” and “hierarchical” . These are intended as paradigm cases of a wider range of possibilities , rather than be fully comprehensive; we analyse their characteristics empirically and theoretically . One particularly important aspect for both codes is the balance between allocating units to storing information about individual feature values , and storing binding information to link each item’s features together . This translates , through the medium of probabilistic recall , into a balance between two of the types of experimentally observed error mentioned above: the small displacements from the target item , and the more theoretically elusive misbinding , here rendered as a ( slightly displaced ) recall of one of the non-target stored items . The third type of random guessing errors also arise in the model via probabilistic recall , even though all items are actually stored . The relative frequencies of these errors varies with the nature of the population code and the number of items stored . A classical way to quantify the quality of population codes is the Fisher information ( FI ) . The FI cannot be used to capture the frequency of misbinding—we therefore provide a thorough empirical characterization of the model’s production of this sort of error . However , the FI does correctly determine the width of the distribution of responses around either target or non-target items—the displacements mentioned above . We show how it may be possible to distinguish between particular population codes based on available experimental data , and so propose new experiments that focus on the interplay between simultaneously-stored stimuli , which could shed light on how items interact in human working memory . Note that the goals of this paper are to introduce and explore population palimpsest memories rather than to fit psychophysical data in quantitative detail . We start by presenting the three key facets of our model: representation , storage and recall . We consider its empirical and theoretical properties , relative respectively to data from existing visual short-term memory experiments and to the Fisher information , which characterizes memory fidelity . This raises the complex issue of misbinding , which we treat in some detail , for both a classical feature-based representation , and a hierarchical representation that we then describe . Finally , we consider specific arrangements of targets in the space of possible memories that are expected to lead to patterns of errors that can help distinguish between different representations .
Consider the case of a population of M units representing the memory of all items seen in a trial . The simultaneous population activity of these units is read during recall to infer the feature of the item of interest . The finiteness of the population , the nature of the representation employed and the influence of noise jointly constitute the limited resource associated with our memory . In terms of the representation , we assume that units have continuous firing rates , and are tuned to specific combinations of features . Unit i has a preferred angle and colour , with separate tuning widths to each feature , and its mean activity follows a Von Mises curve as shown in Equation ( 1 ) . We use Bivariate Von Mises [22 , 23] tuning curves as they provide a convenient parametrisation of the sensitivity to a pair of angular features . Here , ϕ and ψ are respectively the orientation and colour of the item to be represented . θm and γm are the preferred angle and colour of unit m . τ1 , m and τ2 , m are called concentration parameters , which control the size of the receptive field , as well as the sensitivity of each unit to the different features . Units have continuous valued firing-rate responses , and suffer from independent Gaussian noise about these mean activities . To examine the scaling behaviour of the model , we use a normalization scheme that constrains the mean summed overall network activity induced by any item to be constant as the receptive field concentrations change ( although the total activity in the memory grows with the number of items stored ) . We use independent Gaussian noise for simplicity , although it would be straightforward to examine a more neurally plausible , Poisson , noise model . Writing xm as the firing rate of unit m , the population activity x = [x1 , … , xM]T is x|ϕ , ψ∼N ( μ ( ϕ , ψ ) , σ x 2 I ) ( 2 ) Depending on the distribution of τ1 , m and τ2 , m , several types of population code can be generated ( see Fig . 3 and 10 ) . τ1 , m = τ2 , m = τ ∀i corresponds to a “conjunctive” population code , in which each unit is sensitive to a combination of the two features . Conversely , a “feature” population code employs two subpopulations; one has τ1 , m = τ , τ2 , m = 0 , and is sensitive only to the first feature; the other has τ1 , m = 0 , τ2 , m = τ , and is only sensitive to the second . We also consider a “mixed” population code including both conjunctive and feature units , and entertain various possibilities for the relative proportions of the two types . This “mixed” population code provides an easy way to parametrise the relative information required to store feature values accurately ( these are mostly encoded in the feature sub-population ) versus the binding information required to link features together into item-like representations ( only encoded by the conjunctive sub-population ) . Moreover , the different types of population code will require different number of neurons to cover the entire stimulus space appropriately . This will become increasingly important as the number of features increases . We study the effects of different types of representation on the nature and quality of recall , and show that aspects of human experimental data are better accounted for by population codes that might at first seem sub-optimal . According to our restricted palimpsest memory process ( “restricted” , because , as mentioned in the introduction , we do not assume erasure of the palimpsest between storage steps ) , the noisy population activities associated with all the items are simply summed to produce the final memory . As can be expected , the characteristics of the representation used will determine how readily possible it is to extract items when they are overlaid . The storage process is depicted in Fig . 1B , in which N items are stored simultaneously . Again , for simplicity , assuming that the final memory suffers from spherical Gaussian noise , we derive: xi | ϕi , ψi ∼ N ( μ ( ϕi , ψi ) , σx2I ) ( 3 ) yN| x1 , . . . , xN∼N ( ∑i=1Nβixi , σy2I ) ( 4 ) Here , ϕi and ψi represent the feature values of item i . xi is the population representation of item i . Multiple items are then summed to produce the final memory state yN . Extraction of stored information is based on the memory state yN , along with any prior information . Examples of memory states for a chosen set of stimuli and population codes are shown in Fig . 4 . For completeness , these expressions include two generalizations that we do not consider further here: the terms βi allow different items to be stored with different strengths in the memory ( to accommodate tasks involving explicit attentional instructions ) ; however , here we set βi = 1 ∀i . The parameter σy2 allows for extra memory noise , but is set to a very small value in our experiments ( σy = 10−5 ) . Having produced this final memory state , the next step is to recall the correct feature based on the recall cue . Bayes optimal recall would require marginalising over the non-target items that were simultaneously presented . Given the final memory state yN , and a cued feature value ( e . g . a colour ψ ) , this would lead to the posterior distribution over the value of the other feature of this item . However , this marginalisation would be computationally penal , since it would likely require explicitly representing and processing all the non-cued items . Instead , we make the simplifying assumption that only the item to be recalled is explicitly modelled , with the non-targets being collapsed together and treated as background noise . Conceptually , this corresponds to extracting a specific item of interest out of irrelevant noise . This approach was adopted by [21 , 24 , 25] , in the context of retrieval from long-term memory in multistate synapses . The algorithm is illustrated in Fig . 4 . Given a memory state yN and the cued colour ψ , we compute the posterior distribution over ϕ explicitly ( Fig . 4B ) . No closed-form solution exists for this posterior in general , because of the non-linear transform associated with the population code μ ( ⋅ ) . Therefore we sample from it using slice sampling [26] . We treat a single sample as the output of recalling a feature from our model for this trial . The use of sampling instead of a maximum likelihood ( or MAP ) solution has two main consequences: the variance of the posterior has a direct effect on the variance of the recalled orientation , and multi-modal posteriors will reflect situations in which another orientation may be reported in place of the appropriate one . We formalize this process by writing mN–1 as the contribution of the noise process to the mean of the final memory state and ΣN as the contribution of the noise to the full memory covariance , see Fig . 4C . r is the index of the item to be recalled , which we integrate over , as it is unknown during recall . This posterior is usually peaked around the appropriate orientation; however , depending on the population code used and the number of stored memories , additional modes can appear ( see Fig . 4B , middle and right ) . These correspond to the effects of noise and other items on the recall of the item of interest; the latter allows us to study the question of binding . We now consider various characteristics of our models in the context of visual short-term memory experiments . First , the model reproduces the baseline , apparently uniform , component of the distribution of errors ( see Fig . 5 upper row , compared to Fig . 2 ) . However , this does not arise from pure random guessing . Rather , a sample is always taken from the posterior distribution given a memory state composed after storing all items . Nevertheless , interactions between items and the overall background noise in the memory imply that the model sometimes samples values away from the target , so producing output resembling guessing . On the lower row of Fig . 5 , we see that our model can also reproduce misbinding errors , shown by the over-abundance of small errors towards non-target items values during recall . This central tendency is reduced compared to the experimental data from Fig . 2B , but is still significantly present . In addition , the magenta curve and penumbra represent the distribution of samples from the model when inter-items correlations have been removed . The second experimental observation captured by the model is the decrease in recall precision as a function of the set size , which is the number of stored items . Here we study the precision of recall using the procedure defined by [7] . This involves fitting a mixture of Von Mises components on the recall samples , using a procedure based on the EM algorithm [27] . This mixture model consists of one Von Mises component per item ( target or non-target ) and a uniform random component . All Von Mises components share a single concentration parameter κ . This mixture model approach turns out to be substantially more robust to outliers than computing the circular standard deviation of the raw errors directly . We refer to κ as the memory fidelity , and show how it depends on set size . In addition to this memory fidelity , two other types of errors are specifically captured by this analysis: misbinding errors , the probability of recalling from a non-target , and random errors , the probability of recalling from the uniform random component . These will be analysed more thoroughly in the Misbinding errors section . Fig . 6 shows the fit of our model ( in green ) to human data ( dark blue ) from [13] , where we report the memory fidelity . The shaded region indicates one standard deviation , computed over multiple reruns of the model ( or across different subjects for the human data ) . The smooth decay in performance as set size increases is appropriately captured by our model . This decay arises in our model from the increase in recall noise as the number of stored items increases , but also from interference between items in the memory . We report in both cases the memory fidelity , the concentration κ of the Von Mises component obtained from fitting the mixture model on the responses from human subjects and our model . Here , we used a mixed population code , optimizing the fit to the experimental curve by adjusting the ratio of conjunctive to feature units and the encoding noise σx , for a population of M = 200 units ( see Methods for the optimisation procedure ) . The model does not capture the reduced decay rate for 4 and 6 items to its full extent . However , this is a rather specific characteristic of this dataset . For comparison , the inset in Fig . 6 shows the fit of our model to the data from [1] . In this case , the model captures the memory fidelity dependence more accurately . A common theoretical technique used to study the representational capacity of a population code is the Fisher information ( FI ) , which , via the Cramer-Rao lower bound , limits the precision of any estimator based on the output of the code [28–30] . If the posterior distribution in our model can be well approximated as being Gaussian , the FI will accurately characterize memory fidelity , allowing us to examine the effects of different parameters and representations . In our case , the FI should readily be able to characterise the spread of the errors around the correct target value when a single item is stored ( when there is sufficient signal [31 , 32] ) . In this section ( and the Supplementary information ) , we study this case . When there are multiple items , complexity arises from the fact that errors are distributed around both the target feature value and misbound , non-target , features , with the posterior distribution being multi-modal ( and therefore not Gaussian ) . Nevertheless , as we will see in the next section , the Fisher information , calculated assuming storage of just a single item , can still characterise the memory fidelity around each mode . Assuming a population code with Gaussian noise and signal-independent noise , the Fisher information is defined as follows: I F ( θ ) ij =∂μ ∂θ i T C -1 ∂μ ∂θ j ( 7 ) where μ is the mean response of the population , and C the covariance of the population response . In our case , θ = [ϕ ψ]T , so the Fisher information is a 2-by-2 matrix . We can easily compute it for the single item case ( see Methods ) , obtaining: ⇒[I F ] ϕϕ =τ 1 2 σ 2 16π 4 I 0 ( τ 1 ) 2 I 0 ( τ 2 ) 2 ∑ i=1 M sin 2 ( ϕ-θ i ) exp2 τ 1 cos ( ϕ-θ i ) + 2 τ 2 cos ( ψ-γ i ) ( 8 ) In the large population limit in which preferred values have density ρ , it is possible to obtain an analytical closed-form solution for this equation which is easier to interpret , ( see Section , 1 in S1 Text for the complete derivation ) limM→∞[ IF1 ]ϕϕ≈f ( τ1 , τ2 ) ρσ2 ( 9 ) where f ( τ1 , τ2 ) is an increasing , approximately power-law , function of τ1 and τ2 that is given explicitly in the Supplementary information . These values depend on the parameters of the code just as one would expect from classical results for non-circular , uni-dimensional , receptive fields [31 , 33]: Increasing the concentration τ increases the Fisher information . This is easy to interpret , as narrower receptive fields will be more precise in their encoding of the features . Similarly , increasing the coverage density has the same effect , as more units are available to store information . Finally , the item encoding noise σ decreases the Fisher information , as less signal can be extracted from the final memory . The Cramer-Rao lower bound transforms the Fisher information into an estimate of performance in the task . Fig . 7 compares the Fisher information for the finite and large population limit with the curvature of the log-posterior at its maximum value ( as in the definition of the Fisher information ) ; and to the variance of samples given a memory state . We use again the memory fidelity , by fitting the mixture model onto model samples . We convert this memory fidelity from its units of κ into an inverse variance , by converting κ to the σ2 of the approximated Wrapped Gaussian ( see Methods for details ) . Note that the latter procedure , reporting the variance of samples given a memory state , generates a doubly-stochastic process , hence increasing the variance observed . It can be shown that if the posterior is close to being Gaussian , the variance from those samples will be twice that of the curvature of the log-posterior considered above ( see Section , 2 in S1 Text ) . This is shown by the dashed light blue bar . We see that they are all similar on average; the most important being the match between samples from our model and the Fisher information analysis . When more than one item is stored , errors arise from two sources: variance around a mode , and mistakenly reporting the wrong mode ( misbinding error ) . One can adapt the Fisher information analysis to characterize the former , capturing the variability about each mode , conditioned on the fact that the posterior is close to Gaussian . However , it does not capture the component of variance coming from misbinding errors . Further analysis that quantifies both sources of variability will be required to account in a theoretical manner for the full distribution of errors observed in the data . As noted above , several groups have shown that a significant proportion of the errors made by humans can be explained as arising from “misbinding” , i . e . , recalling ( at least approximately ) the appropriate feature of an inappropriate item , i . e . , of a non-target item that also formed part of the array . Such mistakes are shown in Fig . 2B , and could contribute to the appearance of a baseline of errors seen in experiments ( Fig . 2A ) , since these stimuli are drawn randomly from a uniform distribution across all possible angles [7] . The proportion of errors classified as misbinding varies between experiments [1 , 7 , 13 , 34–36] . Although some studies seem to show none at all [37]; in others , they are reported as making up to 30% of all errors when the memory load is high . Misbinding has not been well addressed in the theoretical literature on visual working memory , since current models typically assume distinct subpopulations storing the different items , hence removing any possibility for direct misbinding errors . Our model uses a single population of units for storage , and so can account for misbinding when the posterior distribution ( see Equation 6 ) becomes multimodal . This usually happens when there is insufficient information in the representation of items to bind the features together ( i . e . , when the codes are insufficiently conjunctive ) ; the different modes arise from the different items that are stored . The relative heights of the modes of the posterior determine the frequency of misbinding errors . The classical conjunctive population code represents one extreme , offering near perfect binding information , being limited only by the size of each unit’s receptive field . Feature-based population codes , on the other hand , constitute the other extreme: they do not perform binding at all . For a mixed population code , Fig . 8 shows that the proportion of conjunctive units has a strong effect on misbinding errors and posterior multimodality . We construct a situation with two possible angles , ±3π5 , where 3π5 is to be recalled . In this case , using a mixed population code with around 40% of conjunctive units dramatically reduces the number of misbinding errors produced by the model . This proportion will depend on the number of items to be stored , as more items will require more precise binding information . The widths of the posterior modes depend directly on the amount of information provided by feature and conjunctive units . Feature units are more efficient than conjunctive units at representing single features , and so the cost of reducing misbinding by increasing the proportion of conjunctive units is to increase the width of the posterior over the recalled feature . This can be seen in Fig . 9 , where we fitted the mixture model presented in the Modelling visual working memory experiments section to the recall samples , we report the concentration ( an inverse width ) of the Von Mises component in panel A , and the mixture proportions in panel B . Fig . 9A confirms the relationships of the width of the posterior mode with the proportion of conjunctive units . The concentration of the Von Mises component ( in blue ) , closely follows the theoretical Fisher information ( in green ) , although overestimating it . The Fisher information provides a good local estimate of the variability around a mode , as can be seen in Fig . 8 on the right , where we overlap in red a Von Mises PDF with a concentration predicted from the Fisher information ( with a height set to be aligned with the histogram of the right mode ) . The mixture proportions corresponding to the target , non-target and random responses are shown in Fig . 9B as a function of the fraction of conjunctive units . They show that for around 50% or more conjunctive units , more than 75% of responses are on target . The mixture proportion associated with the random component appears to be overestimated , compared visually to the distribution of the samples of Fig . 8 . However , the mixture model well characterizes the proportion of misbinding errors . Finally , as a last check , we verified that the mixture model estimates of non-target proportions were reliable . To do this , we performed a resampling-based analysis of the mixture of non-target responses , by randomizing the assumed locations of non-target angles and re-fitting the mixture model . Using the empirical cumulative distribution over those samples , we could then compute a p-value for the null hypothesis that the mixture probability for non-target would be zero . The results are shown in Fig . 9C , where the p-values as a function of the proportion of conjunctive units in the mixed population code are reported . For proportions of conjunctive units below 70% , the null hypothesis can be rejected significantly ( at a 5% level ) , consistent with the presence of misbinding errors . We applied this resampling analysis to the human experimental data shown in Fig . 2 , as well as to our model’s fit to these data ( Fig . 5 ) . The p-values for the data collapsed across subjects are shown above the histograms of biases towards non-target angles; they all are significant . Redoing the analysis per subject indicates that for 2 items , 8 out of 12 subjects show significant misbinding errors; for 4 items , 7 out of 12 are significant; and finally for , 6 items 10 out of 12 subjects show misbinding errors . In addition to the “mixed” population code that we have so far described , one might imagine an “hierarchical” population code , shown in Fig . 10 . This uses two layers , the lower of which can either be a conjunctive or feature population , parametrised as described above . Units in the higher layer are randomly connected to a subset of the lower layer units , with activities that are a nonlinear ( sigmoidal ) function of the weighted sum of the sampled units’ activities . More formally , where μ ( 1 ) is the mean response of the lower layer , σΘ the rectified linear function with threshold Θ: x ( 2 ) |ϕ , ψ∼N ( σΘ ( W·μ ( 1 ) ( ϕ , ψ ) ) , σ2I ) ( 10 ) σΘ ( x ) =max ( 0 , x−Θ ) ( 11 ) W˜jk∼Bernoulli ( p ) ·Exp ( λ ) ( 12 ) Wjk=W˜jk∑jW˜jk ( 13 ) Such an hierarchical code can be considered an abstract representation of a layered neural architecture [38] . The “mixed” and “hierarchical” population codes were specifically introduced to parametrise subtly different forms of binding , controlled by the ratio of binding to non-binding units . In the “mixed” population code , conjunctive units introduce binding information independently from the rest of the feature units . In the “hierarchical” population code , the random layer two units bind together the activity of layer one units , generating seemingly arbitrary combinations of feature values , yet providing sufficient conjunctive information . It allows us to check how structured the binding information should be for the results to hold . Fig . 11 shows the behaviour of recall for a hierarchical population code based on a feature population code at the lower layer . The total number of units was fixed ( at M = 200 ) ; the ratio of upper to lower units was varied . The optimal arrangement changes markedly when multiple items must be stored . Having few random binding units is very efficient in the single item case , but this breaks down completely when multiple items are stored and interfere with each other . The dependence of the memory fidelity on the ratio of upper to lower units is similar for increasing number of items , with the exception of the overall scale . Unsurprisingly , memory fidelity is lower when increasing the number of items and conjunctivity , see Fig . 11A . As shown in Fig . 11B , the probability of the response being related to the correct target changes completely going from one to many items , with non-target responses becoming prevalent for small ratios of upper to lower units . Moreover , there is an optimal ratio of upper to lower units when storing multiple items , if one tries to optimise the proportion of correct target angle recall . Fig . 12 shows the fit of the memory fidelity to the experiments in [1 , 7] , as was done in Fig . 6 for the mixed population code . Despite being drastically different in its implementation of conjunctivity , it provides a good fit to the experimental data . The hierarchical code is able to capture the trend of decay in both experiments to a greater extend than the mixed population code ( main plot shows a fit to [7] , inset shows a fit to [1] ) . However , the fit for 4 and 5 items for [1] does show discrepancies with the experimental data . The optimal parameters obtained for those fits resemble those for the mixed population code , namely a high ratio of higher-level binding units and large input noise . These render promising this class of hierarchical codes .
We built a model of short-term visual working memory , assuming a single population of units , an additive , palimpsest , storage scheme and sample-based probabilistic recall . We showed how this model could qualitatively reproduce key aspects of human experimental data , including the decrease in performance with memory load , and also error distributions , including misbinding errors , which have not previously been the focus of theoretical study . It is the next phase of this work to fit human data quantitatively , looking in detail at individual differences in performance and patterns of errors . We studied several different sorts of population code . The most critical question concerns binding , which in our case is performed by conjunctive units that are sensitive to specific combinations of two or more features . Non-conjunctive , feature-based codes , can be more efficient at storing single items , but fail catastrophically whenever multiple items are stored simultaneously . We considered including both single-feature and conjunctive units , and showed that a combination is likely to offer a better characterization of experimental data than either alone . Finally , we considered experiments that would offer useful guidance to discriminating theories . The original such model of this class of experiments was formalised by Wilken & Ma [39] , based on experiments and arguments from Pashler and Luck & Vogel [4 , 40] . This includes a finite set of “slots”; items that are not allocated a slot are therefore not remembered at all ( requiring pure guessing for recall ) . The assumed error distribution was thus a mixture model with two components: a Von Mises centred around the target item , and a random uniform component . The alternative models are based on the notion of a finite resource [1 , 7–11] , arguing against a fixed number of slots , but rather that there is a constraint on the whole collection , such that storage of multiple items leads to interference . More recently , intermediate accounts have been suggested , such as a “slots and averaging” model [5] , letting individual items be stored in more than one slot , with the outputs of all the slots concerned being averaged . By comparison , our model , as a palimpsest , can best be seen as abandoning the notion of slots altogether—be they finite or infinite—and so does not need a mechanism for allocating the slots . There is a finite resource—the population of units that can be active—but this leads to two resource-like limitations on storage , rather than one . The first limitation is noise—this acts just like some of the resource limits in previous models . The second limitation is representational—the fact that the items overlap in the palimpsest in a way that depends on how they are encoded in the population implies a form of interference and interaction that leads to misbinding . This explicit element has been missing in previous treatments . Along with the variability in the process of sampling , it is key to the model’s account of the pattern of errors of human subjects , with heavier tails than a Gaussian/Von Mises distribution . Other factors have also been implicated in this pattern , such as different memory encoding precision on different trials [10 , 41] , or the limited width of neuronal tuning functions [15] . It would be straightforward to extend our scheme to allow for partial information about which item will have to be recalled . We have shown how our model can encode information about each feature separately , with the binding information being provided by another subpopulation . A model along related lines was recently proposed by Swan and Wyble [42] . In this , an associative network , which they call the “binding pool” , provides binding information . However , one could think of other ways to encode and store this binding information , for example by using object-files . If one were to limit how many object-files could be used at a given time , and if object-files made errors in binding the features together , this would provide an hybrid slot-based treatment of the problem . Another related model has been suggested in the context of dynamic field theory [43 , 44] . These authors consider a population of rate-based units with temporal dynamics governed by first order differential equations . Given specific layers and connectivity patterns , they simulate the evolution of bumps of activity through time , which can be used to store information for later recall . In their model , feature binding is completely linked to space in that each feature is stored in different feature-space population bound only to location . A separate working memory population stores the locations of all items seen . Recall relies on using location to couple and constrain the possible features to their original values . This idea resembles “feature integration theory” , proposed by [45] as a model for attention . That the dynamical ( e . g . , drifting ) behaviour of the bumps is the critical focus of the model sits a little uneasily with the observation that performance in visual short-term memory experiments does not drop significantly when recall is delayed [1 , 46] . Further , location cannot be the only variable determining binding given experiments in which items are presented at the same location but at different times . Our model is agnostic about the source of binding in its input , lending itself to the study of different representations . Nevertheless , it would be interesting to model richer aspects of the temporal evolution of the memory state . Here , we assumed that only two features were stored per item , namely colour and angle . However , we report in Section , 5 in S1 Text the effect of using more than two features . One feature that is particularly important is spatial location . In the actual experiments in [7] , space ( which , for simplicity and consistency with [1] , we treated as another angular variable ) was used as the cueing feature , with colour being recalled . It is possible , given the importance of space for object recognition , that spatial tuning has quite different characteristics from that of other cues . Hints of this are apparent in the properties of early visual neurons . This could make it a stronger cue for recall and recognition , something that it would be interesting to examine systematically through experiment and the model . With more features , we could address directly one of the key findings that led support to the slot models , namely the observation of an object benefit in recalling features . That is , despite the sometime fragility of episodic memory [47] , which this functionally resembles , remembering a fixed number of features is easier when those features are parts of fewer conjunctive items . The magnitude of that effect has been the subject of intense debate , but there is broad agreement about a significant object benefit [48–52] . In our model , such effects arise through two mechanisms: first , having fewer items will add less encoding noise to the final memory state , which will directly reduce the overall noise level in recall . Second , the conjunctive units also directly contribute to the storage precision for bound items . Our model would thus also show an object benefit without additional machinery . Our model treats storage as a bottom-up , feedforward process . However certain top-down effects are known , such as directed forgetting [53 , 54] . Such an effect could be accommodated in the model by considering a multiple step process in which following regular storage , recall would be executed based on the cue for the to-be-forgotten item , with the representation of whatever is retrieved being subtracted from the previous memory state . As this would still be a noisy process , the resulting precision for the other items would be less than if the forgotten item had never been stored at all , albeit still greater than if its main influence over the memory state remained . We made a number of simplifying assumptions , notably to do with the noise model and the sampling process . For the former , we only considered additive isotropic Gaussian noise corrupting the encoding . This could be readily extended to more complex noise models , for example to a more neurally plausible Poisson noise model . The key difference from using Poisson noise would be its signal-dependence—storing larger numbers of items would lead to greater activities and thus a higher variance . Signal-dependent Gaussian noise is a related modelling choice [30 , 31 , 55] . Amongst other differences , this would reintroduce the second term in the equation for the Fisher information ( Equation 30 ) . This term can be large compared to the first [55] and it adds extra inferential complexity [56] , hence fully accounting for it can be complicated . We considered a process of recall that involves the full posterior distribution over the responses . Determining how the brain would use and represent distributional information has been an active recent research topic . One set of ideas considers what amounts to a deterministic treatment ( albeit corrupted by noise ) [57–62] . However , there is a growing body of research showing how the brain might instead use samples [63–66] , and we adopted this approach . Inference might involve combining together larger numbers of samples , and thus reporting some ( noisy ) function of the posterior other than just the samples . However , such operations are currently underdetermined by the experimental data , as they would interact with other sources of noise . Sampling from the posterior instead of simply reporting the maximum a-posteriori mode value has the additional benefits of capturing variability around the mode itself , which varies depending on the representation used . Nevertheless , it is important to stress that this sampling scheme is not the main bottleneck in our model . Rather , it is the representation that constrains the nature and magnitude of the errors in recall . The sampling scheme simply provides a mechanism for reporting on the ultimate posterior distribution . A more limited report , such as the MAP value , would likely lack the appropriate characteristics by reflecting too little of this distribution . One of the major tools that we used to analyse the population codes was the Fisher information ( and the associated Cramer-Rao lower bound ) . However , this is only useful if the posterior distribution is close to being Gaussian , and , in particular , unimodal . This will almost always be true for a single item; and often be true when there are multiple items and a conjunctive population code that solves the binding problem . However , as we saw , feature codes lead to multimodality , rendering a direct application of the Cramer-Rao lower bound useless . What is still possible is to use the Fisher information as an indication for the variability around one of the mode . We have shown how it still produces a good approximation to the width of a mode , even in the presence of misbinding errors . We characterized misbinding errors through a mixture model and a resampling-based estimator . It is also possible to assess the multimodality of the posterior itself directly , for example by fitting a parametric mixture model on the posterior . This analysis leads to similar results . But it would then be possible to analyse this multi-modality analytically , and perhaps obtain a closed form expression for the proportion of misbinding errors expected from a given posterior . We considered a case of recalling only a single item given a memory . It would be possible to treat recall differently , with a mixture model , estimating the features associated with all items , and thereby answering the memory query directly . Total recall could be performed using a fixed finite mixture model , e . g . a Gaussian Mixture model , but lends itself well to a nonparametric extension , characterizing the whole collection of elements in an array . Approaches of this sort have been pursued by various recent authors [67–71] . For instance [71] considered both the encoding and recall to be implemented with a Dirichlet process mixture model . They show how this provides a natural account of ensemble statistics effects that can be seen in some experiments , such as regression to the mean of the presented samples . By contrast , our approach is closer to the experimental paradigm , as there is no evidence that subjects recall all features of all items when asked to recall an unique item . Regression to the mean still arises , but from local interactions between items in the representation . Indeed , even for a conjunctive code , when items are close-by the recalled angle will be biased towards the mean of all items , as bumps of activity merge together . There is substantial precedence for the approximation of focusing on a single item , ignoring some or all of the statistical structure associated with other actual or potential items [72–75] . Our results depend crucially on the nature of the underlying population code . As a proof of principle , we tested two schemes—one mixing feature-based and conjunctive codes; the other building a hierarchy on top of feature codes . However , many more sophisticated representations would also be possible—studies of population coding suggest that using multiple scales is particularly beneficial [76 , 77] , and it would be interesting to test these . For our single-scale case , we suggested a particular pattern of three stimuli that we expect to be of particular value in discriminating between different population coding schemes . The pattern was designed to promote misbinding in a way that would also be revealing about the size of the receptive fields . We also expect there to be a strong effect of distance in stimulus space on misbinding probability , if a mixed-like representation is used . On the other hand , by the very nature of our hierarchical population code , it is harder to make specific predictions about the dependence of proximity and other features on misbinding probability . If subjects were too proficient at recall from this pattern , as might be the case for just three items [1] , it would be straightforward to complicate the scheme to include a larger number of items . An interesting extension to this analysis would be to introduce an asymmetry in the pattern of stimuli , in order to displace the mean of the stimuli from the centre stimulus . This would in turn introduce asymmetric biases and deviations for the different items depending on the sources of the errors . Indeed , as briefly mentioned above , it has been shown that the mean statistics of the stimuli have an effect in determining responses characteristics . Such an asymmetric pattern would indicate if the variability is biased towards the mean of the stimuli or to close-by items only . Although our proposal has primarily been grounded on the psychophysical literature , the use of population representations , and the abandonment of anatomical “slots” , makes it appealing to consider the neural basis of the memory . There is substantial work on population-based working memory with a foundation in persistent activity [78] , and even in the gating of storage necessary to make such memories work efficiently [79 , 80] . It would be interesting to study the extra constraints that come from a more realistic neural implementation . In conclusion , we proposed a model which accounts for errors in working memory by considering explicitly the link between storage and representation . We showed it can successfully account for key aspects of the psychophysical data on visual short term memory , and allows for a better understanding of the relationship between being precise in the representation of single features and the representation of binding information across all the features of a single pattern to be able to handle cued recall . Based on observations on the form of the errors arising when recalling information from a palimpsest memory , we proposed a specific stimulus template that would produce different error patterns depending on characteristics of the underlying representation , and so we suggest as an attractive target for psychophysical investigation .
We assume continuous firing-rate style units . They have Bivariate Von Mises tuning curves , corrupted by isotropic additive Gaussian noise: μ m ( ϕ , ψ ) =1 4π 2 I 0 ( τ 1 , m ) I 0 ( τ 2 , m ) expτ 1 , m cos ( ϕ-θ m ) +τ 2 , m cos ( ψ-γ m ) , ( 14 ) ϕ and ψ are respectively the orientation and colour of the item to be represented . θm and γm identify the preferred angle and colour of unit i . τ1 , m and τ2 , m control the size of the receptive field , as well as the sensitivity of each unit to the different features . Let the population firing rate state be x = [x1 , … , xM]T , xm . The firing rate of unit m is: x|ϕ , ψ∼N ( μ ( ϕ , ψ ) , σ x 2 I ) ( 15 ) Differences in the choices of τ1 , m and τ2 , m across the population will generate different types of representation . The hierarchical population code is defined as follows , with μ ( 1 ) being the mean response of the lower layer . The receptive field sizes were set automatically to achieve maximum coverage given a population of M units . Given a fixed number of units with preferred stimuli arranged uniformly over the feature space , the receptive field sizes were modified such that one standard deviation of the receptive field would cover the space uniformly without redundancy . In the case of a conjunctive code , we have: τ=gσ→τ ( 2πM ) where gσ → τ converts the standard deviation of a Wrapped Gaussian into the τ of a Von Mises . No closed-form solution of gσ → τ exists; it can be computed numerically by finding the argminτ ( exp ( −σ22 ) −I1 ( τ ) I0 ( τ ) ) 2 . For a feature code , we set: τ1=gσ→τ ( 2πM/2 ) ( 20 ) τ2=gσ→τ ( 2π ) ( 21 ) Where τ1 and τ2 correspond to the two receptive field sizes of one subpopulations ( here assumed to be sensitive along the τ1 direction ) . The storage process for N items is probabilistic and follows the following model: xi| ϕi , ψi∼N ( μ ( ϕi , ψi ) , σx2I ) ( 22 ) yN |x1 , . . . , xN∼N ( ∑i=1Nβixi , σy2I ) ( 23 ) xi is the representation of item i by the population code . ϕi and ψi represent the feature values of item i . Multiple items are summed to produce the final memory state yN , which is , in turn , corrupted by additional , independent , Gaussian , noise . βi models different strengths of storage in the memory ( to accommodate tasks involving explicit attentional instructions ) . Recall is based on the simplifying assumption that a single item is modelled , while others are collapsed into a single source of noise . mN-1 is the contribution of the noise process to the mean of the final memory state and ΣN is the contribution of the noise to the full memory covariance . r is the index of the item to be recalled , which we integrate over as it is unknown during recall . The posterior over the feature ϕ to be recalled is defined as follows: yN| ϕ , ψ , r∼N ( mN−1+βrμ ( ϕ , ψ ) , ΣN ) ( 24 ) ϕ |yN , ψ∼∫drp ( r ) p ( ϕ ) p ( yN|ϕ , ψ , r ) ( 25 ) We use uniform prior distributions over r and ϕ ( circularly uniform for ϕ ) . The collapsed noise mean mN-1 and covariance ΣN can be estimated from random samples of the storage process . mN-1 is the mean memory built from N−1 , marginalising over feature values: mN−1=E[ yN−1 ] ( 26 ) yN−1∼∫ . . . ∫ϕ1 , ψ1 . . . ϕN−1 , ψN−1P ( yN−1|ϕ1 , ψ1 , ⋯ϕN−1 , ψN−1 ) dϕ1dψ1⋯dϕN−1ψN−1 ( 27 ) Similarly , ΣN is the covariance of N items , marginalising over feature values . We obtain estimates by sampling 5000 memory items from the storage process before estimating those two empirical estimates . We use a slice sampling scheme to obtain samples of ϕ given a memory state . In addition to the classical slice sampling algorithm , we introduce Metropolis-Hastings jumps , which can randomly set the sampler in another part of the state space . This allows to jump between modes in a multi-modal posterior setting . The jump probability is set to 10% and a jump is accepted depending on a Metropolis-Hastings acceptance ratio . We discard the first 500 samples as burn-in steps for the slice sampler . We perform step-out and shrinkage to determine the slice width ( initially set to w=π40 ) [26] . We constrain the sampler to the [−π , π] interval . This allows us to sample appropriately from the full posterior . We use the mixture model of [7] , allowing for a mixture of target , non-target and random responses . We fit the following mixture component , using the expectation-maximization algorithm: P ( θ ) =ptVM ( θ;μt , κ ) +∑kN−1pntVM ( θ;μk , κ ) +pr12π ( 28 ) pt+pr+pnt=1 ( 29 ) where pt is the mixture proportion associated with the target , pr the random mixture proportion and pnt the non-target mixture proportion . μt and μk are the true locations of the target and non-targets . All Von Mises share the same κ; this is because the concentrations ( though not the mixing proportions ) of the posterior modes around each target are determined by the Cramer-Rao lower bound associated with the local Fisher information , which are all identical . The values of pt , pr , pnt and κ are fit during the EM procedure; the μ’s are assumed to be known . To check for the significance of non-zero mixture proportion pnt , associated with non-target responses , we perform a resampling analysis . Given a set of responses , targets and non-target angles , we randomly resample the non-target angles and refit the mixture model . We perform this procedure K times and obtain K samples of pnt ( K = 1000 ) . We then construct the empirical cumulative distribution function Φ ( pnt ) for pnt given those samples . Finally , we compare the mixture proportion pnt* obtained given the original non-target angles , and reject the null hypothesis “pnt = 0” when p=1−Φ ( pnt* ) <0 . 01 . The Fisher information for a population code with Gaussian noise is: I F ( θ ) ij =∂f ∂θ i T C -1 ( θ ) ∂f ∂θ j +1 2trC -1 ( θ ) ∂C -1 ( θ ) ∂θ i C -1 ( θ ) ∂C -1 ( θ ) ∂θ i ( 30 ) where f is the mean response of the population , and C the covariance of the population response . In our case , θ = [ϕ ψ]T , so the Fisher information is a 2-by-2 matrix . Consider the case that the memory only contains a single item , with β = 1 . Then y N |ϕ , ψ∼Nμ ( ϕ , ψ ) , Σ ˜ N ( 31 ) where we assume Σ˜N=σx2I . Since the covariance Σ˜N does not depend on θ , the trace term in the Fisher information is 0 . The FI about the angle is given by [ IF ( θ ) ]ϕϕ=∂μ∂ϕT1σ2I∂μ∂ϕ ( 32 ) [ ∂μ∂ϕ ]i=−τ1sin ( ϕ−θi ) 4π2I0 ( τ1 ) I0 ( τ2 ) exp[ τ1cos ( ϕ−θi ) +τ2cos ( ψ−γi ) ] ( 33 ) ⇒[ IF ]ϕϕ=τ12σ216π4I0 ( τ1 ) 2I0 ( τ2 ) 2∑i=1Msin2 ( ϕ−θi ) exp[ 2τ1cos ( ϕ−θi ) +2τ2cos ( ψ−γi ) ] ( 34 ) The other components of the Fisher information matrix can be derived similarly . By taking a large population limit in which preferred values have density ρ , we obtain a closed-form approximation to the Fisher information ( see Section , 1 in S1 Text for the complete derivation ) : limM→∞[ IF1 ]ϕϕ≈τ12ρσ28π2I0 ( τ1 ) 2I0 ( τ2 ) 2I0 ( 2τ2 ) ( I0 ( 2τ1 ) −I2 ( 2τ1 ) ) ( 35 ) We perform a grid search over several population code parameters to provide a qualitative fit to human experiments . For the mixed population code , we varied σx and the ratio of conjunctivity , as β , σy were kept fixed . For the hierarchical code , we set p = 1 , λ = 1 and Θ = 1 and varied σx and the ratio of conjunctivity ( defined as M2M1+M2 , where M1 ( respectively M2 ) is the size of the layer one subpopulation ( respectively layer two ) ) . A full fit , which is the subject of future work , would require at least the consideration of heterogeneous and multi-scale population representations . | Humans can remember several visual items for a few seconds and recall them; however , performance deteriorates surprisingly quickly with the number of items that must be stored . Along with increasingly inaccurate recollection , subjects make association errors , sometimes apparently recalling the wrong item altogether . No current model accounts for these data fully . We discuss a simple model that focuses attention on the population representations that are putatively involved , and thereby on limits to the amount of information that can be stored and recalled . We use theoretical and numerical methods to examine the characteristics and performance of our model . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | A Probabilistic Palimpsest Model of Visual Short-term Memory |
There is conflicting evidence on the immunologic benefit of treating helminth co-infections ( “deworming” ) in HIV-infected individuals . Several studies have documented reduced viral load and increased CD4 count in antiretroviral therapy ( ART ) naïve individuals after deworming . However , there are a lack of data on the effect of deworming therapy on CD4 count recovery among HIV-infected persons taking ART . To estimate the association between empiric deworming therapy and CD4 count after ART initiation , we performed a retrospective observational study among HIV-infected adults on ART at a publicly operated HIV clinic in southwestern Uganda . Subjects were assigned as having received deworming if prescribed an anti-helminthic agent between 7 and 90 days before a CD4 test . To estimate the association between deworming and CD4 count , we fit multivariable regression models and analyzed predictors of CD4 count , using a time-by-interaction term with receipt or non-receipt of deworming . From 1998 to 2009 , 5 , 379 subjects on ART attended 21 , 933 clinic visits at which a CD4 count was measured . Subjects received deworming prior to 668 ( 3% ) visits . Overall , deworming was not associated with a significant difference in CD4 count in either the first year on ART ( β = 42 . 8; 95% CI , −2 . 1 to 87 . 7 ) or after the first year of ART ( β = −9 . 9; 95% CI , −24 . 1 to 4 . 4 ) . However , in a sub-analysis by gender , during the first year of ART deworming was associated with a significantly greater rise in CD4 count ( β = 63 . 0; 95% CI , 6 . 0 to 120 . 1 ) in females . Empiric deworming of HIV-infected individuals on ART conferred no significant generalized benefit on subsequent CD4 count recovery . A significant association was observed exclusively in females and during the initial year on ART . Our findings are consistent with recent studies that failed to demonstrate an immunologic advantage to empirically deworming ART-naïve individuals , but suggest that certain sub-populations may benefit .
Despite increased access to antiretroviral therapy ( ART ) in sub-Saharan Africa [1] , HIV outcomes in this region have lagged behind those in more industrialized regions [2] . Although a complex interplay of economic , biologic , and sociobehavioral factors underlies this discrepancy , one potential contributing factor is the high rate of endemic , chronic , and overlapping parasitic co-infections , including schistosomiasis and the three major soil-transmitted helminth ( STH ) infections: Ascaris lumbricoides , Trichuris trichiura , and hookworms – caused by two species , Ancylostoma duodenale and Necator americanus [3] . A number of potential pathways have been proposed by which helminth infection might impair immunologic control of the HIV virus and accelerate disease progression . These include suppression of antiviral Th1 lymphocyte responses by helminth-driven Th2 lymphocyte skewing [4] , [5] , helminth-induced stimulation of other immunosuppressive cytokine responses [6] , potentiation of cellular susceptibility to viral entry resulting from increased co-receptor expression [7] , [8] , and augmentation of systemic immune activation [3] , [5] , [9] , [10] . A mechanism whereby helminth infection increases systemic immune activation is particularly intriguing , given the growing evidence that systemic inflammation and immune activation are associated with poor clinical outcomes in HIV-infected individuals on ART [10] , [11] , [12] . The geographic distributions of HIV and STH infection in sub-Saharan are largely overlapping [13] . Existing anti-helminthic drugs are well-tolerated , inexpensive , and easy to administer [14] , [15] . On the other hand , barriers to diagnosing STH infection in resource-constrained settings are considerable [16] , given that the current gold standard method of stool microscopy is both labor intensive and lacks optimal sensitivity [17] , [18] , [19] , [20] . Furthermore , chronic gastrointestinal STH carriage is frequently asymptomic , or presents only with vague and non-specific manifestations that are common among HIV-infected persons – such as weight loss , nutritional deficiency , anemia , abdominal discomfort , or diarrhea [21] , [22] . Therefore , routine empiric treatment of STH infection ( “deworming” ) in HIV-infected individuals living in regions with high prevalence of STH has been suggested as a potential intervention to delay HIV disease progression in ART naïve patients [4] , [22] , [23] . Studies testing this hypothesis in ART-naïve populations have been conflicting [24] , [25] , [26] , [27] . A meta-analysis compiling data from three randomized controlled trials demonstrated a significantly lower plasma viral load and higher CD4 count in individuals receiving definitive therapy for confirmed helminth infection [28] . However , a more recent multi-site randomized controlled trial , reported by Walson and colleagues , demonstrated no beneficial impact of empiric deworming on markers of HIV disease progression in a similar ART-naïve population [29] . Most prior studies investigating the impact of empiric deworming on markers of HIV disease progression have been performed in ART-naïve individuals . There is a paucity of data to demonstrate the impact of deworming on clinical outcomes among HIV-infected persons taking ART . In the past decade over 8 million people living with HIV/AIDS have gained access to ART [1] and this number is expected to continue to grow rapidly as international guidelines recommend earlier treatment at higher CD4 counts [30] . There is increasing evidence that persistent immune activation – perhaps mediated to some extent by the translocation of pro-inflammatory microbial products across a dysfunctional gastrointestinal mucosal barrier [31] , [32] , [33] – is associated with mortality and adverse clinical outcomes , even among those achieving viral suppression on ART [11] , [34] , [35] , [36] , [37] , [38] . In response , a number of interventions are in development and testing which aim to reduce immune activation in this population [39] . Given the potential associations between STH infection , disruption of the gut mucosal barrier , and immune activation , deworming therapy warrants further exploration as such an intervention . Our primary objective was to estimate the association between empiric deworming and change in CD4 count over time in HIV-infected individuals receiving ART in southwestern Uganda . We hypothesized that receipt of deworming therapy ( versus no antecedent deworming ) was associated with greater increase in CD4 count . In the Mbarara district , where the majority of our study population resides , combined prevalence of the three major STH infections is estimated to be 20–50% [40] , [41] , [42] , [43] , [44] . Hookworm is the most commonly identified STH in epidemiologic surveys , both in this district and throughout East Africa [41] , [42] , [43] . Although schistosomal infection is endemic in many Ugandan communities living around low-lying bodies of fresh water , it is relatively uncommon in the higher altitude areas of Mbarara district [45] , [46] . As a result , our clinic does not routinely perform diagnostic testing for Schistosoma species or administer empiric praziquantel . A secondary objective was to estimate the association between empiric deworming and other markers of nutritional status , such as body mass and anemia , during treated HIV infection . Because STH infection is an important cause of malnutrition – specifically , hookworms are known to cause gastrointestinal blood loss resulting in iron deficiency anemia [47] – we hypothesized that deworming was associated with greater increase in total body mass and blood hemoglobin concentration with time on ART .
Ethical approval for all study procedures was obtained from the Committee on Human Research , University of California at San Francisco; the Partners Human Research Committee , Massachusetts General Hospital; and the Institutional Review Committee , Mbarara University of Science and Technology . Consistent with national guidelines , we received clearance for the study from the Uganda National Council for Science and Technology and from the Research Secretariat in the Office of the President . The database used for this analysis is primarily a clinical database . Personal identifiers and protected health information are removed prior to data extraction and analysis . As such , all ethical review committees granted a waiver for informed consent . We conducted a retrospective , observational study among HIV-infected adults on ART at a large , publicly operated , regional HIV clinic located on the campus of the Mbarara Regional Referral Hospital in southwestern Uganda . Clinical data from 1998 through 2009 were analyzed in this study . Depending on clinical status , patients were seen in the clinic approximately two to six times annually . We included all subjects aged 17 years or greater who were on ART and had at least one recorded CD4 count test during the study period . For a given CD4 count measurement , subjects were assigned as having received deworming treatment if they were prescribed either albendazole or mebendazole between 7 and 90 days before the date of the CD4 test . The same 7 to 90 day time window was used to assign credit for deworming with respect to the date of measurement for our secondary outcomes , body mass and hemoglobin . Standard practice in this clinic was to administer a single oral dose of either albendazole ( 500 milligrams ) or mebendazole ( 400 milligrams ) for routine deworming approximately once per year ( which corresponds to approximately every six visits ) . We chose a 7 to 90 day window to assign credit for having received deworming therapy based on evidence that the likelihood of STH re-infection approaches 50% at 90 days post-treatment [48] . Additionally , this time window was chosen in order to maintain consistency with prior studies that examined the effect of deworming on CD4 count at 70 to 112 days after deworming [24] , [25] , [26] , [27] . We compared baseline characteristics between subjects who received at least one deworming treatment and those who received no deworming during the study period . Categorical variables were compared between these groups using chi squared testing , and continuous variables using the equality of medians test . In our primary analysis , we fit a multivariable linear regression model to estimate the association between deworming and CD4 count . The primary explanatory variables of interest were time on ART , receipt ( vs . non-receipt ) of deworming , and an interaction between these two variables . Because graphical depiction of CD4 changes on ART demonstrated a clear leveling of the rate of increase in CD4 count after one year of ART , we implemented a linear spline model for duration of ART with a knot at one year . We then carried out a sub-analysis stratified by gender , to determine if there was a differential effect of deworming in males versus females . Finally , we performed two secondary analyses to estimate the association between deworming and 1 ) body mass ( kilograms ) , and 2 ) blood hemoglobin concentration ( grams per deciliter ) . We fit a separate multivariable linear regression model for each of these secondary outcomes , similarly using time on ART , receipt ( vs . non-receipt ) of deworming , and a time by deworming interaction term as our explanatory variables of interest . To account for within-participant clustering over time , we used cluster-correlated robust estimates of variance [49] , [50] . We adjusted for known correlates of HIV disease progression and CD4 count change , including age , time on ART , co-morbid tuberculosis infection , and pregnancy ( if applicable ) . We considered also adjusting for known socio-demographic predictors of STH infection , such as educational attainment , socioeconomic status , and rural ( versus urban ) residence [18] , [51]; however , in our clinical database these variables were inconsistently documented over the course of the study period . Given the high degree of missing data and the uneven distribution of missingness with respect to other subject characteristics , we did not include these additional co-variables in our final analyses . However , we did employ the following relevant variables in a sensitivity analysis: educational attainment , monthly income , and travel time from home to clinic . To test the robustness of our primary model , we performed several sensitivity analyses . First , we fit a model in which CD4 count outliers were excluded . Outliers were defined as CD4 count measurements that varied by greater than 400 cells/mm3 ( approximately +/−2 standard deviations from the median change in CD4 count ) from the immediately previous or subsequent measurement . Additionally , we considered that the potential benefit of deworming on CD4 count may persist for greater than 90 days , and that some subjects who received deworming may have simply not been scheduled for a CD4 count measurement within the 7 to 90 day time window after deworming . Therefore , we performed analyses in which credit for deworming was extended to CD4 count measurements up 120 days and 180 days after treatment . Finally , although several compelling socio-demographic indicators were missing for the majority of our subjects , we performed a sensitivity analysis in which educational attainment , monthly income , and travel time from home to clinic were added as co-variables to our primary model in a step-wise fashion . For our secondary analyses , we performed sensitivity analyses in which we extended credit for deworming up to 120 and 180 days , stratified by gender , and excluded outliers , defined as body mass or hemoglobin measurements that were +/−3 standard deviations from the median . All data analysis was performed using Stata version 11 . 2 ( StataCorp , College Station , Texas ) .
From 1998–2009 , a total of 5 , 379 subjects on ART attended 21 , 933 clinic visits at which a CD4 count was measured . Sixty-one percent of subjects were female . The median baseline CD4 count and age at the time of ART initiation were 270 cells/mm3 and 38 . 3 years , respectively . During our study period , the median number of clinic visits per subject at which a CD4 count was measured was four . A total of 2 , 781 out of 5 , 379 ( 52% ) subjects received deworming therapy at least once during the observation period , and a total of 668 ( 3% ) CD4 count measurements were preceded by deworming therapy within the 7 to 90 day time window . Baseline characteristics , compared between subjects that received deworming therapy at least one time during the study period and those that received no deworming therapy during the study period , are summarized in Table 1 . In our primary multivariable linear regression analysis ( Table 2 ) , deworming was not significantly associated with a change in CD4 count over time in either the first year on ART ( β = 42 . 8; 95% CI , −2 . 1 to 87 . 7 ) or after the first year of ART ( β = −9 . 9; 95% CI , −24 . 1 to 4 . 4 ) . In this model , statistically significant predictors of CD4 count were age , time on ART , tuberculosis co-infection , and receipt of deworming . Based on estimates derived from our primary model , predicted CD4 count as a function of time on ART is graphically depicted in Figure 1 . In our sensitivity analyses , excluding outlier CD4 values , extending the treatment window , or adjusting for additional socio-demographic variables did not yield qualitatively different results . In the sub-analysis restricted to female gender ( Table 3 ) , deworming was significantly associated with a greater increase in CD4 count during the first year of ART ( β = 63 . 0; 95% CI , 6 . 0 to 120 . 1 ) , but not after the first year of ART ( β = −15 . 4; 95% CI , −32 . 6 to 1 . 8 ) . Statistically significant predictors of CD4 count were time on ART , tuberculosis co-infection , pregnancy , and receipt of deworming . When the analysis was restricted to male gender , deworming was not significantly associated with CD4 count change in either the first year on ART ( β = 13 . 0; 95% CI , −57 . 4 to 83 . 4 ) or after the first year of ART ( β = −6 . 9; 95% CI , −29 . 3 to 15 . 5 ) . Statistically significant predictors of CD4 count for males were time on ART and tuberculosis co-infection . Notably , as compared to males in our study population , females were significantly younger ( median age 36 . 2 vs . 41 . 3; p<0 . 001 ) , more likely to have initiated ART after 2007 ( 48 . 1% vs . 44 . 4%; p = 0 . 009 ) , less likely to have ever had tuberculosis during the study period ( 15 . 2% vs . 25 . 6%; p<0 . 001 ) , and had significantly higher pre-ART baseline CD4 count ( 282 vs . 248; p<0 . 001 ) . Females and males were not significantly different with respect to the likelihood of receiving deworming at least once during the study period , as well as the median number post-ART visits at which a CD4 measurement was obtained . In the secondary analyses , there was no significant effect of deworming on either body mass ( β = −0 . 09; 95% CI , −0 . 40 to 0 . 23 ) or hemoglobin ( β = 0 . 08; 95% CI , −0 . 05 to 0 . 22 ) over time ( Table 4 ) . Statistically significant predictors of both body mass and hemoglobin were time on ART , age , tuberculosis co-infection , and receipt of deworming . The sensitivity analysis in which we extended the time window for assigning deworming credit and simultaneously restricted the population to females resulted in the new observation that deworming was significantly associated with increased hemoglobin concentration over time for both the 120 day ( β = 0 . 15; 95% CI , 0 . 02 to 0 . 27 ) and the 180 day ( β = 0 . 14; 95% CI , 0 . 04 to 0 . 25 ) time windows . Otherwise , the remaining sensitivity analyses for both the body mass and hemoglobin outcomes yielded qualitatively similar results to the original models .
In this retrospective analysis accounting for over 600 deworming events in more than 5 , 000 persons accessing HIV care at a public clinic during a 10 year period in rural Uganda , we found no generalized benefit of empiric deworming on CD4 count recovery among HIV-infected individuals on ART . Although it did not reach statistical significance , it is notable that deworming predicted an increased CD4 count over time during the first year on ART , but not during subsequent years . Additionally , in the sub-population of females , deworming predicted a significantly greater rise in CD4 count during the first year on ART . Not only was the latter effect statistically significant , but compared to our primary model the magnitude of CD4 count increase was greater . In the secondary analysis , deworming conferred no benefit on body weight over time . Similarly , in our original model for the hemoglobin outcome , deworming conferred no benefit on hemoglobin level over time; however , we did observe a small but significant increase in hemoglobin level with deworming when we concurrently restricted the population to females and extended the time window for assigning deworming credit to 120 or 180 days before a given hemoglobin measurement . Interestingly , in both our primary model and the sub-analysis restricted to females , baseline CD4 count at time of ART initiation was significantly lower in subjects who received deworming . One possible explanation for this would be if clinicians were more likely to recommend deworming to patients who were judged to be clinically more ill , or more specifically to those with lower CD4 counts . This notion is supported by our finding that both baseline body weight and hemoglobin were also lower in subjects who received deworming . Given that we observed a non-significant trend toward greater CD4 count recovery during the first year of ART with deworming ( and a significant effect in the female sub-analysis ) , it is intriguing to speculate that our estimate did not reach significance simply due to methodological limitations , such as an unidentified bias toward the null , under-powering , or residual confounding . Additionally , the dominant effect of ART on CD4 count recovery may have masked any smaller , but still potentially significant , effect of deworming . In light of these limitations , it is critical to interpret the above findings in the context of the disparity in baseline CD4 count between those receiving deworming and those not ( Table 1 ) . The fact that , independent of deworming , there was no significant association between baseline CD4 count and rate of CD4 count rise during the first year of ART ( data not shown ) suggests that a true treatment effect may be more likely . It should be noted that females in our study population differed significantly from males with respect to several important baseline characteristics in our model , including age , tuberculosis co-infection , and baseline CD4 count . Our finding that deworming had a significant benefit for females during the first year on ART may be partly explained by the fact that females in our population were younger and less likely to have tuberculosis co-infection , since both age and tuberculosis co-infection were inversely associated with CD4 count in our primary model , and in prior studies [52] , [53] . Notably , our models included both age and tuberculosis co-infection , which should account for potential confounding by these factors . We are unaware of prior data supporting a differential impact of deworming between males and females . The most likely explanation is unmeasured confounding between these two groups . Barring that , a plausible explanation for our findings would be greater prevalence of helminth infection or higher parasite load among females in our population . However , we have no compelling reason to suspect this . In fact , several studies in HIV-infected populations have demonstrated that prevalence of STH infection in some areas of East Africa is actually similar to or lower in females than in males [54] , [55] , [56] , [57] . However , that these studies were conducted in regions outside of our geographic area of scope and may not be generalizable to our study population . Models demonstrating the impact of deworming on changes in body mass and hemoglobin after ART initiation paralleled the results of our primary analysis of CD4 count . Although both models failed to demonstrate a significant effect of deworming , time on ART was strongly associated with both greater body mass and greater hemoglobin levels . Similarly , tuberculosis co-infection was strongly associated with both decreased body mass and decreased hemoglobin level . Taken together , these findings support the validity of the analyses because they are consistent with anticipated results based on known relationships . Interestingly , in a sensitivity analysis in which we permitted an increased duration of deworming therapeutic response from 90 to 120 or 180 days , we found that deworming was associated with increases in hemoglobin in females only . This finding may be explained by the fact that hemoglobin levels are checked less frequently than CD4 count in our clinical population . Therefore , lengthening the deworming therapeutic time window may have increased the number of hemoglobin measurements receiving credit for deworming , thereby increasing the statistical power to allow for measurement of this association . Alternatively , it is possible that a period of time greater than 90 days is required for deworming to sufficiently exert an effect on hemoglobin levels , which is consistent with the known red blood cell life span of approximately 120 days . Alternatively , deworming may confer a benefit on hemoglobin levels directly by reducing the gastrointestinal parasite load , particularly with respect to hookworms . Although this study is strengthened by its large sample size ( 668 deworming visits ) and ability to adjust for key confounding variables , we acknowledge that there are several notable limitations beyond the inherent risks for confounding and bias in a retrospective analysis . First , although STH infection is reported to be high in southwestern Uganda [40] , [41] , [42] , [43] , [44] , actual prevalence in our study population was unknown . Lower rates of infection than previously published would bias the observed effect of any true deworming treatment effect towards the null . Similarly , STH infection intensity ( stool parasite load ) was also unmeasured , although it should be considered as a factor that may modulate any potential effect of empiric deworming . The intensity of STH infection has been shown to correlate with certain markers of clinical disease severity , including anemia and eosinophilia [18] , [19] . Therefore , it is conceivable that deworming may exert a greater beneficial effect on CD4 count recovery in the setting of higher intensity STH infection . Second , because viral load monitoring was not routinely available in our study setting , it is not possible to adjust for the presence or absence of viral suppression , an important predictor of CD4 reconstitution . We would only expect confounding if receipt of deworming therapy were associated with virologic failure , which is unlikely given the nearly equivalent patterns of CD4 reconstitution observed in both groups . Third , a small incremental effect conferred by deworming may be obscured by the expected dominant effect of ART ( and resulting suppression of HIV activity ) on CD4 count recovery . Fourth , although most patients ( 52% ) received deworming therapy at least once during the observation period , deworming therapy was recorded prior to only 3% of visits with a corresponding CD4 count measurement , which is less than what would be expected with annual deworming visits ( i . e . closer to 10–20% ) . Fifth , patients at our clinic site receive only a single dose of either mebendazole or albendazole at each deworming . Therefore , it is possible that high rates of STH re-infection [48] and incomplete eradication of parasite load with standard doses of anti-helminthic therapy [58] , especially in individuals with a large parasite burden , may obscure the potential benefit of deworming on CD4 count recovery . Finally , host-parasite interactions during STH infection are complex and incompletely understood . In addition to causing disease , helminths have been associated with protection from atopic syndromes and other pathologic inflammatory conditions [59] , [60] . Like these conditions , untreated HIV infection is characterized by the presence of exuberant systemic inflammation and immune activation . This lessens with the initiation of ART , but never completely normalizes to the levels seen in HIV-uninfected individuals [12] , [36] . Furthermore , even in the setting of virologic control on ART , higher levels of immune activation are associated with increased risk for HIV disease progression and mortality [11] , [12] , [33] , [36] . Given that there are conflicting data on the association between STH infection and immune activation [3] , [5] , [61] , [62] , it is plausible to consider that STH infection may promote immune activation only in certain circumstances . This is supported by evidence that STH infections may both promote and protect against immune activation – by disrupting the gut mucosal barrier during high-intensity infection , or by reinforcing mucosal barriers in other settings , respectively [63] . Therefore , we cannot rule out the possibility that , depending on host and parasite-specific characteristics ( for example: helminth species , stage of infection , host genetic factors ) , STH infection – and therefore deworming – may alternately confer either beneficial or deleterious effects on HIV disease progression . Indeed , it has been suggested that low-intensity infection with certain helminth species may protect against HIV disease progression [5] , [64] . Thus , it is possible that only under certain circumstances will deworming confer a significant benefit on HIV disease progression or CD4 count recovery . Under certain conditions , deworming could even cause harm by disrupting as yet incompletely understood pathways by which helminths protect against pathologic inflammation and T-cell activation . Alternatively , anti-helminthic agents could have pleiotropic effects with either antiviral or immunomodulatory properties . One might consider a hypothetical model in which deworming is beneficial during the most active period of STH-mediated mucosal disruption ( such as early in the natural history of STH infection ) , but is in fact detrimental or has no effect during chronic , stable , low-intensity infection – when protective anti-inflammatory or mucosal barrier stabilizing effects of STH infection presumably predominate . Consistent with this model , the variation in life cycle and time course of infection between different STH species may explain species-specific differences in the effect of deworming on HIV disease progression that have been observed in certain settings [24] , [55] . Furthermore , several studies have demonstrated an association between STH infection and higher CD4 count in ART-naïve , HIV-infected individuals [18] , [51] , [54] . Although potentially confounded by a number of factors , this finding is consistent with a model in which low-intensity STH infection confers a beneficial effect on CD4 count . In summary , our findings are consistent with previous studies that failed to find a benefit of empiric deworming in ART-naïve , HIV-infected individuals [25] , [29] , [55] , but also suggest that there may be a modest benefit among women on ART . Although our study does not support a role for universal , empiric deworming as a method to improve immune reconstitution in the general population of HIV-infected persons on ART , this strategy warrants further investigation as a potential adjunct to optimize ART effectiveness in females . Regardless of these results , deworming should continue to be a fundamental part of routine care for individuals living in areas highly endemic for STH infection . Given the inherent constraints of our retrospective study design , including the inability to select at least a representative sample of subjects with confirmed and quantified STH infection , there is a need for more thoroughly controlled , randomized , and prospective studies that will avoid such limitations . These future investigations could more clearly establish any deworming treatment effects of targeted benefit to those with confirmed STH infections . Future studies should also examine the role of deworming using optimally effective anti-helminthic drug regimens , the effect of empiric treatment on specific sub-populations , and the impact of deworming on other surrogate and clinical markers or sequelae of HIV disease , including immune activation , systemic inflammation , gut microbial translocation , and cardiovascular disease . Additional investigations are also warranted to confirm our finding of improved CD4 count recovery with empiric deworming in the sub-population of females . Finally , to our knowledge this is the first study to describe the impact of deworming on clinical and immunologic markers of HIV disease in patients on ART . Given the increasing accessibility of ART in sub-Saharan Africa and other regions that are co-endemic for HIV and STH infection , future research on deworming and HIV infection ought to focus on patients initiating or already on ART . | It is estimated that up to half of all people infected with HIV in sub-Saharan Africa are co-infected with one or more gastrointestinal parasites . These parasitic infections may negatively impact the ability of the immune system to combat the HIV virus , leading to worse clinical outcomes in people with HIV . Therefore , routine , universal , empiric treatment of gastrointestinal parasite infections ( “deworming” ) has been suggested as one strategy for optimizing HIV outcomes in this region . Previous studies have provided conflicting results on whether empiric deworming positively impacts markers of HIV disease progression such as CD4 count and viral load , but all of these studies were performed in HIV-infected individuals not yet on antiretroviral therapy . In this study , we measured the association between receipt of empiric deworming and CD4 count over time in HIV-infected adults taking antiretroviral therapy in southwestern Uganda , an area with a high parasite burden . We found that , overall , there was no significant association between deworming and change in CD4 count; however , when we performed a sub-analysis looking exclusively at females during the first year of ART , deworming was associated with significantly increased CD4 count . These results suggest that empiric deworming may not be an effective generalized strategy for improving HIV treatment outcomes in sub-Saharan Africa; however , the possibility of targeted benefit in specific sub-populations deserves further investigation . | [
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] | 2014 | Empiric Deworming and CD4 Count Recovery in HIV-Infected Ugandans Initiating Antiretroviral Therapy |
Neofunctionalization following gene duplication is thought to be one of the key drivers in generating evolutionary novelty . A gene duplication in a common ancestor of land plants produced two classes of KNOTTED-like TALE homeobox genes , class I ( KNOX1 ) and class II ( KNOX2 ) . KNOX1 genes are linked to tissue proliferation and maintenance of meristematic potentials of flowering plant and moss sporophytes , and modulation of KNOX1 activity is implicated in contributing to leaf shape diversity of flowering plants . While KNOX2 function has been shown to repress the gametophytic ( haploid ) developmental program during moss sporophyte ( diploid ) development , little is known about KNOX2 function in flowering plants , hindering syntheses regarding the relationship between two classes of KNOX genes in the context of land plant evolution . Arabidopsis plants harboring loss-of-function KNOX2 alleles exhibit impaired differentiation of all aerial organs and have highly complex leaves , phenocopying gain-of-function KNOX1 alleles . Conversely , gain-of-function KNOX2 alleles in conjunction with a presumptive heterodimeric BELL TALE homeobox partner suppressed SAM activity in Arabidopsis and reduced leaf complexity in the Arabidopsis relative Cardamine hirsuta , reminiscent of loss-of-function KNOX1 alleles . Little evidence was found indicative of epistasis or mutual repression between KNOX1 and KNOX2 genes . KNOX proteins heterodimerize with BELL TALE homeobox proteins to form functional complexes , and contrary to earlier reports based on in vitro and heterologous expression , we find high selectivity between KNOX and BELL partners in vivo . Thus , KNOX2 genes confer opposing activities rather than redundant roles with KNOX1 genes , and together they act to direct the development of all above-ground organs of the Arabidopsis sporophyte . We infer that following the KNOX1/KNOX2 gene duplication in an ancestor of land plants , neofunctionalization led to evolution of antagonistic biochemical activity thereby facilitating the evolution of more complex sporophyte transcriptional networks , providing plasticity for the morphological evolution of land plant body plans .
Gene duplication is thought to be one of the key drivers in generating evolutionary novelty . Following gene duplication , paralogs can undergo a process of neofunctionalization , supplying a genetic basis for morphological novelty [1 , 2 , 3] . Transcription factors can undergo neofunctionalization via either a change in expression pattern or an alteration in functionality , e . g . the derivation of a repressor or inhibitor from an ancestral activator , or vice versa ( e . g . [4] ) . Three amino acid loop extension ( TALE ) homeodomain transcriptional factors , characterized by having a homeodomain that has three extra amino acids between helices 1 and 2 , are found in all eukaryotic lineages [5 , 6 , 7] . Plant TALE homeobox genes are classified into two subfamilies , KNOTTED-like homeobox ( KNOX ) and BELL-like ( BELL ) [8] . Whilst Chlorophyte algal KNOX genes are of a single class , a gene duplication in a common ancestor of land plants , produced two classes of KNOX genes , class I ( KNOX1 ) and class II ( KNOX2 ) [8 , 9] ( Fig . 1A ) . KNOX genes of flowering plants have been studied for over two decades , however , the functional consequences of the KNOX gene duplication have been largely unexplored . The first identified plant homeobox gene was Knotted1 , a KNOX1 gene of maize [10] . Since , KNOX1 genes have been characterized in numerous flowering plants with a conspicuous loss-of-function phenotype being a failure in shoot apical meristem ( SAM ) maintenance . KNOX1 activity is also involved in maintenance of meristematic activity during leaf development , with prolonged activity in leaf margins observed in species with complex leaves and gain-of-function alleles result in more complex leaves . Thus , KNOX1 genes play a critical role in maintaining meristematic properties of cells in flowering plant sporophytes , the diploid generation of the land plant life cycle ( reviewed in [11 , 12 , 13] ) . The KNOX1 genes of the moss Physcomitrella patens are only expressed in the sporophyte and mutants have decreased sporophyte growth , suggesting that KNOX1 genes have a conserved role in tissue proliferation during sporophyte development throughout land plants [12 , 13 , 14] . There is no evidence indicative of KNOX1 function in the gametophyte ( haploid ) generation in any characterized species , including the indeterminate meristems of the moss gametophyte , suggesting the role of KNOX1 is restricted to the diploid , sporophyte generation [14] . A functional distinction between KNOX1 and KNOX2 genes has been postulated from studies based on gene expression patterns in flowering plants . Northern blot analyses in maize demonstrated that KNOX1 gene expression is confined to less differentiated tissues whereas KNOX2 genes are broadly expressed in differentiating tissues and mature organs [9] . Similar broad expression profiles of KNOX2 genes have been reported in Arabidopsis [15 , 16] and tomato [17] . Characterization of spatial expression patterns in Arabidopsis revealed that KNOX2 genes have both overlapping and distinct expression patterns and that they are expressed in most tissues except for meristematic regions [15 , 18 , 19 , 20 , 21] . Despite several reports of expression patterns , comparatively little is known about KNOX2 gene function in flowering plants . One of the four Arabidopsis KNOX2 paralogs , KNAT7 , is involved in secondary cell wall biosynthesis [18 , 19 , 21] , and another , KNAT3 , is reported to modulate ABA responses [22] . While these findings are consistent with the reported expression patterns , there exists a gap between broad expression patterns and known KNOX2 functions . For instance , unlike KNOX1 genes , which are important regulators of growth and development , it is not clear whether or not KNOX2 genes are involved in morphogenesis in flowering plants . These questions have gone unanswered owing to the paucity of functional studies on KNOX2 genes due to extensive genetic redundancy as noted by Truernit et al . [20] . From a wider perspective , a possible ancestral function of TALE homeodomain proteins is the regulation of diploid gene expression upon fusion of gametes , as is observed in the Chlorophyte alga Chlamydomonas reinhardtii and several fungi [23 , 24 , 25] . In C . reinhardtii the plus gamete expresses a BELL protein while the minus gamete expresses a KNOX protein; upon gamete fusion the KNOX and BELL proteins heterodimerize and regulate zygotic gene expression [25] . In the moss P . patens , KNOX2 genes are expressed in the egg cells and the sporophyte . Eliminating KNOX2 activity results in apospory , the development of a haploid body plan during the diploid generation , suggesting KNOX2 genes regulate the gametophyte-to-sporophyte morphological transition , a reflection of the hypothesized ancestral TALE homeodomain gene function [26] . Thus , both KNOX1 and KNOX2 mutant phenotypes in land plants are consistent with the hypothesis that ancestral function of KNOX genes was to regulate diploid gene expression . However , the seemingly different roles of KNOX1 and KNOX2 genes indicate functional diversification among land plant KNOX genes . To gain insight into developmental roles for KNOX2 genes in flowering plants and the genetic relationship between KNOX1 and KNOX2 classes , we undertook a genetic study of KNOX2 genes in Arabidopsis thaliana , a species in which KNOX1 gene function is well characterized . We discuss the implication of our findings on the impact of the gene duplication producing KNOX1 and KNOX2 paralogs in the course of land plant evolution .
KNOX2 mutant phenotypes were characterized using null alleles ( S5 Fig . ) . As reported previously [20] , single mutants lack conspicuous aberrant phenotypes . Amongst double mutants , knat3 knat5 seedlings are distinguishable from wild type by a longer petiole and narrower lamina of cotyledons , and more deeply serrated leaf margins ( Fig . 2A-B ) . Venation pattern is also affected in knat3 knat5 cotyledons ( S6 Fig . ) . knat3 knat4 plants also have serrated leaves ( Fig . 2I ) and are sporophytically female sterile with abnormal integument development . While knat3 knat4/+ knat5 plants are also female sterile , knat3/+ knat4 knat5 plants are phenotypically wild type and produce viable seeds , facilitating characterization of segregating triple mutant plants . Selfed knat3/+ knat4 knat5 plants segregated small , dark-green plants with deeply lobed leaves ( Fig . 2C-G and S14F Fig . ) , a phenotype reminiscent of gain-of-function KNOX1 alleles [12 , 13] . PCR-based genotyping indicated these plants were triple-mutant homozygotes ( knat345 ) . Since only a single mutant allele was available for each gene , we designed an artificial miRNA ( amiRNA , [27] ) targeting only KNAT4 , amiR159-KNAT4 ( S7A Fig . ) , and generated knat3 knat5 plants constitutively expressing amiR159-KNAT4 under the control of the Cauliflower Mosaic Virus 35S promoter ( pro35S ) . pro35S:amiR159-KNAT4 knat3 knat5 lines closely resembled the identified knat345 plants ( S8C-D Fig . ) . Another amiRNA , amiR159-KNAT345–1 , was designed to target KNAT3 , KNAT4 , and KNAT5 ( S7B Fig . ) . pro35S:amiR159-KNAT345–1 plants also show a deeply serrated leaf phenotype ( S8E and S14H Figs . ) . We thus conclude that this is the triple mutant phenotype . Consistent with functional redundancy among these genes , dosage-dependent enhancement of the leaf serration phenotype was observed ( Fig . 2H-L ) . Likewise , venation pattern in cotyledons is more severely affected in knat345 plants ( S6 Fig . ) . Floral organs homologous with leaves are also affected . Sepals and petals are narrower and partially dissected in the knat345 mutant , and integument development is defective as seen in knat3 knat4 plants ( Fig . 2M-N and S9 Fig . ) . Ectopic formation of tracheary elements is observed in knat345 embryo sacs ( Fig . 2R ) . Although these genes are expressed in roots [15 , 16 , 20] , the morphology of primary roots in knat345 plants appeared normal ( S10 Fig . ) . A proKNAT5:KNAT5-GUS translational fusion line was generated to monitor expression patterns ( Fig . 2S-W and S11 Fig . ) . In line with the mutant phenotypes , GUS activity was observed in developing leaves but excluded from the shoot apical meristem ( SAM ) ( Fig . 2S-V and S11C Fig . ) . During early stages of leaf development , GUS activity was not detected in youngest leaf primordia but was observed in older leaf primordia ( Fig . 2U ) . Reduced signal levels were observed in older leaves ( S11B Fig . ) . Prolonged incubation detected GUS signal along cotyledon and leaf veins and in ovules ( Fig . 2T and S11H-I Fig . ) . proKNAT5:KNAT5-GUS expression is nuclear in trichomes , supporting a role for KNAT5 in transcriptional regulation ( S11 Fig . ) . A transcriptional fusion line , proKNAT4:GUS , was generated to examine KNAT4 expression patterns . Ten independent T1 plants were examined , all of which exhibited KNAT4 promoter activity in leaves but not in the SAM ( Fig . 2X ) . A similar expression pattern has been described for KNAT3 using either a GUS reporter line or RNA in situ hybridization [15] . Exclusion of KNOX2 expression from the SAM is also supported by cell-type specific expression analyses of the inflorescence SAM ( S3 Fig . ) . KNOX and BELL heterodimerization plays a pivotal role in regulating their activities as transcription factors [13] . We speculated that the lack of BELL partners may explain why no conspicuous phenotype has been described to date upon ectopic expression of KNOX2 genes [28] ( S12 Fig . ) . The founding BELL gene , BELL1 ( BEL1 ) , and closely related paralogs , SAWTOOTH1 ( SAW1 ) and SAW2 , represent candidates for KNOX2 partners since loss-of-function phenotypes in ovules and leaf margins resemble those of KNOX2 mutants [29 , 30 , 31] . Physical interactions have been previously proposed between these BELL and KNOX2 proteins [22 , 29 , 32] . SAW1 and SAW2 are expressed in leaves but not in meristems [29] ( S3 and S4 Figs . ) . Thus , we co-expressed SAW2 and KNAT3 throughout the SAM by trans-activating SAW2 under the control of SHOOT MERISTEMLESS ( STM ) regulatory sequences ( proSTM>>SAW2; >> denotes the use of transactivation system hereafter ) in pro35S:KNAT3 plants [33] . pro35S:KNAT3 proSTM>>SAW2 plants lack an embryonic SAM and resemble loss-of-function stm or stm knat6 mutant plants [34 , 35 , 36] ( Fig . 3A-C , 3E ) . Combined expression of KNAT5 and SAW2 in the proSTM region resulted in a similar phenotype ( Fig . 3D ) , confirming that the presence of both SAW2 and KNOX2 proteins simultaneously accounts for the phenotype . Collectively , these data indicate that concurrent expression , and by proxy , heterodimerization with BELL proteins , is important for KNOX2 function and that KNOX2 activity may thus be constrained by limited access to corresponding BELL partners . A mutation in KNAT3 suppresses the gain-of-function phenotype caused by ectopic expression of another BELL gene , BLH1 , suggesting that BLH1 is likely a functional partner for KNOX2 proteins [37] . This prompted us to examine genetic interactions between BLH1 and BEL1-related BELL genes , and we found that bel1 blh1 double mutants show color changes in unfertilized gynoecia as seen in knat3 knat4 and knat345 plants ( Fig . 3G-J and S9 Fig . ) . Thus , BEL1 and BLH1 play a redundant role in gynoecium development and perhaps act in association with KNOX2 genes . More comprehensive genetic analyses as well as expression analyses are required to assign specific roles to functionally redundant BELL genes . To further dissect BELL-KNOX interactions , plants expressing BELL and/or KNOX genes in the proSTM region were characterized . Among BELL proteins , PENNYWISE ( PNY ) and POUND-FOOLISH ( PNF ) are expressed in the SAM and act in conjunction with KNOX1 proteins to promote SAM activity [12 , 13] . As expected , plants expressing KNOX1 genes ( STM or KNAT2 ) or PNY in the STM domain appeared wild type ( S13B-D Fig . ) . In contrast , proSTM>>SAW2 plants displayed abnormal floral morphologies , such as fused sepals , reduced petals , and misshapen fruits ( S13E , H Fig . ) , phenotypes often associated with reduced KNOX1 activity , e . g . weak stm mutants [38] . Flower development was not impacted in proSTM>>KNAT5 plants , but fused sepals are also observed in strong pro35S:KNAT3 lines ( S12F Fig . ) . Concomitant expression of KNOX2 with PNY or KNOX1 with SAW2 did not enhance the KNOX2 or SAW2 overexpression phenotypes . We therefore conclude KNOX2 shows selectivity for BELL proteins in vivo . Loss-of-function and gain-of-function KNOX2 phenotypes are reminiscent of gain-of-function and loss-of-function KNOX1 phenotypes , respectively [12 , 13] . To characterize the relationship between the two gene classes , loss-of-function alleles for KNOX1 and KNOX2 were combined . Plants constitutively expressing an amiRNA targeting KNAT3 , KNAT4 , and KNAT5 , pro35S:amiR159-KNAT345–1 , in KNOX1 loss-of-function ( stm or bp knat2 knat6 ) backgrounds were examined . Neither the meristem failure of stm mutants nor the KNOX2 loss-of-function mutant leaf phenotype was suppressed in these plants ( Fig . 3F and Fig . 4A-B ) . Similarly , neither knat2 knat3 knat5 knat6 nor bp knat345 showed significant suppression of the KNOX2 loss-of-function mutant leaf phenotype and the bp inflorescence phenotype ( Fig . 4F-J ) . Thus , loss-of-function phenotypes of KNOX1 and KNOX2 mutants are not due to ectopic activation of KNOX2 and KNOX1 , respectively . Furthermore , BP , STM , and KNAT2 expression was not altered in knat3 knat5 plants ( Fig . 4K-P ) , arguing against mutual repression between KNOX1 and KNOX2 genes . Deeply lobed leaves , a phenotype characteristic of gain-of-function KNOX1 alleles , occur in Arabidopsis plants where STM is driven by the leaf specific promoter , proBLS , proBLS:STM ( [39]; S14B , C Fig . ) . These were crossed with loss-of-function KNOX2 plants ( pro35S:amiR159-KNAT345–1 ) to generate plants with ectopic KNOX1 and reduced KNOX2 activities in the leaves . Compared to the parental lines , F1 plants harboring both transgenes displayed more extreme leaf margin elaboration ( Fig . 4C-E ) . The additive effects , rather than epistatic interactions , suggest it is unlikely that the two subclasses negatively regulate one another . An attractive hypothesis for the antagonism between KNOX1 and KNOX2 is that they regulate shared downstream events in an opposite manner . The complex leaf of gain-of-function KNOX1 alelles is suppressed by reduction in CUP SHAPED COTYLEDON ( CUC ) transcription factor activity [40] ( S14C Fig . ) . Two CUC genes are targeted by the miR164 family of miRNAs , and expression of miR164b in young leaves using regulatory sequences of the FILAMENTOUS FLOWER ( FIL ) gene ( designated as proFIL ) , proFIL:miR164b , flattens the leaf margin in wild-type plants ( S14D-E Fig . ) . Thus , the miRNA-mediated CUC regulation plays a key role in leaf margin elaboration [41] . Introduction of proFIL:miR164b also suppressed the leaf dissection phenotype in a knat345 mutant background ( S14F-G Fig . ) . Among miR164 targets , CUC2 plays a major role in leaf serration development [41] . We find leaf serration is largely suppressed in the cuc2 knat345 and pro35S:amiR159-KNAT345–1 cuc2 backgrounds ( Fig . 5A-C and S14H-I Fig . ) . In addition , constitutive expression of KNOX2 ( pro35S:KNAT3 ) can partially suppress the proBLS:STM leaf phenotype ( Fig . 5D-F ) . Thus , a common developmental program mediates both gain-of-function KNOX1 and loss-of-function KNOX2 leaf phenotypes . As observed in proBLS:STM plants , elevated levels of KNOX1 activity are often associated with increased leaf complexity ( reviewed in [11] ) . In Cardamine hirsuta , a close relative of Arabidopsis , dissected leaf development requires KNOX1 expression in leaves , and additional KNOX1 expression leads to ectopic leaflet initiation [42] . We investigated the outcome of reduction in the level of KNOX2 activity in this species . In Cardamine , leaf shape exhibits heteroblasty with leaflet number increasing in later produced leaves . Although leaflet number can vary for a particular leaf position , the first and second leaves always consist of a single , undivided , lamina , and the third leaf typically consisting of three leaflets ( S15A Fig . ) . An amiRNA , amiR159-KNAT345–2 , was designed to target three Cardamine genes homologous to Arabidopsis KNAT3 , KNAT4 , and KNAT5 . Constitutive amiR159-KNAT345–2 expression ( pro35S:amiR159-KNAT345–2; S7C Fig . ) results in plants with an extra lateral leaflet on the second leaf , observed in approximately 15% of individuals ( 27 of 188 plants derived from 6 independent lines ) , indicating KNOX2 activity influences complexity of dissected leaves in Cardamine ( Fig . 5G-H ) . Furthermore , gain-of-function KNOX2 alleles ( pro35S:KNAT3 ) simplify leaf shape , a phenotype particularly obvious in third leaves , which are undivided in strong lines ( Fig . 5I-J and S15B Fig . ) . Thus , reduction or increase in KNOX2 activity leads to increase or decrease in leaf complexity , respectively , in Cardamine ( Fig . 5 and S15 Fig . ) . This observation and the deduced KNOX1/KNOX2 antagonism are in consistent with the results in Arabidopsis .
Expression of KNOX1 genes in leaves is correlated with increased leaf complexity and has been hypothesized to be influential in the evolution of leaf shape [42 , 43 , 44] . Given that seed plants leaves evolved from ancestral shoot systems , the ancestral seed plant leaf was likely complex , but fossil evidence and phylognetic analyses suggest that the ancestral angiosperm leaf may have been simple [45] . Regardless of the ancestral state , transitions from simple to more complex and vice versa have occurred repeatedly during angiosperm evolution [43 , 44 , 46] . In angiosperms , increase in leaf complexity is associated with increased KNOX1 activity while loss of KNOX1 activity in leaves results in decreasing complexity . While KNOX1 activity has been shown to play a pivotal role , other loci , such as REDUCED COMPLEXITY ( RCO ) in Cardamine and LEAFY ( LFY ) orthologues in legumes either contribute directly to modifying leaf shape or influence sensitivity to KNOX1 activity [11 , 47 , 48] . The lability of angiosperm leaf architecture may reflect that addition or loss of enhancer modules directing KNOX1 activity in leaves does not affect general plant viability . The present study demonstrates that KNOX2 activities can also influence leaf shape—leaf dissection increases with decreasing KNOX2 activity ( Fig . 2 ) in a dose dependent manner—raising the possibility of whether changes in KNOX2 activity could also have contributed to the evolution of leaf morphology . Just as KNOX1 gain-of-function alleles result in increases in leaf complexity , novel gain-of-function KNOX2 alleles that alter temporal or spatial expression patterns within developing leaves could contribute to the evolution from complex towards simple leaf morphology , as suggested by our experimental results in Cardamine , via acquisition of leaf specific enhancers . Alleles resulting in loss of KNOX2 activity could also contribute to increases in leaf complexity as suggested by the dose dependent changes to leaf shape in Arabidopsis , however , this may be less likely due to pleiotropic effects of loss-of-function KNOX2 alleles . Intriguingly , in monilophytes KNOX1 gene expression is broadly similar to that of seed plants , with expression limited to less differentiated tissues including the shoot apical meristem , developing leaves , and procambial tissues [43 , 49 , 50] . KNOX2 gene expression has not been studied in detail , but similar to the situation in angiosperms , is reported to be throughout the sporophyte body [50] . In parallel with seed plants , simple leaves have evolved from more complex ancestral leaves within monilophytes [51] . Whether changes in KNOX1 or KNOX2 gene expression may be related to evolution of leaf form in monilophytes is presently unknown . One plausible explanation for the opposing action of KNOX1 and KNOX2 genes is an epistatic relationship between the gene classes . While non-overlapping expression patterns have been observed between KNOX1 and KNOX2 genes , we found no evidence for mutual repression . Alternatively , KNOX1 and KNOX2 proteins may interfere one another’s activity . Such a mode of action was proposed for KNATM in Arabidopsis and PETROSELINUM ( PTS ) /TKD1 in tomato , both of which are KNOX-related proteins that lack a DNA-binding homeodomain [52 , 53] . It is suggested that these mini KNOX proteins act as passive repressors and interfere with formation of a functional complex composed of canonical KNOX and BELL proteins . That KNOX2 function depends on the availability of appropriate BELL partners to be active , argues against a similar mechanism for the KNOX1/KNOX2 antagonism . Instead , our data favor a model whereby the antagonistic roles of KNOX1 and KNOX2 are at the level of opposing modes of transcriptional regulation . Since addition of a repressor domain causes a dominant negative phenotype , KNOX1 proteins can act as activators [39 , 54] . Conversely a KNOX2 protein , KNAT7 , can repress transcription in a transient protoplast system [18 , 19] , and a motif similar to known repression domains is found in the ELK domain of all KNOX2 proteins [55] ( S16 Fig . ) . Comparison of KNOX1 and KNOX2 homeodomains reveals that the third helices , an important determinant of DNA binding specificity , are highly conserved , indicating similar DNA binding properties , at least in vitro ( S16 Fig . ) . Concurrently expressed KNOX1 and KNOX2 proteins could thus conceivably compete with each other at some target genes . Indeed , a putative KNOX2-SAW2 complex can overcome endogenous KNOX1 activities in the meristem , as does a dominant-negative form of KNOX1 ( e . g . , TKN2-SRDX [39] and en298-STM [54] ) . However , as KNOX1 proteins have also been reported to act to repress gene expression , the activity of KNOX proteins may be modified by either BELL partners , or third parties , such as OVATE proteins that interact with KNOX/BELL heterodimers and influence both their cellular localization and transcriptional activity [32 , 37 , 56] . In a related scenario , KNOX1 and KNOX2 could act on different sets of paralogs of downstream targets . These hypotheses are not mutually exclusive , and depending on the cellular contexts , different modes of action could operate , as is the case for the yeast TALE protein , Matα2 , which has different partners in different cell types ( reviewed in [23] ) . Phylogenetic analyses indicate land plant KNOX1 and KNOX2 genes are derived from a single , ancestral KNOX gene . We hypothesize that subsequent to the KNOX1/KNOX2 gene duplication , accumulating structural differences endowed a new mode of action to at least one paralog . Therefore a possible evolutionary scenario could have an ancestral KNOX protein acting primarily as a transcriptional activator , with the evolution of a transcriptional repressor following gene duplication and neofunctionalization . The evolution of a repressor from an ancestral activator may be a common event , with several instances documented in plant transcription factor families [52 , 53 , 57 , 58 , 59 , 60] . Thus , within the context of land plant KNOX genes two types of negative regulators , in which the modes of repressor action are mechanistically different , may have evolved . Mini KNOX proteins act to inhibit KNOX activity by interacting with and sequestering BELL proteins [52 , 53] , as opposed to antagonistic action at the level of downstream gene expression as we propose for KNOX2 . The latter provides more flexibility due to the potential to act independently . Accompanying divergence in protein functionality , our data provides additional evidence for nearly complementary expression patterns of KNOX1 and KNOX2 genes in Arabidopsis thaliana . In contrast , in P . patens KNOX1 and KNOX2 genes exhibit both overlapping and distinctive expression patterns [14 , 26] . Changes in cis-regulatory sequences must have contributed to the establishment of complementary expression patterns during land plant evolution . Flexibility in gene regulatory networks governing meristematic maintenance and differentition engendered by the combination of changes in protein functionality and expression pattern could provide plasticity enabling morphological evolution . Heterodimerization between BELL and KNOX proteins is important for translocation of the complex into the nucleus [13] . BELL-KNOX2 heterodimerization may also be critical for providing specificity or increasing affinity of DNA binding ( e . g . [61] ) . Although studies based on the yeast two-hybrid technique suggest physical interactions between BELL and KNOX proteins in a rather nonspecific manner [29 , 32] , our genetic data suggest KNOX2 proteins interact in planta with a subset of BELL proteins , including those of the BEL1/SAW1/SAW2 clade . KNOX1 proteins rely on a distinct set of BELL proteins , e . g . PNY and PNF ( reviewed in [12 , 13] ) . Due to an obligate heterodimerization requirement , the activity of a KNOX/BELL pair may be limited by the protein with the more restricted expression domain . In Arabidopsis KNOX2 functions appear to be regulated by restricted availability of corresponding BELL partners [29] ( Fig . 3 ) . Similar to KNOX genes , land plant BELL genes evolved from a single gene in the algal ancestor [9] . However , the diversification of paralogs followed a different trajectory in the two families since BELL genes do not fall into discrete functional clades ( S17 Fig . ) . For instance , KNOX1-interacting BELL genes ( PNY and PNF ) form a sister clade with KNOX2-interacting BELL genes ( BEL1 and SAW1/2 ) . Moreover , genetic interactions implicate BLH1 , from a phylogenetically distinct clade , as a KNOX2 partner since knat3 alleles suppress the phenotype induced by ectopic BLH1 embryo sac expression [37] . These phylogenetic relationships might be expected if the genome of the land plant common ancestor encoded a single BELL protein that interacted with both KNOX1 and KNOX2 proteins . As the BELL gene family diversified , subfunctionalization would have restricted interactions of BELL paralogs to specific KNOX1 or KNOX2 partners . The defining feature of land plants is the formation of an embryo—a multicellular diploid generation . One prominent feature within land plant evolution is the transition from a gametophyte-dominant life cycle to a sporophyte-dominant life cycle [62 , 63] . This process is regarded as progressive sterilization and elaboration of vegetative organs [62] , and in flowering plants , the gametophyte is reduced to a ephemeral structure of only a few cells that is dependent on a sporophyte body that can live up to thousands of years . If the ancestral KNOX-BELL genetic program regulated gene expression in a single celled zygote [25] , it follows that during the course of land plant evolution , the KNOX/BELL module has been recruited to control numerous aspects of sporophyte development , with KNOX1/BELL modules promoting meristematic maintenance and continued growth and KNOX2/BELL modules promoting differentiation . In some cases , there is resemblance to a presumed ancestral function , such as in P . patens where KNOX2 genes regulate the gametophyte-to-sporophyte morphological transition [14 , 26] . In other cases , however , KNOX/BELL modules direct the development of novel structures , such as sporophyte shoot meristems and leaves ( Fig . 6 ) , that evolved later in land plant evolution , suggesting the duplication and diversification of the KNOX/BELL genetic module is linked with the evolution of morphological diversity in the land plant sporophyte . Neofunctionalization , exemplified by opposing activities between KNOX1 and KNOX2 genes in Arabidopsis , may underlie the molecular mechanism of key innovations and modification of body plans in the land plant history , through elaboration of transcriptional networks . The role of TALE genes in fungi and Chlamydomonas can be viewed as promotion of cellular specialization in the diploid zygote and progression towards a meiotic state . The life cycle of land plants arose by an interpolation of mitotic divisions between fertilization and meiosis . Thus there is cell proliferation and a delay in meiosis in the diploid generation . KNOX1 genes prevent differentiation and maintain an undifferentiated state of the cells , enabling the cells to proliferate and develop a multicellular body in the sporophyte generation . In organisms with two heteromorphic multicellular generations , such as land plants , the developmental programs for each must be tightly controlled—a role suggested for KNOX2 genes in preventing the haploid gametophyte genetic program to be active during the diploid sporophyte generation in Physcomitrella . We hypothesize the duplication and diversification of the KNOX/BELL genetic module was instrumental in the evolution of a diploid embryo such that multicellular bodies develop in both haploid gametophyte and diploid sporophyte generations known as alternations of generations [25 , 26] . Alternations of generations have evolved independently in phylogenetically diverse eukaryotic lineages [64 , 65] , prompting the question of whether similar TALE class genetic diversification may be found in these lineages .
Arabidopsis thaliana accessions Columbia and Landsberg erecta ( Ler ) were used as wild type in most experiments . proKNAT2:GUS was generated in the C24 background and introgressed into Ler . Cardamine hirsuta ‘Oxford strain’ is a kind gift of A . Hay and M . Tsiantis . Plants were grown under long-day ( 18 hours light ) or short-day ( 10 hours light ) conditions at 20°C . knat3 and knat5 alleles are gift from V . Sundaresan and G . Pagnussat . bp-9 knat2–5 knat6–1 seeds are gift from V . Pautot . T-DNA insertion alleles for BELL and KNOX genes were obtained from the Arabidopsis Biological Resource Center ( ABRC ) or the Nottingham Arabidopsis Stock Center ( NASC ) . Mutant and transgenic lines have been described previously: bp-9 knat2–5 knat6–1 [66]; stm-11 [67]; proBP:GUS [68]; proKNAT2:GUS [69]; Op:KNAT2 and Op:STM [39]; and proSTM:LhG4 [70] . The mutant and transgenic lines used in this study are listed in S1 Table . Homozygous mutant lines were identified by polymerase chain reaction ( PCR ) -based genotyping . Sequences of genotyping primers are available in S2 Table . The details of the transactivation system was previously described [33] . Multiple mutants combining knat3 , knat4 , and knat5 alleles were generated by crossing , and genotypes were confirmed by PCR-based genotyping . To generate bel1 blh1 double mutant , blh1 plants were crossed with bel1 plants , and the resulting F2 plants were examined . bel1 plants were identified based on self-sterility , and among them , plants with yellow gynoecia segregated and were confirmed to be bel1 blh1 double mutant plants by PCR-based genotyping . knat2 knat3 knat5 knat6 and bp knat345 plants were identified among F2 plants originating from a cross between bp knat2 knat6 and knat345 plants , and their genotypes were confirmed by PCR-based genotyping . cuc2 knat345 plants were identified in a F2 population derived from a cross between cuc2 and knat345 plants . To generate proFIL:miR164b lines in the knat345 mutant , self-fertile knat3 knat5 plants were transformed with the proFIL:miR164b construct , and tranformants were selected by resistance to herbicide Basta . Single insertion lines were selected and crossed with knat3/+ knat4 knat5 plants . Among F1 plants , self-fertile knat3/+ knat4/+ knat5 plants carrying the proFIL:miR164b transgene were selected , and F2 seeds were collected; proFIL:miR164b knat345 plants were identified in the resultant F2 population . To characterize the effects of the pro35S:amiR159-KNAT345–1 transgene in mutant backgrounds , the mutant plants were directly transformed with the pro35S:amiR159-KNAT345–1 construct , and transformants were selected by resistance to Basta . As stm null alleles are seedling lethal , heterozygous plants were used for transformation . More than twenty T1 plants for each background were examined , and phenotypes consistently observed among independent lines were reported . RNA was extracted , using the RNeasy Plant Mini Kit ( Qiagen ) , from 10-day-old seedlings grown on half-strength MS medium supplemented with 0 . 5% sucrose . RNA samples were treated with on-column DNaseI ( Qiagen ) and purified . SMARTScribe reverse transcriptase was used for cDNA synthesis ( Clontech ) , and PCR reactions were performed using Ex Taq ( Takara ) . Oligo sequences used for PCR reactions are described in S3 Table . amiRNAs were designed using the Arabidopsis pre-miR159a backbone ( S7 Fig . ) and synthesized ( GenScript ) . For construction of the proKNAT5:KNAT5-GUS reporter construct , the genomic sequence spanning the KNAT5 locus ( from the next upstream annotated gene [At4g32030] to the next downstream annotated gene [At4g32050] ) was used , and the stop codon was replaced with the GUS coding sequence . For construction of the proKNAT4:GUS reporter construct , an approximately 6 . 6-kb region of the sequence directly upstream of the KNAT4 coding sequence was amplified using BAC T5K6 as PCR template and cloned into pCRII-TOPO ( Invitrogen ) . The KNAT4 upstream sequence was subcloned into the pRITA vector , which contains the GUS coding sequence and the terminator sequence from the nopaline synthase gene . For constitutive expression , the amiRNA sequences or the KNAT3 coding sequence were cloned into the ART7 vector , which contains the Cauliflower mosaic virus pro35S sequence and the terminator sequence from the octopine synthase gene . KNAT5 , SAW2 , and PNY coding sequences were amplified from Ler cDNA and cloned downstream of an Lac Op array [33] to generate responder cassettes used in the transcription activation system . All constructs were subcloned into pMLBART or pART27 binary vector and were introduced into Agrobacterium tumefaciens strain GV3001 by electroporation . Transgenic lines were generated by Agrobacterium-mediated transformation , and transformants were selected on soil on the basis of resistance to the BASTA or kanamycin . Primers used to clone the various cDNAs and promoters are described in S2 Table . Scanning electron microscopy was performed according to Alvarez and Smyth [71] . For light microscopy , cleared samples were prepared . Leaf samples were fixed overnight in 9:1 ( v:v ) ethanol:acetic acid at room temperature . After rehydration in a graded ethanol series , samples were rinsed with water and were cleared with chloral hydrate solution [1:8:2 ( v:w:v ) glycerol:chloral hydrate:water] . For histochemical analysis of GUS activity , samples were infiltrated with GUS staining solution [0 . 2% ( w/v ) Triton X-100 , 2 mM potassium ferricyanide , 2 mM potassium ferrocyanide , and 1 . 9 mM 5-bromo-4-chloro-3-indolyl-β-glucuronide in 50 mM sodium phosphate buffer , pH 7 . 0] and incubated at 37°C . Publically available KNOX and BELL coding nucleotide sequences representing taxa across land plants were manually aligned as amino acid translations using Se-Al v2 . 0a11 ( http://tree . bio . ed . ac . uk/software/seal/ ) . We excluded ambiguously aligned sequence to produce alignments for subsequent Bayesian analysis . Bayesian phylogenetic analysis was performed using Mr . Bayes 3 . 2 . 1 [72 , 73] . Three separate analyses were performed . The first included Chlorophyte algal and land plant KNOX sequences ( S1 Fig . ) ; the second included only land plant KNOX2 sequences ( S2 Fig . ) ; and the third included land plant BELL sequences ( S17 Fig . ) . The fixed rate model option JTT + I was used based on analysis of the alignments with ProTest 2 . 4 [74] . Sequence alignments and command files used to run the Bayesian phylogenetic analyses are provided upon request . | Eukaryotes alternate between haploid ( 1n ) and diploid ( 2n ) stages during their life cycles , and often seen are remarkable differences in morphology and physiology between them . Land plants are multicellular in both generations , in contrast to their presumed ancestral green algae that develop multicellularity only in the haploid stage . TALE class homeodomain transcriptional factors play a key role in the activation of diploid development in diverse lineages of eukaryotes . A gene duplication event within this family in an ancestor of land plants had profound implications for land plant evolution . We show that the two subclasses resulting from the gene duplication event act to pattern , in a complementary manner , most above ground organs of the diploid stage of the flowering plant Arabidopsis . Their opposing activities sculpt the shape of leaves from entire to pinnate and control the architecture of the plant body , and thus providing plasticity for evolutionary tinkering . These results form a foundation for understanding how these genes have been co-opted from an ancestral role of regulating diploid gene expression in a zygote to directing sporophyte land plant body architecture and provide insight into the evolution of various forms of life cycles . | [
"Abstract",
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"Methods"
] | [] | 2015 | Antagonistic Roles for KNOX1 and KNOX2 Genes in Patterning the Land Plant Body Plan Following an Ancient Gene Duplication |
The primary goal in cluster analysis is to discover natural groupings of objects . The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims . Despite its shortcomings in accuracy , hierarchical clustering is the dominant clustering method in bioinformatics . Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective . Hierarchical clustering operates on multiple scales simultaneously . This is essential , for instance , in transcriptome data , where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes . The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise . The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering . The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering . The algorithm separates parameters , accommodates missing data , and supports prior information on relationships . Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units ( GPUs ) for maximal speed . Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems . CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www . genetics . ucla . edu/software/
Pattern discovery is one of the primary goals of bioinformatics . Cluster analysis is a broad term for a variety of exploratory methods that reveal patterns based on similarities between data points . Well-known methods such as k-means invoke a fixed number of clusters . In complex biological data , the number of clusters is unknown in advance , and it is appealing to vary the number of clusters simultaneously with cluster assignment . Hierarchical clustering has been particularly helpful in understanding cluster granularity in gene-expression studies and other applications . In addition to producing easily visualized and interpretable results , hierarchical clustering is simple to implement and computationally quick . These are legitimate advantages , but they do not compensate for hierarchical clustering’s instability to small data perturbations such as measurement error . Cluster inference can be adversely affected as small errors accumulate . All principled methods of clustering attempt to minimize some measure of within group dissimilarity . Hierarchical clustering constructs a bifurcating tree by fusing or dividing observations ( features ) . Fusion is referred to as agglomerative clustering and splitting as divisive clustering . Because of the greedy nature of the choices in hierarchical clustering , it returns clusters that are only locally optimal with respect to the underlying criterion [1] . Solution quality may vary depending on how clusters are fused . There is no guarantee that UPGMA , single linkage , or complete linkage will agree or will collectively or individually give the optimal clusters . A potentially greater handicap is that small perturbations in the data can lead to large changes in hierarchical clustering assignments . This propensity makes hierarchical clustering sensitive to outliers and promotes the formation of spurious clusters . In combination , the presence of local minima and the sensitivity to outliers lead to irreproducible results . Although hierarchical clustering has its drawbacks , completely reformulating it might be detrimental . Recently [2] and [3] introduced convex clustering based on minimizing a penalized sum of squares . Their criterion is coercive and strictly convex . Recall that a function f ( x ) is coercive if lim‖x‖ → ∞ f ( x ) = ∞ . According to a classical theorem of mathematical analysis , a continuous coercive function achieves its minimum . Strict convexity of the convex clustering criterion ensures that the global minimizer is unique . The penalty term in convex clustering criterion accommodates prior information through nonuniform weights on data pairs . The solution paths of convex clustering retain the straightforward interpretability of hierarchical clustering while ameliorating its sensitivity to outliers and tendency to get trapped by local minima . Despite the persuasive advantages of convex clustering , there are two obstacles that stand in its way of becoming a practical tool in bioinformatics . The first is the challenge of large-scale problems . Current algorithms are computationally intensive and scale poorly on high-dimensional problems . A second obstacle is the minimal guidance currently available on how to choose penalty weights . Hocking and colleagues suggest some rules of thumb but offer little detailed advice [3] . In our experience , the quality of the clustering path depends critically on well-designed weights . To address these issues , the current paper describes a fast new algorithm and a corresponding software implementation , convexcluster . Our advice on strategies for choosing penalty weights is grounded in some practical biological examples . These examples support our conviction that convex clustering can be more nuanced than hierarchical clustering . Our examples include Fisher’s Iris data from discriminant analysis , ethnicity clustering based on microsatellite genotypes from the Human Genome Diversity Project and SNP genotypes from the POPRES project , and breast cancer subtype classification via microarrays . In the POPRES data , we first reduce the genotypes to principal components and then use these to cluster . The paths computed under convex clustering expose features of the data hidden to less sophisticated clustering methods . The potential for understanding human evolution and history alone justify wider adoption of convex clustering .
The proximal distance principle is a new way of attacking constrained optimization problems [5] . The principle is capable of enforcing parsimony in parameter estimation while avoiding the shrinkage incurred by convex penalties such as the lasso . In parametric models , shrinkage leads to biased parameter estimates and entices false positives to enter the model . Imperfect models in turn fit new data poorly . The proximal distance principle seeks to minimize a function h ( y ) , possibly nonsmooth , subject to y ∈ C , where C is a closed set , not necessarily convex . The set C encodes constraints such as sparsity . In the exact penalty method of Clarke [6 , 7 , 8] , this constrained problem is replaced by the unconstrained problem of minimizing h ( y ) + ρ dist ( y , C ) , where dist ( y , C ) denotes the Euclidean distance from y to C . Note that dist ( y , C ) = 0 is a necessary and sufficient condition for y ∈ C . If ρ is chosen large enough , say bigger than a Lipschitz constant for h ( y ) , then the minima of the two problems coincide ( Proposition 6 . 3 . 2 in [6] ) . How does convex clustering fit in this abstract framework ? Although the objective function fμ ( U ) is certainly nonsmooth , there are no constraints in sight . The strategy of parameter splitting introduces constraints to simplify the objective function . Since least squares problems are routine , the penalty terms constitute the intractable part of the objective function fμ ( U ) . One can simplify the term ‖ui−uj‖ by replacing the vector difference ui−uj by the single vector vij and imposing the constraint vij = ui−uj . Parameter splitting therefore leads to the revised objective function g μ ( U , V ) = 1 2 ∑ i = 1 n | | x i − u i | | 2 + μ ∑ i < j w i j | | v i j | | ( 2 ) with a simpler loss , an expanded set of parameters , and a linear constraint set C encapsulating the pairwise constraints vij = ui−uj . The proximal distance method undertakes minimization of h ( y ) + ρ dist ( y , C ) by a combination of approximation , the MM ( majorization-minimization ) principle [9 , 10 , 11 , 12 , 13] , and an appeal to a combination of set projection [14] and proximal mapping [15] . The latter operations have been intensely studied for years and implemented in a host of special cases . Thus , the proximal distance principle encourages highly modular solutions to difficult optimization problems . Furthermore , most proximal distance algorithms benefit from parallelization . Let us consider each of the ingredients of the proximal distance algorithm in turn , starting with approximation . The function dist ( y , C ) is nonsmooth even when C is well behaved . For ϵ > 0 small , the revised distance dist ϵ ( y , C ) = ( dist ( y , C ) ) 2 + ϵ is differentiable and approximates dist ( y , C ) well . The MM principle leads to algorithms that systematically decrease the objective function . In the case of minimizing f ( y ) + ρ dist ( y , C ) one can invoke the majorization dist ( y , C ) ≤ ‖y−PC ( ym ) ‖ , where PC ( ym ) is the projection of the current iterate ym onto the set C . By definition dist ( ym , C ) = ‖ym−PC ( ym ) ‖ , and PC ( ym ) is a closest point in C to the point ym . For a closed nonconvex set , there may be multiple closest points; for a closed convex set there is exactly one . According to the MM principle , minimizing the surrogate function 1 2 ∑ i = 1 n | | x i − u i | | 2 + μ ∑ i < j w i j | | v i j | | + ρ ∥ ( U V ) − P C ( U m V m ) ∥ 2 + ϵ ( 3 ) drives the approximate objective function 1 2 ∑ i = 1 n | | x i − u i | | 2 + μ ∑ i < j w i j | | v i j | | + ρ dist [ ( U V ) , C ] 2 + ϵ downhill . The surrogate function Eq ( 3 ) is still too complicated for our purposes . The remedy is another round of majorization . This time the majorization t + ϵ ≤ t m + ϵ + 1 2 t m + ϵ ( t − t m ) ( 4 ) comes into play based on the concavity of the function t + ϵ for t ≥ 0 . This follows from the fact that a differentiable concave function is always bounded by its first order Taylor expansion . As required by the MM principle , equality holds in the majorization Eq ( 4 ) when t = tm . Applying this majorization to the surrogate function Eq ( 3 ) yields the new surrogate h [ ( U , V ) | ( U m , V m ) ] = 1 2 ∑ i = 1 n | | x i − u i | | 2 + μ ∑ i < j w i j | | v i j | | + ρ 2 d m ∥ ( U V ) − P C ( U m V m ) ∥ 2 d m = ∥ ( U m V m ) − P C ( U m V m ) ∥ 2 + ϵ ( 5 ) up to an irrelevant constant . The surrogate function Eq ( 5 ) resulting from these maneuvers separates all of the vectors ui and vij . The derivative of the surrogate with respect to ui is ∂ ∂ u i h [ ( U , V ) | ( U m , V m ) ] = u i − x i + ρ d m ( u i − a n , i ) , where an , i is the part of the projection pertaining to ui . One can explicitly solve for the update u n + 1 , i = d m d m + ρ x i + ρ d m + ρ a n , i . The update of vij involves shrinkage . Let bn , ij denote the part of the projection pertaining to vij . Standard arguments from convex calculus [16] show that the minimum of μ w i j ‖ v i j ‖ + ρ 2 d m ‖ v i j − b n , i j ‖ 2 is achieved by v n + 1 , i j = max { ( 1 − μ w i j d m ρ ∥ b n , i j ∥ ) , 0 } b n , i j . ( 6 ) In the exceptional case bn , ij = 0 , the solution vn+1 , ij = 0 is clear from inspection of the vij criterion Eq ( 6 ) . Both of these solution maps fall under the heading of proximal operators , hence , the name proximal distance algorithm . If a weight wij = 0 , then it is computationally inefficient to introduce a difference vector vij . Thus , in many applications , the weight matrix W = ( wij ) may be sparse . The block descent algorithm for projection , that we discuss next , takes into account the sparsity patterns in W . Again taking the sparsity pattern of W into account enables us to employ fewer difference vectors . Let E denote the set of edges {i , j} with positive weights wij = wji . Divide the neighborhood Ni of a node i into left and right node neighborhoods Li = {j < i:wji > 0} and Ri = {j > i:wij > 0} . Clearly Ni = Li∪Ri , and E = ∪ i = 1 n N i . Projection minimizes the criterion 1 2 ∑ i = 1 n ∥ u i − u ˜ i ∥ 2 + 1 2 ∑ { i , j } ∈ E ∥ u i − u j − v ˜ i j ∥ 2 for U ˜ and V ˜ given . One can minimize this criterion by equating its derivative with respect to ui to 0 . It is unclear how to massage the stationarity equation 0 = u i − u ˜ i + ∑ j ∈ R i ( u i − u j − v ˜ i j ) − ∑ j ∈ L i ( u j − u i − v ˜ j i ) into a solvable form . However , the block updates u i = 1 1 + | N i | ( u ˜ i + ∑ j ∈ R i v ˜ i j − ∑ j ∈ L i v ˜ j i + ∑ j ∈ N i u j ) are available . Here |Ni| denotes the cardinality of Ni . One cycle of the block descent algorithm updates u1 through un sequentially . This cycle is repeated until all of the vectors ui stabilize . Once convergence is achieved , one sets vij = ui−uj for the relevant pairs . In general , clustering methods require complete data . The remedy of pre-imputation of missing values can be sensitive to the model assumptions underlying a given imputation method . A better remedy is to change the clustering criterion to directly reflect missing data . It is then straightforward to accommodate missing data in X by another round of majorization . Suppose Γ is the set of ordered index pairs ( i , j ) corresponding to the observed entries xij of X . We now minimize the revised criterion f μ ( U ) = 1 2 ∑ ( i , j ) ∈ Γ ( x i j − u i j ) 2 + μ ∑ i < j w i j | | u i − u j | | , ( 7 ) which unfortunately lacks the symmetry of the original problem . To restore the lost symmetry , we invoke the majorization 1 2 ∑ ( i , j ) ∈ Γ ( x i j − u i j ) 2 ≤ 1 2 ∑ ( i , j ) ∈ Γ ( x i j − u i j ) 2 + 1 2 ∑ ( i , j ) ∉ Γ ( u m i j − u i j ) 2 , where umij is a component of Um . In essence , the term ( umij−uij ) 2 majorizes 0 . If the n × p matrix Y = ( yij ) has entries yij = xij for ( i , j ) ∈ Γ and yij = umij for ( i , j ) ∉ Γ , then in the minimization step of the proximal distance algorithm , we simply minimize the surrogate function g μ ( U , V ) = 1 2 ∑ i = 1 n | | y i − u i | | 2 + μ ∑ i < j w i j | | v i j | | ( 8 ) The rest of the proximal distance algorithm remains the same . The pairwise weight wij = wji introduced in the penalty term of Eq ( 1 ) determines the importance of similarity between nodes i and j . Two principles guide our choice of weights . First , the weight wij should be inversely proportional to the distance between the ith and jth points . This inverse relationship accords with intuition . As wij increases , the pressure for the ith and jth centroids to coalesce increases . If the weights wij are correlated with the similarity of the feature vectors xi and xj , then the pressure for their centroids to merge is especially great . Second , the weight matrix W should be sparse . Despite the fact that small positive weights and zero weights lead to similar clustering paths , the computational advantages of zero weights cannot be ignored . These observations prompt the following choice of weights . To maintain computational efficiency , it is helpful to focus on the k nearest neighbors of each node . We define the distance dij between two nodes i and j by the Euclidean norm ||xi−xj|| and write i∼k j if j occurs among the k nearest neighbors of i or i occurs among the k nearest neighbors of j . Based on these considerations the weights w i j = 1 { i ∼ k j } e − ϕ d i j 2 ( 9 ) are reasonable , where 1{i∼k j} is the indicator function of the event {i∼k j} and ϕ ≥ 0 is a tuning constant . The case ϕ = 0 corresponds to uniform weights between nearest neighbors . When ϕ is positive , wij strictly decreases as a function of dij . Complete coalescence of the nodes occurs as μ increases if the graph is connected based on all wij . Using squared distances d i j 2 rather than distances dij induces more aggressive coalescence of nearby points and slower coalescence of distant points . In practice we normalize weights so that they sum to 1 . This harmless tactic is equivalent to rescaling μ . This generic framework was proposed by [3] . We now discuss a strategy for leveraging additional information . When expert knowledge on the relationships among nodes is available and can be quantified , incorporating such knowledge may improve the clustering path . This must be done delicately so that prior information does not overwhelm observed data . If xi and yi store the genotypes and GPS ( global positioning system ) coordinates of subject i , respectively , then the weighted average d i j = α ∥ x i − x j ∥ + ( 1 − α ) ∥ y i − y j ∥ , α ∈ ( 0 , 1 ) , ( 10 ) serves as a composite distance helpful in clustering subjects . In Eq ( 10 ) observe that the components of the difference yi−yj must be computed in modulo arithmetic . Given a proper choice of the scaling constant α , an even better alternative replaces ‖yi−yj‖ by the geodesic distance between i and j . One could reverse the roles of the vector pairs yi and xi , but it seems to us that genotype similarity rather than physical proximity should be the primary driver of clustering . GPS coordinates are less informative , crudely estimated , and shared across many cases . Our program convexcluster minimizes the penalized loss Eq ( 2 ) for a range of user specified μ values . For each μ the optimized matrix U of cluster centers is stored in a temporary file for later construction of the cluster path . To facilitate visualization , convexcluster encourages users to project the cluster path onto any two principal components of the original data . The first example of Section 1 relies on the classical Iris data of discriminant analysis [4] . This dataset contains 150 cases spread over three species . The Iris data can be downloaded from the UCI machine learning repository [17] . For purposes of comparison , we also evaluated the clusters formed by agglomerative hierarchical clustering . In contrast to convex clustering , hierarchical clustering results are usually visualized via dendrograms . Hierarchical clustering comes in several flavors; we chose UPGMA ( Unweighted Pair Group Method with Arithmetic Mean ) [18] as implemented in the R function hclust . Although hclust offers six other options for merging clusters , UPGMA is probably the most reliable in reducing the detrimental effects of outliers since it averages information across all cluster members . UPGMA operates on a matrix of pairwise distances defined between nodes . In our genetics examples , we take these to be the distances defined by Eq ( 10 ) . To make a fair comparison between convex and hierarchical clustering , we invoke the composite distance in both methods . We also present results graphically by projecting cluster paths onto the first two principal components of the genetic data in Examples 1 and 2 and the expression data in the last Example . To generate a cluster path for hierarchical clustering , we assigned each fusion node on the tree as as the average of the values of its descendant leaves .
In computing pairwise weights , one is immediately confronted with the question of how to select the constants k ( number of nearest neighbors ) and ϕ ( the soft-threshold effect ) . The answer depends upon one’s research goals . Unlike supervised learning such as classification , clustering is inherently exploratory . In practice it usually looks for coarse-level relationships among the data points before drilling down in coarse clusters to look for fine-level relationships . In hierarchical clustering different levels of granularity can be explored by drawing a line bisecting all branches along a given level of the tree . Our recommendation for convex clustering is to begin with large values of k and then examine the patterns revealed as k is progressively reduced . All points eventually coalesce to a single cluster while k exceeds a particular threshold , which is determined by the separation of the nodes . To get a sense of the impact of the constants k and ϕ on the Iris data , we generated cluster paths for various pairs ( k , ϕ ) . As Fig 2 illustrates , k quantifies the connectivity of the underlying graph . Eventual coalescence only occurs for k = 50; even then the apparent Iris-Versicolor outlier does not coalesce until very late . All values of k support a clear separation of Iris-Setosa from the other two species Iris-Versicolor and Iris-Virginica . Separation of Iris-Versicolor and Iris-Virginica into two different groups becomes discernible at k = 20 . Subgroups within each species are evident for k = 5 and k = 2 . Improved resolution comes at a price; the two small two-member clusters seen in the top right corner of the main Iris-Versicolor cluster never fully coalesce with the main cluster when k = 2 . The distance tuning constant ϕ also exerts a subtle influence along each row of Fig 2 . This influence is more strongly felt for low values of k . For example , for k = 2 and k = 5 with ϕ = 4 , we observe that the two green points at the bottom left of the cluster graph coalesce much later when ϕ is set to smaller values . Examination of the Iris data suggests exploring cluster granularity over a range of k values with ϕ set to 0 . One can find the minimum k ensuring full connectivity by combining bisection* with either breadth-first search or depth-first search [19] . Once the desired granularity is achieved , ϕ can be increased to reveal more subtle details . Note that increasing ϕ sends most weights between k nearest neighbors to 0 . As previously noted , the proximal distance algorithm takes substantially more iterations to converge for large values of ϕ . As the Iris data illustrate , cluster inference is robust over a wide range of k values . Across all four rows in Fig 2 , we would have learned that there are two major classes of Iris , even if the points were plotted in the same color . By decreasing k , we were able to discern relationships within the two classes . The figure also shows that the parameter ϕ is less critical than k . Note , however , that for low values of k , better resolution is achieved by increasing ϕ from 0 . Although agglomerative hierarchical clustering is computationally efficient , it is greedy , and greedy algorithms tend to produce suboptimal solutions [1] . In particular , it can falter in the face of noisy data . To test this hypothesis , we simulated new data from the Iris data . In creating a dataset , we perturbed each row of the data matrix X by adding normal deviates with mean 0 and standard deviation equal to the sample standard deviation s2 of the corresponding feature multiplied by a constant c . We then clustered the data points into three clusters and quantitatively evaluated clustering performance through Normalized Rand Indices [20] . For convex clustering , visual inspection of the converged clustering paths reveals roughly three major clusters for values of k between 5 and 15 . With hierarchical clustering , three clusters were constructed by choosing a cut point on the full tree intersecting three branches . Table 1 summarizes Rand indices averaged over 100 replicates under the two methods . Larger values of the Rand index represent higher accuracy; the maximum value of 1 indicates error-free clustering . Examination of the table suggests that convex clustering is indeed more accurate in the face of noise over a wide range of k values . We carried out a second simulation study on the Iris data to assess accuracy of cluster inference as a function of missingness . Because the Iris data includes only four features ( width and height of sepals and petals ) , simply selecting entries of the data matrix at random can lead to cases retaining no data . To avoid these degeneracies , we randomly selected cases and then a random feature from each case for deletion . Given cases rates of 25% , 50% , 75% , and 100% , the proportion of missing observations consequently ranged from 5% to 25% . Hierarchical clustering with missing data requires that either cases with missing entries be omitted or that missing entries be imputed . We employed the second strategy , filling in missing entries by multiple imputation as implemented in the R package mi [21] . Hierarchical clustering was then applied to the completed data . For convex clustering , we also applied multiple imputation , but for the sole purpose of computing the convex clustering weights . We then applied convex clustering to the original incomplete data under the objective function Eq ( 7 ) . Accuracy for each method was estimated in the same manner as the previous simulations . The Rand indices in Table 2 suggest that convex clustering does indeed outperform hierarchical clustering in the presence of missing data . As genotyping costs have dropped in recent years , it has become straightforward to relate ethnicity to subtle genetic variations . Several software tools are now available for this purpose . For example , the programs structure [22] and admixture [23] estimate a subject’s admixture proportions across a set of predefined or inferred ancestral populations . eigenstrat [24] employs a handful of principal components to explain ethnic variation . Principal component analysis ( PCA ) is attractive due to its speed and ease of visualization . Clustering can also separate subjects by ethnicity if individuals of mixed ethnicity are omitted . The advantage of convex clustering is that one can follow the dynamic behavior of the relationship clusters along the regularization path . In the next two examples on population structure , the data consist of multi-dimensional genotypes . We project our convex clustering paths onto the first two principal components of the data . This produces plots where population substructure aligns with geographic regions of origin . It is well accepted that cancers of a given tissue often fall into different subtypes . In breast cancer for instance , patients with tumors that are estrogen receptor ( ER ) and epidermal growth factor receptor ( ErbB2 ) negative are less responsive to hormone based treatment than those possessing active receptors [37] . High-throughput platforms such as gene-expression microarrays and RNA-Seq have enabled researchers to classify cancer patients based on their molecular phenotypes . Hierarchical clustering by [38] established five gene-expression profiles across 9216 genes in 84 breast-cancer patients . Among the 84 patients , only 16 also had a clinical assessment of hormone receptor status . Here , we attempt to determine whether convex and hierarchical clustering can infer clusters consistent with the clinical outcomes for these 16 patients . Under the tuning constants ϕ = . 5 and k = 1 , convex clustering recovers two distinct clusters . Fig 18 projects the cluster centers along the cluster path on the first and third principal components of the original data . The left and right clusters correspond roughly to ER positive and ER negative tumors , respectively . Two ER negative tumors cluster with the ER positive tumors . Fig 19 depicts results from hierarchical clustering . Based on the order of fusion events , hierarchical clustering does not appear to group the tumors into distinct ER positive and negative groups . This could be an artifact of the hard binary choices imposed by hierarchical clustering . The two ER-B2 positive samples that clustered together in convex clustering appear in distant clusters under hierarchical clustering . For a dataset with a large number of attributes , parallelization can substantially reduce run times . convexcluster includes code written in OpenCL , a language designed to run on many-core devices such as GPUs . For each of the three genetic analyses presented above , we recorded the total run-time along the entire regularization path using standard C++ code for the CPU and OpenCL code for the GPU . For the sake of comparison , we also recorded run-times for clusterpath [3] , an R package that also implements convex clustering , on the same datasets and weighting schemes . Table 3 records the average run time to minimize the objective function averaged over all values of the regularization parameter . We chose this strategy because clusterpath does not allow users to pre-specify a grid of regularization values . The bottom line is that convexcluster required only 16% , 47% , and 75% of the time required by clusterpath to fit the HGDP , POPRES , and breast cancer datasets respectively . When a GPU is available , further improvements can potentially be realized . On an nVidia C2050 GPU , convexcluster enjoys speed improvements of 4 . 6 and 5 . 5 fold over the CPU version for the HGDP and breast cancer examples . In contrast , on the POPRES example , the GPU version is actually 3 . 5 fold slower than the CPU version . In its current form , convexcluster reads the updated matrix U from the GPUs at each point on the μ-regularization path before saving the data to disk . This large I/O overhead can overwhelm gains from parallelization for low-dimensional datasets such as the POPRES data . In general , GPU implementations of standard algorithms require a high degree of parallelization , limited data transfers between the master CPU and the slave GPUs , and maximal synchrony of the GPUs . Depending on the nature of the clustering data , convexcluster satisfies these requirements . It does not in the POPRES data , and computational efficiency suffers in the GPU version .
The literature on cluster analysis is enormous . Each clustering method has advantages in either simplicity , speed , reliability , interpretability , or scalability . If the number of clusters is known in advance , then k-means clustering is usually preferred . In convex clustering one can often achieve a predetermined number of clusters by varying the number of nearest neighbors and following the solution path to its final destination . Alternatively , if the underlying graph is fully connected , then one can follow the solution path until k clusters appear . The downside of k-means clustering is that it offers no insight into cluster similarity . If the goal in clustering is to obtain a snapshot of the relationships among observed data points at different levels of granularity , the choices are limited , and most biologists opt for hierarchical clustering . Hierarchical clustering is notable for its speed and visual appeal . Balanced against these assets is its sensitivity to poor starting values and outliers . Convex clustering occupies an enviable middle ground between k-means clustering and hierarchical clustering . Our extensive exploration of the HGDP and POPRES datasets showcase the subtle solutions paths of convex clustering . These paths offer considerable insights into population history and correct some of the greedy mistakes of hierarchical clustering . Nonetheless , hierarchical clustering can be the more practical choice when noise is low and a premium is put on computational speed . In the Iris data with no introduced noise , the two methods yield equivalent results . Total runtimes for the convex clustering analyses in this paper ranged from 5 minutes to 30 minutes . In contrast , even for the largest datasets analyzed here , hierarchical clustering required no more than 5 seconds to complete . Our perturbations of the Iris data demonstrate sensitivity to noise , so speed comes at a price . Given the novelty of convex clustering [2] , it is hardly surprising that only a few previous programs ( clusterpath [3] and cvxclustr [39] ) , implement it . Our program is unique in that we offer a fast implementation when GPU devices are available . These earlier programs perform similarly to our program on modest problems such as the Iris data . Unfortunately , on large datasets such as the HGDP data , clusterpath depletes all available memory and terminates prematurely . Furthermore , clusterpath lacks two features that work to the advantage of convex clustering . First , it does not support disconnected graphs defined by sparse weights . In our breast cancer example , clustering with disconnected graphs reveals fine-grained details . Second , clusterpath does not allow for missing entries in the data matrix . The current paper documents convexcluster’s ability to scale realistically to dimensions typical of modern genomic data . A combination of careful algorithmic development and exploitation of modern many-core chipsets lies behind convexcluster . The proximal distance algorithm propelling convexcluster separates parameters and enables massive parallelization . OpenCL made it relatively easy to implement parallel versions of our original serial code . Further speedups are possible . For instance , convexcluster spends an inordinate amount of execution time moving matrices over relatively slow I/O channels in preparation for plotting . One could easily project the data to principal components on each GPU itself prior to data transfer . More recent ATI or nVidia GPUs should improve the speedups on high-dimensional data mentioned here . Convex clustering also shows promise as a building block for more sophisticated exploratory tools in computational biology . In a companion paper [40] introduce a convex formulation of the biclustering problem . In biclustering one seeks to cluster both observations and features simultaneously in a data matrix . Cancer subtype discovery can be formulated as a biclustering problem in which gene expression data is partitioned into a checkerboard-like pattern highlighting the associations between groups of patients and the groups of genes that distinguish them . To bicluster a data matrix , hierarchical clustering can be applied independently to the rows and columns of the matrix . Convex biclustering produces more stable biclusterings while retaining the interpretability of hierarchical biclustering . Convex biclustering requires repeatedly solving convex clustering subproblems . The field of cluster analysis is crowded with so many competing methods that it would foolish to conclude that convex clustering is uniformly superior . Our goal of illustrating the versatility of convex clustering is more modest . The reflex reaction of most biologists is to employ hierarchical or k-means clustering . We suggest that biologists take a second look . Convex clustering’s ability to reliably deliver an entire solution path is compelling . The insights discussed here will enhance the careful exploration of many big datasets . The present algorithm , and indeed the present formulation of convex clustering , are unlikely to be the last words on the subject . We encourage other computational biologists and statisticians to refine these promising tools . convexcluster can be freely downloaded from the UCLA Human Genetics web site at http://www . genetics . ucla . edu/software/ for analysis and comparison purposes . | Pattern discovery is one of the most important goals of data-driven research . In the biological sciences hierarchical clustering has achieved a position of pre-eminence due to its ability to capture multiple levels of data granularity . Hierarchical clustering’s visual displays of phylogenetic trees and gene-expression modules are indeed seductive . Despite its merits , hierarchical clustering is greedy by nature and often produces spurious clusters , particularly in the presence of substantial noise . This paper presents a relatively new alternative to hierarchical clustering known as convex clustering . Although convex clustering is more computationally demanding , it enjoys several advantages over hierarchical clustering and other traditional methods of clustering . Convex clustering delivers a uniquely defined clustering path that partially obviates the need for choosing an optimal number of clusters . Along the path small clusters gradually coalesce to form larger clusters . Clustering can be guided by external information through appropriately defined similarity weights . Comparisons to hierarchical clustering demonstrate the superior robustness of convex clustering to noise . Our genetics examples include inference of the demographic history of 52 populations across the world , a more detailed analysis of European demography , and a re-analysis of a well-known breast cancer expression dataset . We also introduce a new algorithm for solving the convex clustering problem . This algorithm belongs to a subclass of MM ( minimization-majorization ) algorithms known as proximal distance algorithms . The proximal distance convex clustering algorithm is inherently parallelizable and readily maps to modern many-core devices such as graphics processing units ( GPUs ) . Our freely available software , convexcluster , exploits OpenCL routines that ensure compatibility across a variety of hardware environments . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Convex Clustering: An Attractive Alternative to Hierarchical Clustering |
Few studies of dengue have shown group-level associations between demographic , socioeconomic , or geographic characteristics and the spatial distribution of dengue within small urban areas . This study aimed to examine whether specific characteristics of an urban slum community were associated with the risk of dengue disease . From 01/2009 to 12/2010 , we conducted enhanced , community-based surveillance in the only public emergency unit in a slum in Salvador , Brazil to identify acute febrile illness ( AFI ) patients with laboratory evidence of dengue infection . Patient households were geocoded within census tracts ( CTs ) . Demographic , socioeconomic , and geographical data were obtained from the 2010 national census . Associations between CTs characteristics and the spatial risk of both dengue and non-dengue AFI were assessed by Poisson log-normal and conditional auto-regressive models ( CAR ) . We identified 651 ( 22 . 0% ) dengue cases among 2 , 962 AFI patients . Estimated risk of symptomatic dengue was 21 . 3 and 70 . 2 cases per 10 , 000 inhabitants in 2009 and 2010 , respectively . All the four dengue serotypes were identified , but DENV2 predominated ( DENV1: 8 . 1%; DENV2: 90 . 7%; DENV3: 0 . 4%; DENV4: 0 . 8% ) . Multivariable CAR regression analysis showed increased dengue risk in CTs with poorer inhabitants ( RR: 1 . 02 for each percent increase in the frequency of families earning ≤1 times the minimum wage; 95% CI: 1 . 01-1 . 04 ) , and decreased risk in CTs located farther from the health unit ( RR: 0 . 87 for each 100 meter increase; 95% CI: 0 . 80-0 . 94 ) . The same CTs characteristics were also associated with non-dengue AFI risk . This study highlights the large burden of symptomatic dengue on individuals living in urban slums in Brazil . Lower neighborhood socioeconomic status was independently associated with increased risk of dengue , indicating that within slum communities with high levels of absolute poverty , factors associated with the social gradient influence dengue transmission . In addition , poor geographic access to health services may be a barrier to identifying both dengue and non-dengue AFI cases . Therefore , further spatial studies should account for this potential source of bias .
Approximately 2 . 5 billion people worldwide live in dengue-endemic areas and are at risk for acquiring the infection [1] . Every year , as many as 390 million dengue infections occur , resulting in an estimated 96 million symptomatic cases [2] . In the Americas , dengue incidence has continuously increased since the reintroduction of its vector , the mosquito Aedes aegypti , in the 1970s [3–5] . Brazil accounts for the largest number of dengue cases in the region . In 2013 alone , Brazil reported more than 1 . 46 million cases of dengue; 61 . 5% of the total number of cases recorded in the American continent [6–8] . Rapid urbanization , with subsequent increases in population density and poor living conditions , has been associated with the re-emergence of dengue [9] . Currently , approximately one third of the urban population in developing regions live in urban slums and , according to United Nations projections , about 2 billion people will reside in urban slums by 2030 [10 , 11] . In Brazil , a marked increase in the number of people living in impoverished urban slum communities occurred during the 20th century as a consequence of intense rural to urban migration and population growth [12] . The United Nations estimated that 26 . 4% of Brazilians lived in slums in 2010 [13] . In Brazil and elsewhere , several studies with ecological design have found associations between increased dengue risk and demographic , socioeconomic , and environmental characteristics , such as high population and household densities [14–17] , wide social inequality and low socioeconomic status [18–25] , low levels of population education [24–26] , presence of a precarious sanitary system [16 , 17] , lack of garbage collection [15 , 18 , 27] , and low coverage of piped water [28 , 29] . The majority of these studies have examined large urban areas and compared dengue occurrence among states , counties , or cities . However , dengue transmission is highly focal in space , as the vector typically disperses within a short range ( <100 meters ) [30 , 31] . Up to now , it is unknown whether group-level factors are associated with dengue at smaller geographic scales , such as within a neighborhood . In addition , prior studies , particularly those performed in Brazil , used secondary data from the national dengue reporting system . As dengue usually presents with nonspecific clinical manifestations , the disease burden may have been underreported during interepidemic periods and over-reported during epidemic periods [7] , a limitation of studies using official surveillance data . In Salvador , Brazil , dengue has been transmitted endemically since 1995 , with approximately 5 , 000 cases reported each year between 2008 and 2012 [32 , 33] . We estimated the spatial distribution of symptomatic dengue in an urban slum community in Salvador , and assessed whether group-level demographic , socioeconomic , and geographic factors influenced dengue distribution . Additionally , to investigate whether any associations were specific for dengue , we repeated the spatial distribution analyses and assessed group-level associated factors using cases of non-dengue acute febrile illness ( AFI ) as the outcome .
Between January 1 , 2009 and December 31 , 2010 , we conducted enhanced community-based surveillance to detect patients with laboratory evidence of dengue infection among those seeking medical care for AFI at the only public emergency health unit ( São Marcos Emergency Center [SMEC]; 38°26'09"W , 12°55'32"S ) serving the Pau da Lima slum community in Salvador , Brazil ( Fig 1A ) . The study site for the community-based surveillance was arbitrarily defined to have common boundaries with census tract territories , allowing use of official social and demographic population data to determine if AFI patients who sought medical attention at SMEC lived within the study site . In 2010 , we performed a community survey and found that 84% ( 284 of 337 ) of the study site residents seek medical assistance for AFI at SMEC . According to the 2010 national census , the population of Salvador was 2 . 7 million and 76 , 352 ( 3% ) people lived in the Pau da Lima study surveillance site [34] . The study site was comprised of 98 census tracts ( CTs ) in an area of 3 . 7 km2 within the Sanitary District of Pau da Lima , a delimited administrative area with a population of 218 , 706 in 294 CTs [34] . The site’s topography is characterized by valleys and hills , with an elevation range of 60 meters ( S1 Table ) . Population density was >215 , 000 inhabitants per km2 for 75% of the study site’s CTs [34] . On average , 71 . 9% of the families living within the study area had a per capita monthly income lower or equal to the Brazilian minimum wage ( R$ 510 . 00; equivalent to US$289 . 77 , in 2010 ) ( S1 Table ) [34] . Demographic and socioeconomic characteristics of the study site varied among the CTs . In general , CTs located around the study health unit presented higher population densities per household and higher percentages of younger inhabitants , black population , illiteracy , and poverty ( S1 Fig ) . Lack of sanitation was more frequent among CTs located in the northeast region of the study site ( S1 Fig ) . The Zoonosis Control Center at the Municipal Secretary of Health conducted vector control actions within the study site , according to the national guidelines for dengue control and prevention [35] . Vector control activities included community education on vector control measures and bimonthly household visits for entomological surveys and vector control . These actions were routinely performed throughout the study period , except for three months between August 2 and November 3 , 2010 , when a strike of the dengue control agents interrupted their activities . Although we informed the Pau da Lima Health District about the participants’ laboratory dengue results , we were not able to provide this information in a timely enough fashion to guide the activities of the Zoonosis Control Center agents . AFI surveillance was performed at SMEC from Mondays to Fridays , from 07h30 to 16h00 . During surveillance hours , the study team used medical charts to prospectively identify patients with the following inclusion criteria: age of five years or more , reported fever or measured axillary temperature ≥37 . 8°C of up to 21 days of duration , and household address inside the study area . Patients who agreed to participate in the study and provided informed consent had an enrollment blood sample collected and were invited to return 15 days later for convalescent-phase blood sample collection . For patients unable to return to SMEC , a study team visited their domiciles to collect convalescent-phase blood samples . Blood samples were maintained under refrigeration and were processed on the same day of collection . Sera were stored at -20°C and -70°C for dengue serological and molecular testing , respectively . The study team retrospectively reviewed medical charts of enrolled participants to collect data on presumptive diagnoses , hospitalization , and death during hospitalization at SMEC . We also reviewed medical charts for every patient attended to at SMEC in 2009 and 2010 to ascertain the number of patients who were eligible for but were not enrolled in the study . Residential addresses for enrolled patients were confirmed by household visits and their positions were marked onto hard copy 1:1 , 200 scale maps , which were then entered into an ArcGIS database [36] . This database was merged with a cartographical database provided by IBGE [37] to identify the CT of residence of the study patients . CT-level aggregated data for demographic , socioeconomic , and geographic variables were obtained from the 2010 national census [34] . Topographical data were obtained from IBGE [38] . Demographic variables examined were the mean age of CT population , percentage of residents ≤15 years of age , household density in hundreds ( households/100/km2 ) , and population density in hundreds ( inhabitants/100 km2 ) . Socioeconomic variables analyzed were the percentage of households with monthly per capita income ≤1 times the national minimum wage , percentage of illiteracy among residents ≥15 years of age , mean number of residents per household , percentage of residents who self-identified as black , percentage of households with inadequate sewage disposal ( households without a closed connection to the sewage system or without a closed septic tank ) , percentage of households without public water supply , and percentage of households not covered by a garbage collection service . We also examined the following geographic variables: mean elevation in relation to sea level , range of elevation ( measured as the difference between the highest and lowest points of the CT ) , and two-dimensional linear distance from the CT centroid to the health unit , which was used as a proxy for health care access . Acute-phase sera were tested by enzyme linked immunoassay ( ELISA ) for detection of dengue NS1 antigen and dengue IgM antibodies ( Panbio Diagnostics , Brisbane , Australia ) . Convalescent-phase sera were also tested by dengue IgM ELISA to identify seroconversions . Acute-phase sera from patients who were positive by NS1 ELISA or by IgM ELISA in either the acute- or the convalescent-phase sera were also tested by reverse transcriptase polymerase chain reaction ( RT-PCR ) [39] to identify the infecting serotype . We defined a dengue case as an AFI patient with a positive NS1 ELISA , acute- or convalescent-phase IgM ELISA , or RT-PCR . We estimated the population risk of symptomatic dengue as the ratio between the number of dengue cases detected by our surveillance and the area population . We also estimated the risk of non-dengue AFI using the number of enrolled patients without laboratory evidence of dengue as the numerator . Risks were estimated for each CT . Dengue and non-dengue AFI standardized morbidity ratios ( SMR ) were calculated indirectly by dividing the estimated risk for each CT during the two-year study period by the estimated risk for the overall study area . The SMRs were plotted in study site maps . Bivariate and multivariable regression analyses to assess associations with estimated risk of dengue were performed using Poisson log-normal models [40] . This model is an extension of the Poisson model , which allows for data overdispersion . Demographic variables associated with dengue risk in the bivariate analysis ( P value ≤0 . 20 ) were entered into a demographic multivariable backwards regression model . The same approach was used to build socioeconomic and geographic multivariable regression models . Variables with P values ≤0 . 10 in the demographic , socioeconomic , or geographic multivariable regression models were selected for entry into a final backwards Poisson log-normal model to identify significant associations ( P ≤0 . 05 ) . A conditional autoregressive model ( CAR ) [40 , 41] was then used to account for the presence of spatially correlated residuals . The CAR model is an extension of the Poisson log-normal model , with the addition of a spatial component that is dependent on the neighboring structure of the spatial units of analysis . This component assumes that neighboring areas have similar risks , which often results in a smoothed risk map . In our CAR model , we assumed adjacency-based neighborhood spatial weights . A Bayesian approach with non-informative prior distribution for all parameters was applied in the model . Calculations were made using integrated nested Laplace approximation ( INLA ) [42] . A backwards selection method was also applied to the CAR model to select associated variables ( P ≤0 . 05 ) . Relative risks and 95% confidence intervals ( 95% CI ) were calculated for all the models . Model fitness was assessed by the deviance information criterion ( DIC ) [43] . We repeated all the steps previously described to identify demographic , socioeconomic , and geographic variables associated with the estimated risk of non-dengue AFI . Risks of dengue and non-dengue AFI , predicted by the final multivariable Poisson log-normal and by the CAR models , were used to calculate adjusted SMRs for each CT , which were plotted in maps . Statistical and spatial analyses were performed using Maptools and INLA packages in the R software ( The R Project for Statistical Computing ) [42] . The dataset was imported to Quantum GIS software to produce the maps [44] . This project was approved by the Research Ethics Committee at the Gonçalo Moniz Research Center , Oswaldo Cruz Foundation , the Brazilian National Council for Ethics in Research , and the Institutional Review Board of Yale University . All adult subjects provided written informed consent . Participants <18 years old who were able to read provided written assent following written consent from their parent or guardian . All study data were anonymized before analysis .
During the two-year study period , a total of 12 , 958 study site residents ≥5 years old received medical care for an AFI at SMEC . Among these residents , 3 , 459 ( 26 . 7% ) were evaluated for study inclusion ( Fig 2 ) . Age and sex distributions for the groups of patients who were and were not evaluated were similar ( both groups had median age of 18 years old and were 47% male ) . Of the assessed patients , 2 , 962 ( 85 . 6% ) were enrolled in the study . Patients who were enrolled in the study were older ( 19 versus 13 years ) and were more likely to be male ( 48% versus 44% ) compared to those not enrolled . An acute-phase blood sample was collected from 2 , 874 ( 97 . 0% ) enrolled patients . Paired blood samples were obtained from 2 , 523 ( 85 . 2% ) patients . Laboratory testing identified 651 ( 22 . 0% of 2 , 962 ) patients with evidence of dengue infection; the remaining 2 , 311 ( 78 . 0% ) patients were classified as having non-dengue AFI . Among the dengue cases , 380 ( 58 . 4% ) were acute-phase IgM ELISA positive , 505 ( 77 . 6% ) were convalescent-phase IgM ELISA positive , 103 ( 15 . 8% ) were NS1 ELISA positive , and 247 ( 37 . 9% ) were RT-PCR positive . IgM seroconversion was observed for 207 ( 31 . 8% ) of the dengue cases . For RT-PCR confirmed cases , 20 ( 8 . 1% ) were infected with DENV1 , 224 ( 90 . 7% ) with DENV2 , 1 ( 0 . 4% ) with DENV3 , and 2 ( 0 . 8% ) with DENV4 . Dengue was less prevalent among patients enrolled in 2009 ( 152 of 1 , 466; 10 . 4% ) compared to patients enrolled in 2010 ( 499 of 1 , 496; 33 . 4% ) . The estimated risk of symptomatic dengue in the study site was 21 . 29 and 70 . 23 cases per 10 , 000 inhabitants in 2009 and 2010 , respectively . The socio-demographic and clinical characteristics of dengue and non-dengue AFI patients are shown in Table 1 . Compared to non-dengue AFI , dengue cases more frequently presented with myalgia , retro-orbital pain , arthralgia , and rash . Only 16% of the dengue cases had a presumptive diagnosis of dengue recorded in their medical charts . Yet , the likelihood of dengue suspicion was 7 . 5 times higher among dengue cases than among non-dengue AFI patients ( P <0 . 001 ) . We were able to locate the census tract of residence for 570 ( 87 . 6% ) of the 651 dengue cases and for 1 , 948 ( 84 . 3% ) of the 2 , 311 non-dengue AFI patients . The estimated risks for both dengue and non-dengue AFI were higher for the population living in the census tracts located in the central region of the study site ( Fig 1B and 1C ) . Multivariable Poisson log-normal models identified the following CT-level factors associated with increased risk of dengue: a shorter linear distance between the centroid of the census tract and the emergency unit , a higher percentage of inhabitants who self-identify as black , and a higher percentage of families earning lower or equal to one Brazilian minimum wage per household inhabitant per month ( Table 2 ) . Estimated risk for non-dengue AFI was independently associated with the same CT-level factors , with higher mean age of the CT population as an additional risk factor ( Table 2 ) . Addition of a spatial component to the multivariable models for both dengue and non-dengue AFI improved their fitness and seemed to capture the spatial pattern of dengue and non-dengue AFI , since the non-structured residuals of both models were randomly distributed in space . Dengue risk , assessed by the CAR model , increased 2% ( RR: 1 . 02; 95% CI: 1 . 01–1 . 04 ) for each 1% increase in the percentage of CT families with a monthly income ≤1 times the Brazilian minimum wage per household inhabitant , and decreased 13% ( RR: 0 . 87; 95% CI: 0 . 80–0 . 94 ) for each 100 meter increase in the linear distance between the CT centroid and the surveillance health unit ( Table 2 ) . Non-dengue AFI risk also increased as the percentage of families with a monthly income of ≤1 times the minimum wage per household inhabitant increased ( RR: 1 . 03; 95% CI: 1 . 01–1 . 04 ) and decreased as the linear distance between the CT centroid and the surveillance health unit increased ( RR: 0 . 87; 95% CI: 0 . 80–0 . 93 ) . In addition , non-dengue AFI was positively associated in the CAR model with higher mean age of the CT population ( RR: 1 . 10; 95% CI: 1 . 02–1 . 19 ) , and with a higher percentage of inhabitants who are black ( RR: 1 . 02; 95% CI: 1 . 01–1 . 04 ) ( Table 2 ) . Although the spatial distribution of SMRs adjusted by the final Poisson log-normal and CAR models were smoother than the non-adjusted SMRs for both dengue and non-dengue AFI , the CTs located in the central region of the study site maintained a higher relative risk for both conditions ( Fig 3 ) .
This enhanced surveillance study highlights the large burden of symptomatic dengue in a poor urban slum community of Salvador , the third largest city in Brazil . Even though the study site was relatively small and characterized by high levels of absolute poverty , the spatial distribution of the detected dengue cases was not homogenous , being influenced by neighborhood characteristics; namely , the gradient of social status and proximity to health services . These findings were not specific for dengue , as the spatial distribution of non-dengue AFI presented the same pattern . During the study period , the case definition for suspected dengue in Brazil was a patient who lived or traveled to endemic areas and presented with fever up to seven days of duration plus two of the following symptoms: headache , retro-orbital pain , myalgia , arthralgia , rash , or prostration [45] . However , underreporting of patients fulfilling clinical and epidemiological criteria for dengue is common in Brazil and elsewhere [46 , 47] . Furthermore , dengue reporting tends to be influenced by disease severity and the availability of dengue laboratory testing [48] . We used an enhanced surveillance design to detect AFI patients with laboratory evidence of dengue infection . This approach allowed identification of dengue cases that were unlikely to be reported , as only 16% of the detected cases had a clinical suspicion of dengue recorded in their chart , and also provided more complete epidemiological disease data . Enhanced surveillance was only conducted during working hours , resulting in an underestimation of dengue and other non-dengue AFI risks . Furthermore , enrolled patients were older than those evaluated but not enrolled , which also might have influenced dengue risk estimation as dengue risk is not equal for all age groups . However , compared to the incidence of reported dengue in the whole Sanitary District of Pau da Lima , the study detected greater dengue risk in 2009 ( 17 . 3 and 21 . 3 cases per 10 , 000 inhabitants , respectively ) and in 2010 ( 44 . 1 and 70 . 2 cases per 10 , 000 inhabitants , respectively ) [33] . This finding is noteworthy , since the study only enrolled 22 . 9% of the 12 , 958 AFI subjects from the study site seeking medical attention at SMEC . As the AFI patients who were assessed for study inclusion had comparable age and sex distribution to those not assessed , we can assume that dengue prevalence was similar between these two groups and infer that the actual risk for a dengue episode requiring medical attention in the study site was about four times greater than estimated . We found a higher risk of dengue associated with poorer areas in the Pau da Lima slum community . Although some population-level studies based on reported dengue cases have also shown an association of symptomatic dengue risk and lower socioeconomic status [21–23 , 49] , others have found an inverse association , where greater incidence occurred in areas of higher income [25 , 50] , or even no association [51] . Discrepancies have been observed in individual-level studies , where dengue occurrence has not been associated [52 , 53] or was positively [54–56] or negatively [24 , 57] associated with income and socioeconomic status . It has been speculated that these contradictory results were due to the specificities of each study location , such as level of dengue susceptibility in the population , implementation and coverage of vector control measures , as well as differences in the study spatial unit or the socioeconomic variables considered [18 , 22 , 29] . However , poor communities typically have environmental characteristics that facilitate Aedes spp . breeding , including presence of refuse deposits and containers for water storage [58 , 59] . Therefore , the social gradient we found in association with increased risk of dengue may have acted as a surrogate for other proximal factors involved in dengue transmission . Proximity of the CTs to the health unit was the variable most strongly associated with detection of dengue . This finding may be due to the fact that CTs located around the health unit had higher population densities per household , and higher percentage of inhabitants <15 years of age ( a proxy for susceptibility to dengue infection ) ( S1 Fig ) ; together these facts might favor dengue transmission as they increase opportunities for interactions between infected and susceptible hosts via the mosquito vector . In bivariate , but not in multivariable and CAR analyses , both population density per household , and percentage of inhabitants <15 years of age were associated with dengue detection . However , the distance between the CTs and the health unit was also positively associated with non-dengue AFI cases detection , suggesting that this association was not specific for a vector-born disease . Therefore , CTs proximity to the study health unit most likely indicates increased opportunity for case detection . Measured distances between households and health facilities have previously been associated with dengue occurrence [28] , as well as with poorer colorectal cancer survival [60] , lower clinic attendance and a higher degree of dehydration due to diarrhea [61] , and decreased use of antenatal healthcare [62] , among others . Geographic accessibility to health care is usually observed on a broader scale , especially in developing countries where greater inequalities in health care access are observed in smaller towns distant to large urban centers [63 , 64] . Our study demonstrates that this phenomenon may also be present at finer geographic scales , such as within urban communities . This finding may be particularly important in spatial distribution studies that use reported cases of mild and self-limited diseases , and that rely on passive surveillance . In this context , areas of higher disease risk may actually represent areas of greater provision of health services and greater opportunity for case detection rather than a true difference in disease frequency . Other studies have identified a higher occurrence of dengue in areas that lack or have infrequent garbage collection [15 , 27 , 65] , that have a lower coverage of closed sewer systems [17] , and those with low coverage or irregular water supplies [28 , 57 , 66] . These associations may be explained by ecological preferences of the mosquito vectors , which find more favorable larval development sites in areas with poorer sanitation infrastructure . In bivariate analysis , we found an association between increased dengue risk and inadequate garbage collection; however , a significant independent association was not observed after adjusting for other covariates . We were unable to identify associations with the coverage levels of piped water supply and sewer provision . The divergence between our findings and those from other studies may be explained by the low variability in the characteristics of the CTs comprising our study site , or by colinearity with other socioeconomic variables included in the model ( S2 Table ) . Alternatively , the inclusion of the distance from the health care unit and the CT centroid in the model may have overshadowed weaker associations . This study has several limitations . Despite SMEC being the sole public emergency unit in the community , with the second closest public emergency unit located >1 . 5 km outside from the study site’s boundaries , Pau da Lima residents may have sought care elsewhere . In addition , we were not able to investigate dengue in all AFI patients seeking medical assistance at SMEC . However , AFI patients who were and were not evaluated for study inclusion were similar regarding age and sex distribution , suggesting that selection bias had a minor influence on our results . The CT of residence was not identified for all study participants , but we georeferenced the majority of them ( 87 . 6% ) and ensured accuracy of the locations of CTs through household visits . We used different laboratory approaches to identify dengue cases . Even though it is likely that we missed some dengue cases by only performing RT-PCR on patients who were NS1 or IgM ELISA positive , the method we used to simultaneous test dengue by IgM and NS1 assays has been shown to increase diagnostic sensitivity [67] . Use of IgM ELISA to confirm dengue is consistent with the Brazilian Ministry of Health guidelines [68 , 69]; however , dengue IgM may remain detectable up to two months after an infection , and we may have classified patients with recent dengue infection as dengue cases . To account for the possible inclusion of recent asymptomatic infections among dengue cases , we repeated the multivariable Poisson-log normal and the CAR regression analyses using only patients confirmed by IgM ELISA seroconversion , NS1 ELISA , and RT-PCR and found similar associations ( S3 Table ) . Finally , in our model , we could not include data from the Larval Index Rapid Assay for Aedes aegypti ( LIRAa ) , a national survey for positive mosquito breeding sites in a random sample of dwellings [70] , because this index is recorded in spatial units that do not align with CTs boundaries . Strengths of this study include the laboratory testing of all enrolled patients and the assessment of group-level characteristics associated with non-dengue AFI . Additionally , we used conditional auto-regressive models , which increased model fitness by adding a spatially structured component . The increases in model fit indicate that there were residual spatial variations in the risk distributions that had not initially been captured by the studied variables . Official surveillance systems based on passive reporting underestimate dengue burden; thus , enhanced surveillance is a useful tool to provide more accurate estimates of disease occurrence and its spatial distribution . According to the World Health Organization guidelines for dengue prevention and control , estimating the true burden of the disease is a critical step to achieve the goal of reducing dengue disease burden [71] . Our findings corroborate those of other studies showing that implementation of sentinel health unit-based enhanced surveillance for dengue is feasible and may be employed to obtain high quality information on disease trends and circulating serotypes as well as increase opportunities for timely detection and intervention during epidemics , which may not be achieved by passive surveillance [46 , 72] . In several settings , low socioeconomic status has been observed to impact dengue transmission [21 , 54 , 73] , emphasizing that the disease burden is likely to be greatest in vulnerable populations such as urban slum dwellers , and as we found in this study , the poorest segments of such populations . Until initiatives address social inequity and the underlying poverty-associated environmental determinants of dengue transmission , specific vector control actions that are difficult to apply citywide , such as biological larvae control , may target groups at higher disease risk , such as those living in the poorer areas of urban communities . Finally , studies aiming to assess spatial distribution and group-level associated factors of diseases should account for potential detection bias . With the popularization of GIS and spatial analysis tools , the distance between each area unit and the closest health service is a viable proxy for health care accessibility and its use may help explain the spatial distribution of health and disease . | Dengue is influenced by the environment; however , few studies have investigated the relationship between neighborhood characteristics and the spatial distribution of dengue within small urban areas . We examined whether specific characteristics of an urban slum community were associated with dengue risk . From January 2009 to December 2010 , we conducted community-based surveillance in a slum in Salvador , Brazil to identify patients with acute febrile illness ( AFI ) and to test them for dengue . We identified 651 ( 22 . 0% ) patients with laboratory evidence of dengue infection among 2 , 962 AFI patients . All the four dengue serotypes were detected , but DENV2 predominated ( DENV1 8 . 1%; DENV2 90 . 7%; DENV3 0 . 4%; DENV4 0 . 8% ) . Estimated risk of symptomatic dengue was 21 . 3 and 70 . 2 cases per 10 , 000 inhabitants in 2009 and 2010 , respectively . We found that neighborhood poverty level and proximity to the health center were associated with higher risk of detection of dengue and other AFI . This study highlights the large burden of dengue in poor urban slums of Brazil and indicates that socioeconomic development could potentially mitigate risk factors for both dengue and non-dengue AFI cases . In addition , we found that residential proximity to a health care facility was associated with improved case detection . Therefore , further studies on disease distribution should consider household proximity to health care facilities when assessing risk . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Spatial Distribution of Dengue in a Brazilian Urban Slum Setting: Role of Socioeconomic Gradient in Disease Risk |
Plasmodium knowlesi is the most common cause of malaria in Malaysian Borneo , with reporting limited to clinical cases presenting to health facilities and scarce data on the true extent of transmission . Serological estimations of transmission have been used with other malaria species to garner information about epidemiological patterns . However , there are a distinct lack of suitable serosurveillance tools for this neglected disease . Using in silico tools , we designed and expressed four novel P . knowlesi protein products to address the distinct lack of suitable serosurveillance tools: PkSERA3 antigens 1 and 2 , PkSSP2/TRAP and PkTSERA2 antigen 1 . Antibody prevalence to these antigens was determined by ELISA for three time-points post-treatment from a hospital-based clinical treatment trial in Sabah , East Malaysia ( n = 97 individuals; 241 total samples for all time points ) . Higher responses were observed for the PkSERA3 antigen 2 ( 67% , 65/97 ) across all time-points ( day 0: 36 . 9% 34/92; day 7: 63 . 8% 46/72; day 28: 58 . 4% 45/77 ) with significant differences between the clinical cases and controls ( n = 55 , mean plus 3 SD ) ( day 0 p<0 . 0001; day 7 p<0 . 0001; day 28 p<0 . 0001 ) . Using boosted regression trees , we developed models to classify P . knowlesi exposure ( cross-validated AUC 88 . 9%; IQR 86 . 1–91 . 3% ) and identified the most predictive antibody responses . The PkSERA3 antigen 2 had the highest relative variable importance in all models . Further validation of these antigens is underway to determine the specificity of these tools in the context of multi-species infections at the population level .
Plasmodium knowlesi is a simian parasite which can cause zoonotic malaria in humans [1] . Recent evidence suggests that human P . knowlesi infections are a growing public health threat in South East Asia , particularly in Malaysia [2] . P . knowlesi has the potential to cause severe disease in endemic regions [3] , and is now the most common cause of clinical malaria in Malaysia [4] . P . knowlesi is morphologically similar to P . malariae [5] , historically leading to the misdiagnosis of P . knowlesi infections as P . malariae [6] . Recent publications have also demonstrated misdiagnosis of P . knowlesi as P . vivax and P . falciparum [7 , 8] with potential delay of appropriate treatment associated with case fatalities [3 , 9 , 10] . Studies have shown that antibodies to Plasmodium proteins persist for long periods [11] , even in the context of limited exposure or absence of infection . Such antibodies can be utilised in serological assays , accurately estimating the incidence and exposure to Plasmodium parasites [12 , 13] . One key requirement for serological studies is the identification of Plasmodium species-specific biomarkers , particularly in regions where multi-species infections are likely to occur . It is important to distinguish between human serological responses to different Plasmodium species to improve our understanding of immunity to these infections , as well as define the geographical spread of infection . Such information can also help to evaluate the impact of how control measures targeting a single species might affect the transmission and immunological profile of other co-endemic species . The few recombinant protein reagents that do exist for P . knowlesi have a high level of sequence homology with orthologues from other Plasmodium species and , as such , are not applicable to identifying species-specific antibody responses . For example , PK66 ( PkAMA1 ) [14] and PkSPATR ( secreted protein with altered thrombospondin repeat ) [15] share 86% and 85% amino acid identity respectively with P . vivax ( https://is . gd/MzISez ) , potentially making it difficult to distinguish between the two species where infections are co-endemic . The 2011 WHO consultation panel on the public health importance of P . knowlesi recommended the urgent development of P . knowlesi-specific diagnostic tools [16] . Key to achieving this goal would be the development of sensitive and accurate tools to help monitor the transmission of infection . In this study , we describe the development and evaluation of a panel of novel recombinant antigens based on P . knowlesi-specific amino acid sequences , using publicly available in silico tools . The development of such well-validated species-specific tools represent a potentially important serosurveillance tool to help monitor for historical P . knowlesi infections in endemic areas . To illustrate how these data can be used to identify seropositive individuals , we utilise data-adaptive statistical methods ( boosted regression trees ) to classify exposed individuals . By assessing relative variable importance within these models , we identify the antigen responses contributing most to model predictions and potential serological tools for use in epidemiological studies . These reagents will also serve as an important set of tools to help identify correlates of immunity to P . knowlesi .
Fig 1 outlines the experimental strategy used in the identification of the target sequences of interest . Known markers of seroincidence were selected based on available evidence from P . falciparum: AMA1 [17] , MSP1 [18] , SSP2/TRAP [19] and SERA [20] ( PkAMA1 ( PKNH_0931500 ) , PkMSP1 ( PKNH_0728900 ) , PkSSP2/TRAP ( PKNH_1265400 ) , SERA3 ( PKNH_0413400 ) and TSERA2 ( PKNH_0413500 ) , respectively ) . Full-length protein sequences for each gene were initially screened using the BlastP search tool ( Plasmodb: https://is . gd/XOs7vd [21] and NCBI: https://is . gd/MzISez ) . Amino acid sequences were used to generate maximum likelihood phylograms to summarise the relatedness of each gene target between species ( S1A–S1E Fig ) . Alignments were also generated for each target using amino acid sequences from other plasmodia matching the query sequence using the MUltiple Sequence Comparison by Log-Expectation ( MUSCLE ) software ( http://www . ebi . ac . uk/Tools/msa/muscle/ ) [22] ( S2A–S2E Fig ) . Each alignment was then interrogated to identify regions of identity primarily with P . vivax and P . falciparum but also with P . malariae and P . ovale . Regions or entire sequences showing high levels of identity were excluded from further analysis and the P . knowlesi-specific truncated regions were again screened using BlastP to validate sequence specificity ( Fig 1 and S1 Table ) . Each target sequence was analysed using domain prediction software ( http://gene3d . biochem . ucl . ac . uk/ and http://smart . embl-heidelberg . de/ ) to help define putative domain boundaries , where possible . The aim was to limit the level of potential antibody cross-reactivity , which would limit the usefulness of the antigens as serological tools due to the high level of identical amino acids between species . A particular problem in co-endemic settings . Simultaneously , sequences were also screened using the TMHMM server ( http://www . cbs . dtu . dk/services/TMHMM/ ) to help confirm the presence , or absence , of signal peptides and transmembrane regions . Previous experience from our group and others has shown that the presence of signal peptides and/or transmembrane domains can significantly impede protein expression and solubility [23] . Based on this , each confirmed target construct was designed to exclude both the signal peptide and transmembrane domains , which in addition to the GST solubility tag was designed to aid expression of soluble proteins [24] . An additional selection criteria step was to determine the transcriptional status of the candidate genes . Blood stage messenger RNA was collected and analysed using the human red blood cell culture adapted P . knowlesi A1-H1 line [25] , grown in human blood obtained from the United Kingdom National Blood Transfusion Service . First strand synthesis was carried out using SuperScript IV Reverse Transcriptase ( RT ) ( Thermo Fisher Scientific ) using oligo d ( T ) 20 for priming ( RT+ ) according to the manufacturer’s instructions . As a negative control ( RT- ) , a second identical reaction was set up in parallel without the addition of the SuperScript IV RT . For PCR analysis of cDNA transcripts , RT+ and RT- samples were used as templates for transcript specific PCR primers for the candidate gene sequences alongside genomic DNA controls . In addition , both PkCTRP ( circumsporozoite protein and thrombospondin-related adhesive protein [TRAP]-related protein ) and PkCSP ( circumsporozoite protein ) , both shown to be pre-erythrocytic stage targets , were included in the panel as negative controls . Where possible , primer pairs were designed to flank introns so that amplicons from cDNA and gDNA could be distinguished . Sequences of primer pairs used to amplify each transcript are listed in S2 Table alongside the expected cDNA and gDNA amplicon size . Amplicons were PCR amplified using GoTaq Green Master Mix ( Promega ) and analysed on a 1 . 2% agarose gel ( S3 Fig ) . Four new constructs were designed ( Table 1 and Fig 2 ) based on three genes . Two sequences within SERA3 ( PKNH_0413400; nucleotide positions 73–419 ( Antigen 1 ) and 2476–2994 ( Antigen 2 ) based on the reference P . knowlesi H strain ) , SSP2/TRAP ( PKNH_1265400; nucleotide positons 1141–1500 ) and TSERA2 ( PKNH_0413500; nucleotide positons 178–751 ( Antigen 1 ) ) and were PCR amplified from P . knowlesi genomic DNA ( H strain ) . Vector compatible primers were designed for each completed target sequence ( S3 Table ) . Cloning of amplified sequences is as described previously [26] . Briefly , purified inserts were cloned into a TA vector ( pGEM-T Easy , Promega ) and sequence verified . Correct sequences were restriction digested and sub-cloned into a GST expression vector ( pGEX-2T , GE Healthcare ) and sequence verified before transforming into BL21 ( DE3 ) Escherichia coli expression cells ( Novagen ) . Validated expression clones were expressed automatically using an autoinduction media based on established protocols [27] . Following expression , protein purification was as described [28] . Briefly , GST-tagged proteins from clarified bacterial lysate were purified by affinity chromatography ( Glutathione sepharose 4B; GE Healthcare ) and fractions from each protein analysed ( Bradford assay reagent , BioRad ) to identify protein-containing fractions . Pooled protein positive fractions were dialysed against PBS and the protein content quantified ( Bicinchoninic acid assay ( BCA ) , Thermo Fisher ) . The dialysed purified proteins were analysed on a 4–20% gradient gel ( NuPAGE Bis-Tris acetate ) under denaturing conditions and visualised using the Coomassie blue staining method ( BioRad BioSafe , USA ) ( Fig 3 ) . The empirical sizes of each protein were calculated using the ImageLab ( BioRad ) software with the PageRuler prestained marker ( Fermentas ) as a reference standard ( Table 1 ) . Full-length sequence data from Plasmodb and construct-specific truncated sequences generated in-house using Sanger sequencing were mapped to an in-house reference sequence strain ( Pk-H strain ) using the Burrows-Wheeler Aligner ( BWA ) software ( v0 . 7 . 5a-r405 ) [32] . Single nucleotide polymorphisms ( SNPs ) ( S4–S8 Tables ) were called using the SAMtools ( v1 . 3 ) ( Sequence Alignment/Map ) software using default settings [33] and were filtered to increase stringency and target only high quality variants ( missingness<10% , mixed calls<10% ) . Custom Perl scripts identified overlap between these SNPs and each gene candidate . Variants were annotated using snpEFF ( v4 . 3i ) ( http://snpeff . sourceforge . net/ ) [34] to retrieve the amino acid position and type effect of the variant . Maximum likelihood phylogenetic trees were constructed from protein sequences using RAxML [35] with a fixed empirical substitution matrix and 200 bootstraps and was visualised using iTOL ( http://itol . embl . de ) [36] . The indirect enzyme-linked immunosorbent assay was performed to screen for antibodies to P . falciparum , P . vivax and P . knowlesi antigens using previously described methods [37] . Briefly , antigens were coated at 50 ng/well and serum samples ( diluted from frozen serum stocks ) assayed at 1/1000 dilution for both the P . knowlesi recombinants and the PvMSP1-19 ( donated as a kind gift from Tony Holder ) positive control antigen . Polyclonal rabbit anti-human IgG-HRP ( Dako , Denmark ) was used at 1/15 , 000 dilution and plates were developed using TMB ( One component HRP microwell substrate , Tebu-bio ) . All assays were performed in duplicate . Negative and positive controls , including blank ( buffer only ) wells were used to help standardise across assay runs . Values in excess of 1 . 5 CV between duplicates were considered fails and re-ran . Written informed consent was obtained from all study participants [18 , 38] . Samples were collected as part of a hospital-based clinical trial in Malaysia , Sabah ( www . clinicaltrials . gov: #NCT01708876 ) ( Fig 1 ) [38] . Serum samples were collected at Day 0 ( n = 92 ) , 7 ( n = 72 ) and 28 ( n = 77 ) following hospital admission , with drug treatment also taking place at Day 0 . The human research ethics committees of Malaysia ( MREC ) ( #NMRR-12-537-12568 ) , the Menzies School of Health and Research ( Australia ) ( #HREC-2012-1814 ) and the London School of Hygiene and Tropical Medicine ( UK ) ( #6244 ) approved the study . Twenty-six P . vivax-positive Ethiopian samples [18] based on positive responses to PvAMA1 and PvMSP1-19 were used as the P . vivax-positive , P . knowlesi-negative control group . In addition , 29 malaria naïve ( Public Health England; LSHTM ethics approval #11684 ) serum samples were used as the P . knowlesi-negative control group . For the scatterplot presented in Fig 4 , both negative control groups were compared to the responses from the P . knowlesi-exposed hospital clinical case samples . All samples used in the study were anonymised . Descriptive analysis of serological data was performed using STATA/IC 14 . 2 ( StataCorp LP , USA ) and PRISM ( GraphPad PRISM 7 ) . P values were generated using the Wilcoxon signed rank and Wilcoxon-Mann Whitney tests ( STATA/IC 14 . 2 ) . Scatter plots showing reactivity between P . knowlesi recombinant antigens and P . vivax MSP1-19 were created using STATA ( Fig 4 ) and dot plots showing reactivity to P . knowlesi recombinant antigens were created using GraphPad PRISM ( Fig 5 and S4 Fig ) . Final optical density ( OD ) values were obtained by subtracting blank OD values , reducing background reactivity . Cut off values for each P . knowlesi-specific antigen were calculated based on the average ODs of Public Health England negative control sera samples ± ( 3xSD ) . Ensemble boosted regression trees were fit to determine predictive power of antibody responses for classification of P . knowlesi exposure . To quantify uncertainty around estimates , 100 datasets were assembled including all seronegative individuals from the malaria unexposed population and an equal number of randomly selected P . knowlesi seropositive individuals ( from all time points ) . All models were fit using stratified 10-fold cross validation with model predictive ability assessed by the area under the receiver operating curve ( AUC ) . The learning rate was set at 0 . 001 and tree complexity set at 4 , to allow for interactions within the dataset . Contribution of responses to each antigen to models was assessed using relative variable importance as described by Elith et . al . [39] . In this method , the relative importance of individual predictor variables is calculated as the number of times a variable is selected for splitting , weighted by the squared improvement to the model and averaged over all trees and scaled to 100% . Boosted regression tree analysis was completed in R statistical software ( v 3 . 4 . 2 ) using the gbm package . Amino acid sequence alignments were generated using MUltiple Sequence Comparison by Log-Expectation ( MUSCLE ) ( http://www . ebi . ac . uk/Tools/msa/muscle/ ) [22] .
Sequences associated with known immunological markers in P . falciparum were selected based on existing evidence ( AMA1 [17 , 40] , MSP1 [40 , 41] , SSP2/TRAP [42] and SERA antigens [20 , 43] ) , by interrogating existing P . knowlesi databases[21 , 44] and supporting literature [45] ( Fig 1 ) . AMA1 is expressed in the micronemes of both the merozoite ( invasive asexual blood stage ) and sporozoite ( invasive pre-erythrocytive stage ) forms [17] . MSP1 is a major protein located on the surface of the merozoite[41] . SSP2/TRAP is also expressed on the surface of the sporozoite forms [42] , and the SERA antigens are soluble parasitophorous vacuole proteins [20 , 43] . Each sequence was processed using available in silico analytical tools ( Fig 1 ) . Gene3D [46] and SMART ( http://smart . embl-heidelberg . de/ ) were used to obtain domain prediction information for each gene which helped with the design of truncated fragments ( Fig 2 ) . This approach ensured that the design of truncated sequences properly accounted for the presence of any potential domains within each sequence , avoiding unintended truncation of domains which could impact on the solubility of the recombinant proteins . To ensure that expressed products would be specific for P . knowlesi , target sequences were interrogated multiple times using the BlastP algorithm [47] against both the Plasmodium specific ( Plasmodb: https://is . gd/XOs7vd [21] ) and non-redundant databases ( NCBI: https://is . gd/MzISez ) . Maximum likelihood phylogenetic trees were constructed using the P . knowlesi H reference strain , highlighting the relationship of each gene between Plasmodium species ( S1A–S1E Fig ) . Specifically , for both PvAMA1 ( bootstrap value: 100% ) and PvMSP1-19 ( bootstrap value: 87% ) , there is a strong relationship between different Plasmodium species , particularly between P . knowlesi and P . vivax ( S1A Fig ) , highlighted further by corresponding near identical amino acid alignments ( S2A Fig ) . Amino acid alignments were generated using available sequences for human-pathogenic Plasmodium spp . , which clearly highlight the level of sequence identity for both genes between P . knowlesi and P . vivax ( S2A–S2E Fig ) . Although the bootstrap value strongly supports the grouping of P . knowlesi with P . vivax and P . simiovale ( P . simiovale was used when data for P . ovale was lacking ) ( S2C–S2E Fig; bootstrap value: 100% ) , the alignments for SSP2/TRAP and the SERA antigens ( PKNH_0413400 and PKNH_0413500 ) , help identify regions specific for P . knowlesi ( S2C–S2E Fig ) . Based on these screens , any sequences showing high amino acid sequence identity to other Plasmodium spp . , specifically P . ovale , P . malariae , P . falciparum and P . vivax , were re-edited to focus on P . knowlesi-specific regions only , where possible . All the antigens were expressed in Escherichia coli as soluble products with final yields ranging from 11 . 9–20 . 5 mg/L ( Fig 3 , Table 1 ) . Based on their predicted molecular masses ( including the GST tag ) , SDS PAGE analysis of the purified proteins clearly suggested multimerisation of the purified products ( both monomer and dimer ) ( Fig 3 and Table 1 ) . The Coomassie stained profiles also illustrated that there is very little non-specific degradation of the recombinant proteins ( Fig 3 ) , suggesting that the proteins are stable under the conditions used . The protein sizes for each protein were larger than predicted , so called “gel shifting” when ran on SDS PAGE , which is not uncommon . All though not fully explained for all proteins classes evidence suggests that the presence of acidic residues , net hydropathy or protein aggregation can reduce the effectiveness of SDS in altering the charge , and therefore the migration of proteins through the gel [48 , 49] . The fact that all four protein constructs exhibited signs of protein aggregation supports the suggestion that aggregation may affect protein migration on polyacrylamide gels ( Fig 3 and Table 1 ) . By way of further validation each protein construct was sequence verified to confirm each sequence and the position of the stop codons to ensure that the departure from the predicted sizes was not due to sequence errors in the construct . The results of the Reverse Transcriptase-Polymerase Chain Reaction ( RT-PCR ) confirmed that both the SERA3 and TSERA2 candidate genes were actively transcribed in the blood stage ( S3 Fig ) . By contrast , SSP2/TRAP , a sporozoite stage along with the PkCTRP and PkCSP pre-erythrocytic stage controls , were negative by RT-PCR ( S3 Fig ) . The existence of three major subpopulations of P . knowlesi have been recently described , two associated with clinical human infections from separate macaque species reservoir hosts and the third from long-term laboratory isolates [50] . The presence of amino acid polymorphisms biased towards a single cluster would likely limit the utility of any reagents generated to function as P . knowlesi-specific , for all P . knowlesi-strains . Therefore , we characterised the presence of SNPs associated with the clusters , focussing on non-synonymous positions within the P . knowlesi-specific truncated constructs . S4 Table summarises both the synonymous and non-synonymous SNPs associated with the three clusters ( S5–S8 Tables shows the raw SNP data for all four constructs; SERA3 Ag1 , SERA3 Ag2 , SSP2/TRAP and TSERA2 respectively ) . For all antigens , the vast majority of the non-synonymous SNPs lie in regions not covered by the antigen design . By omitting the majority of these cluster-specific SNPs we hoped to avoid segregation of detectable antibodies according to the defined clusters . The relevance of these genetic clusters in the context of immunity , and the potential relevance to host preferences is yet to be defined . Serum samples were collected from 97 Malaysian adults and children hospitalised with P . knowlesi malaria on day of diagnosis ( day 0 ) , 7 and 28 days post-treatment . Hospital case samples were assayed by enzyme-linked immunosorbent assay ( ELISA ) using the P . knowlesi-specific protein panel . Ethiopian non- P . knowlesi malaria endemic children’s sera ( n = 26 ) and adult UK malaria naïve sera ( n = 29 ) were used as a P . knowlesi-negative control panel . The P . knowlesi-negative malaria endemic controls were all reactive with the PvMSP1-19 antigen due to previous P . vivax exposure . The malaria naïve controls showed no reactivity to any of the antigens tested ( Fig 4 , top row and S4 Fig ) ( SERA3 ag1 OD = 0 . 124; SERA3 ag2 OD = 0 . 131; SSP2/TRAP OD = 0 . 117; TSERA2 ag1 OD = 0 . 118 ) . With the exception of one weakly positive sample to SERA3 ag 1 and SSP2/TRAP , there was no other detectable antibody reactivity in the control group to the P . knowlesi-specific antigens ( Fig 4 ) . Antibody reactivity to all four antigens appeared to peak at day 7 ( Figs 4 and 5 and S4 Fig ) , although prevalence of antibody responses to SERA3 antigen 1 , PkSSP2 and TSERA2 antigen 1 remained relatively low ( 18 . 1% ( 13/72 ) ; 33 . 3% ( 45/72 ) and 43 . 1% ( 31/72 ) respectively ) ( Fig 4 , columns 1 , 3 and 4 ) , compared to SERA 3 antigen 2 ( 63 . 8% ( 46/72 ) ) . The PkSERA3 antigen 2 had a higher prevalence compared to controls at all time-points ( p<0 . 001 ) ( Fig 4 and S4 Fig ) . Antibody responses measured at day 7 and 28 to SERA3 antigen 2 demonstrated a significant increase when compared to day 0 ( p<0 . 001 for both comparisons ) , with fold changes as high as 50 observed for some samples ( Fig 5 ) . In comparison , the fold changes observed in serum responses to the TSERA2 antigen 1 ( day 7 and 28; p = <0 . 001 and p = 0 . 005 respectively ) , SERA3 antigen 1 ( day 7; p = 0 . 008 ) , and PkSSP2 ( day 7 and 28; p = 0 . 001 and p = 0 . 013 ) , although statistically significant had comparatively lower fold changes with a maximum of 15 ( Fig 5 ) . To assess the predictive ability of responses to these antigens to identify P . knowlesi exposed individuals , we used boosted regression tree analysis , an ensemble modelling method combining aspects of machine learning and statistical analysis shown to have strong predictive performance and reliable identification of variable importance [39] . Similar data-adaptive statistical models are increasingly being used for classification and identification of patterns in large datasets and have previously been applied to identify predictive antigens [51] . Although the samples size is small , boosted regression trees have been used for classification with similarly small training data sets [39] . To further compensate for the small dataset , we fitted 100 models of random samples of equal numbers of sero-positive and sero-negative samples within this training dataset and cross-validated these model predictions . Out of the 100 models fitted for randomly sampled equal numbers of exposed and unexposed individuals , the median classification accuracy was 88 . 9% ( IQR: 86 . 1–91 . 3% ) , calculated as the cross-validated area under the receiver operator curve ( AUC ) . Relative variable importance was calculated for all models . SERA3 antigen 2 contributed most to the models ( median relative variable importance: 50 . 4% ( IQR 43 . 3–61 . 4% ) ) , followed by TSERA2 antigen 1 , PkSSP2/TRAP and SERA3 antigen 1 ( Fig 6 ) .
P . knowlesi is a naturally occurring infection of long-tailed and pig-tailed macaques , historically associated with forested areas of Southeast Asia [52] . Increased deforestation of their natural habitat is thought to have led to increased interaction between macaques and the human population in endemic areas [53] . Changes in village level forest cover and historical forest loss has been associated with an increase in P . knowlesi clinical cases in Sabah [54] , with malaria caused by P . knowlesi increasingly reported in Southeast Asia [8] . Conversely , there has also been a steady decline in the prevalence of P . falciparum and P . vivax infections in the same region [55] . The recent efforts of the malaria community towards achieving malaria elimination means that tools to help monitor the impact and effectiveness of intervention strategies are an urgent requirement [56] . The development of species-specific tools for P . knowlesi would allow accurate assessment of the levels and geographical limits of infection with this zoonotic species [57] . There is an urgent need to develop a comprehensive discovery strategy to help identify P . knowlesi unique antigenic markers of exposure in order to further characterise this organism and develop stronger and better identification methods . Currently , there are no specifically designed biomarkers for the serosurveillance of P . knowlesi infections . Recombinant proteins are available [PkCSP [58] , PkAMA1 [59] , PkDBP [60] , PkSPATR [15] , PkLDH [61] , Pk1-Cys peroxiredoxin [62] , Pk knowpains [63] , PkMSP1-42 [64] , PkMSP1-33 [65] , PkMSP1-19 [66] , Pk tryptophan-rich antigens ( PkTrags ) [67] , PkMSP3 [68] and PkSBP1 [69] , but are limited in number and are generally not species-specific . As a result , their utility as serological diagnostic tools is generally secondary to their original design . The reported level of amino acid sequence conservation to other Plasmodium spp . in some currently available P . knowlesi proteins is > 60% across large stretches of continuous sequence . Such reagents could not be specific to P . knowlesi [70] and would be unable to reliably discriminate between antibody responses to different parasite species in co-endemic settings . High levels of amino acid identity ( 83% ) between PvMSP1-19 and PkMSP1-19 , meant we were unable to use these reagents to dissect the species-specific immune responses due to the inevitable cross-reactive antibody responses . This is consistent with a proportion ( 48 . 9% ( 45/92 ) ) of the confirmed P . knowlesi-exposed clinical samples in this study reacting with PvMSP1-19 at day 0 , although it is unknown whether these participants had previously been exposed to P . vivax . However , this limitation simply reflects the paucity of available serological reagents for use in assessing exposure to infection , a deficit this study aims to address . Although the small number of clinical case samples do not give sufficient statistical power to assess either the duration of antibody responses to the panel of antigens or population-level exposure , the P . knowlesi clinical case samples represent a unique dataset with which to validate the immunogenicity of our antigen panel . The use of the boosted regression tree model was able to discriminate between P . knowlesi exposed and unexposed individuals for the purposes of classification of seropositivity rather than to assess individual-level risk factors . While this dataset is sufficient for classification as exposed or unexposed , it is not sufficiently large enough to stratify by age , gender or previously reported malaria status . In order for us to assess these types of risk factors , we would first need to apply an approach ( using known negatives , mixture or probability models ) to classify antibody responses as sero-positive or sero-negative and then assess risk factors within the population . Based on this result the PkSERA 3 antigen 2 recombinant was used to survey ~2500 samples across three site; Limbuak , Pulau Banggi and Matunggung , Kudat , Sabah , Malaysia and Bacungan , Palawan , the Philippines [71] . One of the key elements from this study using this reagent was the indication of community level patterns of exposure that differed markedly from reported cases , with higher levels of exposure among women and children [71] . The panel of reagents developed for this study focussed on immunologically relevant orthologous targets previously described in P . falciparum . The serine repeat antigen ( SERA ) family had previously attracted attention as a source of both drug and vaccine candidates [72] . In P . falciparum , SERA 5 is the most abundant parasitophorous vacuole protein and is essential to blood stage growth of the parasite [73] , with antibodies against this antigen thought to inhibit parasite growth [74] . Although possessing a papain-like enzymatic domain , recent evidence suggests that the protein plays a non-enzymatic role [73] . SERA 3 has also been shown to be a highly immunogenic antigen with an important , although not essential role in the erythrocytic cycle [75] and has also been implicated as having a role in liver stage merozoite release in P . berghei [76] . Similarly , evidence for the sporozoite surface protein 2 ( SSP2/TRAP ) suggested an immunogenic antigen involved in protection from disease in mice [77] . Although we were unable to confirm active transcription of SSP2/TRAP due to the lack of available material , we were able to validate active transcription of both the SERA3 and TSERA2 candidate genes . Collectively , the evidence provided by studies on Plasmodium supports the design of seroepidemiology tools based on these targets . Despite the targeted approach used in designing the recombinant constructs , the SERA3 antigen 2 construct was by far the most promising candidate . The differences in the performances of the antigens could be due to a number of factors: ( 1 ) variation in the inherent immunogenicity of the regions selected , ( 2 ) variations in the expression status of the P . knowlesi antigens compared to P . falciparum or ( 3 ) the loss of immunoreactive epitopes due to the truncation of the protein . There are a number of potential limitations of the study . The small sample size of the clinical samples used prevented detailed analysis of the samples , such as monitoring the impact of factors such as age , on the profile of reactivity to the reagents under test . In addition , the lack of repeated samples per individual ( i . e . longitudinal samples ) prevented us from investigating the longevity of antibody responses to each target , across individuals and age groups . The availability of supporting biological information on P . knowlesi , such as functional data , transcriptional or RNA seq data would have helped with the rational selection of additional candidates for further study and the design recombinant tools . This is the first study to describe the development a panel of P . knowlesi-specific serological tools using freely available in silico software . We have demonstrated the importance of targeting species-specific reagents at the amino acid level and highlighted the potential of such proteins as serosurveillance tools . Using these tools we have been able to measure specific immune responses to these reagents and described the change in antibody profile following treatment . As such , we have already demonstrated the utility of the SERA3 antigen 2 reagents as a potential seroepidemiological tool . Studies are also currently in development to expand the existing panel of P . knowlesi species-specific reagents to identify additional serological tools . Beyond this we envisage employing high throughput antigen discovery approaches such as the protein microarray to help identify additional important targets of immunity [51 , 78] . Further validation of the SERA3 antigen 2 at the population level has recently been performed [71] . Further studies are also planned to characterise the wider immunoglobulin responses , such as IgG subclasses and IgM , to these and future antigens . | Malaria caused by Plasmodium knowlesi is the most common form of the disease in Malaysia . The parasite is transmitted from monkeys to humans via the bite of an infected mosquito , with the resulting P . knowlesi infection potentially leading to severe symptoms and in some cases , death . Although adult males working close to areas with infected monkeys are at the greatest risk of infection , the true extent of the geographical boundaries of P . knowlesi transmission is as yet unknown . The ability to measure antibodies to infection is a powerful technique that would help to address this deficit . However , currently available recombinant proteins lack the required specificity for this role . Here , we have developed a panel of recombinant proteins for eventual use as serological tools , strongly supported by robust statistical methods . We envisage that these tools will complement existing approaches to identifying the geographical limits of P . knowlesi transmission . | [
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"pa... | 2018 | Identification and validation of a novel panel of Plasmodium knowlesi biomarkers of serological exposure |
In recent years the functions that the p53 tumor suppressor plays in human biology have been greatly extended beyond “guardian of the genome . ” Our studies of promoter response element sequences targeted by the p53 master regulatory transcription factor suggest a general role for this DNA damage and stress-responsive regulator in the control of human Toll-like receptor ( TLR ) gene expression . The TLR gene family mediates innate immunity to a wide variety of pathogenic threats through recognition of conserved pathogen-associated molecular motifs . Using primary human immune cells , we have examined expression of the entire TLR gene family following exposure to anti-cancer agents that induce the p53 network . Expression of all TLR genes , TLR1 to TLR10 , in blood lymphocytes and alveolar macrophages from healthy volunteers can be induced by DNA metabolic stressors . However , there is considerable inter-individual variability . Most of the TLR genes respond to p53 via canonical as well as noncanonical promoter binding sites . Importantly , the integration of the TLR gene family into the p53 network is unique to primates , a recurrent theme raised for other gene families in our previous studies . Furthermore , a polymorphism in a TLR8 response element provides the first human example of a p53 target sequence specifically responsible for endogenous gene induction . These findings—demonstrating that the human innate immune system , including downstream induction of cytokines , can be modulated by DNA metabolic stress—have many implications for health and disease , as well as for understanding the evolution of damage and p53 responsive networks .
The p53 master regulator is responsive to a variety of DNA metabolic stresses resulting in induction or repression of over 200 genes as well as several LINC- and micro-RNAs [1] , [2] . In its role as tumor suppressor and “guardian of the genome” many of the target genes in humans influence cell cycle progression or apoptosis . Over the past decade the p53 network has been extended to transcriptional regulation of genes associated with a wide variety of biological functions including DNA repair , angiogenesis , cellular metabolism , autophagy , stem cell renewal , fertility , differentiation and cellular reprogramming . To better understand the broad role that p53 can play in human biology , we have pursued “functionality rules” for identifying target response element ( REs ) sequences where p53 can directly influence transactivation . Using in vivo transactivation systems based in yeast and human cells as well as binding in human cell extracts [3] , [4] , we recently found that functionality of the binding consensus RRRCWWGYYYnRRRCWWGYYY ( R , pyrimidine; Y , pyrimidine; W , A or T; n , spacer of 0 to 13 bases ) is greatest when there is at most a single base spacer and if “WW” is “AT . ” Furthermore , we defined functionality for half-sites ( RRRCWWGYYY ) and found in cis synergy when another transcription factor , estrogen receptor , was bound nearby ( for a description of noncanonical REs including half-sites see [3] , [5] and summary in [6] ) . Based on these functionality rules , we found that the evolution of p53 control of at least one gene family , DNA metabolism and repair , is limited to primates [7] . Furthermore , we identified single nucleotide polymorphisms in REs that are predicted to modify responsiveness of genes to p53 mediated stress [8] . Our functionality studies of canonical and noncanonical promoter p53 REs suggested that p53 may have a role in human Toll-like receptor ( TLR ) expression . We had reported that a single nucleotide polymorphism ( ChrX:12923681 , rs3761624 A/G ) in the promoter of the TLR8 gene creates an RE that can be targeted by p53 in yeast and cell line reporter systems [8] although we could not detect endogenous expression of the TLR8 gene . In addition , the TLR3 gene in epithelial cancer cell lines was found to be induced by p53 following exposure to 5-fluorouracil ( 5FU ) [9] . Innate immunity is paramount to infection and tissue injury responses [10] . The TLR gene family mediates innate immunity to a wide variety of pathogenic threats . The TLRs recognize conserved exogenous pathogen-associated molecular patterns ( PAMPs ) and endogenous danger-associated molecular patterns ( DAMPs ) [11] , while adaptive immune receptors ensure specific antigenic responses through clonal expansion . TLR function is associated with circulating immune cells such as monocytes and dendritic cells , tissue phagocytes that include alveolar macrophages and with nonimmune cells ( such as epithelial cells of gut , skin , lung , etc . ) that are exposed to environmental injury or pathogens . The roles of TLRs in mammalian biology are continuously expanding and they are now understood to function in such diverse processes as inflammation , cell differentiation and cell survival [11] . Altered TLR function is implicated in human diseases such as systemic lupus erythematosus , inflammatory bowel disease ( IBD ) and cancer [11] , [12] , and agonist/antagonist manipulations of the TLR system are being pursued to alleviate various diseases ( reviewed in [13] ) . Given the varied functions of TLRs , factors that regulate their expression are expected to shape immune responses [14] . However , there are few examples of TLR gene induction and these are limited to specific TLR-stimulus interactions [15] , [16] . The TLR-dependent innate immune response is thus generally considered to be “hard-wired . ” Although environmental stress can influence gene expression indirectly , e . g . , through epigenetic mechanisms , to our knowledge there are no reports of TLR induction by environmental factors in primary human cells [15] , [17]–[20] . These collected observations led us to investigate the responsiveness of TLR genes as a class to common DNA stressors . We have examined expression of the entire TLR gene family following exposure to anti-cancer agents that induce the p53 network in primary immune cells obtained directly from human subjects as well as the impact on downstream cytokines . Agents were applied to T-lymphocytes and alveolar macrophages ex vivo . Prior to this there were no reports that we are aware of addressing DNA damage-induced responses of any TLR genes in cells directly associated with innate immunity . As part of this study , we establish that in the evolution of the TLR responses , the p53-mediated expression of TLRs is unique to primates .
We selected ionizing radiation ( IR ) , 5FU and Doxorubicin ( Doxo ) because these agents represent a cross-section of DNA metabolic stressors , such as damage or replication inhibition , that may occur endogenously or environmentally and are well-known to activate p53 and its network of target genes . Furthermore , these agents are often employed in cancer treatments . To address TLR responses ex vivo in primary human immune cells , T-lymphocytes were expanded by phytohemagglutinin ( PHA ) stimulation of peripheral blood mononuclear cells ( PBMC ) freshly isolated from the blood of healthy human volunteers ( see Figure S1A; for demographics of volunteers see Table 1 ) ; alternatively , T-lymphocytes were obtained from the PBMC fraction using anti-CD3 antibody-based purification as described in Figure S1B . Presented in Figure 1 are the expression responses of the entire family of TLR genes in stimulated lymphocytes for 18 subjects ( except where noted ) following exposure to IR ( 4 Gray ) , 5FU ( 300 µM ) , and Doxo ( 0 . 3 µg/ml ) ( Figure 1A , 1B , and 1C , respectively ) . These responses are relative to no treatment controls and normalized to 18S ribosomal DNA . As additional controls , we also examined the expression of the beta-glucuronidase , GUSB , and actin genes ( Figure S2 ) that are considered to be nonresponsive to chromosomal and/or p53 stress; they showed little variation after doxorubicin and nutlin exposure . The doses chosen were similar to therapeutic doses or doses commonly used in the literature . By way of comparison , we also examined the response of the cyclin-dependent kinase inhibitor gene p21WAF1 ( CDKN1A ) , a prototypical p53 target gene induced by these agents in a variety of human cells . Notably , these results establish that expression of all TLRs can be responsive to DNA metabolic insults , even exceeding p21 induction . However , there is considerable variability in the individual responses between subjects , TLRs , and treatments as summarized in the “box and whiskers” presentation of Figure S2 . For example , the IR induction of the ten TLR genes varies from 1- to over 4-fold for each TLR except TLR3 ( Figure 1 and Figure S2 ) , which is not detected in the PHA-stimulated lymphocytes of most subjects . This agrees with the large differences in expression of IR-inducible genes between human lymphoblastoid cell lines [21] . The variability in gene expression among the 18 subjects can represent a continuum of responses ( e . g . , TLR4 expression after IR ) , or exhibit more of a binary induction pattern ( e . g . , TLR8 response after IR; see Figure 1 ) . Specifically , for the TLR8 gene , approximately half the subjects respond strongly and half exhibit a much lower response , a finding that appears to be genetically determined [8] , as discussed below . Different agents also elicit differential TLR gene expression responses . For example , TLR1 is responsive to IR by more than 2-fold in most subjects , but generally there is only a small level of induction in response to 5FU or Doxo . To better assess each TLR gene response across subjects , we portrayed the induction of TLRs for each subject in a format akin to a heat map , as described in Figure 1E for IR , where 2 . 5-fold induction is indicated in red and <2 . 5 fold in black ( this value corresponds to the minimal p21WAF1 response for nearly all agents and subjects; see Figure S3 for other agents ) . Among three subjects ( BS4 , 5 , 21 ) , all TLRs were induced at least 2 . 5-fold by IR exposure , while only 1 ( BS2 ) , 2 ( BS9 ) or 3 ( BS8 , 13 ) TLRs were induced in four others . There is no obvious pattern to the differences between TLRs or subjects for IR as well as for 5FU and Doxo , although it is clear that TLR1 is generally much less responsive to the last two agents ( Figure 1 , Figures S2 and S3 ) . Consistent with TLR3 induction by DNA damage in cancer cell lines [9] , its expression was induced in 5 out of 7 subjects; however , TLR3 gene expression was not detected in 11 subjects ( Figure 1 and Figure S3 ) . Even though all TLRs are responsive in at least one subject , only subjects BS4 , 5 and 21 exhibited high responses for most TLRs for all agents tested ( Figure S3 ) . We also addressed statistically the responsiveness of the population as a whole ( 18 subjects , Figure 1A , 1B , 1C , except TLR3 which was expressed only in 7 subjects ) to IR , 5FU , and Doxo employing a t-test ( see Material and Methods ) to assess the ability of each agent to induce expression of each TLR gene in the population . As expected , induction of the p21 gene in the population was highly significant for all the treatments ( p<0 . 0001 ) . While there was variation between individuals , all the TLR genes in the population were responsive to IR ( p<0 . 0001 ) . A similar likelihood of responsiveness ( p<0 . 0001 ) was observed for 5FU and Doxo treatment for all but the TLR1 and 7 genes ( p<0 . 003 ) and TLR9 ( p = 0 . 011 for 5FU and 0 . 053 for Doxo ) ; however , there were substantial responses for the TLR 9 genes of several individuals . Collectively , these results suggest that multiple pathways may influence TLR expression after DNA damage , and these are specific to the mode of chromosomal stress . Since p53 mediates many DNA damage responses and given our finding that all TLRs respond to at least one DNA metabolic disruptor , we sought to examine in more detail the ability of p53 to induce TLRs . PHA-stimulated lymphocytes were exposed to the p53 activator nutlin ( 10 µM ) to increase p53 levels . Stabilization of p53 normally occurs through stress-induced post-translational modifications affecting both p53 and MDM2 [22] . Nutlin can directly prevent p53 destruction by interfering with the MDM2-p53 interaction [23] . As expected , all treatments activated the p53 pathway in the lymphocytes of all individuals as assessed by immunodetection of p53 and p21 proteins ( Figure S4; Figure 1F is a representative example ) . As shown in Figure 1D , Figures S2 and S3 , nearly all TLR genes in most subjects are induced over 2 . 5 fold after nutlin treatment , except for TLRs 1 and 7 . The TLR1 gene much less responsive while the TLR7 gene is generally repressed by p53 induction . Interestingly , TLR7 is induced by the DNA damaging treatments , suggesting p53-independent induction mechanisms . While TLR3 is not detected in 11 subjects , it is induced by nutlin in 5 of the remaining 7 subjects . Although there was variation between individuals , we found that most of the TLR genes in the population were responsive to nutlin . There was no statistically significant induction of the TLR9 gene ( p = 0 . 11 ) ; however , there was statistically significant repression of the TLR7 gene ( p = 0 . 006 ) . The expression of all the remaining TLR genes was significantly induced in the population ( p<0 . 003 ) . A role for p53 in TLR induction is further indicated by the finding that co-treatment with the p53 inhibitor pifithrin-alpha [24] represses the induction of TLRs by IR and other agents ( Figure 1G for IR; Figure 2 for Doxo , 5FU and nutlin ) . Notably , samples taken on two occasions from the same individuals in a 4 month-interval , blind study revealed a striking consistency in TLR gene expression patterns ( Figure 3 ) , thereby excluding technical or temporal variables as significant sources of variability in findings . The p53 inducibility of most TLRs led us to investigate if p53 could act directly on transcription . As discussed in the Introduction , the commonly accepted consensus target RE consists of two decamers composed of ( RRRC A/T A/T GYYY ) separated by up to 13 bases ( reviewed in [25] , [26] ) . We had established rules for predicting in vivo functionality of p53 REs including separation of <2 bases and dependence on a core CATG ( summarized in [3] , [6] ) . Among several potential p53REs identified by in silico search and bioinformatic tools , we found that each TLR has at least one p53 target sequence within ±5 kb of the transcription start site that is predicted to provide at least a weak-to-modest p53 responsiveness ( Table 2 contains the sites predicted to have the greatest functional responses ) . As shown in Figure 4A , each of these target sequences ( TLR7 was not examined because we did not find any p53-like binding sequences ) could support p53-driven transcription of a luciferase reporter co-transfected , along with a p53 expression plasmid , into p53 null H1299 cells . For half of the TLRs , the induction levels were comparable to those obtained with the moderately responsive p53 target response element of AIP . Based on these results , we examined whether the functional REs of Figure 4A are indeed targets of induced p53 using chromatin immunoprecipitation ( ChIP ) analysis . We chose a representative subject BS4 whose lymphocytes exhibited strong Doxo-induced expression ( Figure 1 ) for all TLRs except TLR1 and TLR7 . As shown in Figure 4B , there is clear binding following exposure of cells to Doxo at the established p53RE on p21WAF1 as well as at the TLR2 , 4 , 5 , 6 , 8 , 9 and 10 sequences described in Figure 4A . Thus , all TLR genes are subject to DNA-damage associated transcriptional regulation and most are directly targeted by p53 . ( We also identified other p53-like sequences in the analyzed regions; however , these were predicted to be less functional than the sequences examined ) . Furthermore , a direct role for p53 in TLR gene expression was demonstrated using p53 null SaOS2 osteosarcoma cells that have p53 under the control of a tetracycline inducible promoter [27] . Expression of wild-type p53 , but not the transcriptionally inactive G279E mutant protein , results in induction of all TLRs except TLR7 ( TLR8 was not tested because it is not expressed by SaOS2 cells; Figure S5 ) . We also determined whether p53 induction of TLR genes by nutlin can lead to a corresponding increase in protein . ( Nutlin was chosen because it typically led to the largest increase in p53 , as shown in Figure S3 . ) Using western blot analysis with TLR specific antibodies ( see Materials and Methods ) , the levels of TLR2 and TLR5 proteins were examined in the membrane fraction from stimulated lymphocytes of volunteers BS25 and B26 ( sufficient cells were obtained from these subjects to enable the protein measurements; the TLR2 and TLR5 proteins were only detected in the membrane fraction as noted in the Material and Methods ) . Nutlin treatment resulted in a substantial increase in both proteins in the membrane fraction which corresponded well with induced expression of the TLR 2 and TLR5 genes , as described in Figure 5 . To address the generality of DNA damage-induced TLR expression and the role of p53 , we also examined alveolar macrophages since they are well-known to play a pivotal role in pulmonary innate immunity and are susceptible to DNA damage from environmental exposures including cigarette smoke and particulate matter [28]–[30] . The cells were collected by bronchoalveolar lavage of healthy human subjects and treated ex vivo . Among 6 subjects , there were considerable differences between TLRs with little or no induction of TLR1 and TLR6 by Doxo or nutlin , as described in Figure 6 , and a dramatic induction of TLR9 by Doxo , suggesting cell-specific factors that determine damage-TLR response profiles . In general , fewer of the TLR genes in the macrophages responded to Doxo and nutlin as compared to lymphocytes and the response levels were not as large . Induction of cytokines is a prototypical functional response that is downstream of TLR activation [11] , [31] , [32] , and increases in TLR expression can enhance PAMP or DAMP-induced signaling and innate-immune mediated effects . As shown in Figure 7 , pretreatment of freshly isolated CD3+ T-lymphocytes ( see Figure S1 ) with nutlin to induce TLR2 by p53 resulted in 2- to 5-fold increased expression of interleukin IL-1 and IL-8 by the TLR2 ligand PAM3CSK4 , suggesting that TLR induction by p53 can directly affect innate immune function . ( The TLR2 induced cytokine response was examined because of our observation that T-lymphocytes from all subjects experience nutlin-induced TLR2 expression , as shown in Figure 1 and Figure S3 ) . The magnitude of cytokine induction varied between subjects ( see Figure S6 ) , suggesting that other factors , besides p53-induced gene expression , affect innate immune responsiveness . Our previous report [8] of a SNP in a potential p53 response element of the TLR8 promoter ( AAACAT ( G/A ) TCa; see Table 1 ) provided a unique opportunity to directly assess the relationship between p53 and TLR expression . While we had established large differences in the potential p53-responsiveness of the two SNP sequences [8]; see Figure 4A for the “positive” , p53-responsive G-allele ) , their cellular impact could not be assessed because the TLR8 gene was not expressed in the human cell systems previously examined . Unlike the results with cell lines , we observed a dichotomous response in TLR8 expression in lymphocytes following IR and nutlin treatment ( Figure 1 ) , and in alveolar macrophages following nutlin exposure ( Figure 6 ) , with some subjects showing low TLR8 induction and others robust induction . We , therefore , determined which p53 response element alleles were present and their relationship to TLR8 induction . Since TLR8 is located on the X-chromosome , males carry only a single copy and females 2 copies ( the specific alleles for each volunteer are described in Table 1 ) . As shown in Figure 8 and Figure S3 , the ability of nutlin and IR to induce TLR8 correlates well with the presence of the G-allele . The frequency of this allele in our study is 0 . 43 ( 13/28 , which corresponds to the frequency in the general population ( Table 1 ) . Importantly , TLR8 induction was always high when only G-alleles were present ( both alleles in females or the single allele in males ) and absent if there were only A-alleles . However , among the 3 female subjects that were heterozygous for these alleles , only one responded poorly to both nutlin and IR . Possibly , the variability between female subjects heterozygous for the SNP is related to X chromosome inactivation . While these results demonstrate differences in the impact of the polymorphic alleles , the differences do not extend to 5FU or Doxo , suggesting that other p53-related target sequences are responsible for the induction or involvement of additional p53-independent mechanisms ( Figure 8 and Figure S3 ) . Notably , these findings with TLR8 provide the first direct demonstration , to our knowledge , in humans of the ability of a specific RE in a p53 target gene to drive transcription . The SNP-associated differences in expression may provide opportunities to identify other factors in human primary tissue that determine the ability of specific REs to support p53-driven transcription [33] , [34] . Although there are SNPs in the REs of TLR5 and TLR6 , as described in Table 2 , they are predicted to have no functional impact ( see [6]–[8] ) . Our results with primary human cells have been confirmed for DNA damage induction of TLRs ( to be presented elsewhere ) in several human cell lines including the previously reported TLR3 [9] . However , as shown in Figure S7 , they do not extend to murine cells . The TLR responses to Doxo , 5FU and IR were found to be small in mouse peritoneal elicited macrophages , bone marrow-derived macrophages , and embryonic fibroblasts ( MEFs; except for TLR9 in MEFs ) . The low induction level appears to be p53-independent based on results with nutlin and on the similar responses in p53-positive and -null MEFs . These results are consistent with the lack of sequence conservation between humans and rodents of functional p53 response elements in TLR genes ( Figure S8 ) even though the coding sequences are well-conserved . Although we were able to identify p53 RE-related sequences in the vicinity of the transcription start site of several mouse TLRs , none were predicted to be functional p53 targets .
Cellular stress and the inflammatory response are intricately linked pathways subject to endogenous and exogenous challenges . Here , we provide the first evidence that DNA stressors may also modulate inflammatory responses at a fundamental level in primary human cells , namely , by altering TLR expression . All members of the human TLR gene family tested ( TLR1-10 ) are responsive to at least one disruptor of chromosome metabolism . While there are considerable variations between individuals , for each TLR gene there is a group of several subjects that is responsive to at least one of the stressors ( 5FU , doxorubicin , IR and/or nutlin ) . Note , for example , that there is at most a low level of TLR1 induction by 5FU and nutlin in the primary cells from 18 subjects and there is even a general repression of TLR7 by nutlin . We thus propose that , contrary to the paradigm that innate immunity is hard-wired , the TLR system in humans actually has a complex , robust responsiveness to environmental conditions that challenge the integrity of the genome . We establish that induction of TLRs by DNA damage is a class effect that in many cases can be mediated by p53 ( including repression of TLR7 ) and prevented by the p53 inhibitor pifithrin . In support of these findings , several potential p53 binding sites were identified in the proximity of the transcription start sites for all TLRs , except TLR7 . The new p53REs were identified using functionality rules to predict p53RE responsiveness [6] , [7] . These were confirmed with reporter assays and by the binding of p53 in human lymphocytes following stress activation . In addition the functional TLR8 SNP in the p53 target response element directly confirms a role for p53 . Interestingly , for TLR2 and TLR10 , the most functional sites were predicted to be noncanonical , containing only a half–site p53RE ( i . e . , one decamer ) . Previously , the genes FLT1 [35] and RAP80 [36] were shown to be directly controlled by p53 through half-site REs and several other target genes have been identified that are predicted to be regulated by noncanonical p53 RE ( summarized in [6] ) . The inclusion of TLR genes expands the universe of genes and biological functions that fall within control of the p53 master regulator . The addition of the set of TLR genes identifies a biological gene niche regulated by the p53 master regulator that is distinct from rodents whose TLR genes appear to lack functional p53 REs . It is interesting that a similar niche was identified for all the DNA metabolic genes that are p53-responsive in humans in that none of them respond to p53 in rodents [7] , [36] . p53 provides a rapid , integrated signaling mechanism for increasing or maintaining gene responses to acute and chronic DNA damage stress . In a more general sense , we suggest that the evolutionary inclusion of sets of genes with related functions may provide an efficient means of dealing with sudden , temporary challenges that can result from DNA damage and/or DNA damage itself may be a modulator of broader biological threats , as for the case of infection . We speculate that there may be feedback loops that integrate this newly identified role for p53 in DNA damage and innate immune responses , as described in Figure 9 . In this scheme , DNA damage from environmental agents or potentially from TLR-elicited reactive oxygen species ( ROS ) may amplify the responsiveness of the innate immune system by promoting TLR upregulation . p53 itself displays a variety of roles in mediating ROS signals [37] , [38] . On the other hand , TLR upregulation may also sensitize tissues to maladaptive aseptic inflammation in the setting of environmental injury . For example , tissue injury is reported to induce inflammation through release of DAMPs that act upon TLR2 , TLR4 , and TLR9 [39] , [40] . We speculate that , during tissue injury , upregulation/activation of TLRs may serve as a cell-fate counterbalance to p53-mediated pro-apoptotic responses by leading to activation of the pro-survival factor NF-κB [39] . This may be particularly relevant to cancer therapy , as stimulation of TLR5 , 7 , 8 , and 9 have been shown to modify cellular radio- and chemoresistance [41] , [42] . These findings demonstrating that p53 can increase an inflammatory response differ from the generally held view relating to the antagonistic affect of p53 on inflammation directed by NF-κB [41] . However , the mechanism here is quite different in that it involves the p53-mediated increase in a receptor that translates ligand interactions into cytokine responses . Our results may be particularly relevant to diseases in which variations in T-cell function can impact pathogenesis , such as autoimmune disease , asthma , and IBD . For example , recent reports suggest that intestinal inflammation can induce genotoxicity in circulating leukocytes [43] , while increased DNA damage is also detected in lymphocytes obtained from rheumatoid arthritis patients [44] . The heightened pro-inflammatory status in such patients , as well as the common finding of systemic or multi-organ inflammation during exacerbations of autoimmune disease , might be mediated by circulating immune cells which have suffered DNA damage during passage through inflamed tissues . Beyond the TLR gene and cell subset variability in response to DNA damage and p53 activation , we also demonstrate considerable inter-individual variation . This variability may be relevant to inter-individual differences in susceptibility to a wide spectrum of diseases and therapies . Our results suggest that various anti-cancer agents may yield different patterns of responses across TLRs and between subjects . Since TLR ligands are increasingly used as adjuvants for vaccines ( TLR4 and TLR9 ) and cancer treatment ( TLR3/7/9 ) ( reviewed in [45] ) , the ability to detect and predict genetically determined inter-individual variability in TLR induction may prove a useful therapeutic tool . Future studies are warranted to determine whether single agents such as nutlin , or even factors that induce chromosome stress , may serve as useful immune adjuvants through manipulation of TLR expression in human subjects .
Healthy adult volunteers were recruited to the NIEHS Clinical Research Unit and underwent phlebotomy . Subjects were excluded if they had a history of recent infection , were on anti-inflammatory medications , or tested positive for hepatitis B , C or HIV . Up to 300 ml of whole blood were withdrawn from an antecubital vein into citrated tubes . Lymphocytes were isolated using percoll ( Sigma ) and anti-CD3-coupled Magnetic Beads ( Miltenyi Biotec ) as per manufacturer's protocol . Cell purity was >98% after percoll/magnetic bead isolation based on flow cytometry . We maintained lymphocytes in RPMI supplemented with 10% FBS . For T cell stimulation , cells were activated with phytohemagglutinin-M ( PHA , Invitrogen , 3% vol/vol ) for 72 h . The total number of lymphocytes available per treatment conditions after PHA was typically around 10 million cells or less . Cells were treated starting at 48 h post PHA addition and cell cultures were harvested 24 h later . Freshly isolated CD3+ cells were treated with nutlin for 20 h or DMSO as a vehicle control , then washed and exposed to TLR1/2 ligand PAM3CSK4 ( 1 µg/ml ) at 1×106 cells/ml . Total yield of CD3+ cells from a single subject was typically around 10–15 million cells or less . Protocol and procedures were approved by the NIEHS Institutional Review Board . Healthy , nonsmoking male volunteers , 18 to 40 yr of age , underwent fiberoptic bronchoscopy with lavage to procure alveolar macrophages . The screening procedures for each subject included a medical history , physical examination , and routine hematologic and biochemical tests . None of the subjects had a history of asthma , allergic rhinitis , chronic respiratory disease , or cardiac disease . Subjects were excluded from the study if they had suffered a recent acute respiratory illness and were asked to avoid exposure to air pollutants such as tobacco smoke and paint fumes . A fiberoptic bronchoscope was wedged into a segmental bronchus of the lingula . Six 50-ml aliquots of sterile saline were instilled and immediately aspirated . The procedure was repeated on the right middle lobe , again using 300 ml of saline . Samples were put on ice immediately after aspiration and centrifuged at 300 x g for 10 min at 4–8°C . Cells from all aliquots were pooled , washed twice with RPMI 1640 , and re-suspended in RPMI 1640 at 1 . 0×106/mL . Total yield was typically around 10–15 million cells or less . Around 2 . 0×106 cells per well were seeded in a 12 well plate . After 2 hr , cells were washed twice with warm PBS and 2 ml of growing media was added to each well . Cells were then treated with nutlin or doxorubicin . Cells were harvested 24 hr post-treatment . The protocol and consent form were approved by the University of North Carolina School of Medicine Committee on the Protection of the Rights of Human Subjects . Prior to participation in the study , subjects were informed of the procedures and potential risks and each signed a statement of informed consent . H1299 lung cancer cells ( American Type Culture Collection ) were routinely maintained following standard conditions and procedures for culturing mammalian cells . All cultures were incubated at 37°C with 5% CO2 . p53 tetracycline inducible SaOS2 TET-off cell lines expressing the wild-type or the G279E mutant protein were cultured as described previously [27] . p53 expression was kept “off” by 2 mg/ml doxycycline ( Clontech ) . To induce the p53 expression , cells were washed 3X with phosphate-buffered saline ( PBS ) and placed in medium lacking doxycyline during 24 h . For drug treatment and p53 activation , cells were incubated with doxorubicin ( Sigma 0 . 3 µg/mL ) , nutlin3 ( Sigma , 10 µM ) and 5-fluorouracil ( Sigma , 300 µM ) . For ionizing radiation treatment , cells were irradiated at 1 . 56 Gy/min from a Shepherd cesium irradiator in PBS at room temperature at final dose of 4 Gy . Where indicated cells were also pretreated 2 h with p53 inhibitor pifithrin-alpha ( Sigma , 40 µM ) . Total RNA was isolated by RNEasy kit ( Qiagen ) . Real-time PCR was performed in triplicate with Taqman PCR Mix ( Applied Biosystems ) in the 7000 ABI sequence Detection System ( Applied Biosystems ) . All human and mouse primers were purchased from Applied Biosystems ( information available upon request ) . For PHA stimulated lymphocytes and alveolar macrophages expression of TLR genes was normalized to 18S ribosomal RNA gene while for freshly isolated CD3+ T cells , the glucuronidase-beta gene was used for normalization . Pairs of complimentary oligonucleotides for the desired p53RE from selected TLRs and containing restriction sites were cloned into the open reading frame of firefly luciferase pGL4 . 26 plasmid ( Promega ) previously double digested by Xho I/Kpn I restriction enzymes . The identity of the inserts was confirmed by DNA sequencing . Luciferase activity was measured 48 h after Fugene6- mediated co-transfection of the TLR p53RE constructs in the presence of p53 ( pC53-SN3 ) or empty vector pCMV NEO-BAM3 along with pRL-TK Renilla as a transfection efficiency control into p53 null H1299 cells , as previously described [5] . Forty-eight hours post-transfection extracts were prepared using the Dual Luciferase Assay System ( Promega ) following the manufacturer's protocol and luciferase activity was measured on a Victor Wallac multilabel plate reader ( PerkinElmer ) . Relative luciferases activities for each construct was defined as the mean value of the firefly luciferase/Renilla luciferase rations obtained from 4 independent experiments performed in triplicate . Whole cell extracts were quantified using the Bradford protein assay kit and gamma globulin as a reference standard ( BioRad ) . For TLR protein detection , cellular pellets were subjected to subcellular protein fractionation ( Thermo Scientific ) following the manufacturer's instructions and protein was quantified using BCA protein assay kit ( Thermo Scientific ) . For TLR western blot analysis ∼30 µg of total membrane fraction was used , while for the analysis of other proteins ∼25 µg of total cell extract were used . As expected , we did not detect TLR2 and TLR5 in the cytosolic fractions; therefore , those data are not included . Proteins were resolved on 4–12% BisTris NuPAGE and transferred to polyvinylidene difluoride membranes ( Invitrogen ) and were visualized with primary antibodies followed by horseradish peroxidase–conjugated goat anti–mouse or donkey anti-goat immunoglobulin ( Santa Cruz Biotechnology ) through the use of enhanced chemiluminescence reagents ( Amersham Biotechnology ) . The primary antibodies used in these studies were against p53 ( DO1 , Santa Cruz Biotechnology , ) , p21 ( SXM30 , BD Biosciences Pharmigen ) and Actin ( C-11 Santa Cruz Biotechnology ) . The following is the list of TLR antibodies tested in this study in order to detect TLR protein expression in whole cell extracts as well as membrane and cytosol protein fractions: TLR8 ab24185 and TLR10 ab45088 from Abcam , Inc . ; TLR1#2209 , TLR2#2229 , TLR7#2633 and TLR9#2254 from Cell Signaling . We also used a Toll-like receptor detection kit that includes antibodies for all human TLRs ( TLR1 to TLR10 antibodies from ProSci , Inc . , as well as TLR3-4H270 and TLR5-H1-27 antibodies from Santa Cruz Bitoechnology . The TLR3-IMG-315A , TLR4-IMG6370A antibodies were from IMGENEX . Only the TLR2 ( Cell Signalling ) and TLR5 ( Santa Cruz ) gave clear results . Attempts to detect other TLRs with these antibodies were unsuccessful and appear to be a general problem with TLR antibodies from our collective experience . One of the antibodies enabled us to detect induction of full length TLR4 by nutlin; however , those results are not presented due to the appearance in both untreated and treated samples of nonspecific bands . ChIP assays were done as previously described [35] using ChIP kits ( Millipore ) . Approximately 40×106 PHA stimulated lymphocytes were used for each experimental sample . Cell lysates were sonicated using conditions that yield chromatin fragments 200–500 bp long . One microgram of DO-7 p53-specific monoclonal antibody ( BD Biosciences Pharmigen ) was used per ChIP assay . As a negative control , mouse Ig ( Santa Cruz Biotechnology ) was used . PCR amplifications were performed on immunoprecipitated chromatin using primers to amplify specific regions on the TLRs promoters ( sequence information available upon request ) . The PCR cycles were as follows: initial 10 min Taq polymerase ( Invitrogen ) at 95°C followed by 40 cycles of 95°C for 15 s and 60°C for 1 min . The PCR products were then run on a 1 . 8% agarose gel . Cells were resuspended in 100 µl of PBS and incubated with 5 µl of fluorescent antibody per sample for 30 min , then washed and fixed with 0 . 5% paraformaldehyde . The fluorescence intensity was evaluated using a Becton Dickinson LSR II Flow Cytometer . All antibodies used for FACS were from BD Pharmigen . SNPs were assessed by three different approaches . In RFLP assays , genomic DNA was extracted from Percoll-isolated lymphocytes by DNeasy kit ( Qiagen ) . For the SNP in the TLR8 p53RE#6 ( AGGCAAGATGAAACAT ( G/C ) TCA ) , the G-SNP creates a unique restriction cutting site for NspI ( R CATG Y ) . PCR was performed with 100 ng of DNA , 50 pmol of each primer , 1 . 5 mM MgCl2 , 1 µL 10× PCR buffer , and 0 . 0125 U of Taq ( Invitrogen ) . After 10 min at 94°C , 35 cycles were repeated as follows: 94°C 30 s , 60°C 30 s , and 72°C 35 s; this cycling was followed by a final extension at 72°C for 10 min . The PCR product was digested with 5 U Nsp I ( New England Biolabs , Ipswich , MA ) , at 37°C for 4 h . Since Nsp I recognizes the polymorphic sequence , a G allele is demonstrated by the presence of two fragments 109 and 69 bp in a gel . The A allele is revealed by the presence of a single 177 bp band . The following primers were used for amplifying the region containing the p53RE on TLR8 promoter region: Forward: 5′TCATAACAAGGTGTTCCACAGTC-3′ Reverse: 5′-ATCTGGCCCTTTACAGAAAAAGTT-3′ . The status of this SNP was determined also by using a Taqman SNP genotyping assay . All primers were purchased from Applied Biosystems ( Assay ID:C_27497635_10 ) . For direct sequencing the region containing the p53RE was first PCR amplified using the following pairs of primers: Forward: 5′-TTGAATTCCCTTAGGGTGTGA-3′ , Reverse: 5′-AAACTGCCTTCGATTATTATTATTACA-3′ This was followed by running the samples on a TEA-agarose gel . The expected product ( 397 bp ) was cut out and cleaned using QIAquick gel extraction kit ( QIAGEN ) . The sequencing reactions used Big dye ( Applied Biosystems ) per manufacturer recommendations and the following primers: Foward: 5′TCATAACAAGGTGTTCCACAGTC-3 Reverse: 5′-ATCTGGCCCTTTACAGAAAAAGTT-3′ p53+/+ and p53−/− mouse embryonic fibroblasts ( MEFs ) were cultured in DMEM media and 10% of FBS . Female C57BL/6 mice were purchased from Jackson Laboratories . All experiments were performed in accordance with the Animal Welfare Act and the U . S . Public Health Service Policy on Humane Care and Use of Laboratory Animals after review of the protocol by the Animal Care and Use Committee of the National Institute of Environmental Health Sciences . For murine peritoneal macrophage harvests , mice were injected i . p . with 2 ml of 4% Brewer's thioglycollate and euthanized 96 h later . The peritoneum was washed with 10 ml ice cold PBS three times . Cells were centrifuged ( 1 , 000x RPM , 6 minutes , 4°C ) and washed twice with sterile PBS . Peritoneal exudate macrophages were resuspended in DMEM/0 . 1% FBS , counted , and plated at 2×106 cells/well in a 12-well plate . Cells were allowed to settle for 2 h ( 37°C/5% CO2 ) before replacing media with DMEM complimented with 10% FBS . For bone marrow-derived macrophages ( BMM ) , marrow was flushed from femoral and tibial bones using bone marrow media ( DMEM/2 mM L-glutamine/10% L929-conditioned medium/10% FBS ) . Cells were spun down ( 2200x RPM , 5 min , 4°C ) , brought up in 1 ml sterile ACK buffer , incubated 4°C for 1 min after which 10 ml PBS was added . Cells were spun as above , resuspended in bone marrow medium , counted , and plated at 1×106 cells/well in a 12-well plate . Cells were cultured at 37°C and 10% CO2 in 2 ml bone marrow medium/well and fed on Day 5 with addition of 1 ml medium/well . Experiments were performed on Day 6 . At 24 h post-treatment , cells were harvested for RNA extraction . To examine statistically whether the average mRNA fold-change at each locus in the population sampled differed from 1 for the various exposures , we applied one-sample Student's t tests to log-transformed values of mRNA fold change . The logarithmic transformation helps the data meet the distributional assumptions for the t test . This procedure , in effect , tests the null hypothesis that the geometric mean mRNA fold change at the locus is equal to 1 against the two-sided alternative that the geometric mean differs from 1 . | Among the most prominently studied regulators of gene function is the p53 tumor suppressor , which has many roles in human biology . The transcriptional master regulator p53 directly targets expression of >200 genes . Previously , we sought to define the p53 network in terms of functionality , specifically the ability of target response element sequences ( REs ) to support p53 transactivation . Here we identify p53 target canonical and noncanonical REs in the family of Toll-like Receptor ( TLR ) innate immune response genes and establish p53 regulation of most TLR genes . We address p53 responsiveness in primary human lymphocytes and alveolar macrophages collected from healthy volunteers . Notably , all TLR genes show responses to DNA damage , and most are p53-mediated . However , there is considerable variability between individuals , suggesting that DNA and p53 metabolic stresses can markedly differ in impact on the innate immune system as well as downstream appearance of cytokines . Indeed , we report a SNP in a p53 RE within the TLR8 promoter that alters p53 responsiveness in primary human cells . Furthermore , the p53-mediated expression of TLRs is unique to primates . Overall , these findings identify a new , pivotal role for the well-known human tumor suppressor p53 , namely , integration of DNA damage and innate immune responses . | [
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"... | 2011 | The Toll-Like Receptor Gene Family Is Integrated into Human DNA Damage and p53 Networks |
The picornaviruses coxsackievirus A24 variant ( CVA24v ) and enterovirus 70 ( EV70 ) cause continued outbreaks and pandemics of acute hemorrhagic conjunctivitis ( AHC ) , a highly contagious eye disease against which neither vaccines nor antiviral drugs are currently available . Moreover , these viruses can cause symptoms in the cornea , upper respiratory tract , and neurological impairments such as acute flaccid paralysis . EV70 and CVA24v are both known to use 5-N-acetylneuraminic acid ( Neu5Ac ) for cell attachment , thus providing a putative link between the glycan receptor specificity and cell tropism and disease . We report the structures of an intact human picornavirus in complex with a range of glycans terminating in Neu5Ac . We determined the structure of the CVA24v to 1 . 40 Å resolution , screened different glycans bearing Neu5Ac for CVA24v binding , and structurally characterized interactions with candidate glycan receptors . Biochemical studies verified the relevance of the binding site and demonstrated a preference of CVA24v for α2 , 6-linked glycans . This preference can be rationalized by molecular dynamics simulations that show that α2 , 6-linked glycans can establish more contacts with the viral capsid . Our results form an excellent platform for the design of antiviral compounds to prevent AHC .
Coxsackievirus A24 variant and enterovirus 70 , members of the Picornaviridae family , cause acute hemorrhagic conjunctivitis , a highly contagious eye infection [1] , [2] . During the last decades CVA24v has been responsible for several outbreaks and two pandemics [3]–[9] . Besides hemorrhagic conjunctivitis , the two viruses can also cause symptoms in the cornea , upper respiratory tract , and neurological impairments such as acute flaccid paralysis [2] , [5] , [6] , [10] . Despite the recurring appearance of AHC caused by CVA24v , to date , neither vaccines nor antiviral drugs are available for the prevention or the treatment of the disease . Most picornaviruses engage protein receptors such as decay-accelerating factor ( CD55 , DAF ) [11] , [12] , intercellular adhesion molecule 1 ( ICAM-1 ) [13] , the low-density lipoprotein receptor ( LDL-R ) [14] , the coxsackie and adenovirus receptor ( CAR ) [15] , and integrins [16] , [17] . However , the AHC-causing human picornaviruses CVA24v and EV70 use glycan-containing receptors for cell attachment [1] , [18] . Both viruses engage glycans that terminate in the sialic acid 5-N-acetyl-neuraminic acid ( Neu5Ac ) . Cell binding and infection studies showed that EV70 binds Neu5Ac in the context of an α2 , 3 linkage [18] , while CVA24v is able to use both α2 , 3- and α2 , 6-linked Neu5Ac as receptors , with some preference for the α2 , 6-linkage . The sialic acid-containing receptor is used by CVA24v on corneal but not conjunctival cells [19] . Until now , the molecular determinants underlying these interactions have been unknown for either virus .
To establish a structural basis for the recognition of sialic acid by CVA24v , we first determined the structure of intact CVA24v , an isolate of the Malaysia outbreak occurring during the 2002–2004 AHC pandemic [5] , at 1 . 40 Å resolution ( Table 1 , Figure S1 ) . As is typical for picornaviruses , the four capsid proteins ( VP1-4 ) assemble into an icosahedral pseudo T3-capsid [12] , [20] , [21] ( Figure 1 ) . VP1 is close to a fivefold axis of the capsid; VP2 , close to a twofold; and VP3 , close to a threefold . VP4 lies inside the capsid and carries a myristyl group at its N-terminus . Picornaviruses have so-called non-polar pocket factors that modulate the interactions to receptors of the immunoglobulin superfamily , e . g ICAM-1 and DAF since they bind in regions that lie beneath the receptor binding site of the picornavirus capsid ( so-called “canyon” ) [22] . An unbiased ( Fo-Fc ) -omit map clearly reveals the presence of a branched , elongated pocket factor in CVA24v ( Figure S2 ) . However , the identity of this molecule remains unclear , perhaps due to multiple conformations or an inhomogeneous mixture of pocket factors present in the virion . A structural comparison [23] of ten different homologous picornavirus capsids ( Figure S3 , Tables S1 and S2 ) shows that CVA24v differs primarily at the N-terminal and C-terminal regions and at the solvent-exposed loops of the “jelly-roll” fold proteins VP1 , VP2 , and VP3 . Compared to the structural homologues the most substantial structural differences are observed in the BC- and DE-loops of the CVA24v capsid . Neu5Ac is required for infectivity of CVA24v; the roles of additional sugar moieties and the preferred Neu5Ac linkage have not been determined . In order to advance an understanding of the requirements for CVA24v binding to sialylated glycans , we derivatized CVA24v crystals with eleven physiologically relevant , commercially available sialyloligosaccharides ( Figure 2A ) that differ in glycan composition and linkage . We determined all structures to high resolution . In unbiased ( 2Fo-Fc ) -omit maps , we observed a contoured electron density at a σ-level of 1 . 0 for the glycan only for α2 , 6-sialyllactose ( 6SL ) and disialyllacto-n-tetraose ( DSLNT ) ( Figure 2A ) , a hexasaccharide that carries an α2 , 3 and α2 , 6-linked Neu5Ac . In contrast , we observed very weak binding ( corresponding to a σ–level of 0 . 7 in a ( 2Fo-Fc ) -omit map ) for α2 , 3-sialylated glycans , and no detectable binding for any β–branched or α2 , 8-α2 , 3-disialylated glycan . Thus , our analysis indicated that CVA24v preferentially engages glycans that contain α2 , 6-linked sialyloligosaccharide epitopes . Neu5Ac binds to VP1 near the fivefold axis , at a solvent exposed , protruding region of the virion ( Figure 1 ) . The shallow , positively charged binding site ( Figure 1 ) is formed by the BC- ( residues 95-99 ) and DE-loop ( residues 145–151 ) of one VP1 monomer and the HI-loop ( residues 247-254 ) of a clockwise ( cw ) rotated VP1 protomer ( Figure 2B ) . All 60 Neu5Ac-binding sites in the pseudo T = 3 CVA24v particle are free of crystal contacts and feature unequivocally defined electron density ( Figure 2B ) for Neu5Ac in the complexes with 6SL ( CVA24v-6SL ) and DSLNT ( CVA24v-DSLNT ) . We detected additional difference electron density towards the pentameric VP1 channel in all structures . This electron density does not result from glycan binding and is most likely a remainder of the virus preparation , as it is observed in a different crystal form ( CVA24v-acidic , Table 1 ) . Moreover , flexibility of the residues 147–150 might contribute to the observed electron density . Neu5Ac is bound with a set of hydrogen bonds to the side chains of S147 and cwY250 . Additionally , two main chain interactions contribute to the glycan recognition . The nitrogen atom of S147 participates in binding to the carboxylic group , whereas the carbonyl oxygen of Y145 accepts a hydrogen bond from the acetamido -NH group of Neu5Ac . Moreover , two water-mediated hydrogen bonds are formed to the carbonyl oxygens of cwP252 and cwY250 ( Figure 2C ) . Residues Y145 , A146 , and cwP252 also contribute hydrophobic contacts with non-polar portions of the receptor . An extensive π-π-stacking network involving the side chains of Y145 , R254 and F95 appears relevant for Neu5Ac binding as this interaction stabilizes the conformation of Y145 . A comparison of the observed interactions with other virus-sialic acid complexes shows that the Influenza A virus hemagglutinin , which has an entirely different fold , recognizes the Neu5Ac moiety [24] with a similar main chain interaction pattern [25] . The glycine-serine-motif of Influenza A virus hemagglutinin is substituted by an Y145-A146-S147 motif in CVA24v but remains functionally conserved ( Figure S4 ) . The carboxylate group of the sialic acid is fixed by hydrogen bonds from a side chain Oγ of a serine residue and a main chain NH , and the main-chain carbonyl of the adjacent residue accepts a hydrogen bond from the acetamido-NH of Neu5Ac . This situation is similar for influenza virus hemagglutinin ( pdb-code: 1HGG ) , although the serine residue interacts with the carboxyl group from the opposite side . Although the asymmetric unit of the crystals contains 15 crystallographically independent copies of the binding site , the electron density beyond Neu5Ac is not well defined in any of these sites in either structure . The Neu5Ac atom C2 , to which additional sugars are attached in 6SL and DSLNT , projects away from the virion surface ( Figure 2B ) . The electron density in the vicinity of the Neu5Ac C2 atom also leads towards the solvent , suggesting that additional carbohydrates attached to Neu5Ac project away from the virus , without engaging in significant contacts . To complement the results from the structural analysis , we performed binding inhibition assays using 35S-labeled CVA24v virions that were pre-incubated with different glycans ( Figure 3A ) . Compared with untreated virions , pre-incubation of the virus with DSLNT and 6SL substantially decreased the attachment to human corneal epithelial cells while α2 , 3-linked glycans such as 3SL and a2 , 3-sialyllactosamine ( 3SLN ) had much lower effects , demonstrating the relevance of the observed interactions and specificity . A similar preference of α2 , 6-linked sialic acid glycans compared to 2 , 3-linked sialic acid compounds was very recently described in a glycan array analysis of human enterovirus 68 ( EV68 ) [26] . The authors speculated that the observed preference for α2 , 6-linked sialic acid glycans in EV68 might result in an affinity for the upper respiratory tract . In order to rationalize the differences in binding we investigated the molecular interactions between 3SL , 6SL and DSLNT and the CVA24v virion also in silico . A pre-generated conformational ensemble of each glycan was positioned into the binding site using Neu5Ac for superimposition . Histograms of an interaction score , which is based on AutoDock grid maps , were calculated ( Figure 3B , for details see methods section ) . It was found that all three glycans can establish additional favourable contacts beyond Neu5Ac , which is shown by a significant population of conformations with negative interaction scores . The ranking is DSLNT> 6SL> 3SL , which is in good qualitative agreement with the competition experiments ( Figure 3A ) . Additionally , we performed molecular dynamics simulations of the virus pentamer in complex with each of the three glycans in explicit solvent . A detailed intermolecular atom-atom contact analysis confirmed that DSLNT and 6SL can establish more favorable contacts than 3SL ( Figure S5 ) . These contacts are mainly transient , which is in excellent agreement with the observed lack of electron density beyond Neu5Ac .
We provide a structural basis for understanding the interactions of CVA24v with sialic acid-bearing glycan receptors at high resolution . Our data show that the preferred CVA24v receptor terminates in α2 , 6-linked Neu5Ac . Receptors terminating in α2 , 8-α2 , 3-disialylated glycans , such as GD1b and GD3 , can clearly not engage CVA24v as these glycans would clash with protein residues when superimposing them onto the Neu5Ac entity , irrespective of which of the two Neu5Ac residues in GD3 of GD1b is used ( Figures S6A and S6B ) . Moreover , steric restraints appear to interfere with the binding of β–branched α2 , 3-sialylated glycans ( GM1 , GM2 , GD1a ) to the virus , in line with our observation that these compounds do not bind CVA24v ( Figures S6C and S6D ) , although MD simulations indicated that binding of GM1 to the virus seems possible . Finally , linear α2 , 3-linked sialyloligosaccharides such as 3SL bind less well to the virus and are also less efficient in blocking virus binding than their α2 , 6-linked counterparts . The crystal structures alone do not offer a straightforward explanation as only the Neu5Ac moiety is clearly visible in the electron density maps , and the CVA24v binding site could accommodate a range of glycan structures terminating in either α2 , 6 and α2 , 3 linked Neu5Ac . Sabesan and coworkers have reported a higher flexibility of α2 , 6-linked sialyloligosaccharides compared to their α2 , 3-linked counterparts [27] , and our molecular dynamics simulations demonstrate that the increased flexibility of α2 , 6-linked glycans ( 6SL and DSLNT ) yields a larger number of virus to receptor interactions and thereby likely favors the binding to α2 , 6-linked glycans . It is clear that the energetic differences are very subtle , which is reflected in our experiments that show weak binding to α2 , 3-linked glycans at the same binding site . However , it is important to bear in mind that the virus can simultaneously engage many glycans , and a small energetic difference in each binding site is therefore amplified in a cellular setting . It is remarkable that CVA24v binds Neu5Ac in a surface-exposed , protruding region that appears to be an easy target for a neutralizing antibody response . The CVA24v binding site differs strikingly from the canyon-like areas that engage DAF [12] , CAR [15] and ICAM-1 [13] in other picornaviruses . These deeply recessed “canyons” are thought to engage receptors in regions that are shielded from immune surveillance , and that can accommodate slender protein receptors [28] . However , not all picornaviruses engage their receptors via canyons . The LDL receptor binding site on human rhinovirus 2 ( HRV2 ) [14] is located near the five-fold symmetry axes and does not bind to the canyon perhaps since this receptor is larger and would not fit into the narrow canyon structure . A closer look on the structure reveals that the LDL receptor binding site is located at the same position as the glycan binding site of CVA24v utilizing the BC- , DE- , HI-loop for recognition . LDL receptor binding to CVA24v is unlikely on the basis of a structural based sequence analysis , as the sequence differs substantially in these areas . Moreover , the BC-loop of CVA24v is significantly larger forming a rigid α-helix that would interfere with LDL receptor binding . While CVA24v represents the first structure of a human picornavirus bound to a glycan receptor , two animal picornaviruses , persistent Theiler's Virus DA strain [29] and a cell-culture adapted foot-and-mouth disease virus ( FMDV ) [20] , [30] , have been shown to bind glycans at the interface between VP1 and VP2 and the C-terminus of VP3 . Although also solvent-exposed , this area is distant from the sialic acid binding site in CVA24v ( Figure S7 ) . Since none of the glycan receptors in picornaviruses target canyon residues , it seems that the principles that guide the engagement of picornaviruses with glycans might differ from those that underlie protein receptor binding . Glycan receptors such as sialylated oligosaccharides and glycosaminoglycans ( GAGs ) are conformationally flexible and negatively charged , and offer almost no options for hydrophobic contact formation , in contrast to protein receptors . In agreement with this observation , a survey of available virus-sialic acid complexes shows that they typically bind to shallow , surface-exposed regions of the capsid proteins [24] . It is tempting to speculate that the two picornaviruses that cause AHC engage related sialyated glycans that are expressed on ocular cells , thus linking their receptor binding specificity to tropism . To date , limited data are available about the glycan composition on such cells . Analysis of mucin-type O-glycans showed a highly unequal distribution of α2 , 6- and α2 , 3-linked sialylated glycans in tear films ( 48% and 8% , respectively ) , which contrasts with the distribution on conjunctival epithelial cells ( 3% and 47% , respectively ) [31] . Based on our analysis , we would predict that CVA24v recognizes an easily accessible , unbranched α2 , 6-linked sialylated glycan motif on target cells , and that the high content of α2 , 6-linked sialylated glycan in tear films might facilitate virus spread within the eye . CVA24v , EV70 and EV68 are the only human picornaviruses that use sialic acid-based receptors . The unique character of the CVA24v glycan binding site is revealed by a structure-based sequence comparison ( Figure S8 ) . The sequences that contribute to the Neu5Ac binding site shape a binding pocket not found in other CV strains , even if we take into account a replacement of amino acids that would functionally retain the sialic acid binding site . This analysis would exclude a similar binding of the same glycan receptor . Although EV70 binds α2 , 3-linked sialic acids [18] , its sequence differs profoundly from that of CVA24v , including the CVA24v receptor binding region ( Figure S9 ) . We therefore expect that EV70 binds sialic acids in a mode that is distinct from the one observed here , which explains at least in part why CVA24v-caused infections more commonly includes respiratory symptoms than EV70-caused infections [1] . An anti-viral strategy identified by our results could target cell entry . Such an approach has been employed recently to develop potent inhibitors for ocular virus Adenovirus type 37 [32] , [33] . A potential inhibitor of CVA24v could likewise consist of multivalent α2 , 6-linked Neu5Ac entities that in this case occupy the pentameric glycan binding site with substantial higher affinity , thereby blocking the cell attachment of the virus . A high affinity attachment caused by multivalent glycans has also been shown by Kitov and coworkers [34] , and their pentameric STARFISH design of the Shiga-like toxin inhibitor might serve as template for the future development of drugs to treat AHC . Since the sialic acid binding sites cluster near the five-fold symmetry axis of CVA24v , such a molecule could still be similarly small and useful for topical applications .
Total RNA was extracted using Aurum Total RNA Mini Kit ( BioRad ) and cDNA was generated with Superscript III ( Life Technologies/Invitrogen ) . The sequence was determined twice using dideoxy sequencing of PCR-amplified cDNA , each time on independent PCR products . The CVA24v genome sequence was deposited to the GenBank of the National Center for Biotechnology ( http://www . ncbi . nlm . nih . gov/genbank/ ) with accession code KF725085 . The CVA24v strain ( 110390 ) used in this work originate from Malaysia and was isolated during the 2002–2004 pandemic [5] . 35S-labeled CVA24v virions were generated as previously described [1] . Briefly , normal human conjunctival ( NHC ) cells [35] were infected with CVA24v in serumfree medium containing Dulbecco's modified Eagle's medium ( DMEM; Sigma-Aldrich ) , HEPES ( pH 7 . 4; EuroClone , Milan , Italy ) , and penicillin-streptomycin ( PEST; Gibco , Carlsbad , CA ) with gentle agitation for one hour at 37°C . Cells were washed with phosphate-buffered saline ( PBS; Medicago AB , Uppsala , Sweden ) to remove non-bound virions and then starved of methionine and cysteine in Met/Cys-free medium ( Sigma-Aldrich ) . After three hours , 35S-Met/Cys mixture ( NEG-772 Easytag express protein-labeling mix; Perkin-Elmer , Wellesley , MA ) and 1% fetal calf serum ( FCS; Sigma-Aldrich ) were added to the cells . Thirty hours after infection , Triton X-100 ( Sigma-Aldrich ) was added to a final concentration of 0 . 5% and centrifuged for 15 min at 3 , 000×g . Sodium dodecyl sulfate ( VWR , Leicestershire , United Kingdom ) was mixed with the supernatant to a final concentration of 0 . 5% and the mixture was laid onto a 30% sucrose solution and centrifuged for 3 h at 113 , 000×g at 18°C . The pellets were dissolved in 2 ml of 10 mM Tris-HCl , pH 7 . 5 , and sonicated for 20 s . The mixture was loaded onto a discontinuous gradient of 1 . 2 and 1 . 4 g/ml CsCl and centrifuged at 107 , 000×g for 17 h at 4°C . The virion band was harvested and desalted on a NAP-10 column ( Amersham Biosciences , Uppsala , Sweden ) and stored in Tris-buffered saline ( TBS ) with 10% glycerol at −80°C . To produce non-labeled CVA24v virions , the 35S-Met/Cys-labeling step was neglected from the process described above . To concentrate the non-labeled virions for crystallization , 6 mg virions were laid onto a 20% sucrose solution and centrifuged for 3 h at 113 , 000×g at 18°C . The pellet was dissolved in 0 . 6 ml of TBS 10% glycerol to a final concentration of 10 mg/ml . The virions were stored in −80°C until use . Crystallization screening was performed at 4°C by hanging drop vapor diffusion on siliconized cover slides . Therefore , the virus solution ( 10 mg/mL , 1 µL ) was mixed with the crystallization buffer-I ( 200 mM Magnesium chloride , 3 . 4 M 1 , 6-Hexanediol , 100 mM HEPES pH 7 . 5 ) in a 1:1 ratio and placed over the reservoir solution . Rod-like crystals appeared after four days and grew to the final size of up to 80×80×300 µm3 within three weeks . To identify the glycan binding site of CVA24 , we tested 6SL ( 15 mM ) , 3SL ( 15 mM ) , 3SLN ( 15 mM ) , LSTc ( 15 mM ) , Gd1a ( 15 mM ) , Sialyl-LewisX ( 15 mM ) , Gd1b ( 8 mM ) , DSLNT ( 8 mM ) , GM1 ( 8 mM ) , GM2 ( 8 mM ) , and GD3 ( 8 mM ) for binding ( Figure 2A ) . Therefore , the crystals were incubated in the glycan-containing solution for 1 h at 4°C , harvested and stored in liquid nitrogen until data collection . The same method was applied to obtain trigonal bipyramidal crystals . These crystals were obtained in acidic crystallization buffer-II ( 200 mM Calcium chloride , 20% ( v/v ) 2-Propanol , 100 mM Sodium acetate trihydrate pH 4 . 5 ) and grew to a final size of 150×150×250 µm3 within several days . Data collection was performed the beamline I03 at the Diamond Light Source in Didcot , UK . Special care has to be taken to avoid spot overlapping . Data were reduced by the XDS/XSCALE package [36] . All data sets were scaled to the native data set as reference . We obtained a data set of the native virus to a resolution of 1 . 40 Å and bound to 6SL and DSLNT diffracting up to at least 1 . 96 Å resolution . All crystals resulting from crystallization buffer-I are of orthorhombic spacegroup I222 containing two virus particles in the unit cell . We used a CHAINSAW [37] modified model of coxsackievirus B3 [12] as search model . This template structure was placed into the unit cell of the search model . The capsid of the template model was constructed from the NCS rotation translation matrices and the center of mass was calculated by a PYTHON script using PYMOL [38] modules to obtain the vector ( t1 = 148 . 32 Å , −85 . 63 Å , −271 . 07 Å ) to translate the template structure into the origin of the orthorhombic unit cell . Next , the template capsid was rotated around the x-axis by ε = 20 . 905° to orient the icosahedral two-fold axes of the virus capsid along the orthorhombic unit cell axes a , b and c . Finally , the template structure was translated into the center of the unit cell ( t2 = a/2 , b/2 , c/2 ) and the asymmetric unit was generated to include 15 copies of VP1 , VP2 , VP3 , and VP4 using the NCS-operators of the search model . Initial phases were established by rigid body refinement procedure as implemented in REFMAC5 [39] with the pre-oriented asymmetric unit of the search model followed by a simulated annealing approach using PHENIX [40] . This approach yielded an R-factor of approximately 41% . Strict NCS-parameterization was applied . Several cycles of manually model correction with COOT [41] and refinement using REFMAC5 completed the model . Water molecules were placed using the COOT:find_waters algorithm and manually checked . Finally , the ligand molecules were placed into the unbiased ( Fo-Fc ) -difference omit map . All structure models were refined to possess excellent geometry with R-factors below 16% ( Table 1 ) . Figures were generated with PYMOL [38] . The electrostatic potential was mapped onto the surface by the use of APBS [42] . 35S-labeled CVA24v virions were used as previously described [1] . Briefly , 5000 35S-labeled CVA24v virions/cell were incubated with or without different concentrations of glycans ( 6SL , 3SL , 3SLN , DSLNT; Carbosynth , Berkshire , United Kindom ) ( N-acetylneuraminic acid ( sialic acid ) ; Dextra , Reading , United Kindom ) at 4°C , diluted in 50 µl of binding buffer ( BB ) containing DMEM , HEPES , and 1% bovine serum albumin ( BSA; Roche , Stockholm , Sweden ) for one hour with gentle agitation . Meanwhile , adherent human corneal cells ( HCE cells ) [7] were washed and detached with PBS containing 0 . 05% EDTA ( PBS-EDTA; Merck , Darmstadt , Germany ) . The cells were recovered in growth medium ( 50% DMEM , 50% HAMs-F12 , 1 mg/l human insulin , 100 µg/l cholera toxin , 2 µg/l human epidermal growth factor , 5 mg/l hydrocortisone , 10% FCS ( all from Sigma-Aldrich ) , 20 mM HEPES and PEST ) at 37°C with agitation . After one hour , 1×105 cells/sample were washed with BB and added to the pre-incubated glycan-virion mixture . After 1 h of incubation at 4°C , cells were washed with BB to remove non-bound virions before the radioactivity of the cells was measured using a Wallac 1409 scintillation counter ( Perkin-Elmer , Waltham , MA ) . Conformational ensembles of 3SL , 6SL and DSLNT were derived from 100 ns molecular dynamics simulation at 310 K using TINKER ( http://dasher . wustl . edu/tinker/ ) . The MM3 force field [43] and a dielectric constant of 4 were applied . The size of each ensemble was 100000 frames . Affinity grids for the virus receptor were calculated using AutoDockTools ( http://mgltools . scripps . edu ) and the autogrid program of AutoDock 3 . 05 [44] . All further processing was performed using Conformational Analysis Tools ( CAT ) ( http://www . md-simulations . de/CAT/ ) . The conformational ensembles were positioned into the binding site of the crystal structure using Neu5Ac for superimposition . For DSLNT the α2 , 6-linked Neu5Ac was positioned into the binding site . Gasteiger atom charges were assigned to the glycan atoms by CAT using OpenBabel ( http://openbabel . org ) . Interaction scores for each frame were determined from the AutoDock affinity grids by using all glycan atoms except the atoms of Neu5Ac . Explicit solvent molecular dynamics simulations of the virus pentamer in complex with 3SL , 6SL and DSLNT were performed at 310 K using YASARA [45] . AMBER03 [46] forcefield was used for the protein and GLYCAM [47] for the carbohydrates . Different representative glycan conformations were positioned into each of the five binding sites of the virus pentamer using CAT . 20 ns were sampled for complexes of 3SL and 6SL and 30 ns for DSLNT . Atom-atom contact analysis was performed using CAT . Simple atom-atom distance criteria were used for counting favourable interactions ( hydrophobic: C-C distance <4 . 0 Å; H-Bond: donor-acceptor distance <3 . 5 Å ) . Structure factors and atomic coordinates have been deposited in the Protein Data Bank ( rcsb . org ) with accession codes 4Q4V , 4Q4W , 4Q4X , and 4Q4Y . | Coxsackievirus A24 variant ( CVA24v ) and enterovirus 70 ( EV70 ) are responsible for several outbreaks of a highly contagious eye disease called acute hemorrhagic conjunctivitis ( AHC ) . These viruses represent a limited set of human picornaviruses that use glycan receptors for cell attachment . Until now no data has been available about the binding site of these glycan receptors . We therefore determined the structure of the entire virus capsid in its unbound state and also together with several glycan receptor mimics and could establish the structure of the receptor binding site . CVA24v recognizes the receptor at a solvent exposed site on the virus shell by interactions with a single capsid protein VP1 . Moreover , we identified a glycan motif favoured for CVA24v binding and confirmed this preference biochemically and by in silico simulations . Our results form a solid basis for structure-based development of drugs to treat CVA24v-caused AHC . | [
"Abstract",
"Introduction",
"Results",
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] | 2014 | A Sialic Acid Binding Site in a Human Picornavirus |
Human intervention has subjected the yeast Saccharomyces cerevisiae to multiple rounds of independent domestication and thousands of generations of artificial selection . As a result , this species comprises a genetically diverse collection of natural isolates as well as domesticated strains that are used in specific industrial applications . However the scope of genetic diversity that was captured during the domesticated evolution of the industrial representatives of this important organism remains to be determined . To begin to address this , we have produced whole-genome assemblies of six commercial strains of S . cerevisiae ( four wine and two brewing strains ) . These represent the first genome assemblies produced from S . cerevisiae strains in their industrially-used forms and the first high-quality assemblies for S . cerevisiae strains used in brewing . By comparing these sequences to six existing high-coverage S . cerevisiae genome assemblies , clear signatures were found that defined each industrial class of yeast . This genetic variation was comprised of both single nucleotide polymorphisms and large-scale insertions and deletions , with the latter often being associated with ORF heterogeneity between strains . This included the discovery of more than twenty probable genes that had not been identified previously in the S . cerevisiae genome . Comparison of this large number of S . cerevisiae strains also enabled the characterization of a cluster of five ORFs that have integrated into the genomes of the wine and bioethanol strains on multiple occasions and at diverse genomic locations via what appears to involve the resolution of a circular DNA intermediate . This work suggests that , despite the scrutiny that has been directed at the yeast genome , there remains a significant reservoir of ORFs and novel modes of genetic transmission that may have significant phenotypic impact in this important model and industrial species .
During its long history of association with human activity , the genomic makeup of the yeast S . cerevisiae is thought to have been shaped through the action of multiple independent rounds of wild yeast domestication combined with thousands of generations of artificial selection . As the evolutionary constraints that were applied to the S . cerevisiae genome during these domestication events were ultimately dependent on the desired function of the yeast ( e . g baking , brewing , wine or bioethanol production ) , these multitude of selective schemes have produced large numbers of S . cerevisiae strains , with highly specialized phenotypes that suit specific applications [1] , [2] . As a result , the study of industrial strains of S . cerevisiae provides an excellent model of how reproductive isolation and divergent selective pressures can shape the genomic content of a species . Despite their diverse roles , industrial yeast strains all share the general ability to grow and function under the concerted influences of a multitude of environmental stressors , which include low pH , poor nutrient availability , high ethanol concentrations and fluctuating temperatures . In comparison , non-industrial isolates such as laboratory strains , have been selected for rapid and consistent growth in nutrient rich laboratory media , thereby producing markedly different phenotypic outcomes when compared to their industrial relatives [3] . The outcomes of these very different selection pressures are therefore most evident when comparing industrial and non-industrial yeasts . As an example , laboratory strains of S . cerevisiae , such as S288c , are unable to grow in the low pH and high osmolarity of most grape juices and therefore cannot be used to make wine . This is a clear difference between industrial and non-industrial strains of S . cerevisiae , however there are numerous subtle differences not only between industrial strains , but also between strains used within the same industry [4] , [5] , highlighting the overall genetic diversity found in this species . There have been several attempts to characterize the genomes of industrial strains of S . cerevisiae which have uncovered differences that included single nucleotide polymorphisms ( SNPs ) , strain-specific ORFs and localized variations in genomic copy number [6]–[14] . However , the type and scope of genomic variation documented by these studies were limited either by technology constraints ( e . g arrayCGH relying on the laboratory strain as a “reference” genome ) , or by the resources required for the production of high-quality genomic assemblies which has limited the scope and number of whole-genome sequences available for comparison . In addition , to limit genomic complexity to a manageable level , previously published whole-genome sequencing studies on industrial strains used haploid representations of diploid , and often heterozygous , commercial and environmental strains [9]–[13] . We sought to address these shortcomings by sequencing the genomes of four wine and two brewing strains of S . cerevisiae in their industrially-used forms . The industries of winemaking and brewing were targeted for this work as they have the longest association with S . cerevisiae ( measured in the thousands of years ) and each industry has accumulated large numbers of phenotypically distinct strains for which genetic comparisons can be made . This study demonstrates that industrial yeasts display significant genotypic heterogeneity both between strains , but also between alleles present within strains ( i . e . heterozygosity ) . This variation was manifest as SNPs , small insertions and deletions , and as novel , strain and allele-specific ORFs , many of which had not been found previously in the S . cerevisiae genome and may provide the basis for novel phenotypic characteristics . Interestingly , several ORFs were shown to comprise a gene cluster that was present in multiple copies and at a variety of genomic loci in a subset of the strains examined . Furthermore , this cluster appears to have integrated into genomic locations by a novel circular intermediate , but without employing classical transposition or homologous recombination , which we believe represents the first time such an element has been characterized in S . cerevisiae . Overall , this work suggests that , despite the scrutiny that has been directed at the yeast genome , there remains a significant reservoir of ORFs and novel modes of genetic transmission which may have significant phenotypic impact in this important model and industrial species .
Rather than being strictly diploid , many industrial yeast strains display chromosomal copy number variation ( CNV ) [18] . In order to catalogue CNV in the industrial yeast genomes , the depth of sequencing coverage determined for each sequence contig were calculated such that areas of CNV could be detected as localized variations in that coverage ( Figure 1 ) . There were several large areas of increased copy number across the strains including six potential whole-chromosome amplifications ( chrI of AWRI796 , chrVIII of VL3 , chrIII of FostersO and chrIII , V and XV of FostersB ) and one potential reduction in chromosomal copy number ( chrXIV of FostersO ) . There were also several partial chromosomal CNVs , including amplification of 200 kb of chrXIV in AWRI796 , 600 kb of chrII and 200 kb of chrX in FostersO and a 400 kb reduction from chrVII of FostersO ( Figure 1 ) . However , while the ale strains had a higher number of large CNVs than wine strains , the overall fold change of these CNVs was generally reduced . This reduction can be most easily explained by the brewing strains having a polyploid genetic base while the wine strains are diploid , an observation which has been seen previously in these industrial yeasts [18] . As existing published industrial yeast genome sequences were either generated from haploid derivatives of industrial strains [9]–[12] or had heterozygous regions discarded during analysis [13] , the level of genome-wide heterozygosity present in industrial strains remains largely unknown . However , as the assemblies performed in this study retained genomic heterozygosity , it was possible to determine the level of allelic differences within each of these strains ( Table 2 ) . While every industrial strain contained heterozygous single nucleotide polymorphisms ( SNPs ) , the proportion of these varied over thirty-fold between wine strain AWRI796 ( 1041 total heterozygous bp ) and the brewing strain FostersB ( 33071 bp ) . Heterozygous insertions and deletions ( InDels ) were also present and ranged from single base pair variants to large InDels of up to 35 . 3 kb . Strains were also shown to contain heterozygous instances of Ty element insertion , although , due to the repetitive nature of these elements , their presence in the genome could generally only be estimated through paired-end information ( data not shown ) . In addition to the intra-strain variation that was present between homologous chromosomes within individual strains , there was also significant nucleotide variation between strains . As seen for the allelic variation , both SNPs and InDels were found between strains , with inter-strain InDels of up to 45 kb being observed . Many of the smaller InDels ( both heterozygous and homozygous ) were located in regions comprising tandem repeats ( Figure 2A , Table S1 ) and primarily in the expansion and contraction of di- and tri-nucleotide tandem repeats ( Figure 2B ) . Indeed , when using chromosome XVI as an example , over 86% of the instances of di- and tri-nucleotide repeats displayed variable length in at least one of the strains . As the size of tandem repeats has been associated with differences in gene expression [19] , this suggests that there are both strain and allele-specific differences in the expression of genes proximal to these repeat-associated InDel events . SNP variation was also common throughout the strains with a total of 165 , 913 non-degenerate SNPs ( unique points of nucleotide variation ) that were present in at least one allele of the twelve strains investigated ( ∼1 . 3% of the total genome length ) . However , given the influence of large , strain-specific InDels ( which were filtered out of the SNP analysis ) the apparent SNP density is much higher than 1 . 3% , such that these SNPs were shown to display a median inter-SNP distance of only 37 bp . By using the number of SNPs separating any two isolates as an estimation of their relatedness ( Figure 3A ) , we were able to show that industrial yeasts are distinct from both the laboratory and human pathogenic strains and were also found to group by industry . This was especially true of the brewing strains which displayed a high degree of genetic distance not only from the laboratory and human isolates , but also from the wine and bioethanol strains . The only exception to this pattern of grouping by industry or environment niche was with the ‘natural’ isolate RM11-1a which grouped closely with wine strains . However , given that it is descended from a strain sourced from a vineyard , RM11-1a may well share genetic origins with those strains used in winemaking . In order to put the genetic variation observed in these genomic alignments in a larger population context , twelve strains were selected to represent each of the six main S . cerevisiae population groups as proposed by Liti et al [12] for further SNP comparison ( Figure 3B ) . In this broader context , wine strains sequenced in this study were shown to also group tightly with the wine/European strains DBVPG1106 and DBVPG1373 , showing that the data produced across these two studies are directly comparable . However , while the ale strains were still shown to be distinct from the wine isolates they were found to be far closer to the wine strains than isolates such as those used in sake production , which display the greatest level of nucleotide diversity when compared to the wine strains . Indeed , when the SNP data from these additional strains in included in the calculations of SNP density , the total number of non-degenerate SNPs increases to 216 , 207 ( ∼1 . 7% ) with a median inter-SNP distance of only 27 bp . However , despite comparisons to eighteen other diverse strains of S . cerevisiae 15 , 576 of these SNPs were found solely in this study ( 2 , 501 in more than one strain ) and with the vast majority of these SNPs being present in a heterozygous form ( only 1 , 864 novel SNPs were homozygous in at least one strain ) . To determine how inter-specific variation at the nucleotide level translated into protein-coding differences , the predicted coding potential of each strain was compared . ORFs were predicted from each sequence ( including the pre-existing whole genome sequences ) using Glimmer [20] and compared using a combination of BLAST [21] homology matches and genomic synteny to differentiate instances of orthology from gene duplication ( Table S2 ) . When using the laboratory strain S288c as a reference , there was an average of 92% ORF coverage across the strains . The majority of S288c ORFs without a match in other strains were shown to be located in repetitive regions of the S . cerevisiae genome such as in the sub-telomeric zones or the numerous Ty retrotransposons that are present in S288c genome relative to other strains . Due to the repetitive nature of these regions it was often impossible to unambiguously position these sequences in the industrial yeast genome assemblies and they remain within repetitive , unmappable contigs in the various genome assemblies . It therefore appears that , due to its persistent propagation in the laboratory , the genome of S288c may represent a reduced genomic state as it does not appear to contain additional genes that provide unique metabolic or cellular potential outside of those present in other strains . It does however contain a far greater number of Ty transposons relative to all of the other strains suggesting that transposon proliferation occurred on at least one occasion during the development of this laboratory strain . While the laboratory strain S288c is considered the reference for the genomic complement of S . cerevisiae , it is becoming apparent that it lacks a multitude of ORFs which exist in other strains of S . cerevisiae [9]–[13] , [22] , [23] . This is confirmed n the present study with between 36 ( FostersB ) and 110 ( Lalvin QA23 ) ORFs lacking significant homology to the S288c genome but for which there were clear matches to sequences in other S . cerevisiae strains or microbial species ( Table S2 ) . Orthologs of 102 out of 218 of the non-degenerate set of these ‘non-S288c’ ORFs have been identified previously in S . cerevisiae strains , mainly through whole-genome sequencing of AWRI1631 , EC1118 and RM11-1a and YJM789 [8] , [9] , [13] ( Table S2 ) . These include genes encoding proteins such as the Khr1 killer toxin [24] which is found in YJM789 , EC1118 , Vin13 , VL3 , FostersB and FostersO and orthologs of the MPR1 stress-resistance gene ( which was originally identified in the Sigma 1278b strain[23] ) in RM11-1a , EC1118 , AWRI1631 , JAY291 , QA23 and VL3 . Interestingly , in addition to these ORFs there were at least three proteins present in the human pathogen YJM789 and the FostersB and FostersO ale strains but which were lacking from the wine , biofuel and laboratory strains ( Figure 4C ) . These included the YJM-GNAT GCN5-related N-acetyltransferase [8] and a separate gene cluster which is predicted to contain both RTM1 , which was identified previously as a distillery-strain specific gene that provides resistance to an inhibitory substance found in molasses [22] , and a large ORF of around 2 . 3 kb which , despite its large size and high-degree of conservation across the brewing and human pathogenic strains , lacks significant homology to any other protein sequences except for six isolates from the large S . cerevisiae population genomic screen which also appear to encode this protein [12] ( Figure S1 ) . In addition to these two conserved ORFs , in the ale strains this cluster also appears to encode an invertase that would be expected convert sucrose into the sugars glucose and fructose . Despite the presence of at least two existing high-coverage wine strain sequences and at least an additional six low coverage genomes , the entire repertoire of ORFs present in wine strains of S . cerevisiae , let alone the species as a whole , is far from complete . In addition to expanding the strain range of previously identified non-S228c proteins , it was possible to identify at least eleven ORFs that lacked homology to existing proteins from S . cerevisiae , in addition to many new paralogs of existing S . cerevisiae genes . These novel ORFs often clustered in large InDels , the largest of which was a 45 kb fragment in the wine strain AWRI796 . This novel genomic region is located adjacent to a large repetitive element present on chromosomes XIII , XV and XVI , which hampered initial efforts to assign this region to a specific chromosome . However , through the application of a 20 kb paired-end library , it was possible to bridge the repetitive region and position this novel region at the end of the right arm of chromosome XV . This fragment is predicted to encode nineteen ORFs ( Figure 4A ) , three of which are predicted to encode aryl-alcohol dehydrogenases ( AADs ) . AADs have been extensively characterized in filamentous fungi where they catalyze the reversible reduction of aldehydes and ketones to aromatic alcohols during lignin-degradation [25] , [26] . These new AAD homologs are phylogenetically distinct from other AAD enzymes that have been identified , including the seven predicted AADs that are present in the S288c genome [27] , [28] ( Figure 4B ) . One particularly curious feature of many of the industrial yeast strains analyzed in this study , was a cluster of five conserved ORFs that was present in all of the wine strains , RM11-1a and the bioethanol strain JAY291 , and potentially in at least four of the strains present in the Liti et al [12] study ( Figure 3 ) . This cluster is predicted to encode two potential transcription factors ( one zinc-cluster , one C6 type ) , a cell surface flocullin , a nicotinic acid permease and a 5-oxo-L-prolinase , and has been suggested to be horizontally acquired by S . cerevisiae from Zygosacharomyces spp [13] . In this study we have been able to show that while the sequences of the individual genes within this cluster are highly conserved between strains , the cluster itself is actually highly diverse with respect to copy number , genomic location and overall gene order ( Figure 5 , Table S3 ) . The cluster was present in one to at least three copies across strains , with individual clusters being located in at least seven different genomic loci ( Figure 5A ) . For example , wine strain Lalvin QA23 was shown to contain at least three copies of the cluster , found in three different genomic loci and with at least two copies being heterozygous . However , despite this diversity , the sequence of the ORFs and intergenic regions of the cluster were highly conserved , with only fifteen nucleotide substitutions ( 0 . 01% ) recorded across the eleven known copies of the cluster ( Figure 5B , Figure S2 ) . In addition to the differences in copy number and location , the exact order of the ORFs within the cluster differed in a location dependent manner ( Figure 5B , 5C ) . However , all of these different ORF arrangements could be resolved into a syntenically-conserved order if the linear genomic copy of each cluster resulted from the differential resolution of a common circular intermediate , with a unique breakpoint in this circular arrangement being observed for each genomic location ( Figure 5B–5D ) . However , despite the differential location of these clusters these integration events appear to select for functional conservation of the genes with the majority of the breakpoints being located within intergenic regions ( Figure 5B ) . Of the two exceptions to this , one of these events occurs at the extreme 3′ end ( ∼100 bp from the predicted stop codon ) of one ORF such that a functional protein is likely to still be produced from this gene . Adding further interest to the mode of transfer of this cluster , its integration into the genome appears to occur without the production of the terminal repeated sequences that would be expected if integration of this element occurred by either homologous recombination or classical mobilization via a transposon-like mechanism . In fact , for at least three of the seven different integration events characterized in this study , integration of the cluster has occurred between two directly adjacent , conserved nucleotides , with a further two events showing only single nucleotide indels at the junction between the cluster and the flanking genomic sequences ( Figure 5E ) .
While S . cerevisiae is one of the most intensively studied biological model organisms and economically-important industrial microorganisms , many characteristics of its genome remain unknown , especially in strains other than the laboratory reference S288c . Through the analysis of six industrial strains , it was possible to show that the industrial members of this species are distinct , with wine and brewing strains being almost as distantly related at the DNA level as they are to either the laboratory or human pathogenic strains . This suggests that despite their roles in performing industrial fermentations , the two groups comprise genetically separate S . cerevisiae lineages . While this is a situation similar to that proposed previously for wine and sake strains of S . cerevisiae [2] , the wine and ale strains were much more closely related to each other than to strains with origins outside of Europe [12] , and this may reflect a distant common European-type ancestor . The bioethanol strain JAY291 displays an intermediate level of sequence relatedness to the wine strains ( compared to ale strains ) and also contains the five-gene cluster , suggesting that this strain shares at least some of its genomic origins with the wine isolates . With the relatively recent development of the bioethanol industry , it is not entirely unexpected that yeasts used in this process may well have their origins in commercial strains used in established ethanologenic industries . Wine strains would therefore make a logical choice for this starting point given their highly efficient production of ethanol and relatively high tolerance to a variety inhibitory substances , such as ethanol or polyphenols , that also exist in bioethanol fermentations [29] . In addition to mapping the relationships between these strains , this study uncovered a number of genetic elements not previously identified in the S . cerevisiae genome , as well as expanding the range of several strain specific elements that had been identified previously . This highlights the fact that the genetic variation that underlies the phenotypic diversity of S . cerevisiae goes well beyond that of SNPs or small InDels and is similar to the situation observed with many bacterial species where the pan ( species-wide ) genome is larger than that observed in any single strain [30] . As for the situation observed with single nucleotide variation , several of these genetic elements link strains to specific industries ( e . g . the RTM1 cluster in the ale strains and the five-gene cluster in the wine strains ) . It would therefore be expected that these ORFs provide selective advantage within specific industries that have favored their retention . For some of these ORFs , such as the RTM1 cluster , the phenotypic benefits that they have historically provided in one industry may be advantageous in modern incarnations of others . For example , modern wine production generally makes use of inoculated commercial strains ( rather than the historical use of wild yeast ) , which are produced on a large scale using molasses as a feedstock . Genes such as the RTM1 cluster may therefore provide advantages in the production of modern commercial wine yeast , but which are lacking from the genomic complement of this group of strains due to the historical practices of winemaking . While other strain-specific ORFs were shown to have much narrower strain ranges ( often single strains ) , it was possible to predict industrially-relevant roles for some of these genes . For example , the novel AAD proteins that were identified in the wine strain AWRI796 may have a direct impact on the range of volatile aromas produced during fermentation , as the aromatic alcohols produced through the action of the AAD enzymes can present very different aromas profiles to their corresponding aldehydes and ketones [31] . The presence of these AADs in specific industrial yeasts may therefore alter the profile of volatile aromas produced during winemaking or brewing , contributing to strain-specific aroma characteristics that are vitally important to many flavor and aroma-based industrial applications . The role of ORFs such as those present in the wine yeast five-gene cluster are less clear but , given the potential regulatory role for at least two of these proteins , they could produce significant phenotypic effects . The generally similar characteristics of high sugar and ethanol tolerance of Zygosacharomyces spp and the wine and bioethanol strains of S . cerevisiae [29] , [32] , may provide a selective advantage for growth under these conditions . However , understanding the function of individual ORFs is overshadowed by questions regarding the origins of this novel cluster in addition to its effect on genome structure and dynamics . It was recently proposed that this cluster entered the S . cerevisiae genome from Zygosacharomyces spp [13] . Our data suggests that if this is the case , the transfer has either occurred on multiple occasions via a conserved circular intermediate that has integrated randomly into different genomic loci , or the fragment has entered the S . cerevisiae genome on a single occasion but has subsequently mobilized to new genomic locations via a circular intermediate ( Figure S3 ) . Alternatively , this cluster is a mobile feature of the S . cerevisiae genome that has been lost from many strains and was transferred to Zygosacharomyces spp . Regardless of the direction or precise mode of transfer it appears that this genetic cluster may mobilize throughout the genome via a method which has yet to be characterized in yeast and therefore provides an entirely new mechanism for the generation of variation in the S . cerevisiae genome . A thorough understanding of the scope of plasticity of the yeast genome is a vital prerequisite for the systematic understanding of yeast biology or for the development of the next generation of yeasts for industrial applications . As more S . cerevisiae strains are sequenced , the suitability of S288c as a “reference” strain for this species is becoming less clear , especially as it appears to lack a large numbers of ORFs found in many other S . cerevisiae strains while containing an abnormally high number of Ty transposable elements [8] , [9] . Given the ubiquitous nature of the S288c genome for the design of ‘omics experiments , these novel elements have generally not been considered when studying strains other than S288c . Thus , little data exists regarding the functional contributions of these proteins . As such , they represent a significant knowledge gap with respect to cellular and metabolic modeling strategies . This is especially true for proteins such as the ORF located next to RTM1 which is large ( ∼800 amino acids ) and highly conserved but has no significant homologs outside of a small subset of S . cerevisiae strains on which a function can be based . Fortunately , the continued development of next generation sequencing , such as that applied in this work , have provided the means to now characterize large numbers of yeast strains to provide this information and outline the true scope and variability of this species .
Each commercial strain was obtained from the original mother cultures from the supplier . Genomic DNA was prepared by zymolase digestion and standard phenol-chloroform extraction . Library construction and sequencing was performed at 454 Life Sciences , A Roche Company ( Branford , CT ) using a pre-release development version of the GS FLX Titanium series shotgun and 3 kb paired-end protocols . Sequences were assembled using MIRA ( http://sourceforge . net/apps/mediawiki/mira-assembler/index . php ? title=Main_Page ) and manually-edited using Seqman Pro ( DNAstar ) . Regions of chromosomal CNV were determined by calculating the per-base sequencing coverage across each sequencing contig with median smoothing ( 1001 bp window , 100 bp step size ) . The ratio between the coverage at each genomic location and the overall median genomic coverage was the calculated to determine the level of over-representation for each location . Large-scale chromosomal aneuploidies were detected by screening for regions in which median ratio for a contiguous stretch of at least 101 individual segments differed from the overall genomic median by either 1 . 25 ( 5∶4 ratio representing at least 1 extra genomic copy in a tetraploid ) or 1 . 4 fold ( 3∶2 ratio representing at least 1 extra genomic copy in a diploid ) . Chromosomal scaffolds from each yeast strain were aligned using FSA [33] . Diploid sequences were assigned into two haploid alleles by converting any degenerate bases into their non-degenerate pairs . Heterozygous regions were divided into both an insertion and deletion allele . A chromosomal consensus was computed for the alignment based upon the most frequent allele at each position in the alignment . Nucleotides that varied from the consensus in each strain were scored as sequence variants and were subsequently divided into SNPs ( nucleotide substitution ) or InDels ( nucleotide insertion or deletion ) . To enable the comparison to strains with low coverage sequences [12] , SNPs that were calculated for each strain relative to S288c ( imputed SNPs ) were used to create synthetic S288c-based genome sequences that contain the SNPs present in these strains . The genetic relationship between the strains was calculated by editing and concatenating the nucleotide alignments of all sixteen chromosomes using Seaview [34] followed by calculating the distance tree using the NJ algorithm of Clustalw ( ignoring gapped regions in the alignment ) . Tandem repeats were predicted from the chromosomal alignment of all twelve yeast strains using Tandem Repeats Finder [35] using default parameters ( match weight , 2; mismatch , 7; indel , 7; pM , 0 . 80; pI , 0 . 10; minimum alignment score , 50; maximum period size , 500 ) . Individual repeats were then scored as either being variable if the specific tandem repeat region contained strain- or allele- specific InDels . ORFs were predicted using Glimmer [20] with the predicted ORFs of S288c being used to build the prediction model ( See Datasets S1 , S2 , S3 , S4 , S5 , S6 , S7 , S8 , S9 , S10 , S11 for actual CDS sequences for each strain ) . Initial ORF designations were made by identifying the best sequence match for each ORF when compared to S288c using BLASTn [21] . Glimmer was also used to predict ORFs from the sequence of S288c ( Accession numbers NC001133-NC001148 ) to correct for false-negatives in the predictions when compared to existing ORF designations in S288c . ORFs with no match to S288c were searched against the full list of non-redundant Genbank proteins to identify a closest existing homology match . ORFs from each strain were then arranged in syntenic order ( Table S2 for a full list of ordered ORFs ) . For protein sequence comparisons , predicted protein sequences were aligned using Clustalw [36] ( http://align . genome . jp ) . | The yeast S . cerevisiae has been associated with human activity for thousands of years in industries such as baking , brewing , and winemaking . During this time , humans have effectively domesticated this microorganism , with different industries selecting for specific desirable phenotypic traits . This has resulted in the species S . cerevisiae comprising a genetically diverse collection of individual strains that are often suited to very specific roles ( e . g . wine strains produce wine but not beer and vice versa ) . In order to understand the genetic differences that underpin these diverse industrial characteristics , we have sequenced the genomes of six industrial strains of S . cerevisiae that comprise four strains used in commercial wine production and two strains used in beer brewing . By comparing these genome sequences to existing S . cerevisiae genome sequences from laboratory , pathogenic , bioethanol , and “natural” isolates , we were able to identify numerous genetic differences among these strains including the presence of novel open reading frames and genomic rearrangements , which may provide the basis for the phenotypic differences observed among these strains . | [
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] | 2011 | Whole-Genome Comparison Reveals Novel Genetic Elements That Characterize the Genome of Industrial Strains of Saccharomyces cerevisiae |
According to a prominent view of sensorimotor processing in primates , selection and specification of possible actions are not sequential operations . Rather , a decision for an action emerges from competition between different movement plans , which are specified and selected in parallel . For action choices which are based on ambiguous sensory input , the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action . These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning , and show signatures of competitive value-based selection among these goals . Since the same network is also involved in learning sensorimotor associations , competitive action selection ( decision making ) should not only be driven by the sensory evidence and expected reward in favor of either action , but also by the subject's learning history of different sensorimotor associations . Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output . Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies . We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm . We show that the model accurately simulates the dynamics of action selection with different reward contingencies , as observed in monkey cortical recordings , and that it correctly predicted the pattern of choice errors in a control experiment . With our adaptive model we demonstrate how network plasticity , which is required for association learning and adaptation to new reward contingencies , can influence choice behavior . The field model provides an integrated and dynamic account for the operations of sensorimotor integration , working memory and action selection required for decision making in ambiguous choice situations .
Actions beyond simple reflexes are generally not the direct consequence of a sensory input . Instead , the association of a specific sensory input with an appropriate action has to be learned from experience , and depends on the behavioral context . Often these context-dependent associations can be described in terms of a general mapping rule . In most situations , subjects can choose among more than one associated action . This requires a process for action selection , a form of decision making . We propose that a reward-based learning mechanism for forming new sensorimotor associations is integrated in the action selection system . Through this integration in a common neural substrate , the learning history directly influences the decision process . While traditional psychological theories tended to view decision making as the outcome of a higher cognitive process which is separate from perception and action [1] , more recent neurophysiologically motivated ideas emphasize the integrative nature of sensorimotor processing and action selection [2]–[5] . Several cortical areas form frontoparietal networks for making goal-directed saccades , like the lateral intraparietal area ( LIP ) and the frontal eye fields ( FEF ) , or goal-directed reaches , like the parietal reach region ( PRR ) and the dorsal premotor cortex ( PMd ) [6]–[9] . At the same time , neurons in these areas show signatures of valuation and selection of action , since their neural responses are modulated by the subject's choice preference based on reward expectancy or other decision variables [5] , [10]–[19] . We present a dynamic neural field ( DNF ) model that unifies the processes of sensory integration , working memory formation , associative learning and action selection in a context-dependent mapping task . The model implements a reward-driven Hebbian mechanism that allows it to learn simple associative sensorimotor mappings from reward history . The model selects from a continuum of ‘behavioral’ options through an integrated competition process between potential action plans . This framework reflects the conceptual idea of integrated sensorimotor and decision processing [2] , [20] . With this model , first , we explain adaptive decision behavior and its neural underpinnings in tasks which require rule-based selection of spatial motor goals . As an example , we use the model to mimic the behavioral and neural findings of a previous monkey experiment . In this experiment , the authors investigated the preparatory neural activity in situations in which in response to an ambiguous visual cue , two potential motor goals could be ‘freely’ chosen according to two different spatial transformation rules [5] . Varying reward contingencies lead to different choice behavior and neural activity patterns in this experiment . Previous models of decision making did not utilize learning in decision tasks with ambiguous choice situations , hence could not adapt to different reward contingencies . Conceptually , they either did not implement neural-inspired mechanisms of sensorimotor mapping , like threshold-models of decision making ( see [21] for review ) , or were limited to solve predefined target-selection tasks [2] . Other models , which implemented sensorimotor association learning , did not investigate decision making in ambiguous situations [22]–[24] . Second , we used our model to make predictions about specific patterns of choice errors in a generalization task . We tested the predictions which result from these assumptions in an additional behavioral monkey experiment . Our results provide support for two assumptions , which are more general than the specific examples for which we directly demonstrate the suitability of our approach . The first assumption regards the neural mechanism underlying context-specific “rule-based” spatial remapping in visuomotor tasks . It is in general unclear if rules that can be derived by abstraction from concrete examples are encoded as such in the monkey brain , or if instead the brain stores the individual underlying associations that constitute the rule . We propose that spatial mapping rules are learned , at least in our monkey experiments , by local associations . The nature of local associations limits the ability to generalize a mapping rule and imposes interactions between novel cues and already trained cue locations , which lead to specific patterns of choice errors . The second assumption regards the interaction of sensorimotor learning with adaptive choice behavior in action selection tasks . We propose that the same reward-driven Hebbian learning mechanism which allows learning of arbitrary stimulus-response mappings also contributes to adapting the choice behavior to changing probabilistic reward contingencies in a free-choice task , in addition to other biasing factors for adapting choice behavior . As an inevitable consequence , the learning and reward history influences the decision process , and biases the behavior in free-choice situations .
This study was granted permission to carry out experiments on vertebrates by the Niedersächsische Landesamt für Verbraucherschutz und Lebensmittelsicherheit , No 33-9-42502-047-064/07 . All animal work was conducted according to the German Animal Welfare Act and all experiments were conducted in conformity with the European Communities Council Directive of November 1986 ( 86/609/ECC ) . Our approach addresses sensorimotor association learning and decision making in situations in which context-dependent remapping of a spatial sensory ( e . g . visual ) location onto different motor ( e . g . reach ) goals is required , and the mapping is achieved according to geometric transformation rules . Different variants of the task were employed in previous studies [5] , [25]–[27] and are discussed in more detail below , but they all share the same basic structure ( Figure 1 ) : Two cues are presented , a spatial and a contextual cue , that together determine the rewarded goal location for a reach movement . The spatial cue is located at one of four equally spaced positions representing directions in the center-out workspace . The contextual cue can have two different colors and determines the mapping rule for the current trial . The mapping rule is either ‘direct’ ( green ) , meaning that the rewarded motor goal is located at the same position as the spatial cue , or ‘inferred’ ( blue ) , which means the rewarded goal is located in the direction opposite to the spatial cue . The reach movement has to be executed after a memory period upon a ‘go’-signal . We use a model architecture that consists of multiple dynamic neural fields ( DNF ) to capture the neural processes underlying cue perception , working memory for visual locations , movement plan formation , and movement initiation ( Figure 2A ) . Each DNF describes neural activation patterns at the population level . Its functional properties are determined by lateral interactions within each field ( Figure 2B ) , which are predefined , and its connections to other fields in the architecture , which are partly plastic . This model is not intended to serve as a comprehensive and strict anatomical model , which is why we will have to refrain from drawing simple one-to-one links between individual DNFs and corresponding cortical areas . Nonetheless , the model architecture captures the general structure of spatial processing pathways in the primate frontoparietal cortex ( see Discussion for a comparison to neurophysiology ) , and is largely analogous to a previous neurodynamic model that explicitly aimed to reproduce activation patterns in specific cortical areas [2] . The goal of the study is to emphasize general principles , which likely can be found in several sensorimotor subsystems , and to highlight these principles for a specific finding in specific cortical areas for which we have detailed knowledge . The model is largely pre-structured in its inter-field connectivity ( white projection arrows in Figure 2A ) . The pre-structuring allows it to perform basic functions without any initial training: These basic functions allow the model to produce a memory-guided reach directly towards a previously cued goal position as a default behavior . In addition to this , the model must be flexible enough to learn different spatial mappings from the spatial sensory input onto the motor output . This is achieved through additional plastic connectivity ( red connections in Figure 2A ) between input and output via a cue integration and association field . Plastic connections are adapted by a reward-driven Hebbian learning mechanism ( see below ) . DNFs describe neural activation patterns through the evolution of continuous activation distributions over time , emphasizing the role of attractor states and instabilities [20] , [29] . DNFs are based on the concept of population coding , in which a value along a certain feature dimension , e . g . the location of a visual stimulus or the endpoint of a planned movement , is represented through the distribution of activity within a population of neurons . These neurons have different tuning functions that sample the underlying feature space [30] , [31] . Abstracting from the discrete spiking neurons , DNFs directly describe the activation distributions over the underlying feature space [32]–[35] . This activation distribution evolves continuously in time under the influence of external input and lateral interactions , governed by a differential equation of the form Here , is the activation at time t for a position x along the underlying feature dimension , is its rate of change over time , which is scaled with a time constant , and is the ( negative ) global resting level for the field activation . Any point in the field receives external input , as well as endogenous input from other parts of the field . Furthermore , each point in the field is affected by additive noise , drawn from a normal distribution , that represents unspecific input and spontaneous activity . The lateral interactions are characterized by an interaction kernel , which consists of a local excitatory and a long-range inhibitory component . The lateral connectivity pattern reflects the mutual excitation between neurons with similar tuning curves and inhibition between those with dissimilar tuning curves . This interaction kernel is convolved with the output of the field , which is computed from the field activation via a sigmoid function , The field output is close to zero for low activation levels , rises around a soft threshold ( arbitrarily placed at zero ) , and saturates for higher activations . The specific pattern of lateral interactions promotes the formation of localized peaks of activation as attractor states of the field dynamics ( Figure 2B ) . Depending on the interaction parameters ( see Text S1 , Table S1 and S2 ) , different dynamic regimes can be achieved ( for a quantitative analysis see [35] ) : With moderately strong local interactions , multiple simultaneous peaks can provide a representation of ( multiple ) current inputs that is stabilized against fluctuations . For stronger self-excitation ( balanced by sufficient inhibition ) , peaks may become self-sustained in the absence of input , yielding a model of working memory ( similar to the implementation with spiking neurons described by [36] ) . If strong global inhibition is present in a field , a competitive regime is created in which only a single peak can form , implementing a winner-take-all selection that is stabilized over time . For numerical simulations , the conceptually continuous field dynamics have to be discretized in space and time . To perform comparisons with electrophysiological data , the field output at one point in the field is equated to the firing rate of neurons with corresponding selectivity profile . Evidence for a cortical organization that supports the neural field dynamic have been shown by [37] . The dynamic model for context-dependent reaching consists of a set of interconnected DNFs and discrete nodes that can be organized into four levels: Perception ( spatial and context input fields ) , memory and association ( association field ) , movement planning ( motor preparation field ) and movement initiation ( motor field ) , which are shown in Figure 2A . A complete formal description of the model with all parameter values is given in Text S1 , Table S1 and Table S2 . A direct pathway from the spatial input field to the motor preparation field and further to the motor field implements a default sensorimotor mapping that is functional prior to any task-specific learning . The direct pathway comprises three DNFs defined over a one-dimensional space . In our case this space represents the angular direction ( with circular boundary conditions ) of either the location of the spatial cue ( as direction from the central fixation point ) or the direction of a reach movement in a center-out reach task . The projections between the fields along this direct pathway are topologically organized , that is , the output at a certain point in one field drives activation at the corresponding point ( coding for the same direction ) in another field , and to a lesser degree in the direct neighborhood of that point . The spatial input field features relatively weak local interactions to form a stabilized representation of a currently presented spatial cue . It projects in a topological fashion onto the motor preparation field . The motor preparation field has moderate local excitatory and global inhibitory interactions , producing a soft competition behavior between different regions of the field . While these competitive interactions promote the concentration of activation in a single region , they still allow multiple activation peaks to exist simultaneously if they are driven by multiple localized inputs . The motor preparation field in turn reciprocally projects to the motor field in a topological manner . The motor field itself features stronger self-excitation and global inhibition , producing a strong selection behavior that only allows a single stabilized activation peak to prevail . The motor field is held at a low resting level during most of the time , so that it cannot form a peak from the motor preparation field's input alone . Only after the ‘go’-signal has been provided , the motor field is globally excited and an activation peak can form , simulating a gating mechanism for movement initialization . Similar gating mechanisms have been described for saccade generation [38] . When a peak has formed in the motor field , it projects back to the motor preparation field , such that the actually selected motor plan is reinforced in that field and others are suppressed . An additional indirect pathway from the spatial input to the motor preparation field runs through the association field . This field spans two dimensions . The first dimension of the association field corresponds to the angular spatial representation also used in the spatial input field , motor preparation field , and motor field . The second dimension of the field is initially ( i . e . , before training ) not associated with a specific feature , but instead provides redundancy in the existing representation to allow further specialization through learning . The association field receives one of its inputs from the spatial input field . This input is organized topologically along the spatial dimension , and is homogeneous along the second dimension . That means that a localized peak of activation in the spatial input field induces a vertical ridge of activation at the corresponding spatial location in the association field . The lateral interactions ( local excitation and global inhibition ) will produce a localized peak of activation from this ridge-like input ( see Figure 2A ) . The association field receives a second input from a set of two context input nodes . These nodes provide a simple , discrete representation of the context for the current trial ( direct or inferred ) , indicated by the color cue . The nodes feature self-excitation and mutual inhibition , such that if one node becomes sufficiently activated by external input , it will remain active and suppress activation of the other node . There is an all-to-all connection from the context input nodes to the association field , which is initially unspecific ( with small , random weights from each node to every point in the field , shown in Figure 3A ) . These connections are modified during the learning phase as detailed below , and can then influence where along the spatial input ridge an activation peak will form . When a peak has formed in the association field , it remains stable even without exogenous inputs due to strong self-excitation . This peak provides a second input to the motor preparation field . The projection from the association to the motor preparation field is initially topologically organized along the spatial dimension , so that it supports a delayed reach movement to the memorized location of a previously presented spatial cue , but it is likewise subject to learning . The projections from the context neurons to the association field and from the association field to the motor preparation field are adapted according to a reward-driven Hebbian learning rule [24] , [39]–[41] . We use two variants of the basic Hebbian rule that incorporate an implicit limit of weight growth , the ‘instar’ and ‘outstar’ learning rules in the formulation of Marshall [42] . These rules have successfully been used in topographical dynamic neural networks that are comparable to DNFs [43] , [44] . We further adapted them to be used in a reward-dependent manner: As in the original rules , the weights between active regions are strengthened if the reward signal is positive , but in addition they are weakened if the reward signal is negative . Physiologically , a teaching signal could be conveyed by dopaminergic neurons via the cortico-basal circuitry ( for review see [45] ) . It has been shown that dopamine neurons signal rewards through phasic activity and lack of expected reward through depressed activity [46] . However , our teaching signal does not habituate . Instead , we manually stop the learning process once the task has been trained . The learning rules are applied once for each trial after the system has selected a response , which is defined as a sufficiently strong peak in the motor field ( smoothed field output at one position exceeds a threshold ) . The direction of the planned reach , given by the position of the activation peak in the motor field , is compared to the rewarded goal location according to the task requirements . The trial is considered a success , with a reward signal of , if the reach direction falls within a tolerance window ( ±8° ) around the desired goal direction , and a failure , with a reward signal of , otherwise ( this corresponds to the omission of the actual reward in the electrophysiological study ) . The connection weights from the context neurons to the association field are updated according to the reward-dependent instar rule: Here , is the weight from the context node to position in the association field , is the change of that weight in one trial , is the association field output at position and the output of context node . The learning rate depends on the reward signal for that trial , and takes a larger value for and a smaller value for . In the case of negative reward signal , a normalization is introduced to ensure that the overall weight changes are comparable for successful and fail trials . With the instar learning rule , only those neurons in the association field that are active during a trial adapt their incoming connection weights from the context nodes . In the case of a positive reward signal , the weights of these neurons are adapted in such a way that the weight patterns become more similar to the current output pattern of the set of context nodes . The neurons whose weights have been adapted will be driven more strongly if the same output pattern of the context nodes appears again in subsequent trials , and will receive proportionally less input from different output patterns of the context nodes . Note that there is no normalization on the presynaptic side , such that multiple regions in the association field can form preference for the same context input without competition between them . This means that the instar rule supports development of divergent projections from the context nodes to the association field . The weights from the association field to the motor preparation field are adapted according to the reward-dependent outstar rule: Analogously , is the weight from position in the association field to position in the motor preparation field , is the change of that weight , and is the output of the motor preparation field . With the outstar rule , the normalization of the weights is reversed compared to the instar rule . Again , weights are only adapted for those neurons in the association field that are active in a given trial ( these are now the presynaptic neurons ) . If the reward signal is positive , outgoing weights of these neurons are adapted in such a way that the weight patterns become more similar to the postsynaptic output pattern in the motor preparation field , which reflects the actually performed reach . In the case of failed trial with negative reward signal , the connections from the active regions in the association field to the active region in the motor preparation field is weakened and the projection to all inactive regions is strengthened . This increases the probability that a different motor response is chosen in the next trial with the same conditions . Due to the normalization in this learning rule , each region in the association field can only strongly support a single motor response , but different regions may support the same response without competition between them . This means that the outstar rule supports development of convergent projections from the association field to the motor preparation field . In the first step , we will use our model to reproduce and explain the behavioral and neural observations of a previous monkey experiment , in which the authors investigated neural selectivity in the frontoparietal cortex during selection of rule-based spatial motor-goals [5] . The following scenarios were implemented to simulate this experiment . The monkey behavioral and neuronal data which we refer to in this study are taken from a previous electrophysiological study and are described in detail elsewhere [5] . Previously unpublished behavioral data is presented from one of the same monkeys to test predictions of the model ( see section Generalization in Results ) .
During the IR training task the model acquired the initially unknown inferred mapping rule , in addition to the default direct mapping . The model forms the required stimulus-response associations in the following way: The spatial cue induces an activation peak at the corresponding location in the association field , and at the same time in the motor preparation field ( via the direct pathway ) . The association field peak remains self-sustained after the input disappears , and keeps supporting the activation in the motor preparation field , due to the a-priori topology of this projection ( Figure 3B ) . The simultaneously presented contextual cue activates the corresponding context neuron , which likewise retains its activation through the neuron's self-excitation . In the trials early during IR training , a salient target cue then appears at the desired reach goal location . This new stimulus also drives activation in the motor preparation field via the direct pathway – for inferred trials at a location shifted by 180° from the original spatial cue location – and overrides the default reach plan which was induced by the first cue . In contrast , the memory peak in the association field remains largely unchanged , as it is stabilized by the lateral interactions , and suppresses the formation of new peaks . The movement onset is triggered at the end of the target cue presentation by a general disinhibition of the motor field ( reflecting the disappearance of the central fixation stimulus in the monkey study ) . This forces the selection ( activation ) of a single location due to the strong inhibitory interactions in the motor field . In correct trials , i . e . when the surviving peak in the motor field ( = the selected reach ) matches the goal location , the projections between active context nodes and active regions in the association field , as well as between active regions in the association and the motor preparation field , are strengthened , while others are weakened . Over the course of learning , the initially random connections from the context neurons to the association field ( Figure 3A ) are replaced by a more specific connection pattern: Early during training , patches with a preference for the inferred context form for the trained cue directions in the association field , dominating the central part of the context dimension due to the high proportion of inferred training trials ( Figure 3C ) . For these patches , the connection weights to the motor preparation field are changed accordingly , such that the original topological projections to the motor preparation field ( Figure 3B ) have shifted by 180° to implement the inferred reach response ( Figure 3D ) . Subsequently , patches with a preference for the direct context also appear , which retain the original topological projections to the motor preparation field . Through repeated reinforcement of initially random variations in the association peak positions , and under the influence of the lateral interactions in the fields , these coherent patches self-organize along the context dimension for each of the trained cue directions ( Figures 3E , G ) . The projections to the motor preparation field keep adapting to reflect the context preferences of different regions in the association field ( Figures 3F , H ) . The spatial positions that had not been trained ( i . e . spatial locations at which cues had never appeared ) do neither show a shift of their projections to the motor preparation field , nor do they show sensitivity for one of the context inputs . The IR trained model was then tested in the DMG task . It reached a performance of 99% ( n = 4000 ) . This successful training confirms that the model can perform both the direct and inferred reach in a flexible context-depending manner , by re-learning local associations . To test whether this architecture and its integrated learning mechanism can solve a more general class of tasks , we also tested the system with a larger number of different contexts , all indicating different mapping rules . For three different contexts ( with associated rotations of 0° , 180° , and 90° ) , the model still reached a performance of 94% in the DMG task after an analogous training procedure . For four contexts ( indicating required rotations of 0° , 180° , 90° and 270° ) , a performance of 90% was reached ( n = 4000 ) . The decrease in performance for a higher number of different contexts is a consequence of interference between different context preferences in the association field: If the number of contexts becomes too high , the context specific regions that form during learning are no longer cleanly separated , and the corresponding projections to the motor preparation field do not form correctly . This limitation could be overcome in the model either by making the interactions in the associations field sharper ( decreasing the kernel width in the context dimension ) or by increasing the field size along the context dimension . In a biological system , the former would correspond to a sharpening of tuning properties of the neurons and the latter would correspond to the recruitment of a larger number of neurons for the association task . A mechanism which learns spatial transformations via local associations instead of global geometrical rules is limited in its ability to generalize to new cue locations . We tested the generalization limits of our model and compared it to that of a monkey that performed the same task . The model was IR-trained with four spatial cue locations ( e . g . the four cardinal directions ) as described before . The model was then tested with four novel cue locations at positions between the trained locations ( oblique directions ) . The model was unable to fully generalize and perform the task to the new locations . Importantly , the model made specific goal selection errors ( Figure 4 ) . Trials with a direct context cue to trained cardinal directions were not impaired ( Figure 4a ) , and generalized to oblique goals with little performance deficits ( Figure 4c ) . This is not surprising , given the pre-existing default mapping via the direct pathway . Inferred generalization trials , instead , showed a particular error pattern: In inferred trials to the trained cardinal directions the model also showed errorless performance ( Figure 4b ) . Yet , in trials to an oblique inferred goal , the model either performed reaches to the direct reach goal ( context error , approx . 40% of trials ) , or to a learned inferred reach goal at an adjacent cardinal direction ( adjacent direction error , approx . 60%; Figure 4d ) . The monkey control experiment was performed accordingly ( previously unpublished data from one monkey ) . After learning context-specific direct and inferred reaches ( as described in Methods ) to four cardinal directions over the extended period of several weeks , the monkey was then tested with four oblique cue positions . Our reasoning was that the monkey should be able to generalize to the new locations with relative ease if the behavior was learned as a general , abstract rule . Conversely , if the inferred mapping was learned through local associations , proper performance should be restricted to the trained locations . The result was that the monkey performance remained high in blocks of trials in which the cardinal directions were used in either context ( >90% , Figure 4A , B ) , or the oblique directions were used in the direct context ( >95% , Figure 4C ) . But performance was clearly reduced in blocks in which the oblique directions were used in an inferred context ( Figure 4D ) . The same two dominant types of errors as in the model could be observed in the monkey: >20% context errors and approx . 60% adjacent direction errors . In summary , in the way we implemented a context-specific mapping task via local association learning in our model , it predicted specific spatial generalization errors which we could confirm in the monkey behavior . The model provides a mechanistic explanation for these particular error types . As detailed in the previous section , the association field has formed two context-specific regions for each trained spatial cue direction . This is the result of the Hebbian learning . The regions which are specific for the inferred context project to the reach field at a position opposite to the spatial cue direction . If spatial input arrives from the spatial input layer for an untrained direction , together with an inferred-context signal from the context input nodes , it will create a peak between two of the context specific sub-regions in the association field ( Figure 5A ) . This peak , which is self-sustained without exogenous input , may remain stable at this location . In this case , its projection to the motor preparation field will be centered on the position at which the spatial cue was presented ( since the direct projection is the default before learning ) . This will result in a direct reach instead of the instructed inferred reach ( context error ) . Alternatively , the peak in the association field may shift during the memory period to one of the regions that are selective for the ‘inferred’ context . These regions are moderately activated by the input from the context node , and if the activation peak slightly overlaps with one of them , it can get pulled towards it ( Figure 5B ) . These regions implement the ‘inferred’ projection to the opposite direction in the motor preparation field , but only for the trained cardinal directions . The result will therefore be a reach in an inferred direction that is adjacent to the goal direction ( adjacent direction error ) . The ratio of these two types of errors is determined by the ratio between the size of the field and the width of the lateral interactions . We note that the adjacent direction error can also occur in oblique trials with the ‘direct’ context signal , and does appear in the simulation results in a small proportion of trials ( Figure 4C , approx . 3% of trials ) . As in the inferred trials , this error is caused by a shifting of the peak in the association field from the untrained oblique direction to a trained cardinal direction under the influence of the input from the context nodes . However , the regions in the association field with a ‘direct’ preference are smaller than those with an ‘inferred’ preference , and typically situated at the borders of the field . They are therefore much less likely to overlap with the peaks that form in the oblique trials . Nonetheless , this type of error cannot be completely precluded . A core idea of our approach is that the mechanisms which are implemented for learning sensorimotor associations allow the network to also adapt its reward-based choice behavior . We tested this by confronting an IR-trained model with different reward schedules . To emulate the scenario of the previous monkey experiment [5] , we picked a specific constellation of reward schedules , but the results are not restricted to this case . After IR-training , the model is capable not only of correctly performing DMG trials , but also instructed trials in which the context cue appears later than the spatial cue ( PMG-CI trials ) . In these trials , the model achieved 92% ( n = 4000 ) correct choices ( monkey performance in electrophysiological study was >98% ) . We then probed the model's free-choice behavior by presenting a spatial cue but no context cue ( PMG-NC trials ) . Results show that our training procedure induces an inherent bias ( 93% ) for inferred choices in free-choice situations ( see below for systematic analysis of this effect ) , like was the case in the monkeys ( 85%±2% inferred trials; Figure 6A ) . For probing the inherent bias we used the equal-probability reward schedule ( EPRS , see Methods ) , which creates no incentive to change the choice behavior . The bias for selecting inferred reaches is also apparent in the output pattern of the motor preparation field in the model ( Figure 7A ) , which qualitatively reproduces the observed neural activity in monkeys' PRR ( Figure 7B ) : When the spatial cue is presented , activation initially rises for the direction of this cue ( corresponding to the preparation of a direct reach ) . In the model , this is the result of the direct pathway from the spatial input field to the motor preparation field . However , this direct plan is quickly replaced by activation for the inferred reach ( in the opposite direction ) , and this activation remains throughout the memory period . If a context cue is given at the end of the memory period ( PMG-CI trials ) , the activation in the motor preparation field can undergo another change: If the cue for the direct context is given , the field activation rises strongly for the direction of the spatial cue ( the rewarded goal direction for this case ) , and the activation supporting the inferred reach ceases . If the cue for the inferred context is presented , the peak of activation retains its position , and rises further when the motor response is selected . We will further investigate the underlying mechanism for the bias in the following section . We then switched to a bias-minimizing reward schedule ( BMRS ) . The model parameters ( connection weights ) that had developed in the previous testing phase were taken as starting conditions . The model developed a balanced choice behavior under the new reward schedule ( Figure 6B; model: 41% direct reaches , n = 4000; monkeys: 46%±3% direct reaches ) , and , correspondingly , the two potential motor goals are equally represented in the motor preparation field during the memory period ( Figure 7C ) . These two effects were also seen in the monkey data ( Figure 7D ) . The adaptation to the new reward schedule confirms – as we expected based on the reward-dependent learning rule that is used – that the DNF model is capable of adapting its choice behavior to increase its overall reward probability , in a fashion that is consistent with the experimental data . A major implication of an overlapping neural substrate and shared learning mechanism for sensorimotor association learning and reward-based action selection is that the learning history must inevitably influence the choice behavior . A surprising finding in our previous monkey study was the strong bias of the well-trained monkeys to almost exclusively prepare and execute the inferred reach in free-choice situations with EPRS reward . We hypothesized that this bias arose from the higher number of inferred reach trials during early training [5] . Similar effects can also be observed in human behavior [47] . We used our adaptive DNF model to show how the reward-dependent Hebbian-type learning can reproduce this bias in the decision process as an effect of the input statistics during training . In the model , the initial presentation of the spatial cue induces the formation of a sustained activation peak in the association field ( Figure 8A ) . In the absence of context input , e . g . in the memory period of PMG trials , the separate regions with different context preferences do not influence the activation distribution in the DNF , and the peak typically spans both regions . In trials with later context instruction ( PMG-CI ) , the subsequent presentation of a context input changes the attractor states of the DNF , and the activation peak shifts towards the region that has a preference for the given context ( Figure 8B ) . The projections from that region to the motor preparation field then select the appropriate action . In free-choice trials without context instruction ( PMG-NC ) , the choice behavior depends on the connectivity structure which was imposed by the earlier training . As presented above , when the model is trained with a ratio of 80% inferred trials to emulate the intense inferred reach training procedure in the electrophysiological study , it develops a bias to prepare the inferred reach in PMG trials , with a time course of activation in the motor preparation field that qualitatively reproduces the recorded neural activity in monkeys ( Figure 7A , B ) . The cause for this bias in the model is that the regions in the association field that have developed a preference for the inferred context are substantially larger than those for the direct context , as a result of the inferred reach being performed more frequently during training ( see Figure 3b ) . When the activation peak in this field forms before the presentation of the context cue , it typically covers a larger area of the inferred-context region ( Figure 8A ) , resulting in a stronger projection to the opposite reach direction in the motor preparation field . The competitive interactions in the motor preparation field then suppress the activation for the weakly excited direct reach direction . We systematically varied the ratio of direct to inferred trials during the IR training in the model and tested the resulting spatial response profiles in the motor preparation field and the choice behavior in PMG-NC trials ( Figure 9 ) . The number of inferred choices increases continuously with the ratio of inferred trials during IR training ( Figure 9A ) , in an approximately sigmoid fashion ( logistic function fit: m = 0 . 633 , β = 23 . 4; MSE = 2 . 19 * 10−4 ) . Note that the sigmoid curve is not centered at 50% inferred training trials , but at close to 60% . This is an effect of the direct reaches being the default action before training , implemented by the initial connection pattern from the association field to the motor preparation field . The difference in the underlying activation strength for inferred versus direct goal representations in the motor preparation field during the memory period also increases monotonically and in an approximately sigmoid fashion with the fraction of inferred trials during IR learning ( Figure 9B , fit with a scaled and shifted sigmoid curve , : m = 0 . 664 , β = 7 . 22 , a = 13 . 2 , b = −7 . 20 , MSE = 0 . 0897 ) . Note that this result is indeed an effect of the input statistics , not of the expected reward for different choices . Even in training sets with 100% reward rate for both direct and inferred reaches the described biases still developed in the model ( data not shown ) .
Delineating a 1-to-1 correspondence between our model and the neurophysiological functional architecture of the primate brain can obviously only be coarse for several reasons . For example , it is yet unclear to a large extent , how many , which and in which way brain areas ( cortical and subcortical ) contribute to such high-level tasks as context-dependent , rule-based , and reward-driven visuomotor reach-goal selection . For example , similar task-related neural activation patterns during spatial goal selection tasks can be found in parietal and in premotor areas ( e . g . [5] , [10] ) , with the mutual roles of these areas not being clear yet . Also , similarity in neural activation patterns during manual and ocular selection tasks suggests that equivalent mechanisms are implemented in the oculomotor systems [21] , [54] , [55] , yet both systems comprise different cortical and subcortical structures . The intra- or interareal connectivity pattern of the recorded neurons is typically not available with the currently available recording techniques , and many other areas in the cerebral cortex are not explored yet at the required level of detail provided by single cell electrophysiology . These factors impair model validation . Yet , the model should be seen as a rough sketch of cortical frontoparietal visuomotor processing . The spatial input field is retinocentrically organized , and mimics the organization of extrastriatal visual cortex and the available dorsal-stream visual input to the frontoparietal reach network via areas V6/V6a in the parieto-occipital sulcus [56] , [57] . The context input field provides a simplified color/rule representation . This input reflects the currently valid mapping rule and could originate in frontal lobe areas ( e . g . , dorsolateral prefrontal cortex , PFC ) in which the task-relevant stimulus features have already been extracted , and the task rule rather than the actual color of the stimulus is represented [58] , [59] . On the output side of the model , the motor preparation and the motor field employ a population code over possible movement directions . Such encoding of a movement plan or motor preparatory signals is based on neural population activity patterns during reach preparation in cortical motor areas [60] , [61] . The direct pathway in our model can be seen as reflection of the forward projection along the dorsal visual stream and via dorsal premotor cortex to the primary motor cortex . Alternatively , the direct pathway could be motivated by low-level integration paths [62] , e . g . , the retinotectal visual pathway in case of saccadic tasks , which can bypass cerebrocortical processing especially during stimulus-triggered oculomotor behavior ( for review see [63] ) . To draw connections to empirical findings in our specific task , we compare the field output of the motor preparation field to electrophysiological data from the posterior parietal cortex [5] , but we note that the premotor cortex shows very similar activity patterns [5] , [10] . We assume that the motor preparation field provides a functional representation that might be anatomically distributed over the frontoparietal sensorimotor cortex . The motor field could be equated to parts of the primary motor cortex ( M1 ) and caudal parts of PMd . The implemented gating mechanism in the motor field ( gain change as result of ‘go’ cue processing ) , might be a function provided by subcortical structures . It has been suggested that modulation of motor activity similar to a gating mechanism , i . e . facilitation or inhibition , could be provided by the basal ganglia via the so called ‘direct’ or ‘indirect’ pathway ( not to be confused with our use of the terms ) ; for review see [64] . The indirect pathway in our model allows for flexible , context-specific , goal-directed behavior . The two-dimensional association field , which implements the working memory and the actual rule learning , is reminiscent of processing in the cortico-basal loops between PFC and the premotor cortex ( PMC ) with the basal ganglia [65]–[68] . Certain aspects of the association field could also be localized in the frontoparietal loop , since especially PMd was shown to be relevant for learning abstract visuomotor associations ( for example see [6] , [48]–[52] ) . Also , the development of a combined selectivity for reach direction and context input is consistent with a gain modulation by context described for neurons in PMC and posterior parietal cortex ( PPC ) areas [26] . We note that the second ( context ) dimension of the association field in the model is initially simply providing redundancy in the representation , and only through the learning process it takes on the role of separating different context preferences . Corresponding redundancies may well exist in pre-motor areas in the cortex , such that the combined direction/context selectivity can develop through specialization of neural response properties . We assume that similar redundancies would also exist in the other representations in the model , but are not made explicit since they are not critical for the model's behavior . When we designed the adaptive DNF architecture , we assumed that the behavior of the monkeys in the experiment did not rely on an explicit representation of a geometrical transformation rule to achieve the visuomotor mapping , but rather on specific associations between individual stimulus combinations and the rewarded motor response . This may at first seem counterintuitive for a task that can be described unambiguously through a simple rule . It should be noted , however , that from a computational perspective the forming of concrete associations ( which can be achieved by established mechanisms like Hebbian learning ) is much more straightforward than the recognition and implementation of a general rule . Our assumption was supported by the control experiment , in which the monkey had to generalize the learned mapping “rule” to untrained positions . If the monkey applied a geometric transformation rule , one would have expected easy generalizing to novel goal directions . Instead , the monkey showed a highly specific pattern of errors that the model was able to predict , and which in the model was an emergent effect of the local association learning . Note that the observed failed reaches could not be explained by a failure of the monkeys to identify the proper context , since direct trials in all directions and inferred trials in cardinal directions were conducted correctly . Instead , the associated motor responses to the untrained oblique goal positions in the inferred context were undefined . This led to responses which were either guided by the default behavior ( a seeming ‘context’ error ) , or which resulted in the selection of a neighboring trained motor association ( adjacent direction error ) . These observations suggest that the context-dependent reach task in monkeys was not learned through the application of a general mapping rule to the spatial cue positions , but rather by individual , local associations between the spatial and context cue and the rewarded reach location . The adaptable DNF model implements such association learning in that it develops specialized attractor states in the association field , with dedicated sub-regions which prefer different mapping “rules” . In this implementation the context errors originate in the initial topological connection pattern from the association field to the motor preparation field , which is still prevailing after the IR-training for those spatial cue directions that have not been trained . The adjacent direction errors can be explained by a spread of activation from such untrained regions in the association field to neighboring sub-regions which were affected by the IR training and are now associated with the inferred context . In the model , the adjacent direction error also occurs in small percentage of the oblique direct trials , due to the same mechanism . The fact that this error is not observed in the experimental data may indicate that some aspect of the task is not fully captured by the computational model . For instance , the representation and processing of the direct and inferred context signals may not be as symmetrical in the biological system as it is in the model . In particular , the context signal might affect the processing more globally , e . g . by strengthening the direct pathway for the ‘direct’ context cue and the indirect pathway for the ‘inferred’ context cue . This would decrease the impact of training certain directions on the behavior in direct trials . Such a mechanism of executive control has previously been employed in a DNF model of task switching [69] . We note that this implementation of the spatial mapping rule in the association field does in principle not have to be locally restricted . If the sub-regions that implement the ‘inferred’ mapping were expanded over the whole spatial dimension , and if their projections to the motor preparation field were changing in a more continuous fashion , they could implement the general mapping rule for arbitrary spatial cue directions . Forming such a connection pattern would require a sufficiently large number of training directions , which would provide the necessary fine sampling of the directional space to generalize the mapping rule to all directions through averaging . Conversely , the model in its current state is not capable of generalization in a stricter sense , such as the transfer of a rule to completely novel stimuli . Introducing such capabilities would require a substantial extension of the current architecture . This does not mean that the mechanism we presented cannot also be involved in the learning of abstract rules . It is conceivable ( e . g . in the case of humans performing this task ) that generalized connection patterns as described above for different mapping rules accumulate and prevail in the system . Learning a specific variation of a mapping task then only requires the association of the context cue with the appropriate known mapping . This would allow a fast generalization from few examples . In general , however , we propose that the learning via local associations may be the default case , and that forming of true generalizations is an extension that builds on previously learned associations and additional neural structures . With the adaptive DNF model , we integrate two behavioral functions in a single neural architecture . On the one hand , we provide a process model of movement plan formation and action selection . It is in this respect similar to another recent modeling study of decision making in the fronto-parietal cortex [2] . It extends this previous approach to allow the selection of motor goals that were not explicitly spatially cued ( inferred reaches ) in a context-dependent manner . On the other hand , the model also incorporates a learning mechanism that allows it to acquire new visuomotor associations and thereby at the same time become adaptable to different reward schedules in ambiguous choice situations . The learning mechanism allows a close emulation of the training procedure in the monkeys . In particular , it does not require an explicit teaching signal for the desired motor response , as has been used in neural network models of the same task [22] . Instead , the desired behavioral response is shaped by using a second visual cue ( which is processed by the system in the same way as other visual cues in the task ) and reinforced through reward . Other theoretical accounts that focus on the learning process deal only with a small number ( typically just two ) of possible response choices , represented by discrete nodes [24] , [40] , [70] , [71] . They are therefore less suited to capture the process of action selection from a continuous space of motor acts in the fronto-parietal network , and could not possibly explain the resulting consequences for the generalization behavior that we found . Most of these models also do not investigate behavioral biases and free choice tasks , although Soltani and Wang [72] showed how the posterior probability for a choice alternative being rewarded , given a set of cues , could be computed by synapses trained with a reward-dependent learning rule comparable to the one used in our system . Again , this model dealt only with a two-alternative choice and the cues independently predicted the rewarded choice , whereas in our task two different types of cues must be combined to determine the rewarded response . A reward-driven Hebbian learning algorithm enables the model to adapt to changes in the reward schedule in a manner similar to what is called the ‘matching law’ . This means , biases in the reward schedule can produce biases in choice behavior and thereby adapt the choice to the reward probabilities [11] , [73] , [74] . Since the model learns the sensorimotor associations and the reward contingencies via the same projections , the model's sensitivity to the reward history in free-choice trials interacts with its learned associations , and vice versa . For example , if the ratio of free-choice trials is very high , it can happen that the model ‘unlearns’ the initially trained mapping because the context-sensitivity of the association field and the conjunction of the context with specific projections to the motor preparation field slowly decay in the free-choice trials ( data not shown ) . The observation that errors can cancel the learned mapping has similarly been made in a model by Fusi et al . [24] . Reducing the learning rates after initial learning of multiple associations would slow such unlearning process but also decrease the sensitivity to changing reward schedules . If the susceptibility to changing reward schedules in free-choice should stay high , then it is necessary to also present regular instructed trials along with the free-choice trials to preserve the learned associations , which is what we did here and in our previous monkey experiments . Interestingly , in free-choice tasks which do not encourage balanced behavior ( i . e . , choice-independent reward schedules like our EPRS ) , the learning algorithm can easily lead to a biased behavior . Even small imbalances in the probabilities of either choice can self-enhance the probability of the same choice in later trials . This is especially true if the reward probability is high ( e . g . 100% ) , in which case an initially randomly chosen option will be more likely to be chosen again . Such a behavioral bias in free-choice trials is evident in our electrophysiological study [5] and has also been reported in other studies [16] , [75] . Not only does the free-choice reward learning affect the learned associations , but , vice versa , the input statistics during learning of the stimulus-response associations also have an impact on the free-choice behavior , as our model results show . For example , the model's free-choice behavior can be biased even if the model is perfectly able to solve the instructed tasks . Humans rely on prior probabilities if they have to base their decision on lacking or ambiguous evidence [47] , [76]–[78] . From a Bayesian point of view , the activation distribution in our model during the memory period of PMG trials can be interpreted as a representation of the prior distribution . If in a potential motor goal trial no further information is provided , the model decides according to this prior distribution . If further evidence is provided , as is the case at the end of the memory period in our instructed trials , then the prior distribution is over-ruled ( Figure 7 ) . Since the probabilities for direct and inferred trials were equal during the recording experiments in the electrophysiological study , but the monkeys still both showed the same strong bias in favor of inferred reaches , we assume that the inferred bias was acquired during the training of the task , when more inferred than direct trials were presented ( unpublished observation ) . Our model successfully integrates sensorimotor processing and working memory formation with decision making . The reward-driven Hebbian learning mechanism which we use for learning context dependent visuomotor mappings is sufficient to also explain adaptation to probabilistic reward contingencies and at the same time creates susceptibility for input statistics during learning . With our model we could reproduce the electrophysiological results from a previous study [5] , which showed a similar dependency on reward contingencies . Since continuous reward-driven neuronal weight adaptations change the behavior in free-choice trials , we can also explain how manipulations of the reward schedule produce any ratio of biased behavior , as has been observed in other physiological studies [11] , [73] , [74] and could be the source of matching behavior in foraging tasks . From this integrated approach , we can also provide a concept for how biased behaviors in decision tasks can emerge from the learning history of the system . | Decision making requires the selection between alternative actions . It has been suggested that action selection is not separate from motor preparation of the according actions , but rather that the selection emerges from the competition between different movement plans . We expand on this idea , and ask how action selection mechanisms interact with the learning of new action choices . We present a neurodynamic model that provides an integrated account of action selection and the learning of sensorimotor associations . The model explains recent electrophysiological findings from monkeys' sensorimotor cortex , and correctly predicted a newly described characteristic pattern of their choice errors . Based on the model , we present a theory of how geometrical sensorimotor mapping rules can be learned by association without the need for an explicit representation of the transformation rule , and how the learning history of these associations can have a direct influence on later decision making . | [
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] | 2012 | Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making |
Calprotectin , the most abundant cytoplasmic protein in neutrophils , suppresses the growth of Staphylococcus aureus by sequestering the nutrient metal ions Zn and Mn . Here we show that calprotectin can also enhance the activity of the SaeRS two component system ( TCS ) , a signaling system essential for production of over 20 virulence factors in S . aureus . The activity of the SaeRS TCS is repressed by certain divalent ions found in blood or neutrophil granules; however , the Zn bound-form of calprotectin relieves this repression . During staphylococcal encounter with murine neutrophils or staphylococcal infection of the murine peritoneal cavity , calprotectin increases the activity of the SaeRS TCS as well as the production of proinflammatory cytokines such as IL-1β and TNF-α , resulting in higher murine mortality . These results suggest that , under certain conditions , calprotectin can be exploited by S . aureus to increase bacterial virulence and host mortality .
S . aureus is an important Gram-positive human pathogen colonizing the skin , anterior nares and other mucosal surfaces in approximately 30% of the human populations , causing a wide variety of diseases [1] . The pathogenesis of S . aureus requires multiple virulence factors , and the expression of those virulence factors is controlled by multiple regulatory systems such as SarA family transcription factors , the agr quorum sensing system , and the SaeRS two component system ( TCS ) [2 , 3] . The SaeRS TCS is composed of the sensor kinase SaeS and the response regulator SaeR along with two auxiliary proteins SaeP and SaeQ [4 , 5 , 6] . Conserved in all clinical isolates of S . aureus , the SaeRS TCS controls the production of more than 20 virulence factors ( e . g . , hemolysins , leukocidins , coagulases and immune evasion molecules ) and plays an essential role in staphylococcal survival and pathogenesis [7 , 8 , 9] . S . aureus appears to use the SaeRS TCS to adapt to hostile host environments such as innate immune responses . The sensor kinase SaeS is activated by human neutrophil peptides ( HNPs ) , small peptides with antimicrobial activity found in the primary granules of human neutrophils [5 , 10] . In addition , several sae-regulated gene products show anti-neutrophil properties [11 , 12 , 13 , 14] . Neutrophils are the most abundant white blood cells in human blood , consisting of 40–70% of the total white blood cell count . As the first line of defense at the site of bacterial infection , neutrophils phagocytose invading bacteria and kill them using reactive oxygen species , granule proteins ( including HNPs ) , enzymatic intracellular degradation , or via neutrophil extracellular traps ( NETs ) [15 , 16] . In addition , the neutrophil cytoplasmic protein , calprotectin ( CP ) , has antimicrobial activity toward various infectious fungi and bacteria including S . aureus [17 , 18 , 19] . CP is a heterodimeric S100 class EF-hand Ca-binding protein composed of S100A8 and S100A9 subunits ( also called Mrp8/14 ) . In addition to its four Ca binding sites , CP contains two transition metal binding sites S1 and S2 at the subunit interface . S1 can bind to both Zn and Mn whereas S2 binds only Zn [20] . CP is produced primarily by neutrophils and monocytes and released at the sites of inflammation [21 , 22] . As a main component of NETs and tissue abscesses , its concentration can reach over 1 mg/ml [19] . In tissue abscesses , the sequestration of Zn and Mn by CP suppresses the growth of S . aureus [19] and impairs the activity of Mn-dependent superoxide dismutases , rendering S . aureus more susceptible to oxidative stresses [23] . As a ligand for Toll-like receptor 4 ( TLR4 ) , CP can amplify inflammatory responses [21 , 24 , 25] and increase the migration of neutrophils to inflammation sites without affecting their effector functions [26 , 27 , 28] . During our study on the impact of nutrients on the SaeRS TCS activity , we determined that the divalent ions Zn , Fe , and Cu repress the SaeRS TCS . As a result of its ability to bind Zn , we predicted that CP would affect the SaeRS TCS . Therefore , using a clinical isolate of USA300 , the predominant PFGE ( pulsed-field gel electrophoresis ) type of community-associated methicillin-resistant S . aureus ( CA-MRSA ) in the United States [29] , we examined the role of CP in the activation of the SaeRS TCS . In addition , we also investigated how the proinflammatory property of CP affects host survival during staphylococcal infection . Our results suggest that , during murine peritoneum infection , the antimicrobial protein CP enhances the activity of the SaeRS TCS and , by inducing the production of proinflammatory cytokines , increases host mortality .
The activity of the P1 promoter of the sae operon and the expression of SaeQ are indicators for the activity of the SaeRS TCS [4 , 5] . When the expression of SaeQ was analyzed in three different growth conditions , the strain USA300 showed a much higher expression of SaeQ in RPMI ( Roswell Park Memorial Institute medium ) than in either TSB ( tryptic soy broth ) or human serum ( Fig 1A ) . The distinct SaeQ expression pattern was not observed with the strain Newman , which carries SaeS L18P , a mutant SaeS with constitutive kinase activity [4 , 30] ( Fig 1A ) , suggesting that SaeS is responsible for the distinct expression of SaeQ in USA300 . As compared with TSB or human serum , RPMI contains a low level of divalent metal ions [31] . To examine whether the higher SaeQ expression in RPMI is due to the lower content of divalent metals , we added EDTA to the culture media and examined the SaeQ expression . Although no significant change was observed with TSB and RPMI , the SaeQ expression was increased in human serum ( Fig 1A ) , suggesting that , in human serum , divalent metal ions repress the SaeRS TCS . No change in TSB indicates that the SaeRS TCS is suppressed in the growth medium by hitherto unidentified factor ( s ) different from divalent ions . Addition of Fe to RPMI reduced both the P1 promoter activity and the SaeQ expression , whereas the repression was abolished by EDTA treatment ( Fig 1B ) . To find additional biologically relevant metal ions that repress the SaeRS TCS , various metal ions present in either human blood or neutrophil granules were added to RPMI medium at their physiological concentrations [32 , 33] . As shown , P1 promoter activity and SaeQ expression were repressed by Cu , Fe , and Zn , and the metal-mediated repression was relieved by EDTA ( Fig 1C–1E ) . This result also confirms the previous report that Cu can repress the SaeRS TCS [34] . As a sensor kinase , SaeS possesses autokinase , phosphotransferase , and phosphatase activities . To further understand the metal-mediated repression of the SaeRS TCS , we purified MBP ( maltose-binding protein ) -SaeS and examined the response of the enzymatic activities of SaeS to the metal ions . The autophosphorylation of SaeS was significantly inhibited by 10 μM Zn or 50 μM Cu ( Fig 2A ) , suggesting that the divalent Zn and Cu ions represses the autokinase activity of SaeS . Fe did not inhibit the SaeS autokinase activity until the concentration reaches 500 μM , indicating that , at its physiological concentration , Fe does not inhibit SaeS autokinase activity . Neither the transfer of phosphoryl group from SaeS to SaeR ( i . e . , phosphotransferase activity ) nor the level of phosphorylated SaeR ( i . e . , phosphatase activity ) was affected by the metal ions ( S1 Fig ) , suggesting that Cu and Zn specifically inhibit the autokinase activity of SaeS . Since SaeS is embedded in the cell membrane , we overexpressed SaeS in the strain USA300 , purified the cell membranes and repeated the autokinase assay for Zn with the purified cell membranes . As shown in Fig 2B , Zn inhibited the phosphorylation of SaeS in the cell membrane in a concentration-dependent manner . Since the SaeRS TCS is activated by neutrophils [35] , in the following studies , we focused our investigation on Ca , Mn , Fe , and Zn , the major divalent ions found in neutrophil granules [32] . We reasoned that , since CP binds Zn with high affinity , it might be able to reduce the Zn-mediated repression of the SaeRS TCS . To test this possibility , we grew S . aureus cells in RPMI supplemented with CP and one or all the four metal ions and measured P1promoter activity and SaeQ expression . When the growth medium was supplemented with Fe , as expected , CP failed to restore the activity of the SaeRS TCS ( Fig 3A ) . However , when the medium was supplemented with Zn , CP restored the activity of the SaeRS TCS ( Fig 3A ) , demonstrating that indeed CP can protect the SaeRS TCS activity from the Zn-mediated repression . Intriguingly , when the growth medium was supplemented with all four metal ions , CP restored the SaeRS TCS activity , despite the presence of Fe ( Fig 3A ) . We noted that , in the experiment above , we added 364 times more Zn ( 400 μM ) than CP ( 1 . 1 μM ) to the growth medium . Therefore , most of Zn ions ( > 99% ) are expected not to be bound to CP , and simple sequestration of Zn by CP cannot explain the restoration of the SaeRS TCS activity . In addition , despite the fact that CP does not bind to Fe , when CP was added to the growth medium supplemented with all the four metal ions , it restored SaeRS TCS activity ( Fig 3A ) . Based on these results , we hypothesized that , by binding to Zn , CP gains the ability to protect the SaeRS TCS not only from Zn but also from Fe . To test this hypothesis , we grew the strain USA300 in RPMI , added Fe and Zn-CP mixture of various ratios ( 0–364 ) , and then measured the SaeRS TCS activity . Indeed , CP restored the SaeQ expression only when Zn was also present ( Fig 3B ) , and the restoration of SaeQ expression required more than 2 h incubation ( Fig 3C ) . Moreover , a mutant CP incapable of binding to Zn failed to protect the activity of the SaeRS TCS , while CP mutants retaining one binding site still protected [20] ( Fig 3D ) . These results suggest that binding of Zn confers CP with the ability to protect the SaeRS TCS from repression by Zn and Fe . Since the SaeRS TCS is activated by human neutrophil peptides ( HNPs ) [5] , we examined the effect of Zn-CP on the HNP1-mediated activation of the SaeRS TCS . As shown in Fig 3E , Fe inhibited the HNP1-mediated activation of the SaeRS TCS; however , the addition of Zn-CP abolished the inhibition by Fe , and the P1 activity and SaeQ expression were rather further increased . Based on these observations , we concluded that CP and HNP1 can cooperatively increase SaeRS TCS activity . To understand the effect of CP and Zn on staphylococcal gene expression , we treated the strain USA300 at its exponential growth phase with CP , Zn , or Zn-CP and assessed the transcriptome changes at 4 h post treatment by RNA-seq analysis . The CP treatment altered the transcript level of 221 genes ( Fig 4A , S1 and S2 Tables ) , indicating that the effect of CP is global and not limited to the SaeRS TCS . The Zn and Zn-CP treatments induced profound changes in the staphylococcal gene expression affecting 949 and 871 genes , respectively ( Fig 4A , S3–S6 Tables ) . The Zn and Zn-CP treatments shared 396 ( up-regulated ) and 305 ( down-regulated ) genes ( Fig 2B ) , indicating that , when Zn and CP were added together , Zn exerts a dominant effect on the staphylococcal transcription . The dominant effect of Zn over CP is also corroborated by the principal component analysis ( S2 Fig ) . Nonetheless , the addition of CP to Zn also elicited up-regulation of 45 genes and down-regulation of 133 genes , which was not observed in the Zn-treated cells ( Fig 4B ) . When only the sae regulon was analyzed , CP up-regulated 7 genes , while Zn down-regulated 28 genes ( Fig 4C ) , confirming the positive and negative effects of CP and Zn on the SaeRS TCS . When treated with Zn-CP , overall , the sae regulon remained repressed , as compared with control medium condition ( Fig 4C ) . However , as compared with Zn treatment , all genes showed a higher level of transcripts to disparate extents . In particular , two genes , SAUSA300_1055 and 1757 , showed transcript levels similar to that in the control medium ( Fig 4C ) . When compared with Zn treatment , the Zn-CP treatment significantly up-regulated 14 genes and down-regulated 43 genes ( S7 and S8 Tables ) . Of the 14 up-regulated genes , 10 contain the SaeR binding sequence in their promoter region ( S7 Table ) , indicating that , although the CP induces global changes in staphylococcal gene expression , the protective effect in the presence of Zn is rather specific to the SaeRS TCS . To investigate the role of CP in the activation of the SaeRS TCS by neutrophils , we generated a GFP reporter system for the P1 promoter and integrated it in the chromosome of the strain USA300 ( S1A Fig ) . The resulting reporter strain showed significantly higher GFP signal , as compared with no-promoter control ( S3B Fig ) , and responded to the repression by Fe and Zn ( S3C Fig ) . When the reporter strain was mixed with murine neutrophils purified from either wild type or CP-deficient mice , a higher GFP signal was observed in the presence of wild type neutrophils at 4 h post incubation , and it was more pronounced at 16 h ( Fig 5A ) , suggesting that CP indeed contributes to the activation of the SaeRS TCS during encounter with murine neutrophils . As an endogenous ligand for TLR4 , CP amplifies the endotoxin-induced secretion of TNF-α and other proinflammatory cytokines [24 , 36] . In addition , the SaeRS TCS is reported to induce the production of proinflammatory cytokines including interferon-gamma ( IFN-γ ) from murine neutrophils [37 , 38] . To examine the role of CP in cytokine production during staphylococcal infection , we measured proinflammatory cytokines released by murine neutrophils at 16 h post infection . In the CP-deficient mice , the production of IL-1β and MIP-2 was decreased while that of IL-6 and TNF-α was increased ( Fig 5B ) , indicating that CP can affect the production of proinflammatory cytokines by murine neutrophils . Since CP was required for full activation of the SaeRS TCS , we further asked the question whether or not CP also affects the function of neutrophils , notably migration and bacterial killing . Flow cytometry analysis showed no significant difference in neutrophil recruitment at the infection site ( S4A Fig ) . The number of recruited neutrophils was also comparable between the two mice strains ( S4A Fig ) . In addition , upon contact with S . aureus , both wild type and the CP-deficient neutrophils formed neutrophil extracellular traps ( NETs ) [15] at a similar efficiency ( S4B Fig ) , resulting in equivalent secretion of DNA ( S4C Fig ) . We also found that both wild type and the CP-deficient neutrophils killed the bacteria with a similar efficiency ( S4D Fig ) . Taken together , these results demonstrate that CP does not affect either migration or the bactericidal activities of neutrophils . To further investigate the role of CP in the activation of the SaeRS TCS in vivo , we infected wild type and the CP-deficient mice with the P1-gfp reporter strain via intraperitoneal injection; then the GFP signal was measured in the host cell-free or host cell-associated fraction of peritoneal fluid by flow cytometry . Wild type mice showed a higher GFP signal than did CP-deficient mice in both host cell-free and host cell-associated fractions at both 2 h and 12 h post infection ( Fig 6A and S5 Fig ) , confirming that CP contributes to the activation of the SaeRS TCS during staphylococcal infection . Host cell-associated fraction showed 6–20 times higher GFP signal than host cell-free fraction , indicating that the SaeRS TCS is activated mainly upon contact with host cells . In addition , the GFP signal at 2 h was 2–4 times higher than that at 12 h , suggesting decreased SaeRS activity or bleaching of GFP-signals [39 , 40] . To confirm the higher activity of the SaeRS TCS in the wild type mice , we collected S . aureus from peritoneal fluid at 12 h post infection; then the transcription of the known sae target genes ( saeP , coa , hla , fnbA , sak , and lukS-PV ) and two non-sae target genes ( psmα and spa ) were analyzed . Indeed , all sae target genes showed lower transcription in CP-deficient mice ( Fig 6B ) . Of the two non-sae targets , the transcription of psmα was not affected by CP; however , the transcription of spa was significantly decreased in the CP-deficient mice , indicating that the effect of CP is not limited to the SaeRS TCS . CP has two important qualities: metal chelation and proinflammatory properties . Whereas it is known that CP restricts staphylococcal growth in the abscess and confines spread of the bacterium through nutrient metal chelation , it is not known how the proinflammatory properties affect staphylococcal pathogenesis . Therefore , we first measured the production of six proinflammatory cytokines during staphylococcal peritoneal infection . Indeed , as compared with CP-deficient mice , the wild type mice produced significantly higher levels of proinflammatory cytokines: IL-1β , IL-6 , and MIP-2 at 2 h , and all 6 cytokines at 12 h post infection ( Fig 6C ) . When infected by the Δsae mutant strain , both mouse strains showed much lower production of proinflammatory cytokines , except for TNF-α , at 12 h post infection ( compare USA and Δsae at 12 h in Fig 6C ) , confirming that the products of the sae regulon also contribute to the production of proinflammatory cytokines[37] . Since CP enhanced the activity of the SaeRS TCS and the production of several proinflammatory cytokines , both of which can be detrimental to the survival of the host , next we examined the effect of CP on the mortality of the infected mice . When infected with wild type USA300 , all wild type mice died by 14 h post infection , whereas 70% of CP-deficient mice were alive ( Fig 7A ) . At 24 h post infection , 20% of CP-deficient mice were still alive . These results suggest that indeed CP is detrimental for murine survival during staphylococcal peritoneal infection . On the other hand , when infected with the Δsae mutant , no mice died , regardless of the genetic background ( Fig 7A ) , demonstrating the importance of the SaeRS TCS in the bacterial virulence . To test whether the detrimental effect of CP on host survival depends on infection route , we administered S . aureus cells into mice via retro-orbital injection . When infected with wild type USA300 , only 10% of wild type mice survived by day 14 , whereas yet 60% of CP-deficient mice were still alive ( S6 Fig ) . When infected by Δsae mutant , all mice survived again ( S6 Fig ) . This observation demonstrates that the increased mortality of wild type mice infected with S . aureus USA300 is dependent on CP but independent of the infection routes . To examine the role of the proinflammatory property of CP in the increased murine mortality , we administered antibodies against IFN-γ , IL-1β , or TNF-α at 2 h post infection and compared the murine mortality . No significant effect was observed with anti- IFN-γ antibody ( Fig 7B ) . However , the administration of either anti-IL-1β or anti-TNF-α antibody caused a small but statistically significant delay in the death of the infected mice ( Fig 7B ) . When the mixture of all three antibodies was administered , the delay of the death was much more pronounced , and infected mice survived significantly longer ( Fig 7C ) . These results demonstrate that the proinflammatory property of CP is , at least in part , responsible for the higher mortality of the wild type mice . S . aureus contains multiple proinflammatory PAMP ( pathogen-associated molecular pattern ) molecules such as lipoproteins , lipoteichoic acid ( LTA ) , and peptidoglycan [41 , 42 , 43 , 44] . To examine whether CP can increase murine mortality in the absence of the SaeRS TCS , we infected the wild type and CP-deficient mice with the sae-deletion mutant at 5 times increased dosage . As shown in Fig 7D , the CP-deficient mice still showed a lower mortality than wild type mice , demonstrating that , in the presence of excess PAMP molecules , the CP can increase murine mortality in a sae-independent fashion .
CP is a multi-functional protein with a broad range of antimicrobial activities and proinflammatory properties . In particular , its antimicrobial activities against S . aureus are well documented . In this study , however , we show that , in certain infection conditions , the antimicrobial activity of CP can lead to the activation of the SaeRS TCS and the proinflammatory property of CP can increase murine mortality . Among the metal ions present in either serum or neutrophil granules , Cu , Fe , and Zn were able to repress the SaeRS TCS . How then do the metal ions repress the SaeRS TCS ? Like other sensor histidine kinase , SaeS requires Mg as a cofactor for enzymatic activities . Enzyme assays show that Cu and Zn can inhibit the autokinase function of SaeS , while Fe does not ( Fig 2A ) . The radius of divalent Fe ( 0 . 78 Ǻ ) is larger than that of Mg ( 0 . 72 Ǻ ) , whereas the radii of divalent Zn ( 0 . 74 Ǻ ) and Cu ( 0 . 73 Ǻ ) are more similar to that of Mg [45] . Hence Zn and Cu may inhibit the SaeS autokinase activity by displacing Mg in the catalytic center , although conformational changes or allosteric effects are also equally possible . Since Fe did not inhibit either autokinase or phosphotransferase activity of SaeS , it is likely that the Fe inhibits the SaeRS TCS indirectly . This indirect effect of Fe on the SaeRS TCS is supported by the fact that the SaeRS TCS is also repressed by haemin [46] . Johnson et al reported that Fur , a transcription regulator responding to iron availability , is required for expression of multiple sae regulon in low-iron growth condition [47] raising the possibility that Fur mediates the Fe-mediated repression of the SaeRS TCS . However , we observed that Fe can repress the expression of SaeQ in a fur mutant of USA300 ( S7 Fig ) , ruling out the involvement of Fur in the Fe-mediated repression of the SaeRS TCS . Therefore , it still remains to be determined how Fe suppresses the SaeRS TCS . CP shows its protective effect only when Zn is present ( Fig 3A–3C ) . In addition , the mutant CP lacking Zn binding sites failed to restore the SaeRS TCS activity ( Fig 3D ) , suggesting that the Zn-bound CP , not the Zn-free CP , protects the SaeRS TCS from the metal-mediated repressions . Since it takes more than 2 h to show its protective effect ( Fig 3C ) , it is likely that the Zn-bound CP protects the SaeRS TCS indirectly . SaeS has a linker peptide of 9 amino acids between its two transmembrane helices [4] and is thought to respond to physicochemical changes in the cell membrane . Due to the small size of the linker peptide , the entire sensor domain ( i . e . , two membrane helices and the linker peptide ) is expected to be buried in the membrane and interact intimately with membrane lipids and surface molecules . For instance , Omae et al recently reported that the apolipoprotein from silkworm represses the SaeRS TCS by binding LTA [48] , indicating that LTA can affect the SaeR TCS activity via direct interaction with SaeS . Indeed , mutational changes in the extracellular linker peptide altered the SaeS response to LTA [48] , suggesting that the linker peptide is involved in the interaction with LTA . By analogy , it is possible that the Zn-bound CP renders SaeS resistant to the metal-mediated repression by binding to surface molecules in the cell envelope . However , since it takes more than 2 h for the protective effect by Zn-CP to occur ( Figs 3C and 4C ) , we suspect that the surface molecule , if there is any , does not directly interact with SaeS and , instead , it might cause alteration of the cell membrane environment of SaeS ( e . g . , the compositions of membrane proteins and lipids ) , in which SaeS might be protected from the metal-mediated repression . This hypothesis is clearly speculative and requires experimental verification . During peritoneal infection by S . aureus , proinflammatory cytokines play an important role in the CP-mediated increase of murine mortality ( Fig 7B and 7C ) . Since CP enhanced not only the SaeRS TCS activity but also the production of proinflammatory cytokines ( Fig 6 ) , and the SaeRS TCS also played a key role in the production of five out of six proinflammatory cytokines tested in this study ( Fig 6C ) , it is likely that the reduced activity of the SaeRS TCS in CP-deficient mice contributed to the higher survival of the mice ( Fig 7A ) . In fact , when wild type mice were infected with a mutant S . aureus strain where the expression of the sae target genes was reduced by 70% , the survival rate of the infected mice was improved by 40% -70% [49] . Therefore , it is expected that the 30%- 40% reduction of the sae activity in the CP-deficient mice ( Fig 6A ) also contributed to the survival of the mice ( Fig 7A ) . Recently Watkins et al showed that , during peritoneal infection by S . aureus , the SaeRS TCS induces the production of IFN-γ from murine neutrophils [38] . Although we confirmed the critical role of the SaeRS TCS in the production of IFN-γ in mice ( Fig 6C ) , we did not find any evidence that purified murine neutrophils can produce IFN-γ . Instead , we found that the SaeRS TCS plays a significant role in the production of not only IFN-γ but also IL-1β , IL-6 , MIP-2 , and IL-17A/F during murine infection ( Fig 6C ) . It is known that the sae-regulated cytolytic toxin Hla ( alpha-hemolysin or alpha-toxin ) activates the NLRP3 ( nucleotide binding domain and leucine rich repeat containing protein ) -inflammasome pathway in human and murine monocytic cells [50] and induces the production of IL-1β , IL-6 , and IL-8 [50 , 51 , 52 , 53] . Since the sae-regulon also includes many lipoproteins [7] , an important TLR2 ligand , the SaeRS TCS is expected to induce proinflammatory cytokine production via TLR2 . In addition , some staphylococcal super antigen-like ( SSL ) proteins are expected to be regulated by the SaeRS TCS [7 , 54] . Therefore , we believe all of these sae-target gene products contribute to the cytokine production during staphylococcal infection . In addition , it is possible that the cellular damages induced by the sae regulons also contributed to the proinflammatory host response . S . aureus is well known for its exploitation of host factors to promote bacterial survival . During host tissue invasion , S . aureus activates prothrombin with two coagulases , Coa and vWbp , and surrounds its cell mass with fibrin-deposit called pseudocapsule , blocking the access of neutrophils [11 , 55] . S . aureus uses the nuclease Nuc to degrade DNA in NETs , and induces apoptosis of macrophages by converting the released nucleotides into deoxyadenosine [56] . The SaeRS TCS is essential for these host exploitations because all of those bacterial factors ( i . e . , Coa , vWbp , and Nuc ) are the products of the sae regulon [7 , 8 , 57] . In this study , we add the neutrophil cytoplasmic protein CP to the list of the host factors exploited by S . aureus . CP is important to restrict growth through metal chelation in abscesses; however , our data also suggest that , during high dose systemic infections , its antimicrobial activity increases the activity of the SaeRS TCS and its proinflammatory properties can lead to higher mortality of the host , shedding new light on the evolutionary tug-of-war between microbial pathogens and host .
The animal experiment was performed by following the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The animal protocol was approved by the Committee on the Ethics of Animal Experiments of the Indiana University School of Medicine-Northwest ( Protocol Number: NW-34 ) . Every effort was made to minimize suffering of the animals . The bacterial strains and plasmids used in this study are listed in S9 Table . Escherichia coli was grown in Luria-Bertani broth ( LB ) medium while S . aureus was grown in tryptic soy broth ( TSB ) , human serum ( Sigma-Aldrich ) , or RPMI 1640 ( Corning ) with shaking ( 250 rpm ) . To measure bacterial growth in human serum , cells were washed twice and suspended in phosphate buffered saline ( PBS ) before OD600 measurement . When necessary , antibiotics were added to the growth media at the following concentrations: ampicillin , 100 μg/ml; erythromycin , 10 μg/ml; and chloramphenicol , 5 μg/ml . To generate the P1 promoter-gfp reporter plasmid , the gfp gene was PCR-amplified from pSW4-GFPopt [58]by Phusion DNA polymerase ( NEB ) with the primer pairs P974 ( 5’- GATGGTACCAAA AGGAGAACGCATAATGTCAAA AG-3 ) and P975 ( 5’- CGGGCT CCGCGGGCAGCCGAAT TCTTACCCCCCG-3’ ) . The amplified fragments were digested with KpnI . The resulting PCR product was ligated with the integration plasmid pCL55 digested with KpnI and SmaI , resulting in pCL-gfp . The P1 promoter sequence was PCR-amplified by Phusion DNA polymerase ( NEB ) with the primer pairs P1096 ( 5’-AACGGTACCTTGGTACTTGTATTTAATCGTCTATC-3’ ) and P785 ( 5’- AAAGGTACCGTTGTGATAACAG CACCAGCTGC-3’ ) . The PCR product was digested with KpnI and inserted into pCL-gfp digested with KpnI . The plasmid with correctly oriented P1 sequence was identified by PCR analysis and named pCL-P1gfp . pCL-gfp and pCL-P1gfp were electroporated into S . aureus strain RN4220 and then transduced into USA300-P23 with ϕ85 . To generate the SaeS overexpression plasmid pYJ-saeRS , the saeRS region was PCR-amplified with the primers P2856 ( 5’- GAGTATAATTAAAATAAGCTTGAT AGAGGTGAAAAAATAGATGACCCACTTACT-3’ ) and P2857 ( 5’-AACGACGGCCAGTGAATTCGAGCTCGGTACCCG CGGTTATGACGTAATGTCT-3’ ) using pCL-saeRS as a template [59] . The PCR product was digested with EcoRV and KpnI , and cloned into pYJ335 digested with the same enzymes[60] . Cells carrying the plasmid pCL-P1-lacZ [7] were grown and collected at a desired time point by centrifugation . The collected cells were washed with AB buffer ( 60 mM K2HPO4 , 40 mM KHPO4 , 100 mM NaCl , pH 7 . 0 ) , suspended in 100 μl of AB buffer , and mixed with 5 μl of lysostaphin ( 2 mg/ml ) . After 15 min incubation at 37°C , the samples were mixed with 900 μl of AB buffer containing 0 . 1% ( v/v ) Triton X-100 , and the β-galactosidase assay was performed at room temperature . As a substrate , 4-methyl umbelliferyl β-D galactopyranoside ( MUG , Sigma ) was used in the hydrolysis reaction , which was read at 366 nm excitation and 445 nm emission wavelengths . S . aureus cells were collected by centrifugation , and normalized to 1 ml of 1 . 0 OD600 . Western blot analysis of SaeQ was carried out as described previously [61] . Briefly , cells were collected by centrifugation and suspended in 50 μl of Tris HCl ( pH 8 . 0 ) ; then 2 μl lysostaphin ( 2 mg/ml ) was added . After incubation at 37°C for 30 min , 50 μl of 2× SDS-PAGE sample buffer was added . The samples were separated by SDS-PAGE and the proteins were transferred onto a nitrocellulose membrane ( 0 . 45 μm , Whatman ) . The membrane was blocked with 10% skim milk and incubated with SaeQ antibody for 1 h at room temperature; then the blot was incubated with horseradish peroxidase ( HRP ) -conjugated secondary antibody . Signals were detected by a luminal enhancer solution detection kit ( Thermo ) . Unless indicated otherwise , strains were grown in RPMI for 16 h in the presence or absence of the metal ions present in human blood or in neutrophil granules . When necessary , CP ( 1 . 1 μM ) or HNP1 ( 5 μg/ml , Bachem ) was added to the growth medium . Recombinant CP was expressed , purified , and tested for activity as described previously [20] . Maltose-binding protein fused SaeS ( MBP-SaeS ) was expressed in E . coli BL21star ( DE3 ) and purified with MBPTrap HP column ( GE Healthcare ) by following the column manufacturer’s recommendations . The purified MBP-SaeS ( 3 μM ) was suspended in the reaction buffer ( 10 mM Tris-HCl , pH 7 . 4 , 50 mM KCl , 10 μM MgCl2 , 10% glycerol ) containing various concentrations ( 0–1 mM ) of metal ions ( FeSO4 , ZnSO4 , CuSO4 , ) . After addition of [γ-32P]-ATP ( 2 μCi ) , the samples were incubated at room temperature for 15 min and subjected to SDS-PAGE ( 10% ) and autoradiography . First , MBP-SaeS was phosphorylated as described above in the reaction buffer . After elimination of free [γ-32P]-ATP with a Micro Bio-Spin Chromatography Column ( Bio-Rad ) , SaeR ( 9 μM ) and various concentrations ( 0–0 . 5 mM ) of metal ions were added . The resulting samples were incubated at room temperature for 15 min and subjected to SDS-PAGE ( 13% ) and autoradiography . S . aureus strains harboring pYJ-saeRS were grown to exponential growth phase at 37°C and the SaeS protein was induced by the addition of anhydrotetracycline ( Clontech , 0 . 5 μg/ml ) at 37°C for an additional 4 h . Cell membranes were prepared as described previously [49] . The SaeS phosphorylation assay was carried out as described above except that the purified cell membranes ( 25 μg ) were used as a source of SaeS and the incubation time was 10 min . Cells were grown in RPMI to exponential growth phase; then ZnSO4 ( 20 μM ) , CP ( 1 . 1 μM ) , or the mix of ZnSO4 ( 20 μM ) and CP ( 1 . 1 μM ) was added to the culture . After 4 h incubation of the culture at 37°C , total bacterial RNA was isolated using the RNeasy minikit ( Qiagen ) with optional on-column DNA digestion according to the manufacturer’s instructions . After purification , contaminating DNA was removed with RNase-free DNase I . RNA was then purified again using RNeasy Mini columns . The purified RNA was sent to the Center for Genomics and Bioinformatics at Indiana University . Sequencing libraries were constructed using the ScriptSeq Complete Kit for Bacteria ( Epicentre ) . Bone marrow-derived neutrophils were isolated as described previously [62] . Briefly , mice were euthanized by CO2 asphyxiation; then tibias and femurs were flushed with Hank's balanced salt solution without Ca2+ and Mg2+ ( HBSS ) . After lysing red blood cells with hypotonic solution ( eBioscience ) , the remaining cells were separated by centrifugation at 500 ×g at room temperature for 30 min over discontinuous Percoll ( GE ) gradients ( 55% [v/v] , 65% [v/v] , and 75% [v/v] in PBS ) . Neutrophils at the 75%-65% interface were removed and washed once with HBSS . The purity ( > 90% ) of the purified neutrophils was confirmed by flow cytometry with Gr-1 and CD11b antibodies ( eBioscience ) . Neutrophils ( 1 ×105 ) were seeded into 96 well plates in RPMI and stimulated with 200 nM phorbol 12-myristate 13-acetate ( PMA ) , bacteria ( MOI = 10 ) , or left un-stimulated for 4 h . Then micrococcal nuclease ( 500 mU ml-1 , Worthington ) was added , and the samples were incubated for 10 min at 37°C . After addition of 5 mM EDTA , the released DNA was collected by centrifugation and measured with Picogreen double-stranded DNA kit ( Invitrogen ) according to the manufacturer’s recommendations . S . aureus cells were opsonized with 10% autologous serum at 37°C for 30 min , washed with of PBS , and suspended in RPMI . Neutrophils ( 2 ×105 ) purified from C57BL/6 wild type or S100A9-/- mice were added into 24-well tissue culture plate and allowed to adhere at 37°C for 1 h . S . aureus were added to neutrophils ( bacteria: neutrophil = 10:1 ) , and the plates were centrifuged at 300 ×g for 8 min at 4°C . Samples were incubated at 37°C for the indicated time period . To determine the cfu of surviving S . aureus , after lysis of neutrophils by treatment of 0 . 1% saponin on ice for 15 min , the resulting samples were diluted , spread on tryptic soy agar , and incubated at 37°C overnight . The percentages of killing were calculated by the following formula: [1- ( CFUneutrophils+/CFUneutrophils- ) ] × 100 . Neutrophils ( 2 ×105 ) from wild type or S100A9-/- mice were seeded on 13 mm glass cover slips treated with 0 . 001% polylysine and allowed to settle and stimulated with 200 nM PMA for 4 h . Cells were fixed with 4% paraformaldehyde , treated with 0 . 1% Triton X-100 , and blocked overnight in PBS containing 10% goat serum , 5% cold water fish gelatin , 1% BSA , and 0 . 05% Tween 20 . CP was detected by treatment with S100A9 antibody ( Novus Biological ) and Cy3-conjugated secondary antibody ( Invitrogen ) , while DNA was detected by DRAQ5 ( Cell Signaling ) . Specimens were analyzed with Fluoview confocal microscope ( Olympus ) . C57BL/6 was purchased from the Jackson Laboratory ( Bar Harbor , ME ) . CP deficient ( C57BL/6 S100A9-/- ) mice were acquired from the Skaar laboratory under the permission from University of Muenster . The CP-deficient mice were maintained in house as previously described [24] . S . aureus strains were grown in 3 ml of TSB at 37°C overnight with shaking ( 250 rpm ) . The next day the overnight culture was inoculated into fresh TSB ( 1:100 dilution ) and incubated at 37°C for 2 h . S . aureus were collected by centrifugation , washed with of PBS , and suspended in sterile PBS to 4 OD600 ( 1 ×109 cfu ml-1 , for intraperitoneal injection ) or 0 . 4 OD600 ( 1 ×108 cfu ml-1 for retro-orbital injection ) . Sex matched eight-week-old C57BL/6 mice or S100A9-/- mice were infected via intraperitoneal injection ( 2 ×108 cfu or 1 ×109 cfu ) or retro-orbital injection ( 1 ×107 cfu ) . To test the effects of proinflammatory cytokine-neutralizing antibodies on murine mortality , mice were injected once with the following antibodies at 2 h post infection: 100 μg of anti-TNFα ( MP6-XT22 ) , anti-IFNγ ( R4-6A2 ) , or anti-IL-1β ( B122 ) Ab ( Biolegend ) or 150 μg of the antibody mixture ( 50 μg each ) . A rat IgG ( whole molecule , Jackson ImmunoResearch ) was used as an isotype control . The survival of the infected mice was monitored every hour for 24 h and then every 4 h for an additional 48 h ( peritoneal infection model ) or every 12 h for two weeks ( retro-orbital infection model ) . To determine the P1 promoter activity during peritoneal infection , mice were infected with USA300 ( pCL-P1gfp ) . At desired time points , the peritoneum was washed with 2 ml HBSS using an 18 gauge needle and 5 ml syringe . The collected peritoneal fluid was subjected to centrifugation at 200 ×g for 5 min to separate the host cell-free fraction ( supernatant ) and the host cell-associate fraction ( pellet ) . The supernatant was directly subjected to flow cytometry analysis . To release bacterial cells from the host cells in the pellet , the pellet was suspended in sterile water ( pH 10 . 5 ) for 10 min at room temperature and subjected to vigorous vortex for 1 min . The GFP fluorescence from the P1 was detected in the FL-1 channel . The bacterial cells were harvested from peritoneal fluid as described above at 12 h post infection . Total RNA was isolated from harvested bacterial cells with a FastRNA pro blue kit ( MP bio ) , and contaminating genomic DNA was eliminated with RNase-free DNase set ( Qiagen ) . cDNA was generated from the purified RNA with SuperScript II RT ( Invitrogen ) and random primers ( Applied Biosytems ) . PCR was performed on ABI PRISM 7000 sequence detection system ( Applied Biosystems ) using SYBR Green Master Mix ( Applied Biosystems ) and the primers listed in S10 Table . The transcript levels were calculated relative to that of 16S rRNA using the ΔCT method [63]: [relative expression = 2-ΔCT , where ΔCT = CT ( staphylococcal gene ) -CT ( 16S rRNA ) ] . The experiments were performed on RNA pooled from 3 mice per group and repeated 3 times . To measure cytokines produced from murine neutrophils , neutrophils ( 2 ×105 ) from wild type or S100A9-/- mice were infected with S . aureus for 2 h ( MOI = 10 ) or left uninfected . Then cells were cultured in RPMI containing gentamicin ( 50 μg ml-1 ) for an additional 16 h , and the supernatants were collected . To measure cytokines produced in murine peritoneum , the peritoneal fluids were separated by centrifugation at 200 ×g for 10 min , and the supernatants were collected . Cytokines in the resulting supernatants were quantified with corresponding sandwich ELISA kits by following the manufacturer’s recommendations . The kits for TNF-α , IL-6 , IL-1b , IL-17A/F and IFN-γ were purchased from eBioscience , while the MIP-2 ( CXCL-2 ) ELISA kit was purchased from Sigma . Statistical analyses were performed by using the software Prism 5 ( GraphPad ) . For the analyses of SaeQ expression , P1 promoter activity , and cytokine productions , two groups were compared by unpaired , two-tailed Student’s t-test . For the analyses of animal survival , however , Log-rank ( Mantel-Cox ) test was used . Differences were considered significant when p is smaller than 0 . 05 . | Staphylococcus aureus is an important human pathogen causing skin infections and a variety of life-threatening diseases such as pneumonia , sepsis , and toxic shock syndrome . Previous study showed that the growth of S . aureus in abscesses is suppressed by the host antimicrobial protein calprotectin , which sequesters Zn and Mn from bacterial usage . During bacterial infection , calprotectin also plays an important role in the production of proinflammatory cytokines . Although the antimicrobial activity of calprotectin has been well defined , it is not known how the proinflammatory property of calprotectin affects staphylococcal infection . In this study , we found that the Zn-binding property of calprotectin increases the pathogenic potential of S . aureus by enhancing the activity of the SaeRS two component system in S . aureus . We also found that , under certain infection conditions , the proinflammatory property of calprotectin is rather detrimental to host survival . Our study illustrates that the important antimicrobial protein can be exploited by S . aureus to render the bacterium a more effective pathogen , and provides an example of the intricate tug-of-war between host and a bacterial pathogen . | [
"Abstract",
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"Methods"
] | [] | 2015 | Calprotectin Increases the Activity of the SaeRS Two Component System and Murine Mortality during Staphylococcus aureus Infections |
Rabies is a notoriously underreported and neglected disease of low-income countries . This study aims to estimate the public health and economic burden of rabies circulating in domestic dog populations , globally and on a country-by-country basis , allowing an objective assessment of how much this preventable disease costs endemic countries . We established relationships between rabies mortality and rabies prevention and control measures , which we incorporated into a model framework . We used data derived from extensive literature searches and questionnaires on disease incidence , control interventions and preventative measures within this framework to estimate the disease burden . The burden of rabies impacts on public health sector budgets , local communities and livestock economies , with the highest risk of rabies in the poorest regions of the world . This study estimates that globally canine rabies causes approximately 59 , 000 ( 95% Confidence Intervals: 25-159 , 000 ) human deaths , over 3 . 7 million ( 95% CIs: 1 . 6-10 . 4 million ) disability-adjusted life years ( DALYs ) and 8 . 6 billion USD ( 95% CIs: 2 . 9-21 . 5 billion ) economic losses annually . The largest component of the economic burden is due to premature death ( 55% ) , followed by direct costs of post-exposure prophylaxis ( PEP , 20% ) and lost income whilst seeking PEP ( 15 . 5% ) , with only limited costs to the veterinary sector due to dog vaccination ( 1 . 5% ) , and additional costs to communities from livestock losses ( 6% ) . This study demonstrates that investment in dog vaccination , the single most effective way of reducing the disease burden , has been inadequate and that the availability and affordability of PEP needs improving . Collaborative investments by medical and veterinary sectors could dramatically reduce the current large , and unnecessary , burden of rabies on affected communities . Improved surveillance is needed to reduce uncertainty in burden estimates and to monitor the impacts of control efforts .
Rabies is a fatal viral infection that can infect all mammals , but domestic dogs cause over 99% of all human deaths from rabies [1] . Human rabies can be prevented through prompt administration of post-exposure prophylaxis ( PEP ) to victims of bites by rabid animals [2] , and infection can be eliminated at source through sustained mass vaccination of reservoir populations [3] . Most industrialized countries have eliminated rabies from domestic dog populations . However , in the majority of developing countries , rabies remains endemic in domestic dog populations and poorly controlled [4] . Our focus is on the impacts of canine-adapted variants of the rabies virus , sustained predominantly or entirely by transmission in domestic dogs ( it is unclear whether independent transmission in wildlife might be sufficient for maintenance in some areas [5–7] ) . Our definition therefore includes rabies cases or exposures caused by canine variants of rabies virus also transmitted from wildlife , though these are negligible compared to those transmitted by domestic dogs . The human and economic costs of canine rabies are poorly known [1] . A major challenge to estimating the burden of rabies is the absence of reliable surveillance data for countries where the disease is most prevalent . Basic information on how many lives are lost to rabies and the economic costs of preventing disease amongst those exposed are needed to advocate for sustainable control programmes . Official reporting of incidence data on rabies and rabies exposures remains desperately poor in most canine rabies-endemic countries , and is increasingly recognized to grossly underestimate the true number of cases [8 , 9] . Active surveillance studies highlight the disparities between officially recorded and likely occurring rabies deaths . These include recent studies from both Asia and Africa based on probability decision tree modelling [10 , 11]; extensive verbal autopsy surveys [12]; community surveys [13 , 14] and contact tracing [15] , which all show much higher mortality than officially reported . Specific features of rabies contribute to the problem of underreporting . Death is inevitable following clinical onset and therefore a large number of rabies victims never report to health facilities and are never diagnosed . Misdiagnosis to other neurological syndromes is frequent , especially in malaria endemic regions [16] . Shortages of life-saving PEP [15] and centres that provide PEP for bite victims [17] and poorly monitored sales of PEP to private suppliers all complicate counting the number of rabies diagnoses made and the number of treatments given . These problems of PEP provision particularly increase the risks of disease among the rural poor , an already marginalized sector of society . Moreover , poor infrastructure and a lack of personnel and facilities for rabies surveillance and diagnosis in most developing countries means that only very limited data of questionable reliability are available . In the absence of either reliable mortality reporting systems or more widespread active surveillance studies , extrapolations are required to estimate the global burden of rabies . Predictive methods have been developed to overcome the underreporting of disease , including a probability decision-tree method to determine the likelihood of the onset of clinical rabies in humans following the bite of a suspect rabid dog [8] . Using this method and drawing on data from a limited number of countries , Knobel et al . estimated that canine rabies caused approximately 55 , 000 human deaths annually across Africa and Asia [18] . Since this 2005 study , more data have become available and the disease situation has changed , with concerted control efforts in some parts of the world [19] , increased incidence in others [20] , as well as emergence in previously rabies-free areas [21 , 22] . An updated assessment of the global rabies burden is therefore required . The rabies burden is made up of different components . Societal costs include mortality and lost productivity from premature death , and morbidity from adverse events ( AE ) of vaccination using nerve tissue vaccines ( NTVs ) and psychological effects of exposure to this fatal disease , expressed as disability-adjusted life years ( DALYs ) . Direct costs of PEP ( depending on the use of rabies immunoglobulin ( RIG ) , and the type of vaccine and regimen , for example intramuscular ( IM ) versus intradermal ( ID ) administration ) and indirect costs of seeking PEP ( travel and accommodation for multiple clinic visits and lost income ) fall upon the medical sector and affected communities , whilst the veterinary sector typically incurs costs related to dog vaccination . Veterinary and medical sectors both have responsibility for surveillance costs . Livestock losses depend on the size of at-risk livestock populations and preventative measures taken , and impact both national economies and households . The goal of this study was to make the best possible estimate of the burden of rabies , both globally and on a country-by-country basis , by combining all available data sources in a modelling framework that allows us to estimate missing components . We built upon earlier model frameworks [8 , 18] to assess the current status of canine rabies globally and provide country-specific estimates of disease burden and associated economic costs . Our model relied on data collected from many different sources including published studies , international databases , market data for vaccine use and expert opinion surveyed for this study . We established relationships between rabies mortality and rabies control measures , which we incorporated into our estimation methods . These relationships indicated how interventions could affect the future burden of disease .
We adapted the probability decision-tree framework developed by Cleaveland et al . [8] for Tanzania and used by Knobel et al . [18] to estimate the burden of rabies in Africa and Asia . The model uses the product of bite incidence , the probabilities of ( i ) a biting animal being rabid , RP , ( ii ) a bite victim receiving PEP , PP , and ( iii ) in the absence of PEP , developing rabies , DP , to extrapolate human rabies deaths and DALYs . An economic component is included to calculate the costs of rabies prevention and control , such as PEP administration , surveillance and livestock losses from rabies ( Fig 1 and Table 1 ) . We parameterized the model using country-specific data or aggregated cluster estimates as described below . In endemic areas we assumed that dog rabies incidence depends on vaccination coverage in the dog population , and that the probability that a bite is by a rabid animal depends on incidence . Using concurrent time series from the Americas we identified the best fitting relationship between incidence , I , and vaccination coverage , VC , estimating the asymptote , Imax and exponent , S , of this relationship by maximum likelihood ( Fig 2A ) : I=Imax* ( 1-VC ) s We assume that the probability that a bite is by a rabid animal is proportional to incidence: RP=RPmax* ( 1-VC ) s where RPmax ( 0 . 74 ) is the estimated proportion of bites due to rabid animals in countries with negligible vaccination coverage [23] . Using this relationship we generated country and cluster estimates of RP . We similarly inferred the relationship between livestock rabies incidence and vaccination coverage , based on cross-sectional data from published studies [14 , 23–25] ( Fig 2A inset ) . We assumed that in the absence of PEP , 19% of victims bitten by rabid dogs develop rabies and die ( DP = 0 . 19 , see [26] ) . The probability that a bite victim receives PEP ( anti-rabies vaccine , sometimes supplemented by RIG for severe exposures ) determines the likelihood of progression to rabies and death , however , we know of only one study quantifying this probability , PP [14] . We therefore used an alternative inference method . Reporting of PEP use and of rabies deaths varies according to health infrastructure , however both can be very poor in developing countries . If we assume equivalent reporting rates of rabies deaths , D , and PEP use , T , we can use the ratio D/T , as well as the probability that a reported bite was by a rabid dog , RP , and the probability of developing rabies in the absence of PEP , DP , to calculate the probability that a bite victim receives PEP: PP=RP*DP/ ( ( RP*DP ) +D/T ) We examined whether a relationship exists between country economic/welfare measures ( the Human Development Index , HDI ) and these estimates of the probability of receiving PEP , PP ( Fig 2B ) using a generalized linear mixed-effects model [27] with country as a random effect . We generated country-specific parameter estimates of PP and used bootstrap resampling from the fitted mixed model for sensitivity analyses and to generate prediction intervals . We used these parameter estimates together with data on bite incidence ( see below ) to generate estimates of rabies exposures and deaths . Disease burden was expressed in terms of standard DALYs and calculated in accordance with methods developed by the World Health Organization ( WHO ) . DALY calculations involve two components: Years of Life Lost ( YLL ) , which , for rabies , captures deaths due to infection , and the Years of Life lived with Disability ( YLD ) , which , for rabies , captures disability following AEs due to use of nerve tissue vaccines , which are still used in some parts of the world . Total YLL due to rabies were estimated using the reference-standard life table from the 2010 Global Burden of Disease Study [28] and the age distribution of rabies cases and exposures from previous research [18] ( Table 1 ) . We provide burden estimates based on age- and country-specific mortality rates for comparison ( S1 Table ) . The disability weighting used to calculate AEs from nerve tissue vaccines was based on previous research [18] . We explored an additional component in YLD as the anxiety associated with dog bites that may develop into rabies , assuming that the disability level associated with anxiety was 0 . 108 and that anxiety lasted for 60 days . However , we did not include this in the total disease burden calculation , due to a lack of data to validate these assumptions . The data , parameter values and code to replicate all the analyses are provided in the Supporting Information ( S1 compressed file archive ) . We aggregated countries into clusters on the basis of similar rabies epidemiological situations , socioeconomic conditions and geographical proximity . Countries were classified as canine rabies-free based on historical freedom or literature reporting canine rabies elimination . Oceania , Western Europe , the US and Canada and canine rabies-free countries in Asia ( Japan , Malaysia , Singapore , and Republic of Korea ) comprised four canine rabies-free clusters that were not included in further analysis . The disease burden was estimated in endemic countries within clusters . Where possible , country level data was used to parameterize the model , but , in the absence of country-specific information , we applied the average estimate from countries within the cluster . Data were obtained from: Surveys involving the medical , veterinary and laboratory sectors . The information gathered included reported clinical and laboratory-confirmed rabies cases in humans and animals , protocols for and expenditure related to PEP and costs of control efforts ( vaccinating , sterilizing and killing dogs ) . Surveys were translated into French , Spanish , Portuguese and Russian and made available online . We solicited responses from country representatives , particularly those attending regional rabies meetings or identified through regional networks ( such as the Southern and Eastern Africa Rabies Group , SEARG , and Directors of National Programs to Control Rabies in the Americas , REDIPRA [19] ) , responsible for reporting on rabies surveillance and diagnosis in their countries . Data was collected from 136 respondents ( spanning all sectors and 45 countries ) . These surveys provided valuable data on rabies prevention and control practices , including PEP protocols and unit costs . However , most quantitative data on incidence were incomplete and we therefore used published data instead ( see point 2 ) . An extensive literature search for estimates of human and animal rabies , dog bite incidence , control efforts and associated economic costs . We searched Web of Knowledge and PubMed for publications from 2000 to 2013 using ‘rabies’ AND ‘dogs’ as key words and resulting papers were reviewed to determine their relevance . In addition to scientific publications within the standard search , we collated technical reports and presentations from regional meetings , soliciting data through members of the Global Alliance for Rabies Control Partners for Rabies Prevention ( PRP , rabiesalliance . org/about-us/partners ) . We identified 551 articles addressing canine rabies and its control , but useable quantitative information was only available from a smaller subset ( 113 , see the supporting bibliography , S1 text ) , with considerable underreporting evident in official reports of bite incidence and rabies deaths from low and middle-income countries . We used estimates of bite incidence from empirical studies involving active surveillance and only incorporated official data where no other sources were available and where these were deemed valid by the PRP group . We used the most recent data available since 2000 . Searches were last updated on 1 June 2013 . International databases for country-specific estimates of human populations , demographic rates , economic indicators and livestock ( detailed in Table 1 ) . GDP and 2010 exchange rates were from the International Monetary Fund ( IMF ) databases; 2010 human population estimates and HDI estimates were from the United Nations; health costs were from the WHO ( WHO-CHOICE database; CHOosing Interventions that are Cost-Effective ) , and livestock populations were from the Food and Agriculture Organization ( FAO ) Global Livestock Production and Health Atlas . The World Organization for Animal Health ( OIE ) World Animal Health Information System ( WAHID ) and surveillance databases for specific regions were used to obtain recent country-specific reports of laboratory confirmed rabies cases and diagnostic tests performed . Estimates of regional markets for dog rabies vaccines from Merial and for human post-exposure vaccines and RIG from Sanofi Pasteur . These were used to validate dog vaccination coverage estimates or to provide estimates of coverage for areas where no other source of information was available . If market estimates differed from other sources by >10% , experts were consulted ( amongst the PRP group ) and a 2-round delphi process [29] used to obtain consensus on values used in the analysis . The economic cost of deaths due to rabies was estimated using the human capital approach based on productivity losses . For each rabies death , the number of discounted life years lost was based on the age distribution of rabies deaths and remaining life expectancy using the reference-standard life table from the 2010 Global Burden of Disease Study [28] . Productivity losses were calculated by weighting the life years lost by the country-specific GDP per capita without discounting . Estimates were also calculated using age-weighting and time discounting for comparison with other studies . Time lost by victims and accompanying care-givers ( assuming all minors were accompanied by one adult ) whilst seeking PEP was incorporated into economic losses . Country estimates of unit costs for delivering PEP , dog vaccination and surveillance were largely obtained from surveys ( detailed in 1 ) . The total economic cost of rabies was obtained by combining data on unit cost per case , livestock losses , and costs of control and prevention . We updated reported costs to 2010 US dollars ( USD ) using IMF statistics . We corrected for international differences in medical costs using the WHO-CHOICE database . Indirect costs were corrected for differences in income using the ratio of income per capita ( expressed in International dollars , I$ ) calculated using IMF statistics . Direct non-medical costs were corrected only for differences in purchasing power . Livestock losses due to dog rabies were extrapolated from the inferred relationship between coverage and incidence ( Fig 2A inset ) and using livestock population estimates from FAO . Incidence of rabies in cattle was multiplied by the cost per head of cattle . Using the fitted relationship between dog vaccination coverage and reported rabies incidence in livestock [14 , 23 , 25 , 30] described above , and country and cluster values for dog vaccination coverage we estimated livestock losses . We converted the costs of different livestock into the costs of cattle using FAO livestock unit measures [31] . Uncertainty was modelled by drawing from distributions for each parameter estimate . Bite incidence and dog vaccination coverage were modelled using triangular distributions , with maxima and minima set according to cluster ranges agreed from the 2-round Delphi process . Uncertainty in PEP probability was modelled using bootstrap resampling from the fitted mixed model described above . Uncertainty in the other probabilities ( RP , and DP ) was modelled using permutation-based resampling based on the original binomial sample . The range of variation of the results of our analysis was assessed using 1 , 000 Monte Carlo simulations . Parameters that varied across countries or clusters used the same quantile draw globally for each realization .
Rabies incidence in dogs was best described by the fitted model ( Fig 2A ) : I=0 . 002* ( 1-VC ) 1 . 9 with dog vaccination coverage corresponding to the average from the previous two years . We used this relationship to infer the probability that a bite was due to a rabid animal , RP . We inferred a similar relationship between rabies incidence in livestock , IL and dog vaccination coverage ( Fig 2A inset ) : IL=0 . 0017* ( 1-VC ) 9 We found a strong significant relationship between inferred probability of receiving PEP and both country GDP and HDI ( p<0 . 001 ) . We used the relationship with HDI , which better captures inequalities within countries ( Fig 2B ) to generate estimates of the probability of receiving PEP , PP . The parameter estimates RP and PP are detailed by cluster in Table 2 and by country in S1 Table . Using these relationships and the data described above , we implemented the probability decision tree model to generate burden estimates . Results of predicted rabies mortality , morbidity and DALYs are provided by cluster in Table 2 and by country in S1 Table . We estimated that around 59 , 000 [95% CIs: 25 , 000–159 , 200] human rabies deaths occur annually globally , with the vast majority of these in Africa ( 36 . 4% ) and Asia ( 59 . 6% ) . Less than 0 . 05% of estimated deaths occurred in the Americas [182 , 95% CIs: 84–428] , of which over 70% were from Haiti . India , with 35% of human rabies deaths , accounted for more deaths than any other country , but the estimated per-person death rate was highest in the poorest countries in sub-Saharan Africa . The global distribution of estimated human deaths and death rates due to rabies is illustrated in Fig 3 . The parameters that had the greatest impact on variation in estimated human rabies mortality were bite incidence , followed by the probability of receiving PEP and the probability of developing rabies after a rabid animal bite in the absence of PEP ( Fig 4 ) . Globally , around 3 . 7 [1 . 6–10 . 4] million DALYs were estimated to be lost due to rabies , with over 95% lost in Africa ( 36 . 2% ) and Asia ( 59 . 9% ) and less than 0 . 5% ( 11 , 950 DALYs ) in the Americas . The vast majority ( >99% ) of DALYs lost ( 3 . 68 million ) were due to the premature death of rabies victims ( YLL ) . A very small part ( 0 . 8% ) of the DALY score ( 30 , 400 ) was due to AEs ( in terms of YLD ) of outdated , mostly locally produced , nerve tissue vaccines still in use in at least 10 countries in 2010 . Anxiety due to suspect rabid dog bites potentially accounts for a substantial burden ( 518 , 000 DALYs in terms of YLD ) , although we do not include this component in our total estimate due to a lack of data to validate assumptions about disability weighting ( 0 . 108 ) and its application ( 60 days for true exposures ) . The overall economic costs of canine rabies were estimated as 8 . 6 billion USD ( 95% CIs: 2 . 9–21 . 5 billion ) . These costs were mainly due to productivity losses from premature deaths ( 2 . 27 billion USD ) , direct expenditure on PEP ( totalling 1 . 70 billion USD ) and lost income whilst seeking PEP ( 1 . 31 billion USD ) . However , there was considerable variation in the breakdown of costs by region ( Fig 5 ) : the largest proportion of costs was due to premature death in Asia and Africa; much less was lost due to PEP ( direct costs , and from travel and lost income whilst seeking PEP ) in Africa compared to Asia and the Americas , and in the Americas a large proportion of costs were due to dog vaccination . Livestock deaths amounted to 512 million USD per year , with major losses in African countries with livestock-dependent economies ( e . g . Ethiopia , Sudan , and Tanzania ) and in more populous countries in Asia ( China , India , Bangladesh and Pakistan ) . Table 3 provides a breakdown of estimated costs and a country breakdown is given in S1 Table . Globally , over 70% of the estimated economic burden was societal ( from premature deaths and losses from seeking PEP ) ; 20% fell to the medical sector or to bite victims ( direct costs ) and ~8% to the veterinary sector or directly to communities , from livestock losses and control interventions ( dog vaccination and population management i . e . culling and/or sterilization/ birth control ) . Only around 0 . 01% of costs were from laboratory-based surveillance . The breakdown of costs by region varied dramatically ( Figs 5 and S1 and S1 Table gives country breakdown ) . Dog vaccination accounted for less than 1 . 5% ( ~$130 million ) of the economic burden . In the Americas , average per capita expenditure on dog vaccination was approximately $0 . 11 , amounting to almost 20% of the economic burden . In most other endemic low-income countries , per capita expenditure on dog vaccination was negligible ( less than $0 . 02 or <2% of the economic burden respectively , Figs 3C and 5 inset ) . Unit costs differed greatly between countries both for dog vaccination ( for example costing $6-7/dose in some West African countries , $1/dose in Laos , $0 . 45/dose in the Philippines , $0 . 5/dose in Chad and $0 . 2–0 . 3 in Tanzania ) and human PEP ( range: $11–150 per dose ) , as well as the regimens and types of vaccines and RIG used ( compressed file archive ) . Most countries with a high disease burden reported negligible use of RIG . Countries with more substantial RIG use were in Eastern Europe , North Africa and a few Asian countries ( Sri Lanka , Thailand and the Philippines ) . A few countries reported use of NTVs , notably Ethiopia , several countries in Latin America ( Peru , Venezuela , Bolivia , Honduras and Argentina ) , Myanmar and Pakistan , with Bangladesh discontinuing use of NTVs in 2011 [1] ( though were classified as using NTVs for the year 2010 ) . Only a few countries reported widespread ID vaccination , including the Philippines , Sri Lanka , Thailand , and to a much lesser extent in India .
This study highlights that the mortality risks and per capita rabies burden fall disproportionately upon the poorest regions of the world , with impacts on local communities , public health sector budgets and livestock economies . Mortality and loss of economic productivity due to premature death are the most serious effects of canine rabies . Highest mortality rates occur in areas with limited dog vaccination , where PEP is the only lifeline for at-risk populations , yet PEP supply and distribution systems are wholly inadequate in many of these places and often very costly . PEP costs , the second largest component of the economic burden , could be reduced in many areas through more judicious and cost-effective administration . The methods developed here shed light on important gaps in knowledge , provide a preliminary picture of the distribution of the rabies burden by country and underline the lack of investment in rabies control and prevention measures . Improved surveillance and reporting of bites and rabies cases is needed , both for better burden estimates , and most importantly to monitor the impacts of control efforts . We estimate that annually canine rabies causes around 59 , 000 deaths and 3 . 7 million DALYs , which is considerably higher than previous estimates [18] , however this is largely due to methods . In our study we did not apply time discounting or age-weighting to calculate DALYs for consistency with the Global Burden of Disease ( GBD ) study 2010 [32 , 33] . Our DALY estimates are correspondingly higher than the GBD study as a result of our higher estimates of mortality . Applying 3% discounting and age-weighting we estimated slightly higher but more consistent DALY estimates to the Knobel et al . 2005 study [18] . S1 Table shows differences in DALY estimates due to the life table used and age-weighting and time discounting . We estimated a much higher economic burden of around 8 . 6 billion USD annually , compared to 583 . 5 million USD reported by Knobel et al . [18] . These differences are due to the inclusion of lost income from premature deaths and from all countries ( not just those of Africa and Asia ) , in addition to a major increase in estimated direct PEP costs from 300 million USD [18] to 1 . 7 billion USD . Reasons contributing to this increase include higher prices and increased availability of cell-culture vaccines compared to NTVs , now discontinued in most endemic areas . Furthermore , we estimated higher bite incidence and PEP use ( 29 . 2 versus 4 . 3 million PEP delivered , respectively ) , based on published data , with over 10 million PEP delivered in China alone [34] . According to our estimates , India , the world’s second most populous country ( with close to 18% of the global population ) accounts for over 35% of the global rabies burden ( approximately 20 , 800 deaths ) . This is broadly consistent with a recent verbal autopsy study , that estimated ~12 , 700 deaths from furious rabies alone in India [12] . Generally , our country estimates are in line with active surveillance studies [10 , 13] . However , our estimates of deaths and DALYs are considerably higher than the GBD study , which attributed only 26 , 400 deaths and 1 . 46 million DALYs worldwide to rabies in 2010 [32 , 33] . The GBD study drew upon officially recorded data for rabies , which grossly underestimate the disease burden due to extensive underreporting from countries where rabies is most prevalent . A large proportion of rabies deaths are misdiagnosed in areas with high general mortality [16] . Hospitalization provides little palliative care and death is inevitable , therefore many victims , particularly those from poor communities , either do not attend a facility or do not stay until death . Most victims ( >75% ) die at home [10 , 11 , 13] , and these deaths are absent from official records . Furthermore , few clinical rabies diagnoses will be made from verbal autopsies unless interviewers probe for a history of a dog bite ( as in [12] ) . Therefore even in countries where rabies is notifiable , many rabies deaths are not recorded , and burden estimates must therefore rely on predictive approaches . Our model provides a point of comparison for individual countries , but , due to the nature of extrapolating across large and heterogeneous populations , inaccuracies are likely and active surveillance studies are warranted . A major question is how to quantify anxiety associated with a life-threatening bite from a rabid animal . Previous burden estimates have ignored this component . We were also unable to find empirical evidence to validate a disability weighting . However , using assumptions agreed upon by the PRP group , we show that anxiety could be substantial ( >10% of the total burden , ~518 , 000 DALYs ) , but research is needed to validate this weighting and its application to bite victims . In our study , AEs from NTV use account for a very small proportion of DALYs ( 0 . 8% , 30 , 400 DALYs ) , and this has declined ( 44 , 900 DALYs in 2005 [18] ) due to the discontinuation of NTVs in most countries . Lost productivity due to premature death ( $4 . 7 billion 55 . 2% ) was the largest component of the economic burden followed by direct costs of PEP ( $1 . 7 billion , 20 . 1% ) . Investment in PEP has reduced rabies deaths in some countries , but at a high cost [35] , whilst there has been very little investment in dog vaccination ( Figs 3 and 5 ) . More judicious administration of PEP could substantially reduce PEP costs ( as indicated by the divergence of estimates of PEP use and prevented deaths detailed in Table 2 ) . For example , >1 million PEP are delivered annually in the Americas [36] , but most are precautionary for healthy animal bites . Most countries use IM delivery of PEP , but substantial savings ( >60% ) could be achieved globally by switching to the more cost-effective ID route as recommended by WHO [1] . Indirect costs of seeking PEP ( 1 . 84 billion , both travel and lost income ) are a major cost to households of exposed individuals . This is a particular problem in rural areas , since PEP is typically only available in urban centres ( sometimes only capital cities [10] ) . Recent improvements in PEP provision have reduced mortality in some countries [13 , 37] and should be considered more widely . Vaccination of dogs , the proven way of preventing human exposures and eliminating the disease at source , comprised a very small proportion of the economic burden ( <1 . 5% ) . Outside North America and Europe , a large investment in dog vaccination has only been sustained in one region ( ~$0 . 11/person/year in the Americas ) . The result is that the rabies burden in the Americas is very small ( <200 deaths per year across the continent , mostly in Haiti ) , in contrast to other countries where rabies is endemic and expenditure on dog vaccination is negligible ( <$0 . 02/person/year ) . Unlike the international government-coordinated control effort across Latin America , many developing countries have relegated rabies control and prevention to the private sector with no regulatory requirements or incentives ( for example , as part of structural adjustment programmes in sub-Saharan Africa ) . As a result , rabies has been neglected in comparison to economically important livestock diseases . Generally , medical sector costs were much higher than veterinary costs ( Fig 5 ) , but investment in dog vaccination could bring down costs to the medical sector , demonstrating the need for intersectoral coordination [38 , 39] . The World Bank has been supporting the strengthening of veterinary services ( e . g . through the OIE Performance of Veterinary Services pathway ) and considering zoonoses prevention and control as a ‘public good’ , but resources are still lacking . Standardization of vaccine procurement is greatly needed to assist poorer countries to implement mass vaccinations , given the wide variation in vaccine prices shown by our study . Vaccine banks such as those administered by OIE could have a pivotal role to play . Livestock losses are a relatively small component of the global economic burden ( 6% ) , but represent an important cost to impoverished and livestock dependent communities , particularly in Africa [40] . Our estimates should be considered on the low side due to the limited data , drawing from only a few cross-sectional studies [14 , 23 , 25 , 30] , including laboratory confirmed cases , which underestimate the true burden [24] . Better reporting of livestock cases ( suspect and confirmed ) and further active surveillance studies are therefore necessary . There are number of limitations to our study . The most critical relate to uncertainty surrounding parameter estimates , particularly in relation to bite incidence ( Fig 4 ) . A Bayesian Hierarchical approach could better incorporate uncertainties , but estimates will be constrained by the data scarcity and quality . By fitting the relationship between vaccination coverage and dog rabies incidence phenomenologically , using longitudinal time series , the effect of differences in surveillance quality between locations was reduced . High turnover of dog populations means that single vaccination campaigns have short-lived impacts , whereas sustained campaigns progressively reduce disease incidence [3 , 41] . Using lagged average coverage from consecutive campaigns improved the model fit and meant estimates were less subject to stochastic fluctuations ( Fig 2 ) . However , we had little power to estimate incidence at negligible coverage levels due to large multiannual epidemic fluctuations , compounded by limited surveillance . Consistency in estimates of the basic reproductive ratio for canine rabies [3 , 42] provides reassurance for extrapolations , but localized heterogeneities and landscape and demographic characteristics will influence vaccination impacts . Improved surveillance should enable future use of more mechanistic dynamical models [43] . Nonetheless , the direct relationship between dog vaccination and disease incidence provides a logical means of comparison that frames the problem in terms of investment in disease control and prevention . The proportion of reported bites due to suspect rabid animals contributes uncertainty to our results ( Fig 4 ) , and varies according to rabies incidence and treatment-seeking behaviour ( Fig 2 ) . But we were unable to find data to classify this systematically . By assuming that the maximum probability that a bite is by a rabid animal was 0 . 74 in countries with negligible vaccination coverage [23] , we set an upper limit on the mortality burden , with reduced mortality rates in countries with higher coverage . More generally , major uncertainties in treatment-seeking behaviour , PEP availability and dog bite incidence limited the accuracy of our estimates . For example , little is known about the variation in PEP seeking in different socio-economic and cultural settings . However , cumulative evidence , confirmed by responses to our questionnaire , shows that PEP accessibility is very poor , often restricted to capital cities in the poorest countries [10] , whilst in richer countries , or where PEP is provided free-of-charge , PEP is sought more readily [24 , 44] . We ignored mortality and costs due to imported cases in rabies-free countries , which can be individually expensive [45] , but are negligible compared to endemic rabies . Our use of average per capita GDP will mean that productive losses are overestimated , as rabies disproportionately affects impoverished communities . A further limitation of our study is that the burden is not broken down between urban and rural areas , due to a lack of data . However , dog vaccinations are implemented mostly in urban areas , which are easiest to access; dog:human ratios are typically higher in rural areas [46–48]; and PEP access is best in capital cities . Hence most rabies cases are expected to be from rural areas [18] . Finally , our estimates do not include the impacts of wildlife-transmitted rabies ( from terrestrial wildlife and bats maintaining rabies virus transmission independently from domestic dogs ) . However , as canine rabies accounts for well over 95% of all human cases , our estimates are expected to be close to the overall rabies mortality burden globally . On the other hand , livestock losses due to wildlife rabies ( for example , vampire bat rabies in the Americas [1] ) , will add substantially to the economic burden of rabies in certain parts of the world . This study demonstrates that the global burden of canine rabies is substantial , even though the disease is entirely preventable . Success in tackling the problem is contingent on investment in dog rabies control , which we show has been severely lacking . Long-term mass dog vaccination efforts could reduce medical sector and societal costs , and elimination is feasible with currently available methods [40 , 49] , however innovative financing models are required to overcome institutional barriers . | Rabies is a fatal viral disease largely transmitted to humans from bites by infected animals—predominantly from domestic dogs . The disease is entirely preventable through prompt administration of post-exposure prophylaxis ( PEP ) to bite victims and can be controlled through mass vaccination of domestic dogs . Yet , rabies is still very prevalent in developing countries , affecting populations with limited access to health care . The disease is also grossly underreported in these areas because most victims die at home . This leads to insufficient prioritization of rabies prevention in public health agendas . To address this lack of information on the impacts of rabies , in this study , we compiled available data to provide a robust estimate of the health and economic implications of dog rabies globally . The most important impacts included: loss of human lives ( approximately 59 , 000 annually ) and productivity due to premature death from rabies , and costs of obtaining PEP once an exposure has occurred . The greatest risk of developing rabies fell upon the poorest regions of the world , where domestic dog vaccination is not widely implemented and access to PEP is most limited . A greater focus on mass dog vaccination could eliminate the disease at source , reducing the need for costly PEP and preventing the large and unnecessary burden of mortality on at-risk communities . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Estimating the Global Burden of Endemic Canine Rabies |
Regulation of spatio-temporal gene expression in diverse cell and tissue types is a critical aspect of development . Progression through Caenorhabditis elegans vulval development leads to the generation of seven distinct vulval cell types ( vulA , vulB1 , vulB2 , vulC , vulD , vulE , and vulF ) , each with its own unique gene expression profile . The mechanisms that establish the precise spatial patterning of these mature cell types are largely unknown . Dissection of the gene regulatory networks involved in vulval patterning and differentiation would help us understand how cells generate a spatially defined pattern of cell fates during organogenesis . We disrupted the activity of 508 transcription factors via RNAi and assayed the expression of ceh-2 , a marker for vulB fate during the L4 stage . From this screen , we identified the tailless ortholog nhr-67 as a novel regulator of gene expression in multiple vulval cell types . We find that one way in which nhr-67 maintains cell identity is by restricting inappropriate cell fusion events in specific vulval cells , namely vulE and vulF . nhr-67 exhibits a dynamic expression pattern in the vulval cells and interacts with three other transcriptional regulators cog-1 ( Nkx6 . 1/6 . 2 ) , lin-11 ( LIM ) , and egl-38 ( Pax2/5/8 ) to generate the composite expression patterns of their downstream targets . We provide evidence that egl-38 regulates gene expression in vulB1 , vulC , vulD , vulE , as well as vulF cells . We demonstrate that the pairwise interactions between these regulatory genes are complex and vary among the seven cell types . We also discovered a striking regulatory circuit that affects a subset of the vulval lineages: cog-1 and nhr-67 inhibit both one another and themselves . We postulate that the differential levels and combinatorial patterns of lin-11 , cog-1 , and nhr-67 expression are a part of a regulatory code for the mature vulval cell types .
Complex gene regulatory networks operating in diverse cell types and tissues are crucial for development . Diverse intercellular signals and transcription factor networks control gene expression within individual cell types , acting on cis-regulatory modules of target genes [1] . Understanding such regulation first requires documenting all the regulatory inputs and outputs from each gene [2] . This information allows circuit diagrams to be constructed that provide a global perspective on how diverse cell types acquire their identity . Gene regulatory networks have been well studied in a wide range of biological model systems such as endomesoderm specification in the sea urchin embryo [3] , dorso-ventral patterning in the Drosophila embryo [4] , and mesoderm specification in Xenopus [5] . The common themes that might emerge from these studies would advance our understanding of organogenesis in vertebrates . The Caenorhabditis elegans vulva is postembryonically derived from six vulval precursor cells P3 . p–P8 . p . The central three vulval precursor cells P5 . p–P7 . p are induced to adopt 1° ( primary ) and 2° ( secondary ) vulval fates via epidermal growth factor ( EGF ) and Notch signaling , whereas the remaining precursors fuse with the hypodermal syncytium hyp7 [6] . The vulva is composed of seven distinct cell types , each with its own set of expressed genes and morphogenetic migrations [7–9] . The P6 . p 1° lineages generate the vulE and vulF cells , while the P5 . p and P7 . p 2° lineages generate the vulA , vulB1 , vulB2 , vulC , and vulD cells . The signals that induce 1° versus 2° fates in the primordial vulval precursor cells are known . However , the processes that govern patterning and differentiation of the mature vulval cell types are largely unknown [6] . Both Ras and Wnt pathways are required for the precise spatial patterning of the 1° vulE and vulF cells [10] , and both Wnt/Ryk and Wnt/Frizzled signaling pathways are necessary for patterning the P7 . p 2° vulA–vulD cells [11–13] . Genes expressed in the mature vulval cell types include some with known functions and many others without known physiological roles . lin-3 ( EGF ) is expressed in vulF and is required to signal from vulF to uterine uv1 cells [14 , 15] . egl-17 encodes a fibroblast growth factor ( FGF ) -like protein that is required for migration of the sex myoblasts to their precise final positions [16 , 17] . egl-17 is initially expressed in the 1° vulval lineages and is shut off during the L4 stage . Expression in vulC and vulD is observed during early L4 and persists throughout adulthood . The vulval expression correlates with the sites of muscle attachment . egl-26 encodes a novel protein that contains an H box/NC domain and is expressed in vulB1 , vulB2 , vulD , and vulE cells [18 , 19] . zmp-1 encodes a zinc metalloprotease and is expressed in vulD and vulE during the L4 stage and in vulA in adults [9] . ceh-2 encodes a homeodomain protein that is related to Drosophila empty spiracles and is expressed in vulB1 and vulB2 cells during the L4 stage and in vulC upon entry into L4 lethargus [9 , 20] . pax-2 is a recent gene duplication of the PAX2/5/8 protein EGL-38 [21] and is expressed exclusively in the vulD cells . zmp-1 , ceh-2 , egl-26 , and pax-2 have no known function in the vulva . Transcription factor networks in individual vulval cell types somehow generate a spatially precise pattern of cell fates [19] . Several transcription factors that regulate gene expression in the diverse vulval cell types have already been described [19 , 22–24] . lin-11 , a LIM homeobox transcription factor , regulates gene expression in all seven vulval cell types [25 , 26] . The Nkx6 . 1/Nkx6 . 2 homeodomain gene , cog-1 , regulates gene expression in vulB , vulC , vulD , vulE , and vulF cells [19 , 27] . In contrast , egl-38 encodes a PAX2/5/8 protein that appears to be the only known example of a vulval cell type–specific regulatory factor; it promotes expression of certain target genes and restricts expression of other targets exclusively in vulF cells [14 , 19 , 28] . Additional regulatory factors need to be identified to elucidate the precise spatial patterning of the mature vulval cell types . Here , we identify nhr-67 as a component of the gene regulatory networks underlying vulval patterning and differentiation . nhr-67 is required for the accurate patterning of gene expression and regulation of cell fusion in several vulval cell types and is dynamically expressed in the vulva . nhr-67 interacts genetically with cog-1 , egl-38 , and lin-11 to produce the complex expression patterns of their downstream targets . We demonstrate that the pairwise interactions between these four regulatory genes vary among the diverse vulval cell types . These results indicate that nhr-67 , cog-1 , lin-11 , and egl-38 form a part of a genetic network that generates different patterns of gene expression in each of the seven cell types .
An RNA interference ( RNAi ) screen of 508 known and putative transcription factors encoded in the C . elegans genome ( see Table S1 ) was conducted in a ceh-2::YFP reporter background . At the time we performed the screen , this was the best available set . ceh-2 encodes a homeodomain protein orthologous to Drosophila Empty Spiracles ( EMS ) and vertebrate EMX1 and EMX2 and serves as a readout for vulB fate during the L4 stage [20] . Modifiers of ceh-2 expression are good candidates for genes involved in patterning and/or differentiation of 2° vulval descendents . From this screen , we identified nhr-67 as a gene necessary for negative regulation of ceh-2 expression in the 1° vulE and vulF cells ( Figure 1A–1B ) . Reciprocal BLAST searches indicate that nhr-67 encodes an ortholog of the tailless hormone receptor , which consists of an N-terminal transactivation domain , a centrally positioned DNA-binding domain , and a C-terminal ligand-binding domain . The only other positive was the GATA-type transcription factor egl-18 , which was previously shown to be involved in vulval development [29–31] . Other genes that should have been positive in the screen ( lin-11 and cog-1 ) were not isolated from the RNAi screen , thus indicating a high false-negative rate . Analysis of the nhr-67 deletion allele ok631 revealed severe defects in early larval development ( L1 lethality and/or arrest ) . In order to bypass this early larval arrest phenotype , we resorted to feeding young L1 larvae with nhr-67 RNAi and assayed for defects in vulval gene expression . nhr-67 was also found to be required for negative regulation of two additional L4-specific markers: egl-26 ( wild-type expression in vulB , vulD , and vulE cells ) ( Figure 1C–1D ) and egl-17 ( wild-type expression in vulC and vulD cells ) in the vulF cells . Thus , nhr-67 activity is necessary for the negative regulation of expression of several 2° lineage-specific genes in the 1°-derived vulval cells during the L4 stage . Consistent with previous reports , nhr-67 RNAi results in a highly penetrant protruding vulva ( Pvl ) and egg-laying ( Egl ) defective phenotype [32] ( Figure S1 ) . However , other transcription factors exhibiting a Pvl RNAi phenotype , such as fos-1 , egl-43 , and unc-62 , have normal vulval gene expression ( unpublished data ) . In addition to its negative regulatory role , we also found that nhr-67 is necessary for promoting expression of specific genes . For example , nhr-67 is necessary for zmp-1 expression in vulA during the adult stage ( Figure 1E–1F ) . nhr-67 is also required for vulD-specific expression of pax-2 and egl-17 during the L4 stage ( Figure 1G–1J ) . These examples show that nhr-67 positively regulates gene expression in the secondary vulA and vulD cells . nhr-67 is also required for positively regulating gene expression in the 1° vulval cells , namely vulF-specific expression of lin-3 , an EGF-like protein ( Figure 1K and 1L ) . Therefore , nhr-67 regulates gene expression in at least four of the seven vulval cell types . In the L3 stage , the early 1° and 2° vulval cell fates can be distinguished by the patterns of cell division of their descendents . The 1° fated cell typically gives rise to four granddaughters that divide transversely ( left-right axes ) ; whereas a subset of the granddaughters derived from a 2° cell divide longitudinally ( anterior-posterior axes ) . To determine if nhr-67-dependent alterations in gene expression are a consequence of fate transformations in the early 1° and 2° vulval lineages , we monitored the pattern of the vulval cell divisions in an nhr-67 RNAi background . In the absence of nhr-67 , the vulval cell lineages appear wild-type in terms of both cell number and orientation of cell division ( unpublished data ) . Thus , the perturbations in gene expression caused by reduced nhr-67 function are not the result of gross abnormalities in the early vulval cell lineages . During the L4 stage , the seven vulval cell types invaginate cooperatively to assume a characteristic morphology . The similar cell types subsequently fuse , generating toroid rings that line the vulval cavity [8] . We wanted to ascertain if the observed cell fate transformations in nhr-67 ( RNAi ) animals were possibly due to improper fusion events between the wrong cell types . Cell fusion defects can be assayed using ajm-1::GFP ( an adherens junction marker ) to visualize the cell number and architecture of the vulval toroids . When observing the mid-sagittal plane of wild-type animals , ajm-1::GFP appears as dots between cells . The eight dots on either side correspond to the seven distinct vulval cell types ( Figure 2A ) . Most nhr-67 RNAi–treated animals do not exhibit dramatic defects in cell fusion ( Figure 2B ) . The 2° vulval lineage–derived cells ( vulA , vulB1 , vulB2 , vulC , and vulD ) consistently generate mature toroids . However , inappropriate fusion often occurs ( 65% , n = 17 ) between the presumptive vulE and vulF cells ( indicated by the missing dots at the top of the vulval invagination ) ( Figure 2C ) . Since nhr-67 regulates gene expression in vulval cells other than vulE and vulF , improper cell fusion events cannot fully account for all its altered gene expression patterns . We then wanted to determine if the altered gene expression occurring in the 1° vulval cells was dependent on these improper fusion events . We attempted to address this question using two approaches: ( a ) by analyzing the effect of nhr-67 RNAi on the expression of egl-17 and ceh-2 transgenes in an eff-1 ( hy21 ) background , and ( b ) by monitoring the vulval expression levels of eff-1 in animals with reduced nhr-67 activity . eff-1 is a type I membrane protein necessary for cell fusion [33] . Disruption of nhr-67 function in an eff-1-deficient background is still sufficient to cause upregulation of both egl-17 ( Figure 2D and 2E ) and ceh-2 ( Figure 2F and 2G ) in the 1° vulval cells . Thus , the nhr-67-dependent alterations in gene expression are not dependent on eff-1-mediated cell fusion . We also observed that eff-1 levels ( strong expression in vulA and vulC cells , weak expression in vulF cells ) are highly elevated in vulD and vulF cells when nhr-67 gene activity is compromised ( Figure 2H and 2I ) . However , we also note that eff-1 is not sufficient to rescue the vulE-vulF fusion defects observed in nhr-67 ( RNAi ) background ( unpublished data ) . One possibility is that eff-1 ( hy27 ) is a temperature-sensitive allele that fails to completely eliminate cell fusion . Another possibility is that in addition to eff-1 , nhr-67 negatively regulates other target genes that mediate cell fusion . Previous work reported that an nhr-67 construct containing 6 kb of the promoter region directs expression in several head neurons [34] . We generated several additional transcriptional reporter constructs that tested the entire nhr-67 coding region , introns and the 3′ noncoding region for enhancer activity using the Δpes-10 basal promoter [35] . An 8-kb fragment that consisted of 1-kb 5′ sequence , the entire coding region and introns , and 2 kb of the 3′ noncoding region yielded expression in the vulva , the hyp7 epidermal syncytium , late stage embryos , and the male tail ( Figures 3 and 4A ) . This nhr-67 construct exhibits a dynamic expression pattern in the vulval cells . During the late L4 stage , nhr-67 is first observed in vulA cells ( Figure 3A ) ( and occasionally in vulB1 ) , and this expression is maintained throughout adulthood . Expression in vulC is only seen upon entry into L4 lethargus and persists in adults ( Figure 3B ) . Strong vulB1 and vulB2 expression ( and occasional vulD expression ) is observed only in young adults ( Figure 3C ) . A 4 . 5-kb reporter construct that spans from the fourth intron to the 3′ noncoding region is sufficient to drive expression in the same tissues as seen with the 8-kb fragment ( Figure 4B ) . No expression is seen in the vulC , vulD , vulE , and vulF cells during the L4 stage unless nhr-67 or cog-1 activity is eliminated ( see below ) . Thus , the cis-elements driving the vulval expression of nhr-67 appear to be located in the region spanning the fourth intron to the 3′ noncoding region . We then wanted to confirm if these regulatory elements were capable of interacting with the endogenous promoter of nhr-67 in order to promote its transcription in the vulva . To test this , we generated an nhr-67 transcriptional reporter driven by 1 kb of its native promoter and containing regulatory sequences downstream of the fourth exon in their normal context . The nhr-67 transcriptional construct containing the endogenous promoter recapitulated the vulval and embryonic expression pattern observed with the nhr-67::Δpes-10 constructs ( Figure 4C ) . We also examined whether the upstream regulatory sequences of nhr-67 interact with the downstream regulatory elements to influence its vulval expression . This test was accomplished by coinjecting a transcriptional green fluorescent protein ( GFP ) construct that contains a 6-kb upstream sequence of nhr-67 ( Figure 4D ) with the 8-kb nhr-67::Δpes-10 construct ( Figure 4A ) described above . We find that in the presence of the 6-kb promoter region , the vulval expression is identical to that of the 8-kb nhr-67::Δpes-10 constructs . Besides the previously reported expression in head neurons , we observed expression in the anchor cell ( AC ) ( during mid–late L3 stage ) in hermaphrodites and the linker cell in males ( Figure S2A and S2B ) . We attempted to understand the trans-regulation of vulval expression in the diverse cell types by analyzing the regulation of two target genes in detail: egl-17 and ceh-2 . To dissect the trans-regulation of these target genes , we constructed various double and triple mutant/RNAi combinations and assayed for alterations in gene expression in the 1° vulval cells . During the L4 stage , the egl-17 transcriptional reporter is expressed solely in vulC and vulD , being absent in both vulE and vulF ( Figure 5 and Table 1 ) . nhr-67 RNAi in an otherwise wild-type background results in an increase of egl-17 expression in the vulF cells ( Figure 5 and Table 1 ) . In those nhr-67 RNAi animals , only one of the four vulF cells exhibits this ectopic egl-17 expression during the L4 stage . egl-17 expression is consistently absent in the vulF cells of cog-1 and egl-38 hypomorphic alleles ( Figure 5 and Table 1 ) . In comparison , cog-1 animals treated with nhr-67 RNAi are qualitatively enhanced ( i . e . , several vulF cells misexpress egl-17 ) , whereas egl-38 animals treated with nhr-67 RNAi displayed a qualitatively and quantitatively higher egl-17 expression in the vulF cells ( Figure 5 and Table 1 ) . cog-1 is necessary for negatively regulating egl-17 expression in the vulE cells and acts redundantly with egl-38 to negatively regulate egl-17 in the vulF cells [19] ( Figure 5 and Table 1 ) . We also observed frequent egl-17 upregulation in the vulE cells of egl-38; nhr-67 ( RNAi ) doubly perturbed hermaphrodites ( Table 1 ) , which is invariably absent in either singly perturbed background . Our study provides the first example of egl-38 modulating gene expression in the vulE cell type . Hence , egl-38 , nhr-67 , and cog-1 act together to negatively regulate egl-17 expression in the 1° vulval lineages during the L4 stage . Loss of lin-11 function leads to complete abolition of egl-17 gene expression in all vulval cells [26] ( Figure 5 and Table 1 ) . Lastly , the ectopic egl-17 expression visualized in the 1° descendents of cog-1- , egl-38- , and nhr-67-depleted backgrounds is dependent on lin-11 activity ( Figure 5 and Table 1 ) . Loss of nhr-67 in combination with lin-11 yields rare egl-17 expression in apparently random vulval cell types ( ∼4% of animals ) . In wild-type L4 hermaphrodites , ceh-2::YFP expression is only observed in the vulB cells and is invariably absent in both vulE and vulF cells . nhr-67 RNAi results in a moderate frequency of ectopic ceh-2 expression in the vulE and vulF cells ( Table 2 ) . Eliminating lin-11 function leads to complete loss of ectopic ceh-2 expression in the 1° vulval lineages of nhr-67 RNAi animals ( Table 2 ) . ceh-2 expression is consistently absent in the 1° vulF cells of cog-1 and egl-38 single mutants ( Table 2 ) . cog-1 mutants exhibit a moderate increase of ceh-2 expression in the vulE cells [19] ( Table 2 ) . We also found that 90% of cog-1; egl-38 doubles show increased ceh-2 expression in vulE cells compared to cog-1 ( 32% ) or egl-38 ( 0% ) single mutants ( Table 2 ) . Thus , analysis of these double mutants provides us with a second example of egl-38 regulating gene expression in the vulE cells . As with the egl-17 reporter , simultaneous depletion of cog-1 and egl-38 activities results in a high frequency of ceh-2 misexpression in the vulF cells ( Table 2 ) . Both cog-1 and egl-38 are thus required for negative regulation of ceh-2 expression in the vulF cells . cog-1 , lin-11 , and nhr-67 , all of which regulate different aspects of vulval gene expression , exhibit dynamic spatial and temporal expression patterns in the developing vulva [26 , 27] . egl-38 expression has been observed in the vulF cells [15] . As mentioned previously , nhr-67 expression is primarily restricted to vulA ( and occasionally vulB1 ) cells during L4 stage . Yet numerous perturbations in gene expression are observed in nhr-67 RNAi–treated animals , suggesting that nhr-67 is indeed functional during the L4 stage in other mature vulval cell types besides vulA ( Figure 1 ) . A similar observation can be made about cog-1 . Wild-type animals occasionally exhibit weak cog-1 expression in vulE cells but none in vulF cells ( Table 3 ) . However , cog-1 synergistically interacts with egl-38 and nhr-67 to regulate egl-17 expression in the vulF cells ( Figure 5 and Table 1 ) . One attractive hypothesis is that levels of both these transcription factors are maintained under strict spatio-temporal control . We thus set out to investigate the interactions among these regulatory factors by assaying for alterations in the reporter gene expression in various mutant backgrounds . During the L4 stage , lin-11 is consistently expressed in the 2° vulB , vulC , and vulD lineages , and occasionally in the vulA and vulF cells . Neither cog-1 nor egl-38 mutations alter lin-11 vulval expression [36] . Similarly , reduction of nhr-67 gene activity also does not impact lin-11 expression in the vulva ( Table 3 ) . The cog-1 translational reporter is strongly expressed in vulC and vulD , weakly expressed in vulE , and undetectable in vulF cells during L4 ( Figure 6 and Table 3 ) . We found that cog-1 levels are increased in the 1° vulF cells of nhr-67 RNAi–treated hermaphrodites as well as in lin-11 and egl-38 mutants ( Figure 6 and Table 3 ) . nhr-67 RNAi–treated animals also showed elevated cog-1 expression in the vulE cells ( Table 3 ) . In lin-11 mutants , cog-1 levels in vulD are completely abolished as opposed to the vulC-specific expression , which is only partially affected ( ∼57% of animals ) ( Table 3 ) . Overall cog-1 expression levels in lin-11 loss-of-function mutants are noticeably reduced when compared to the wild-type reporter background . The frequency of vulD-specific cog-1 expression is significantly increased in egl-38 mutants ( Table 3 ) . cog-1 negatively autoregulates in vulA , vulB1 , and vulB2 cells ( Table 3 ) . nhr-67::GFP expression is consistently observed in vulA during the L4 stage ( Table 3 ) . lin-11 mutants only partially eliminate the vulA-specific expression of nhr-67 ( Figure 7 and Table 3 ) . nhr-67 expression in vulA is completely abolished only in the absence of both lin-11 and its positive autoregulatory activity ( Table 3 ) . Overall , nhr-67 expression levels in lin-11 loss-of-function mutants are noticeably reduced when compared to a lin-11 ( + ) background . lin-11 activity is also required for directing the ectopic nhr-67 expression in the 1° lineages when the autoregulatory loop is compromised ( Table 3 , see below ) . Also , loss of lin-11 sometimes caused premature vulC expression of nhr-67 during L4 stage , which can be interpreted either as a cell type or a temporal regulatory defect ( Figure 7 and Table 3 ) . Reduction of cog-1 function results in increased expression of nhr-67 in vulC and vulD during the L4 stage and vulE and vulF during L4 lethargus ( Figure 7 and Table 3 ) . Depletion of both cog-1 and nhr-67 activities leads to a more robust increase in nhr-67 levels in the vulF cells ( Table 3 ) . egl-38 mutants sometimes showed ectopic nhr-67 expression in vulC and vulD cells during the L4 stage ( Figure 7 and Table 3 ) and significantly increased its frequency of expression in vulB1 cells ( Table 3 ) . In addition to the cross-inhibitory interactions between cog-1 and nhr-67 in both the 1° vulE and vulF cells , we also discovered that they both negatively autoregulate in the same cell types . Inhibition of nhr-67 by RNAi feeding results in the robust increase of nhr-67::GFP expression levels in both vulE and vulF cells ( Figure 7 and Table 3 ) . Elevation of nhr-67 transcriptional levels is also visible in the vulC and vulD lineages of nhr-67 ( RNAi ) animals during L4 stage . Upregulation of nhr-67 expression in vulC , vulD , vulE , and vulF cells is also visible with the 4 . 5-kb nhr-67 transcriptional reporter construct ( Figure 4B ) in an nhr-67 ( RNAi ) background ( unpublished data ) . We used fos-1 RNAi feeding as a control to exclude the possibility that the observed negative autoregulation was a nonspecific effect of inducing RNAi . fos-1 RNAi–treated animals exhibited a strong Pvl phenotype ( at least in part due to its AC invasion phenotype ) [37] and did not alter nhr-67 levels in the 1° lineages ( Figure 7 and Table 3 ) . Similarly , ectopic expression of cog-1::GFP in all 1° vulval descendents is consistently observed when cog-1 activity is compromised ( Figure 6 and Table 3 ) . Thus , nhr-67 and cog-1 appear to be activated in all the mature vulval cell types but are then restricted by both autoregulatory and trans-regulatory mechanisms .
nhr-67 encodes a C . elegans ortholog of tailless , a crucial regulator of blastoderm patterning in the terminal pathway of Drosophila embryogenesis as well as neuronal development . We find that nhr-67 activity is required for the regulation of gene expression in several mature vulval cell types and is dynamically expressed in the vulva . For technical reasons , we have been unable to determine whether nhr-67 acts in the vulval cells for these functions . However , the expression of nhr-67 in the vulva and the complexity of the interactions are most consistent with a primarily autonomous action of nhr-67 . However , given the expression of nhr-67 in the AC , it is possible that the effects ( particularly on the 1° lineage ) are nonautonomous . For example , the AC generates EGF and Wnt signals and is required to differentiate vulE and vulF cells , presumably via these signals [10] . Loss of vulF-specific lin-3 expression in an nhr-67 RNAi background is certainly consistent with this model . The AC also promotes 1° over 2° fate [38] . The ectopic expression of 2° lineage-specific genes ceh-2 and egl-17 in the 1° vulval cells is also consistent with this model . However , lineage analysis of nhr-67 ( RNAi ) hermaphrodites argues that these alterations are not full 1° to 2° cell fate transformations in the early vulval lineages . In addition , the observed effects on pax-2 and zmp-1 expression are inconsistent with this model . It remains a formal possibility that some of nhr-67 effects in the vulva are due to a role in the AC . Our data are consistent with the function of Drosophila tailless , which facilitates proper gap gene expression at the posterior end of the blastoderm embryo via its dual activator/repressor activity [39–41] . Specifically , tailless blocks segmentation and maintains the identity of the terminal boundaries via repression of Kruppel and knirps activity and promotes hunchback expression , which is necessary for the establishment of terminal-specific structures [42 , 43] . tailless is also necessary for regulating gene expression during the generation of head segments as well as anterior brain development [44] . We also find that nhr-67 prohibits improper fusion events between related cell lineages , at least partly due to strict spatial regulation of the fusogen eff-1 in certain vulval cell types . As discussed below , nhr-67 interacts genetically with three other transcriptional regulators , cog-1 , egl-38 , and lin-11 , to produce complex patterns of gene expression , probably through trans-regulation of cell type–specific enhancers ( Figure 8 ) . We have uncovered a novel set of genetic interactions between nhr-67 , several transcription factors , and many target genes that contribute to the identity of distinct vulval cell types . For example , nhr-67 appears to be particularly important in the execution of vulF fate and maintaining its cellular identity via regulation of gene expression and fusion events between distinct cell types . Not only does nhr-67 inhibit inappropriate gene expression that is associated with the 2° vulval lineages ( Figure 1 ) , but it also promotes gene expression of the EGF protein LIN-3 , which is necessary for uv1 fate specification and facilitates proper vulval-uterine connection during development [14] . The functional data obtained from numerous RNAi experiments demonstrates that nhr-67 ( like its Drosophila ortholog ) is a versatile regulatory gene that operates on at least four of the seven vulval cell types ( vulA , vulD , vulE , and vulF ) . However , we have not tested whether any of these approximately ten interactions are direct . An interesting feature of the network is our suggestion that both nhr-67 and cog-1 might negatively autoregulate in the same vulE and vulF cells . Drosophila melanogaster tailless does not regulate itself [45] , suggesting that nhr-67 autoregulation is a developmental phenomenon unique to nematodes ( C . elegans ) . This apparent divergence in tailless regulation between phyla suggests that a more precise fine-tuning of tailless levels is required for the execution of accurate patterning in the C . elegans vulva . In contrast to their different autoregulatory properties , we find that certain genetic interactions are indeed conserved between the D . melanogaster tailless and C . elegans nhr-67; namely tailless restricts the expression domain of ems in the head segments [44] , which is comparable to nhr-67 repressing the worm ems ortholog ceh-2 in the inappropriate vulval cells . Additional tailless targets from other organisms [40 , 46 , 47] may also have an impact on vulval patterning . Predictions can also be made in the reciprocal direction and used to elucidate vertebrate development . For example , FGF signaling is required for both vertebrate and inverterbrate heart development [48 , 49] . The LIM domain protein ISL1 promotes differentiation in a subset of cardiac progenitor cells and transcriptionally activates several FGF genes in mice [50] . Our trans-regulation experiments reveal that both egl-17 and ceh-2 contain cis-regulatory elements that are directly or indirectly dependent on cog-1 ( Nkx6 . 1/6 . 2 ) , egl-38 ( Pax2/5/8 ) , nhr-67 ( tll ) , and lin-11 ( LIM ) activity . These data may provide further insights into the elaborate regulation of classic developmental genes such as FGF and EMS , both of which have multiple roles in metazoan development . Previous work demonstrated that patterning of the E and F descendents of the 1° vulval lineage involves both a short-range AC-dependent signal using the Ras pathway as well as lin-17 ( Wnt ) signaling [10] . In the context of egl-17 gene expression , cog-1 single mutants exhibit increased levels in the vulE cells only . In contrast , nhr-67 RNAi appears to exclusively affect egl-17 expression in the vulF cells . The negative regulatory activities of cog-1 in vulF and nhr-67 in vulE only become apparent in an egl-38 mutant background ( which shows no phenotype on its own ) . This difference suggests that cog-1-mediated negative regulation plays a greater role in vulE cells whereas nhr-67-mediated negative regulation functions primarily in vulF cells . One hypothesis is that vulF cells are biased by proximity to the AC to have higher levels of nhr-67 compared to cog-1 ( Figure 9 ) . The genetic regulatory interactions within the vulval network demonstrate that cog-1 levels are negatively regulated in vulF cells via four inputs: lin-11 , egl-38 , nhr-67 , and cog-1 . In comparison , nhr-67 expression in vulF cells is modulated by two antagonistic inputs ( cog-1 and nhr-67 ) and one positive input ( lin-11 ) , thus possibly resulting in its higher levels . These observations are consistent with a model where nhr-67 acts as the major negative regulator in vulF cells . nhr-67 and cog-1 cross-inhibit each other's transcriptional activities , specifically in the vulE and vulF cells , implying that a mutually antagonistic feedback loop exists that exclusively affects the cells of the 1° vulval lineages . Both cog-1 and its mammalian ortholog Nkx6 . 1 have been previously implicated in bistable loops that reinforce one of two possible stable end states [51 , 52] . The cross-inhibitory interactions between nhr-67 and cog-1 might be relevant in the specification of vulE versus vulF cell fates . The nature of the bistable loop between cog-1 and nhr-67 , however , is unknown . In particular , the bistable loop may be a consequence of either direct transcriptional regulation ( as implied in Figure 9 ) or indirect regulation through an unknown intermediate regulatory factor . However , the above observation does not rule out the possibility that additional regulatory factors might also contribute to proper patterning of 1° lineages . These other inputs could presumably operate via several potential mechanisms such as modulating the balance between cog-1 versus nhr-67 levels , being exclusively active in one 1° cell type , and interacting at distinct cis-regulatory elements of the downstream targets . Given the complexity of the observed vulval regulatory interactions , we propose that the network operating on each vulval cell type is unique ( Figure 9 ) . A single regulatory factor may have differential functions in terms of executing accurate spatio-temporal gene expression in diverse cells . For instance , lin-11 may upregulate cog-1 levels in the 2° vulC and vulD cells while antagonizing them in the 1° vulF cells . A similar argument can be made about the lin-11-dependent regulation of nhr-67 . lin-11 may temporally regulate nhr-67 by inhibiting its vulC-specific expression during the L4 stage . In contrast , lin-11 is clearly critical for the positive regulation of nhr-67 expression in both vulE and vulF cells . Both cog-1 and nhr-67 are present at high levels in a subset of the 2° vulval cells , yet are barely detectable in the 1° vulval cells . Nevertheless , the disruption of either factor yields obvious defects in 1° vulval cell–specific gene expression . A cross inhibition circuit , such as we propose for cog-1 and nhr-67 , can be bistable , with stable states that tolerate inherent fluxes in gene expression ( i . e . , it would not randomly oscillate between states ) [53–55] . Negative autoregulatory circuits have been shown to reduce cell–cell fluctuations in the steady-state level of transcription factors [56] and can speed up the response times of transcription networks without incurring the cost of constant protein production and turnover [57] . These two distinct circuits might enable cells to reach a developmental state with built-in flexibility , allowing rapid switching of their fate upon transient inputs ( as opposed to sustained inductive inputs that are metabolically costly ) . In this model , dynamic levels of cog-1 and/or nhr-67 expression could correlate with particular aspects of 1° vulval cell fate execution . This might account for the elaborate autoregulatory and trans-regulatory interactions specifically seen in 1° vulval descendents , as opposed to their 2°-derived counterparts . We postulate that although all the vulval cells appear to use the same regulatory factors , their differential effects on the diverse cell types is what results in accurate gene expression . During the L4 stage , the gradient of nhr-67 expression is opposite to that of either cog-1 or lin-11 . This difference in gene expression domain raises the question of whether the levels of these factors are critical for vulval development . For example , high levels of lin-11 result in misexpression of egl-17 in vulA and abnormal vulval invagination [26] . Different concentrations and combinatorial expression patterns of lin-11 , cog-1 , and nhr-67 might thus encode mature vulval cell types ( Figure 9 ) . For example , differentiation to the 1° vulF cell type may entail low levels of LIN-11 and NHR-67 along with lower levels of COG-1 . In contrast , the 1° vulE cells require medium levels of COG-1 along with low doses of LIN-11 and NHR-67 . vulA and vulB are similar to each other with respect to maintaining low COG-1 levels . However , vulA cells are characterized by their high NHR-67 levels and medium LIN-11 levels as opposed to the reverse situation in vulB1 and vulB2 cells ( medium-low NHR-67 , high LIN-11 ) . Lastly , both vulC and vulD have indistinguishably high levels of LIN-11 and COG-1 , and we are unable to precisely define what distinguishes these two cell types from each other . One hypothesis is the differential regulation of NHR-67 and COG-1 in both cell types: COG-1 levels are impacted by egl-38 in vulD ( but not vulC ) , whereas NHR-67 levels are negatively regulated by lin-11 in vulC ( but not vulD ) . An obvious limitation of this proposed regulatory code is that it does not take into account other transcription factors that may potentially mediate vulval patterning . The intricacies of vulval organogenesis can be deconstructed by rigorously elucidating the genomic networks that operate within the seven mature vulval cell types . Deciphering this regulatory code will provide valuable information on network connections and might provide insights into other examples of organogenesis .
Transgenic worms were anesthetized using 3 mM levamisole and observed using Nomarski optics ( http://www . nomarski . com ) . Photographs were taken with a monochrome Hamamatsu digital camera ( http://www . hamamatsu . com ) and Improvision Openlab 4 . 0 . 4 software ( http://www . improvision . com ) . The fluorescent images were overlaid with their respective DIC images using Adobe photoshop 7 . 0 . 1 ( http://www . adobe . com ) . The vulval expression patterns for all strains except syIs49 were visualized during the late L4 stage . In the case of syEx716 , the vulval expression was also examined during L4 lethargus and adult stage . In syIs49 animals , vulA-specific zmp-1::GFP expression was scored in adults only . C . elegans strains were cultured at 20 °C using standard protocols ( Brenner , 1974 ) . Transgenes used in this study are as follows: syIs54 [ceh-2::GFP] , syIs55 [ceh-2::YFP] , syIs51 [cdh-3::CFP] , syIs49 [zmp-1::GFP] , syIs77 [zmp-1::YFP] , syIs59 [egl-17::CFP] [9] , syIs78 [ajm-1::GFP] [26] , syIs107 [lin-3::GFP] [58] , ayIs4 [egl-17::GFP] [16] , guEx64 [pax-2::GFP] ( gift from Chamberlin lab ) , kuIs36 [egl-26::GFP] [18] , syIs63 and syIs64 [cog-1::GFP] [27] , syIs80 [lin-11::GFP] [59] , syEx716 [8-kb nhr-67Δpes-10::GFP] , syEx749 [8-kb nhr-67Δpes-10::GFP] , syEx744 [nhr-67 intron4 Δpes-10::GFP] , syEx925 [6 kb upstream nhr-67::GFP + 8 kb nhr-67Δpes-10::GFP] , syEx865 [nhr-67p::GFP::nhr-67 int4–3′end] , and syEx756 [unc-53::GFP] . Alleles used in this study: LGI , lin-11 ( n389 ) ; LGII , cog-1 ( sy275 ) , eff-1 ( hy21 ) ; LGIII , unc-119 ( ed4 ) ; LGIV , unc-31 ( e169 ) , egl-38 ( n578 ) , dpy-4 ( e1166sd ) , dpy-20 ( e1282 ) ; LGV , him-5 ( e1490 ) . A complete list of strains is included in Table S2 . Transgenic lines were generated using standard microinjection protocol that produces high-copy number extrachromosomal arrays [60] . syEx756 was generated by injecting the pNP10 construct [61] into unc-119 ( ed4 ) ; him-5 background using unc-119 ( + ) [62] and pBSK+ ( Stratagene , http://www . stratagene . com ) as coinjection markers . A reverse genetics screen was conducted against 508 transcription factors ( Table S1 ) from the Ahringer library ( Medical Research Council Geneservice ) to assay for alterations in vulval expression patterns for the ceh-2::YFP transgene . RNAi feeding protocol is similar to that previously described [32] . Embryos were harvested by bleaching gravid adults and were placed on a lawn of Escherichia coli strain expressing double-stranded RNA at 20 °C . Animals were scored after 36 h ( during the L4 stage ) using Nomarski microscopy . We resorted to nhr-67 RNAi feeding for the rest of this study since the nhr-67 deletion allele ( ok631 ) results in L1 lethality and/or arrest ( International C . elegans Knockout Consortium ) . All subsequent nhr-67 RNAi feeding experiments were done as described above . nhr-67 RNAi feeding experiments that entailed the restriction of cell fusion ( via a temperature-sensitive allele of eff-1 ) were conducted at 25 °C . nhr-67::Δpes-10::GFP reporter gene constructs: The pPD97–78 vector , which includes the Δpes-10 basal promoter driving GFP and the unc-54 3′ UTR ( gift from Fire lab ) , was used as a template to generate 2-kb Δpes-10::GFP products . The primers used for amplification are 5′-GCTTGCATGCCTGCAGGCCTTG-3′ and 5′-AAGGGCCCGTACGGCCGACTAGTAGG-3′ . All nhr-67 gene fragments were amplified from the C08F8 cosmid and were stitched together with the Δpes-10::GFP fragment via PCR fusion [63] and were designated as “pdd-1 constructs . ” Construct ( 1 ) consists of 1-kb promoter sequence , the entire coding region , and introns and 2 kb of the 3′ noncoding region attached to minimal Δpes-10::GFP . The primers used to amplify this template are 5′-CTGCTCAAAACTTTTGCTCC-3′ ( forward ) and 5′-CAAGGCCTGCAGGCATGCAAGCTTAAAGAACTACTGTAGTTTTTG-3′ ( reverse ) . Construct ( 2 ) spans from the fourth intron to the 3′ noncoding region fused to minimal Δpes-10::GFP . This product was generated using the forward primer 5′-GTTCGATCATGGATCCTCTCC-3′ and the same reverse primer as construct ( 1 ) . Construct ( 3 ) is an nhr-67p::GFP reporter that contains 1 kb of the native promoter stitched in-frame with a 700-bp coding fragment of GFP ( amplified from the pPD95–69 vector , a gift from Fire lab ) . The resulting 1 . 7-kb gene product was subsequently fused to 4 . 5 kb of nhr-67 regulatory sequences ( that span from the fourth intron to the 3′ noncoding region ) via PCR . Construct ( 4 ) contains 6-kb sequence upstream of the predicted first ATG of nhr-67 , appended to minimal Δpes-10::GFP . The following primers were used to amplify this product: 5′-GAACCCGGCGACGTTACGGGGCTTC-3′ and 5′-CAAGGCCTGCAGGCATGCAAGCCATCTGTGAAACCGCAGTCATCAT-3′ . Reporter constructs were injected into unc-119 ( ed4 ) ; him-5 worms using unc-119 ( + ) [62] and pBSK+ ( Stratagene ) as coinjection markers . lin-11 ( n389 ) ; syEx749 doubles were constructed by injecting the 8-kb nhr-67::Δpes-10::GFP construct into lin-11 ( n389 ) ; unc-119 ( ed4 ) ; him-5 background using unc-119 ( + ) as a rescue marker .
The WormBase Gene IDs ( www . wormbase . org ) as well as the Refseq accession numbers ( www . ncbi . nlm . nih . gov/entrez/query . fcgi ? db=Nucleotide ) for the genes described in this study are ajm-1:WBGene00000100 ( NM_077135; NM_077137; NM_077136; NM_171966 ) ; cdh-3:WBGene00000395 ( NM_066286 ) ; ceh-2:WBGene00000429 ( NM_059345 ) ; cog-1:WBGene00000584 ( NM_182115 ) ; eff-1:WBGene00001159 ( NM_001026819 ) ; egl-17:WBGene00001185 ( NM_075706 ) ; egl-26:WBGene00001193 ( NM_061251 ) ; egl-38:WBGene00001204 ( NM_069435 ) ; lin-3:WBGene00002992 ( NM_171418;NM_171919;NM_171918 ) ; lin-11:WBGene00003000 ( NM_060295 ) ; nhr-67: WBGene00003657 ( NM_069693 ) ; pax-2:WBGene00003938 ( NM_068112 ) ; unc-53: WBGene00006788 ( NM_001027000;NM_001026999 ) ; and zmp-1:WBGene00006987 ( NM_171138 ) . | During development , in which the single-celled egg generates a whole organism , cells become different from each other and form patterns of types of cells . It is these spatially defined fate patterns that underlie the formation of complex organs . Regulatory molecules called transcription factors influence the fate patterns that cells adopt . Understanding the role of these transcription factors and their interactions with other genes could tell us how cells establish a certain pattern of cell fates . This study focuses on studying how the seven cell types of the Caenorhabditis elegans vulva arise . This organ is one of the most intensively studied , and while the signaling network that initiates vulval development and sets the gross pattern of cell differentiation is well understood , the network of transcription factors that specifies the final cell fates is not understood . Here , we identify nhr-67 , a new transcription factor that regulates patterning of cell fates in this organ . Transcription factors do not necessarily act alone , and we explore how NHR-67 works with three other regulatory factors ( each with human homologs ) to specify the different properties of the vulval cells . We also demonstrate that the interconnections of these transcription factors differ between these seven diverse cell types , which may partially account for how these cells acquire a certain pattern of cell fates . | [
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] | 2007 | The tailless Ortholog nhr-67 Regulates Patterning of Gene Expression and Morphogenesis in the C. elegans Vulva |
Rheumatoid arthritis ( RA ) is a chronic , systemic autoimmune disease affecting both joints and extra-articular tissues . Although some genetic risk factors for RA are well-established , most notably HLA-DRB1 and PTPN22 , these markers do not fully account for the observed heritability . To identify additional susceptibility loci , we carried out a multi-tiered , case-control association study , genotyping 25 , 966 putative functional SNPs in 475 white North American RA patients and 475 matched controls . Significant markers were genotyped in two additional , independent , white case-control sample sets ( 661 cases/1322 controls from North America and 596 cases/705 controls from The Netherlands ) identifying a SNP , rs1953126 , on chromosome 9q33 . 2 that was significantly associated with RA ( ORcommon = 1 . 28 , trend Pcomb = 1 . 45E-06 ) . Through a comprehensive fine-scale-mapping SNP-selection procedure , 137 additional SNPs in a 668 kb region from MEGF9 to STOM on 9q33 . 2 were chosen for follow-up genotyping in a staged-approach . Significant single marker results ( Pcomb<0 . 01 ) spanned a large 525 kb region from FBXW2 to GSN . However , a variety of analyses identified SNPs in a 70 kb region extending from the third intron of PHF19 across TRAF1 into the TRAF1-C5 intergenic region , but excluding the C5 coding region , as the most interesting ( trend Pcomb: 1 . 45E-06 → 5 . 41E-09 ) . The observed association patterns for these SNPs had heightened statistical significance and a higher degree of consistency across sample sets . In addition , the allele frequencies for these SNPs displayed reduced variability between control groups when compared to other SNPs . Lastly , in combination with the other two known genetic risk factors , HLA-DRB1 and PTPN22 , the variants reported here generate more than a 45-fold RA-risk differential .
Rheumatoid arthritis is the most common systemic autoimmune disease affecting approximately 1% of the adult population worldwide , with prevalence varying from 0 . 2–0 . 3% in East Asians to 6% in Pima and Chippewa Indians [1] . The disease is characterized by inflammation of the synovial tissue and local articular damage [2] . Disability in this inflammatory polyarthritis primarily stems from progressive bone erosion and comorbidity with coronary artery disease , infection and lymphoma [3] , [4] . As with many other autoimmune conditions , RA affects women more commonly than men . Although the etiology of RA is presently unknown , studies of RA heritability in two Northern European regions have demonstrated that an average of 60% of the disease variance can be attributed to genetic factors [5] . Through a combination of linkage and association studies , alleles segregating at the human leukocyte antigen ( HLA ) class II DRB1 gene on chr 6p have consistently been shown to have strong RA-predisposing effects [6] , [7] . That said , studies suggest that HLA-DRB1 accounts for at most 50% of the phenotypic variance due to genetic effects [8]; therefore , loci not linked to the HLA region may play a crucial role in RA susceptibility . Utilizing a variety of approaches such as positional mapping , candidate gene experiments and large-scale functional genetic association studies , several recent reports have yielded evidence for additional RA genes . The most robust , non-MHC , RA-associated marker is the R620W missense polymorphism in the PTPN22 gene on chromosome 1p13 , which has been repeatedly associated with RA in individuals of European ancestry [9]–[11] . In addition , positional cloning work has suggested the peptidyl arginine deiminase gene cluster ( including PADI4 ) underneath a linkage peak on chr 1p36 may harbor susceptibility variants [12] , [13] while well-powered association studies identified RA-associated SNPs in STAT4 [14] and the TNFAIP3 region [15] , [16] . A promoter polymorphism of the Fc receptor-like 3 gene , FCRL3 , and a SNP within the RUNX1 binding site of SLC22A4 have also been implicated in RA susceptibility [17]–[19] , both with conflicting reports [20] , [21] . Interestingly , some of these disease-associated polymorphisms appear to have heterogeneity in effect sizes across ethnic groups; for example , the disease-associated variants in PADI4 and FCRL3 have a strong effect in East Asians but little effect in whites of European descent [10] , [22] . Similarly , the PTPN22 W620 risk allele is virtually absent in East Asians and therefore plays no role in RA risk in these populations [11] . As RA is a major cause of disability and is correlated with increased mortality in severe cases , genetic studies promise to improve public health . Importantly , as predicted by careful meta-analyses of linkage studies [23] , some RA-susceptibility variants show pleiotropic effects across many autoimmune diseases [e . g . 11 , 14 , 24 , 25] . Consequently , further identification of RA genetic risk factors should aid in elucidating the underlying mechanisms of autoimmunity , in general , and may substantially impact drug discovery through the development of targeted diagnostics and therapeutics . Arguing that the power of linkage disequilibrium-based designs to map disease alleles is high compared to other approaches , Jorde [26] , Risch and Merikangas [27] and Long and colleagues [28] helped motivate the recent wave of successful genome-wide disease association studies . Propelled by technological developments , this shift has recently transformed common , complex disease gene mapping resulting in a number of convincing susceptibility variants [e . g . 29–31] . We took a large-scale candidate SNP association approach , very similar to that used in our recent study of psoriasis [32] , to interrogate the genome for genetic variants that predispose individuals to RA . This genome-wide SNP panel ( 25 , 966 SNPs ) , which is primarily composed of missense ( 70% ) , acceptor/donor splice site and putative transcription-factor binding site SNPs , was applied to a multi-tiered , case-control association study of RA that incorporated replication of association effects as a key feature of the study design . By directly interrogating polymorphisms with higher likelihoods of producing biologically disruptive effects across multiple large sample sets , our aim was to maximize power to detect RA susceptibility genes . We previously reported the identity of the RA-associated PTPN22 R620W variant which was discovered in the first step ( quality control of all DNA samples ) of our RA scan [9] , [33] . Here , we report our finding of variants in the PHF19-TRAF1-C5 region on chromosome 9q33 . 2 that show strong and consistent association across three independent RA case-control studies ( 1732 cases/2502 controls ) , paralleling and extending the results of a whole-genome association study [34] and a candidate gene study [35] . Combining genetic information from HLA , PTPN22 and TRAF1 variants , we calculate the posterior probability of RA for every possible genotype combination . Results such as these may form the foundation for individualized prognosis and targeted medicine .
We are conducting three sequential case-control studies to identify SNPs associated with RA . In the first study , DNA samples from white North Americans with ( N = 475 cases ) and without ( N = 475 controls ) RA ( sample set 1 , see Table 1 for a breakdown of the clinical characteristics of each sample set ) were genotyped for a set of 25 , 966 gene-centric SNPs utilizing disease-phenotype-based pooled DNA samples ( pooled DNA samples were used to economically increase genotyping throughput while minimizing DNA consumption ) . The allele frequency of each SNP was determined in cases and controls as described in the Methods and 1438 SNPs were significantly associated with RA using an allelic test ( P<0 . 05 ) ; 88 of these SNPs mapped to chr 6p21 between HLA-F and HLA-DPB1 within the major histocompatibility complex ( MHC ) . Of the 1350 non-MHC SNPs , 1306 were evaluated in a second independent white North American sample set ( 661 cases and 1322 controls ) by use of a similar pooling strategy ( 44 SNPs were not genotyped due to insufficient primer quantities ) . Eighty-nine statistically compelling SNPs ( Pallelic<0 . 05 ) with the same risk allele in these two sample sets were then individually genotyped in sample set 1 to verify the results from the pooled DNA phase of the experiment; 55 SNPs retained statistical significance ( Pallelic<0 . 05 ) and 44 have been individually genotyped in sample set 2 . Twenty-eight of these were significant ( Pallelic<0 . 05 ) and are currently being evaluated in a third independent white Dutch sample set ( 596 cases and 705 controls ) . The most significant non-MHC SNP to emerge from a combined analysis of sample sets 1 and 2 after the PTPN22 missense SNP , rs2476601 [9] , was rs1953126 an intergenic SNP located 1 kb upstream of the human homologue to the Drosophila polycomblike protein-encoding gene , PHF19 , on chr 9q33 . 2 near two excellent candidate genes , TRAF1 and C5 ( individual genotyping: Sample Set 1: OR = 1 . 30 , 95% CI 1 . 08–1 . 58 , trend P = 0 . 007; Sample Set 2: OR = 1 . 35 , 95% CI 1 . 18–1 . 56 , trend P = 1 . 69E-05 ) . This SNP was genotyped in sample set 3 showing a nonsignificant trend towards association: OR = 1 . 16 , 95% CI 0 . 99–1 . 36 , trend P = 0 . 066 ( Table S1 ) . No significant deviations from Hardy-Weinberg equilibrium were observed for the genotypes of this SNP in the cases or controls in the three sample sets . The frequency of the minor allele was approximately 30 . 8% in white North American controls increasing to 37 . 3% in white North American cases and 34 . 9% in Dutch controls increasing to 38 . 3% in Dutch cases . A combined analysis across all three sample sets was highly significant ( OR = 1 . 28 , 95% CI 1 . 16–1 . 40 , trend Pcomb = 1 . 45E-06 ) . To further explore the association signal in this region , we used patterns of LD from the CEU HapMap data ( www . hapmap . org ) [36] to define a broad 668 kb region , extending from MEGF9 to STOM on chr 9q33 . 2 , for follow-up individual genotyping . Postulating two different disease models , one where the originally identified SNP , rs1953126 , is in LD with one or more causative SNPs and a second model of allelic heterogeneity where several alleles at a locus independently predispose individuals to disease , we selected a combination of 137 LD and tagging SNPs from this region for follow-up genotyping in Sample Set 1 ( Figure 1 ) ( A detailed description of SNP selection is outlined in the Methods ) . Only four SNPs , all in the RAB14-GSN-STOM region , were mildly out of Hardy-Weinberg equilibrium ( 10−4<P<0 . 01 ) in the controls ( Table S1 ) . Including the original SNP , rs1953126 , 38 of the 138 chr 9q33 . 2-region SNPs genotyped in Sample Set 1 were significant at the 0 . 01 level . To better understand these positive signals and select a subset of informative SNPs for genotyping in our other sample sets , we next investigated the LD architecture around rs1953126 by calculating pairwise r2 values for all 138 SNPs genotyped in Sample Set 1 . Evaluating cases and controls separately revealed very similar LD patterns across this region ( Figure 2A ) . There were two primary haplotype blocks ( LD Block 1 and LD Block 2 ) ( here an LD block is defined as a region in which over 75% of all pairwise r2 LD correlation values exceeded 0 . 3 ) , with moderate LD between pairs of SNPs residing within each of the two blocks . LD Block 1 , which contains the original SNP , rs1953126 , and is approximately 70 kb , extends from rs10985070 , an intronic SNP in the 5′ end of PHF19 , across TRAF1 into the TRAF1-C5 intergenic region to rs2900180 . Approximately 214 kb in length , LD Block 2 ranges from the middle of C5 to the RAB14-GSN intergenic region . Given that haplotype block structures can have complex LD patterns within and between blocks and that we were focused on a single associated SNP in this region ( rs1953126 ) , we present a higher resolution plot shown in Figure 2B where pairwise r2 values were calculated for rs1953126 and each of the remaining 137 SNPs , revealing groups of highly correlated SNPs not readily visible in the LD heat-map . Integrating the Sample Set 1 association results with the LD measures , we found that the original SNP , rs1953126 , was highly correlated ( r2>0 . 95 ) with 17 other SNPs ( Group 1 in Figures 2Band 2C in LD Block 1 . As predicted , these 18 SNPs have similar association results increasing in frequency from approximately 30–31% in controls to 36–37% in cases ( OR = 1 . 29–1 . 35 , trend P∼0 . 002–0 . 009 ) ( Table S1 ) . Of interest was the observation that 20 non-Group 1 SNPs were associated with disease at equal or greater significance including 14 other SNPs from LD Block 1 . Thirteen of these other LD Block 1 SNPs , which were highly correlated with one another ( r2>0 . 95 ) ( Group 2 in Figures 2Band 2C and reasonably correlated with the Group 1 SNPs ( r2 = 0 . 66–0 . 72 ) , had minor allele frequencies of approximately 38% in controls increasing to 46% in cases ( OR = 1 . 34–1 . 39 , trend P≤0 . 002 ) . The fourteenth significant SNP in LD Block 1 , rs7021880 , a TRAF1 intronic SNP , was also highly significant ( OR = 1 . 43 , trend P = 3 . 12E-04 ) increasing in frequency from 27 . 1% in controls to 34 . 7% in cases . This SNP was in LD with both Group 1 ( r2 = 0 . 82–0 . 90 ) and Group 2 ( r2 = 0 . 59–0 . 64 ) SNPs ( Figure 2B ) . The six other SNPs with P values <0 . 01 lie upstream of LD Block 1 ( n = 4 ) or downstream of LD Block 2 in GSN ( n = 2 ) ( Figure 1 , Table S1 ) and , with the exception of the PSMD5 intronic SNP rs10760117 , were not as significant as many of the LD Block 1 SNPs . Given the association results and the LD structure , we selected 72 of the 137 fine-scale mapping SNPs to genotype in Sample Set 2 ( 661 white North American RA patients and 1322 matched white North American controls ) ( Table S1 ) . This subset of fine-scale mapping SNPs was chosen to reduce the genotyping load , while capturing the association signals and retaining full coverage of the genetic variation in this region . Two of these 72 SNPs , rs12683062 ( in CEP110 ) in the cases and rs9409230 ( a RAB14-GSN intergenic SNP ) in the controls , were moderately out of Hardy-Weinberg equilibrium ( P = 2 . 56E-04 and P = 0 . 003 , respectively; Table S1 ) . Including the original SNP , rs1953126 , 23 of these 72 SNPs were significant ( trend P<0 . 01 ) in Sample Set 2; however , the nine significant LD Block 1 SNPs in Sample Set 1 were the most significant , replicated SNPs in Sample Set 2 ( Figure 1 ) . Interestingly there were three SNPs in GSN ( rs10985196 , rs7046030 and rs12683459 ) , all highly correlated with pairwise r2 values >0 . 90 , which were highly significant ( trend P<10−6 ) in Sample Set 2 but only marginally significant in Sample Set 1 ( trend P = 0 . 01–0 . 05 ) . The difference between the two sample sets appears to be the result of disparate control allele frequencies – the case allele frequencies are nearly identical between the two sample sets ( ∼22% ) but the control allele frequencies differ by 3% ( 18–19% in Sample Set 1 vs 15 . 5–16 . 5% in Sample Set 2 ) ( Table S1 ) . Forty-two SNPs were genotyped in Sample Set 3 ( 596 white Dutch RA patients and 705 white Dutch controls ) ; none of these SNPs rejected HWE at the P<0 . 01 significance level ( Table S1 ) . These 42 SNPs span over 600 kb and were selected to cover genetic variability , association patterns and gene boundaries . Four of the 42 SNPs , spanning 286 kb from TRAF1 to RAB14 , were significant at the 0 . 01 level ( Figure 1 ) . Of these four , two SNPs ( rs4836834 and rs7021049 ) were members of Group 2 from LD Block 1 , perfectly correlated ( r2 = 1 ) and both SNPs were highly significant in all three sample sets . The other two significant SNPs , rs1323472 and rs942152 , were only moderately if at all significant in Sample Sets 1 and 2 . The six Group 1 SNPs genotyped in Sample Set 3 were close to the 0 . 05 significance level , with the most significant of these being the synonymous P340P TRAF1 SNP , rs2239657 and the TRAF1-C5 intergenic SNP , rs2900180 ( trend P = 0 . 052 ) ( Table S1 ) . The TRAF1 intronic SNP , rs7021880 , was not significant in this sample set ( trend P = 0 . 102 ) . In a combined analysis of the 43 SNPs genotyped in all three sample sets , including the original SNP , rs1953126 , 25 SNPs , spanning a region of over 525 kb from rs7026635 within FBXW2 to rs10818527 within GSN , were significantly associated with RA ( trend Pcomb<0 . 01 ) ( Table 2 ) . Several of these SNPs exhibited consistent and strong association across all three sample sets ( Table S1 ) . Using either a combined trend or genotypic P-value , the top-ranked five SNPs were: rs6478486 , rs4836834 , rs2239657 , rs7021880 and rs7021049 ( listed in order of position ) . All reside within or near TRAF1 in LD Block 1 , had common odds ratios of approximately 1 . 3 and were highly significant ( trend Pcomb<1 . 5E-07 ) ( Table 2 ) . Since false-positive results can be problematic in any large-scale experiment in which modest nominal significance levels are used , we corrected the results from the combined analysis for multiple testing using the method of Dunn-Sidak [37] . Seven SNPS , all within LD Block 1 , survived a Dunn-Sidak correction for 25 , 966 SNPs at P<0 . 01 . The corrected trend Pcomb values for the five most significant SNPs were: 0 . 004 for rs6478486 and rs223957 ( Group 1 ) , 0 . 002 for rs4836834 and 0 . 001 for rs7021049 ( Group 2 ) , and 1 . 3E-04 for rs7021880 . Given that our fine-scale-mapping SNPs cluster into various groups based on their pairwise r2 values and that under many models haplotypes can be more informative than single-markers [38] , we used the Haplo-Stats package [39] to run a 5-SNP sliding-window haplotype association analysis on the 43 SNPs genotyped in all three sample sets separately for each sample set and then combined the statistical evidence across all three sample sets . The combined analysis revealed a 29 kb-wide maximum peak of global association for haplotypes comprised of alleles segregating at rs6478486-rs4836834-rs2239657-rs7021880-rs7021049 in LD Block 1 ( Pcomb = 4 . 15E-08 ) ( Figure 3 ) . This region ranges from 9 kb downstream of TRAF1 in the PHF19-TRAF1 intergenic region to intron 3 within TRAF1 . Aside from this peak and a second highly significant peak in the TRAF1 region ( Pcomb = 5 . 45E-08; rs2239657-rs7021880-rs7021049-rs2900180-rs2269066 ) , a second region of interest was centered over the RAB14-GSN region ( P = 2 . 11E-06 ) . Of these two regions , we view the disease association evidence to be stronger for the PHF19-TRAF1 region for several reasons: First , combined analyses across all studies yielded the most significant results for both single markers and haplotypes in this region . Second , the association signal at this region shows a higher degree of consistency across the three studies . Indeed , Sample Set 3 haplotypes in the RAB14-GSN region show very little deviation from the null hypothesis ( Figure 3 ) . Finally , as discussed above , a subset of SNPs in the RAB14-GSN region ( e . g . rs10985196 , rs7046030 , rs12683459 ) displayed substantial differences in control allele frequencies between the two North American groups ( Table S1 ) drawing into question the validity of the association results for these SNPs . While both the single marker and sliding window haplotype analyses pointed to LD Block 1 as harboring RA-associated SNPs , these analyses did not identify a single SNP that was clearly the most significant across all three studies . The TRAF1 intronic SNP , rs7021880 , was the most significant SNP in Sample Sets 1 ( trend P = 3 . 12E-04 ) and 2 ( trend P = 5 . 09E-07 ) and in the combined analysis ( trend Pcomb = 5 . 41E-09 ) ; however , this SNP was not significant in the Dutch sample set ( trend P = 0 . 102 ) where the Group 2 SNPs , rs4836834 and rs7021049 , were the most significant ( trend P = 0 . 004 and 0 . 006 , respectively ) ( Tables S1 and S2 ) . Interestingly , these Group 2 SNPs ranked second in significance in Sample Set 1 and in the combined analysis while in Sample Set 2 they ranked third behind rs7021880 and the Group 1 SNPs . Given these results , we analyzed the haplotype structure of LD Block 1 using a subset of the nine SNPs from this region genotyped in all three studies . Taking into account the LD structure we picked rs2239657 , the P340P TRAF1 synonymous polymorphism to represent the six Group 1 SNPs; rs7021049 , a TRAF1 intronic SNP to represent the two Group 2 SNPs; and rs7021880 for these analyses . Haplotype frequencies for these three SNPs were estimated using the Haplo . Stats package [39] revealing the same four common haplotypes in each study ( Table 3 ) . Two of these haplotypes , AGT and GCG were strongly associated with disease ( Pcomb = 3 . 08E-08 and 8 . 00E-09 , respectively ) , with the former being protective – decreasing in frequency from ∼60 . 9% in North American controls to 53 . 8% in North American cases and 56 . 7% in Dutch controls to 51 . 2% in Dutch cases ( ORcommon = 0 . 76 , 95% CI 0 . 70–0 . 83 ) ; and the latter susceptible – 27 . 0% in North American controls increasing to 34 . 7% in North American cases and 33 . 2% in Dutch controls increasing to 36 . 0% in Dutch cases ( ORcommon = 1 . 32 , 95% CI 1 . 21–1 . 45 ) . These haplotype Pcomb-values were not significantly different from those calculated for the individual SNPs ( Table 2 ) suggesting there is no strong evidence for synergistic cis-acting effects between these variants . To explore the effect of the number of copies of each haplotype at these three sites ( rs2239657 , rs7021880 and rs7021049 ) along with any dominant/recessive effects between haplotypes , we estimated diplotypes using the pseudo-Gibbs sampling algorithm from the program SNPAnalyzer [40] . Analyzing the diplotypes individually , two diplotype combinations achieved statistical significance ( P<0 . 01 ) when compared to all other diplotypes ( Table 4 ) . The AGT/AGT diplotype was strongly associated with protection against RA ( ORCommon = 0 . 68 , 95%CI 0 . 59–0 . 78; PComb = 5 . 35E-07 ) , whereas the less frequent GCG/GCG diplotype was associated with predisposition ( ORCommon = 1 . 42 , 95%CI 1 . 16–1 . 75; PComb = 0 . 005 ) . Assuming a disease prevalence of 1% , we calculated the relative risk of RA in those individuals carrying 2 copies of the protective AGT haplotype compared to those without the AGT haplotype ( RR2 copies AGT = 0 . 77 ) . This homozygous relative risk was substanftially reduced from the relative risk calculated for individuals carrying only one copy of the AGT haplotype ( RR1 copy AGT = 1 . 06 . ) . Similarly , we estimated the relative risks for the susceptible GCG haplotype ( RR2 copies GCG = 1 . 38; RR1 copy GCG = 1 . 15 ) . We used a collection of 749 SNPs informative for European substructure to stratify both the cases and controls in Sample Set 2 [41] . By partitioning cases and controls into similar genetic background groups ( “Northern European” or “Other” ) , our aim was to interrogate the data for strata-specific effects – that is , whether or not association signals were specific to one of these genetic background groups – and avoid potential confounding by population stratification . Although two SNPs demonstrated moderately higher significance levels following stratification – rs16910233 in C5 ( PNorth = 0 . 019 compared to PUnstrat = 0 . 147 ) and rs12685539 in CEP110 ( POther = 0 . 038 compared to PUnstrat = 0 . 115 ) , a Breslow-Day test of effect heterogeneity comparing ORNorth and OROther was not significant . Furthermore , a positional plot of Mantel-Haenszel P-values , testing for association given the genetic background stratification , was very similar to the unadjusted plot ( Figure S1 ) suggesting that stratification of the case and control samples by SNPs informative for European substructure did not change the association patterns in Sample Set 2 . Rheumatoid factor , a circulating antibody to immunoglobulin G , is a key serum analyte used in diagnosis of RA as well as an aid for the prognosis of RA-severity [2] . As the R620W missense polymorphism in PTPN22 appears to have stronger susceptibility effects for RF-positive disease [9]–[11] and since RF is clinically important , we investigated the role of RF status on the chr 9q33 . 2 association patterns for the three LD Block 1 SNPs , rs2239657 , rs7021880 and rs7021049 , testing for both strata-specific effects as well effect size differences between RF-positive and RF-negative disease . To explore the effect isolated to RF-positive patients compared to controls , we performed a strata-specific analysis for all sample sets using a genotypic test . The resulting combined P-values for the RF-positive stratum were highly significant ( Prs2239657 = 4 . 02E-05 , Prs7021880 = 7 . 10E-06 , Prs7021049 = 5 . 68E-06; Table 5 ) , which were slightly less significant when compared to the overall genotypic combined P-values ( Table 2 ) . A similar analysis of RF-negative disease in Sample Sets 2 and 3 yielded genotypic combined P-values of Prs2239657 = 0 . 038 , Prs7021880 = 0 . 013 and Prs7021049 = 0 . 082 . Allelic odds ratios and 95% confidence intervals were also calculated for each individual sample set and the results did not demonstrate a clear pattern of strata-specific effects within a stratum or differential effects between the two strata ( Table 5 ) . A Breslow-Day test was performed on Sample Set 2 ( individually matched cases and controls ) to formalize the test of homogeneity of odds ratios , showing that none of the three SNPs exhibited significant differential effects ( Table 5 ) . Similarly , results for the analogous Monte Carlo-based test performed in Sample Set 3 ( where cases and controls were not individually matched ) also did not reveal significant heterogeneity between RF-positive and RF-negative effects . To further dissect association signals from LD patterns , build predictive models and explore the relative effects of each SNP within the models constructed we used logistic regression . To accomplish this we first minimized the number of SNPs for these analyses by calculating pairwise r2 values for the 43 SNPs genotyped in all three sample sets and divided the SNPs into distinct groups based on their LD structure . SNPs with pairwise r2 values >0 . 90 were grouped together resulting in 27 distinct groups ( Table 6 ) and then the single most significant SNP from each group ( Pcomb from Table 2 ) was chosen for the logistic regression analyses . In the univariate analysis , the TRAF1 intronic SNP rs7021049 , which marks the Group 2 SNPs in LD Block 1 , was the most significant SNP ( P = 1 . 24E-06 ) , followed by rs7021880 ( 1 . 39E-06 ) and then the Group 1 SNP , rs2239657 ( P = 2 . 52E-06 ) ( Table 6 ) . In addition , 11 other SNPs were significant ( P<0 . 01 ) . To assess whether other observed associations in the region were primarily a result of LD with the most significant SNP , we performed pairwise logistic regression on all 27 SNPs adjusting for rs7021049 . One SNP retained modest statistical significance ( P<0 . 01 ) : rs10985196 ( Group 21 ) , a GSN intronic SNP ( P = 0 . 001 ) . To test whether the combination of the rs7021049 and rs10985196 variants fully accounted for the association with RA , we repeated the logistic regression adjusting for both; none of the remaining groups of SNPs were significantly associated with RA . It should be noted , however , that analyses of each individual sample set suggested the evidence for association with rs10985196 ( Group 21 ) was primarily driven by the data from sample set 2 ( data not presented ) . To explore more complex models , we used both forwards and backwards stepwise logistic regression procedures separately on the same 27 SNPs in each individual sample set as well as in a combined analysis of all three sample sets . The final models generated from the stepwise procedures , however , were inconsistent across the sample sets ( Table S2 ) . In fact , seven distinct models were produced; the only instance where the same model was produced was for both the forwards and backwards models of Sample Set 2 . Not surprisingly , the forward model for the combined samples , which included two SNPs , rs7021049 ( the Group 2 TRAF1 intronic SNP ) and rs10985196 ( the GSN intronic SNP ) , was consistent with the results of the pairwise logistic regression analysis presented above . Given that we have begun to witness the application of associated genetic variants to disease prognosis [42] , [43] and thus far we have convincing evidence for three RA-predisposing loci in our studies ( HLA-DRB1 , PTPN22 and the TRAF1 region ) , we estimated the risk of RA given genotypes at these three loci under three different possible unconditional RA risk assumptions ( i . e . RA disease prevalence values ) using Bayes' theorem . In total , there were 18 multi-locus genotype combinations and RA risk was calculated for each combination using data from Sample Set 1 as described in the Materials and Methods . Assuming a 1% RA prevalence , similar to that observed in the white North American general population , the results indicate that individuals with the protective genotype at all three loci ( 0SE for HLA-DRB1 , CC genotype for PTPN22 and the AGT/AGT TRAF1 diplotype ) have a substantially reduced predicted risk of RA ( 0 . 29% vs . 1% ) , whereas those individuals in the highest-risk category ( HLA-2SE , TT or TC genotype at PTPN22 , and the GCG/GCG TRAF1 diplotype ) , have an estimated RA risk of 13 . 06% – representing more than a 45-fold increase in risk ( Table S3 ) . These data are presented as a 3-D plot in Figure 4 where the lowest risk value has been reset to 1 and the other values normalized accordingly . Approximately 19% of the general population will find themselves in the low-risk multi-locus genotype category and only 0 . 06% in the high risk group . In contrast , when the disease prevalence is increased to 30% , as might be observed in high-risk groups such as an early arthritis clinic , the range of risk drops to 7 . 88-fold , with the posterior probability of RA calculated to be 11% for the lowest-risk genotype combination and increasing to 86 . 4% in the highest risk category ( Table S3 ) .
We undertook a large-scale , multi-tiered association study of RA using a panel of putative functional SNPs that have been successfully applied to case-control studies of other disease phenotypes [32] , [44]–[47] . The initial step of this large-scale RA association study , individual genotyping of 87 prioritized SNPs to evaluate DNA quality prior to constructing DNA pools for our scan , led to the identification of the PTPN22 R620W SNP [9] . This SNP has been both widely replicated and associated with multiple autoimmune diseases [11] . The present study focuses on variants in the chr 9q33 . 2 region that were also convincingly correlated with RA status . In particular , three groups of SNPs , represented by rs2239657 ( Group 1 ) , rs7021049 ( Group 2 ) and rs7021880 were highly significant and showed a localized effect to a 70 kb region extending from rs10985070 , in intron 3 of PHF19 , across TRAF1 to rs2900180 in the TRAF1-C5 intergenic region , but excluding the C5 coding region ( LD Block 1 in Figure 1 ) . Examination of the CEU HapMap data identified 16 additional SNPs that were highly correlated ( r2>0 . 95 ) with either the Group 1 or Group 2 SNPs genotyped in this study ( Figure 2C ) and all 16 fall within this 70 kb region ( no such SNPs were found for rs7021880 ) . Across sample sets , the evidence for association at these sites was stronger , maintaining statistical significance after correction for multiple testing , and more consistent than sites in neighboring regions . Additional analyses further buttressed the statistical support for these conclusions: ( i ) a haplotype sliding window analysis of all SNPs genotyped in the chr 9q33 . 2 region demonstrated strong statistical evidence for the TRAF1-region harboring RA risk variants ( Pcomb = 4 . 15E-08 ) and ( ii ) haplotype analysis of SNPs within the 70 kb LD Block 1 , identified a common protective haplotype ( Pcomb = 3 . 08E-08 ) and a less frequent risk haplotype ( Pcomb = 8 . 00E-09 ) . The three representative SNPs ( rs2239657 , rs7021049 and rs7021880 ) were strongly associated with RF-positive disease and trended towards association in RF-negative disease although the small number of RF-negative patients in our study precludes drawing statistically meaningful conclusions about the role of these SNPs in this patient population . To tease apart association signals from LD patterns , we used logistic regression . The pairwise analyses of the combined datasets suggest there may be two independent statistical signals of association to RA at chr 9q33 . 2 – one in the TRAF1 region represented by rs7021049 and one in the GSN region represented by rs10985196 ( Table 6 ) ; however , analyses of the individual sample sets showed rs10985196 was independently associated with disease risk in Sample Set 2 only while rs7021049 showed consistent association across all three sample sets ( data not presented ) . Consequently , additional samples are needed to determine whether the GSN region truly contains RA-predisposing effects . To explore more complex models and assess whether SNPs outside of LD Block 1 were incorporated into these models , we used both forwards and backwards stepwise logistic regression . The sets of SNPs included in the models chosen by the stepwise procedures were inconsistent indicating that the observed association in the region is not clearly explained by a single SNP or set of SNPs included in the tested models . Independently , Plenge and colleagues [34] , using a whole genome association ( WGA ) study , and Kurreeman and coworkers [35] , using a candidate gene approach , have also shown this chr 9q33 . 2 region is associated with RA risk in whites of European descent . Although a partially overlapping subset of samples was used in all three studies ( see Table 1 footnotes ) , each study employed unique experimental designs , analyses and presented different facets of the 9q33 . 2-RA association . Plenge and colleagues [34] identified rs3761847 , a TRAF1/C5 intergenic SNP , as one of two non-MHC SNPs reaching genome-wide significance in their WGA study; not surprisingly , the other significant non-MHC SNP was in PTPN22 . Their follow-up fine mapping of the chr 9q33 . 2 region with nine haplotype tag SNPs in four RA sample sets ( 2519 cases / 3627 controls ) localized the region of interest to 100 kb extending from PHF19 into C5 . rs3761847 , which is a Group 2 SNP in LD Block 1 , remained the most significant SNP in their combined analysis ( P = 4 . 00E-14 ) followed by rs2900180 ( P = 8 . 00E-14 ) , a member of the Group 1 SNPs in LD Block 1 . Taking a candidate gene approach , Kurreeman and colleagues [35] studied 40 SNPs in a 300 kb region surrounding C5 ( from the 3′ UTR of PHF19 to intron 25 of CEP110 ) in a staged-approach in four sample sets ( 2 , 719 cases / 1999 controls ) and concluded that rs10818488 , another TRAF1-C5 intergenic SNP and member of the LD Block 1 Group 2 SNPs , was the SNP most significantly associated with a diagnosis of RA in their study . Association of a Group 1 SNP , rs2416806 , was moderately significant in a combined analysis of three of their sample sets ( P = 0 . 015 ) . Neither the Plenge et al . nor Kurreeman et al . study included rs7021880 . Our analyses , which included more SNPs and incorporated HapMap information for all SNPs highly correlated with SNPs genotyped in our study , permitted a comprehensive analysis of the genetic architecture of 9q33 . 2 region , allowing us to localize the RA-susceptibility effects to a 70 kb region ( LD Block 1 ) that includes a portion of PHF19 , all of TRAF1 and the majority of the TRAF1-C5 intergenic region , but excludes the C5 coding region , narrowing the true region of interest . Our data , however , did not allow us to identify a single SNP or group of highly correlated SNPs ( r2>0 . 95 ) in this 70 kb region that unambiguously explained the association signal in all three independent sample sets . Other sample sets with different patterns of LD or functional studies will be required to resolve this issue . Interestingly , Potter and colleagues [48] , who studied 23 haplotype-tagging SNPs from the 6 TRAF genes , including three from TRAF1 , in a UK case-control study ( 351 RA cases / 368 controls ) failed to see association with both a Group1 ( rs1468671 ) and a Group2 ( rs4836834 ) SNP . Using an overlapping sample set to the Potter et al study , the recent Welcome Trust Case Control Consortium genome-wide association study of RA ( 1860 cases / 2930 controls ) [25] also failed to identify RA-risk variants in this region . However , a more recent follow-up study from the same group of an independent RA sample set from the UK ( 3418 cases / 3337 controls ) confirmed association with four LD Block 1 Group 2 SNPs although the effect size was less ( meta analysis OR 1 . 08 , 95%CI 1 . 03–1 . 14 ) [49] . The original RA-associated , 9q33 . 2 SNP identified in our genome-wide scan , rs1953126 , is located within LD Block 1 , 1 kb upstream of the 5′ end of PHF19 , the human homologue of the Drosophila polycomblike protein , PCL , gene . In Drosophila , the protein encoded by this gene is part of the 1MDa extra sex combs and enhancer of zeste [ESC-E ( Z ) ] complex which is thought to mediate transcriptional repression by modulating the chromatin environment of many developmental regulatory genes such as homeobox genes . While the exact function of this gene in humans remains unknown , it encodes two nuclear proteins that appear to be upregulated in multiple cancers and preliminary evidence suggests that deregulation of these genes may play a role in tumor progression [50] . TRAF1 encodes a protein that is a member of the TNF receptor ( TNFR ) associated factor ( TRAF ) protein family that associates with , and mediates signal transduction from various receptors including a subset of the TNFR superfamily . There are six members of this family of adaptor proteins; however , TRAF1 is unique in that while it contains the hallmark carboxyl-terminal TRAF domain , it has a single zinc finger in the amino-terminal part and the N-terminal RING finger domain , required for NF-κB activation , is missing . TRAF1 appears to have both anti-apoptotic and anti-proliferative effects [51] , [52] . In addition , this protein has been found to be elevated in malignancies of the B cell lineage [53]–[58] . This observation is interesting given the risk of lymphoma , particularly diffuse large B cell lymphomas , appears to be increased in the subset of RA patients with very severe disease , independent of treatment [59] , [60] . Although the precise mechanism of TRAF1 action in various signaling pathways has not been fully elucidated , it is clear that this molecule plays an important role in immune cell homeostasis making it an excellent candidate gene for RA . In fact , in vitro work suggests that TNFα-mediated synovial hyperplasia , a major pathophysiologic feature of RA , may be correlated with upregulation of TRAF molecules , particularly TRAF1 [61] . Given that TNF blockade has proved a highly effective therapy for RA [62] , [63] and response to TNF-antagonists among RA patients is known to vary , investigation of whether the TRAF1 variants identified in this study play a role in this differential response may be a fruitful pharmacogenetic avenue to pursue . C5 is also an excellent RA candidate gene and although our analyses allowed us to exclude the C5 coding region , SNPs in LD Block 1 could differentially regulate the expression of this gene . C5 encodes a zymogen that is involved in all three pathways of complement activation . Traditionally , the complement system has been viewed as a central part of the innate immune system in host defenses against invading pathogens and in clearance of potentially damaging cell debris; however , complement activation has also recently been implicated in the pathogenesis of many inflammatory and immunological diseases . Proteolytic cleavage of C5 results in C5a , one the most potent inflammatory peptides , and C5b , a component of the membrane attack complex ( MAC ) that can cause lysis of cells and bacteria . Genetic studies in various mouse models of RA , including collagen-induced arthritis ( CIA ) and the K/BXN T cell receptor transgenic mouse model of inflammatory arthritis have provided evidence that C5 or a variant in strong LD , plays a role in disease [64]–[66] . More striking is the observation that anti-C5 monoclonal antibody therapy can prevent and ameliorate disease in both mouse models [67] , [68] . In summary , we have independently identified a region on chr 9q33 . 2 as a risk locus for RA . Although the evidence from the SNPs genotyped in our sample sets most strongly points towards TRAF1 variants as being the most highly consistent with a disease model , the high LD that extends from the 5′ end of PHF19 through TRAF1 and into the TRAF1-C5 intergenic region precludes conclusively determining causative genes or functional motifs through genetic means in these samples . Mapping studies in additional sample sets with a different LD architecture and/or functional studies will be required to resolve the molecular relevance of these findings . Aside from the possibility of developing targeted therapies with knowledge of predisposing variants underlying the onset of RA , the identification of RA susceptibility alleles may encourage earlier monitoring and provide an intervention avenue in advance of significant joint erosion . Our initial analysis of the three known genetic risk factors , HLA-SE , PTPN22 and the chr 9q33 . 2 variants described here , suggests a >45 fold difference in RA risk depending on an individual's genotype at these three loci . As additional markers are identified , the ability to accurately predict individuals at increased risk for developing RA , particularly within families with a history of RA , may prove useful . Finally , differential risk variants may prove to be drug response markers .
All RA cases included in this study were white and met the 1987 American College of Rheumatology diagnostic criteria for RA [69]; informed written consent was obtained from every subject . Sample Set 1 , which consisted of 475 RA cases and 475 individually-matched controls , was collected by Genomics Collaborative , Inc . All case samples were white North Americans of European descent who where rheumatoid factor ( RF ) positive . Control samples were healthy white individuals with no medical history of RA , also of European descent . A single control was matched to each case on the basis of sex , age ( ±5 years ) , and self-reported ethnic background . The 661 cases in Sample Set 2 were acquired from the North American Rheumatoid Arthritis Consortium ( NARAC ) ( http://www . naracdata . org/ ) and consisted of members from 661 white North American multiplex families [33] , [70] , [71] . Both RF-positive and RF-negative patients were included in this sample set . Controls for Sample Set 2 were selected from 20 , 000 healthy individuals enrolled in the New York Cancer Project [72] , a population-based prospective study of the genetic and environmental factors that cause disease ( http://www . amdec . org/ ) . Two control individuals were matched to a single , randomly chosen affected sibling from each NARAC family on the basis of sex , age ( decade of birth ) , and self-reported ethnic background . Sample Set 3 was composed of 596 white RA patients from the Leiden University Medical Centre and 705 white controls from the same geographic region in The Netherlands [73]–[75] . Both RF-positive and RF-negative patients were included in this sample set . Table 1 displays the clinical characteristics of all three sample sets and a detailed description of samples that overlap with published studies of this region [34] , [35] . Our functional genome-wide scan included 25 , 966 gene-centric SNPs curated from dbSNP , the Applera Genome Initiative [44] , [76] and the literature . SNPs were included if they appeared in more than one database and had a minor-allele frequency >1% . Approximately seventy percent of the SNPs were annotated as missense polymorphisms . The majority of the remaining SNPs were either located within putative transcription-factor site motifs or within acceptor/donor splice site regions or were nonsense polymorphisms . Allele-specific , real-time quantitative PCR [77] was used to amplify 3 ng of pooled DNAs and infer SNP allele frequencies as previously detailed [44] . Individual genotyping on SNPs was performed on 0 . 3 ng of DNA using a similar protocol . Blinded to case-control status , custom-made in-house software was used to call genotypes , followed by hand-curation . Individual genotyping accuracy has been estimated to be >99 . 8% by comparison with an independent method . HLA-DRB1 genotyping was performed using sequence-specific oligonucleotide probes as previously described [9] . Shared epitope ( SE ) status [78] was determined from the probe hybridization patterns . For this study , DRB1 alleles positive for the SE include: 0101 , 0102 , 0401 , 0404 , 0405 , 0408 and 1001 . To identify SNPs for inclusion in our fine-scale mapping effort of the 9q33 . 2 region , we first postulated two different disease models: 1 ) a model where the originally identified SNP is in linkage disequilibrium with one or more causative SNPs and 2 ) a model of allelic heterogeneity where several alleles at the locus independently predispose individuals to RA . To address both of these models , we first defined the region to be interrogated by calculating pairwise linkage disequilibrium ( r2 ) values between the originally identified SNP 5′ of PHF19 , rs1953126 , and all HapMap-genotyped SNPs ( http://www . hapmap . org/ ) within 500 kb flanking either side for the CEPH samples ( Utah residents with ancestry from northern and western Europe , or CEU individuals ) [36] . With this information , we defined a broad region spanning 668 kb from MEGF9 , 177 kb upstream of rs1953126 , to STOM , 491 kb downstream of rs1953126 , for follow-up genotyping . SNPs within this region were partitioned into those in moderate to high LD ( r2>0 . 20 ) with rs1953126 to address the first model , and those in low LD ( r2<0 . 20 ) with rs1953126 to address the second model . The power-based SNP selection program Redigo [79] was then used on the low LD set of SNPs to identify a reduced number of SNPs ( tagging SNP set ) that retained high power to detect association . Those SNPs in moderate to high LD with the original SNP were reduced by selecting a subset of representative SNPs of any groups exhibiting extremely high inter-group LD ( r2>0 . 98 ) . Further , any putative functional SNPs were automatically included in the fine-scale mapping effort if we were able to construct high-quality genotyping assays for them . The resulting set of 137 SNPs was genotyped in Sample Set 1 and the data analyzed . Additional removal of fine-scale mapping SNPs was performed for evaluation in subsequent sample sets on the basis of association results and refined LD patterns: a subset of 72 SNPs were selected for genotyping in Sample Set 2 and 42 SNPs were genotyped in Sample Set 3 . The Cochran-Armitage trend test [80] was used to calculate P-values for individual SNPs . A William's-corrected G-test [37] was used to calculate P-values for genotypic association . P-values were corrected for multiple testing using the method of Dunn-Sidak [37] . Odds ratios and confidence intervals were calculated according to standard procedures . Hardy-Weinberg equilibrium testing was accomplished through the exact test of Weir [81] . P-values were combined across sample sets using the Fisher's combined P-value , or omnibus procedure [82] . Likewise , Mantel-Haenszel common odds ratios [83] were calculated to combine data across sample sets . To avoid the small-count limitations of asymptotic-derived confidence intervals , a Monte Carlo simulation was written in XLISP-STAT to calculate 95% confidence intervals on the Mantel-Haenszel common odds ratios . We typically performed 20 , 000 iterations of the Monte Carlo for these confidence intervals . The standard measure of pairwise linkage disequilibrium ( the r2 statistic from estimated 2-site haplotypes ) was used to characterize the genetic architecture of the region . The program LDMAX with an EM algorithm was used to perform the r2 calculations [84] . | Rheumatoid arthritis ( RA ) , a chronic autoimmune disorder affecting ∼1% of the population , is characterized by immune-cell–mediated destruction of the joint architecture . Gene–environment interactions are thought to underlie RA etiology . Variants within HLA-DRB1 and the hematopoietic-specific phosphatase , PTPN22 , are well established RA-susceptibility loci , and although other markers have been identified , they do not fully account for the disease heritability . To identify additional susceptibility alleles , we carried out a multi-tiered , case-control association study genotyping >25 , 000 putative functional SNPs; here we report our finding of RA-associated variants in chromosome 9q33 . 2 . A detailed genetic analysis of this region , incorporating HapMap information , localizes the RA-susceptibility effects to a 70 kb region that includes a portion of PHF19 , all of TRAF1 , and the majority of the TRAF1-C5 intergenic region , but excludes the C5 coding region . In addition to providing new insights into underlying mechanism ( s ) of disease and suggesting novel therapeutic targets , these data provide the underpinnings of a genetic signature that may predict individuals at increased risk for developing RA . Indeed , initial analyses of three known genetic risk factors , HLA , PTPN22 , and the chromosome 9q33 . 2 variants described here , suggest a >45-fold difference in RA risk depending on an individual's three-locus genotype . | [
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] | 2008 | A Large-Scale Rheumatoid Arthritis Genetic Study Identifies Association at Chromosome 9q33.2 |
Although glucose uniquely stimulates proinsulin biosynthesis in β cells , surprisingly little is known of the underlying mechanism ( s ) . Here , we demonstrate that glucose activates the unfolded protein response transducer inositol-requiring enzyme 1 alpha ( IRE1α ) to initiate X-box-binding protein 1 ( Xbp1 ) mRNA splicing in adult primary β cells . Using mRNA sequencing ( mRNA-Seq ) , we show that unconventional Xbp1 mRNA splicing is required to increase and decrease the expression of several hundred mRNAs encoding functions that expand the protein secretory capacity for increased insulin production and protect from oxidative damage , respectively . At 2 wk after tamoxifen-mediated Ire1α deletion , mice develop hyperglycemia and hypoinsulinemia , due to defective β cell function that was exacerbated upon feeding and glucose stimulation . Although previous reports suggest IRE1α degrades insulin mRNAs , Ire1α deletion did not alter insulin mRNA expression either in the presence or absence of glucose stimulation . Instead , β cell failure upon Ire1α deletion was primarily due to reduced proinsulin mRNA translation primarily because of defective glucose-stimulated induction of a dozen genes required for the signal recognition particle ( SRP ) , SRP receptors , the translocon , the signal peptidase complex , and over 100 other genes with many other intracellular functions . In contrast , Ire1α deletion in β cells increased the expression of over 300 mRNAs encoding functions that cause inflammation and oxidative stress , yet only a few of these accumulated during high glucose . Antioxidant treatment significantly reduced glucose intolerance and markers of inflammation and oxidative stress in mice with β cell-specific Ire1α deletion . The results demonstrate that glucose activates IRE1α-mediated Xbp1 splicing to expand the secretory capacity of the β cell for increased proinsulin synthesis and to limit oxidative stress that leads to β cell failure .
Type 2 diabetes ( T2D ) is a disease epidemic caused by failure of β cells to produce sufficient insulin to maintain glucose homeostasis [1] . In response to obesity , insulin resistance and hyperglycemia pressure β cells to increase preproinsulin synthesis , processing , and secretion . Although β cells can compensate by increasing insulin production , approximately one-third of individuals with insulin resistance eventually develop β cell failure and diabetes [2] . Unfortunately , the mechanisms leading to β cell failure in T2D are poorly understood , although factors include genetic lesions , hyperglycemia , hyperlipidemia , and inflammatory cytokines [3] . The β cell , unlike other professional secretory cells , is uniquely specialized for glucose-stimulated insulin secretion ( GSIS ) in order to respond to daily fluctuations in blood glucose . Upon glucose-stimulated release of insulin granules , preproinsulin mRNA translation increases up to 10-fold [4–6] . Since glucose has a modest short-term effect on insulin gene transcription [7 , 8] , it is surprising how little is known of the underlying mechanism ( s ) of glucose-stimulated insulin mRNA translation and recruitment to the endoplasmic reticulum ( ER ) , which represents the earliest rate-limiting step in insulin biosynthesis . For the β cell to accommodate increased preproinsulin synthesis , it is necessary to expand the secretory pathway for preproinsulin cotranslational translocation , folding , processing , trafficking , and storage in secretory granules . Recent studies suggest that increased proinsulin synthesis overwhelms the capacity of the ER to properly fold , process , and secrete insulin in response to glucose and activates the unfolded protein response ( UPR ) [3 , 9–12] . The UPR is an adaptive mechanism to prevent accumulation of misfolded protein in the ER [13 , 14] . Inositol-requiring enzyme 1α ( IRE1α ) is the most conserved transducer of the UPR that signals through initiating unconventional splicing of X-box-binding protein 1 ( Xbp1 ) mRNA . Cytosolic splicing of Xbp1 mRNA removes 26 nucleotides to create a translational frame shift that produces a potent basic-leucine zipper-containing ( bZIP ) transcription factor ( TF ) ( XBP1s ) that induces genes encoding functions within the ER , including protein synthesis , folding , and trafficking , N-linked glycosylation , lipid biosynthesis , and ER-associated protein degradation ( ERAD ) [14–16] , while mRNAs inhibited by XBP1s or induced by unspliced XBP1u are mercurial . In addition , the endoribonuclease ( RNase ) activity of IRE1α degrades its own mRNA [17] , as well as additional mRNAs containing CUGCAG or similar RNA recognition motifs in a process termed regulated IRE1α-dependent degradation ( RIDD ) , in theory to reduce the ER protein-folding burden [18 , 19] . Further complicating the pathway is the recent function attributed to IRE1α’s RNase activity in microRNA ( miRNA ) biogenesis and/or degradation; however , these endonucleolytic targets are not conserved in all eukaryotic cell types and are not as essential for cell function as IRE1α-mediated cytosolic splicing of Xbp1 mRNA [20–22] . In metazoans , the UPR signals through two additional ER transmembrane sensors , the PKR-like ER kinase ( PERK ) and the bZIP TF activating transcription factor 6 α ( ATF6α ) , where only the latter is dispensable for organismal survival and β cell function [3 , 9–12 , 23–30] . A physiological requirement for IRE1α/XBP1s in β cell function was suggested from analysis of Wolfram syndrome , also known as DIDMOAD ( diabetes insipidus , diabetes mellitus , optic atrophy , and deafness ) , in which patients experience ~60% mortality by the age of 35 [31] . The Wolfram syndrome 1 ( Wfs1 ) gene encodes an ER-resident protein associated with protein folding , calcium homeostasis , glucose-stimulated cAMP production , and degradation with ATF6α [32 , 33] . As XBP1s activates the Wfs1 promoter [34] , the IRE1α-XBP1s-WFS1 pathway represents a direct link between protein folding in the ER , the UPR , β cell failure , and a diabetic patient cohort . Previous studies on IRE1α function in β cells have not measured the effects deletion of Ire1α in adult differentiated β cells [35–37] . β cell-specific , embryonic deletion of Xbp1 caused hyperactivation of IRE1α RNase to degrade mRNAs encoding proinsulin processing enzymes;prohormone convertases 1 and 2 ( PC1 and PC2 ) and carboxypeptidase E ( CPE ) , leading to the conclusion that IRE1α/XBP1 is required for proinsulin to insulin maturation [38] . In the context of these results , we sought to measure the importance of IRE1α signaling in differentiated primary β cells . Therefore , we employed inducible deletion of Ire1α in mature β cells and massive parrallel sequencing to uncover mRNAs altered upon glucose stimulation in an IRE1α-dependent manner . We then compared our results with previously reported IRE1α/XBP1s and IRE1α/RIDD targets to identify the novel and overlapping changes in mRNAs that are IRE1α-dependent in glucose-stimulated islets . Our results reveal that glucose-inducible , IRE1α-dependent mRNAs encode numerous functions important for the β cell secretory pathway , including ribosome recruitment to the ER , cotranslational translocation , signal peptide cleavage , protein folding , and trafficking , all of which are required for proper glucose-stimulated preproinsulin biosynthesis and conversion of preproinsulin to proinsulin . Indeed , in the absence of IRE1α , there is a defect in translation of proinsulin mRNA . However , mRNA-Seq also revealed many uncharacterized and unexpected mRNAs with diverse non-ER functions that are also dependent on IRE1α and glucose stimulation . In contrast , deletion of IRE1α in differentiated β cells increased expression of mRNAs encoding enzymes that produce reactive oxygen species ( ROS ) , proteins of the plasma membrane , and extracellular matrix ( ECM ) that at least partially account for the oxidative stress , inflammation , and fibrosis measured in adult Ire1α-null islets . Furthermore , we show that oxidative stress is a primary mechanism that causes β cell failure upon collapse of the secretory pathway .
To study the function of IRE1α in differentiated β cells and since Ire1α deletion causes embryonic lethality , we analyzed the requirement for IRE1α in β cells by generating and characterizing mice with one floxed Ire1αFe allele [39] in combination with either a wild-type ( WT ) Ire1α+ or an Ire1α— null allele [40] . Deletion of the floxed Ire1αFe allele is mediated by a Cre recombinase-estrogen receptor fusion protein driven by the rat Ins2 promoter , which is expressed in pancreatic β cells from midgestation ( E9–E11 . 5 ) and activated by tamoxifen ( Tam ) administration [41] . This cross yields Ire1αFe/-; Cre+ ( herein designated KO ) mice at nearly the expected frequency as the Ire1αFe/+; Cre- WT , Ire1αFe/-; Cre- ( full-body heterozygous at birth; Het-B ) and Ire1αFe/+; Cre+ ( Tam-induced β cell-specific heterozygous; Het-I ) littermate controls . This allows more efficient Ire1α deletion as only one floxed allele requires deletion and provides two different heterozygous genotypes for comparison to the WT and KO groups . Undeleted KO mice exhibit normal blood glycemia; however , glucose intolerance became significant at 2 wk post-Tam injection and peaked at 6 wk ( Fig 1A and 1B and S1A Fig ) . A similar diabetic phenotype was observed upon analysis of Tam-induced deletion of homozygous floxed mice Ire1αFe/Fe; Cre+ ( KO ) ( S1A Fig ) . Only the KO mice displayed significantly reduced levels of insulin and proinsulin within the serum , pancreas , and islets compared to the heterozygous or WT mice ( Fig 1A–1I and S1A–S1C Fig ) . Peak glucose intolerance for the KO mice occurred 6 wk post-Tam when Ire1α deletion was most efficient ( Fig 1A and 1J and S1A Fig ) ; therefore , most islet experiments were conducted at this time , unless otherwise noted . Compared to the developmental deletion models of Ire1α and Xbp1 previously reported [35–37 , 42] , Tam-induced Ire1α deletion in β cells of adult mice caused a significantly greater diabetic phenotype ( Fig 1A–1I and S1A–S1C Fig ) . The decreased basal serum insulin in the KO mice became more pronounced after a fast and refeed , indicating a defect in postprandial insulin secretion ( Fig 1C ) . Consistent with β cell failure , the KO mice also exhibited higher levels of serum proinsulin ( S1B Fig ) . Significantly , although the percent islet mass was slightly reduced in the KO mice , this decrease alone could not account for the diabetic phenotype ( Fig 1E ) . A more accurate measure of total insulin and proinsulin content in pancreas extracts by ELISA showed a significant reduction in both insulin and proinsulin in the KO group , with proinsulin being more reduced than insulin ( Fig 1F–1H ) . To determine whether the reduced pancreatic proinsulin content was accompanied by a defect in proinsulin synthesis , isolated islets were radiolabeled for 30 min under high glucose with [35S]-Cys/Met . Compared to WT and heterozygous islets , proinsulin synthesis was significantly reduced in the islets from KO mice ( S1 Data ) . In addition , infection of WT islets with adenoviruses that express either Cre as control ( Ad-Cre ) or a dominant-negative IRE1α-K907A mutant ( Ad-ΔR ) demonstrated only the latter selectively reduced proinsulin synthesis ( Fig 1I and S1 Data ) . Taken together , the slightly reduced islet area coupled with the reduced proinsulin synthesis accounts for the reduced pancreatic insulin and proinsulin contents and abnormal glucose homeostasis in the KO mice ( Fig 1A–1I and S1A–S1C Fig ) . In contrast to the beta cell-specific Xbp1 deletion in which IRE1α RNase hyperactivation occurs [37] , the mRNA levels encoding INS1 and INS2 and the proinsulin-processing enzymes—i . e . , prohormone convertases ( PC1 , PC2 ) and CPE—were not significantly altered upon β cell-specific Ire1α deletion , despite an 88% decrease in the floxed Ire1α mRNA expression and an 81% decrease in Xbp1 mRNA splicing ( Figs 1J and 3A ) . In addition , reduced IRE1α in insulinoma cells was reported to also reduce proinsulin synthesis [35] and is consistent with our findings in islets . Importantly , these results show that adult β cells require functional IRE1α to maintain proinsulin mRNA translation , granule storage , and insulin secretion but not to maintain the expression of Ins1 , Ins2 , and most other β cell-specific mRNAs ( Figs 1J and 3A ) . In addition to the Ad-Cre control used on islets in vitro ( Fig 1I ) , to further ensure the diabetic phenotype did not result from nonspecific expression of the RIP-CreER allele [43] and Ire1α deletion in another tissue in vivo , such as the hypothalamus [44] , we measured levels of serum dopamine , which is synthesized in the arcuate nucleus of the hypothalamus . This analysis did not detect a significant difference between the KO and control mice ( S1D Fig ) . In addition , although we detected Cre positive staining in brain sections , there was little difference in growth hormone-releasing hormone ( GHRH ) expression between the KO mice and the controls ( S1E Fig ) . Finally , the KO islets and the WT islets expressing Ad-ΔR both demonstrated reduced proinsulin synthesis without affecting Ins1 or Ins2 mRNA levels in the KO ( Fig 1I , S1 Data and Fig 1J ) . Therefore , we conclude that Ire1α deletion in β cells reduces proinsulin mRNA translation and is a primary molecular basis for the diabetic phenotype . Generally , quantitative real-time PCR ( qRT-PCR ) of β cell-specific mRNAs did not detect a significant difference upon Ire1α deletion , although Mafa ( v-maf avian musculoaponeurotic fibrosarcoma oncogene homolog A ) mRNA expression was consistently increased within KO islets ( Figs 1J and 3A ) . However , immunofluorescence microscopy demonstrated that the WT islets actually contained higher overall and nuclear MAFA protein than KO islets ( S3A Fig ) , while PDX1 nuclear localization was not affected ( S3A Fig ) . This is consistent with the finding that oxidative stress decreases nuclear localization of MAFA [45 , 46] . Although the peak of hyperglycemia occurred at 6 wk post-Tam injection , there was no significant increase in terminal deoxynucleotidyl transferase dUTP nick end labeling ( TUNEL ) positivity at this time or at 12 wk post-Tam injection . However , long after Tam-induced deletion ( >6 mo ) , the glucose intolerance slightly improved , suggesting β cell recovery , possibly by expansion of nondeleted β cells and/or adaptation of the deleted cells ( S2 Data ) . Therefore , the mechanism for the diabetic phenotype in the KO mice is not due to excessive β cell death but is most likely due to underlying XBP1s-dependent defects caused by Ire1α deletion in the β cell . Taken together , these results indicate IRE1α is required within the β cell for proinsulin mRNA translation , without significantly affecting insulin mRNA steady-state levels . Islets isolated from KO mice at 6 wk post-Tam injection demonstrated reduced expression of previously described XBP1s target genes Atf6α , Pdia1 , Fkbp11 , Erdj4 , and Wfs1 [34 , 39 , 47] and increased expression of ER chaperones Hspa5 ( immunoglobulin binding protein [BIP] ) , Grp94 , and Erp72 and proapoptotic Ddit3 ( Fig 2A ) . Immunofluorescence microscopy , qRT-PCR , and mRNA-Seq analyses also demonstrated increased BIP and GRP94 in KO islets ( Fig 2A and 2B , S3B , S4A and S5C Figs ) . In addition , the KO β cells exhibited increased colocalization of the plasma membrane-resident protein GLUT2 with the KDEL-containing ER chaperones BIP and GRP94 ( Fig 2B and S3B Fig ) , indicative of an ER-to-Golgi trafficking defect in KO β cells . Electron microscopy ( EM ) revealed that KO β cells contain many distended ER/Golgi membranes and/or empty vesicles , a 43% reduction in insulin granules , distended mitochondria , pyknotic nuclei , and multilamellar vesicles suggestive of autophagy ( Fig 2C [yellow outlines] and S2A Fig , bottom ) . These dramatic morphological changes were not observed in Tam-induced Het-I control islets , indicating IRE1α is required to maintain organelle integrity in β cells . Next , we studied the effect of Ire1α deletion on glucose-regulated gene expression , by mRNA-Seq analysis on islets after 72 h incubation in 6 mM or 18 mM glucose to chronically stimulate insulin production and because 18mM glucose was reported to cause insulin mRNA degradation by IRE1α in insulinoma cells [48] . RT-PCR using primers flanking the unconventional intron spliced by IRE1α demonstrated treatment with 18 mM glucose increased Xbp1 mRNA splicing by 56 . 6% in WT islets compared to undetectable levels in KO islets ( Fig 2D ) . Similarly , qRT-PCR confirmed Ire1α deletion and Xbp1 mRNA splicing were >90% decreased in KO islets ( S3A Fig ) . mRNA-Seq on average detected >22 , 000 mRNAs in each sample that were visualized by heatmap ( Fig 2E ) . mRNAs having altered expression with p-values ≤ 0 . 1 or ≤ 0 . 01 reduced the number to ~4 , 500 and ~1 , 700 , respectively . Importantly , consistent with our previous results ( Fig 1J ) , the levels of mRNAs encoding INS1 and INS2 were not significantly altered in KO islets as measured by both mRNA-Seq and qRT-PCR ( Fig 3A and S4A Fig ) . In addition , as measured by qRT-PCR , mRNA-Seq also reported that mRNA expression of the XBP1s target genes Wfs1 , Atf6α , Edem1 , Edem3 , and Erdj4 was significantly reduced in KO islets , whereas the mRNA levels encoding BIP , GRP94 , and DDIT3/CHOP were increased ( Fig 2A , S4A and S5C Figs and S2 Data ) . Venn diagram analysis of the islet mRNA-Seq data was used to determine mRNAs that were Ire1α dependent , high glucose dependent , or dependent on both . The four-way Venn diagrams identified the expression of 613 decreased and 1 , 338 increased mRNAs in KO islets , of which 141 and 368 were simultaneously high glucose-dependent , respectively ( Fig 3B and 3C and S2 Data ) . Among the 141 mRNAs that required both IRE1α and high glucose for induction , the most significant gene ontology ( GO ) terms included ER protein translocation , ER–Golgi protein transport machinery , ribosome and protein biosynthetic components , and the lysosome and glycosidases , as well as other intracellular processes with fewer representative mRNAs of each respective ontological group ( Fig 3C , left ) . Strikingly , 76% of the 141 and 35% of the 368 IRE1α- and high glucose-dependent mRNAs have not yet been functionally characterized or previously associated with the IRE1α pathway ( S3 Data ) . Similarly , only 22 of the 141 mRNAs were previously shown to bind XBP1 within their promoters ( S4B Fig , left and S3 Data ) [16] . In contrast to the mRNAs reduced upon Ire1α deletion , mRNA-Seq also uncovered 368 inversely regulated mRNAs that were increased in Ire1α-null islets and reduced during high glucose in WT islets ( Fig 3B and 3C , right ) . These mRNAs encode proteins that are known RIDD targets , produce oxidative stress , and are involved in the ECM , plasma membrane , and immune cell signaling , as well as many other uncharacterized transcripts ( Figs 3C and 4A , S4C and S5A Figs and S2 Data ) . The Venn diagram also compared the 368 inversely regulated mRNAs with previously identified XBP1s and RIDD targets ( S4B and S4C Fig ) [18 , 19] . These mRNAs increased by high glucose in KO islets but decreased in WT islets upon glucose stimulation could be ( 1 ) degraded by IRE1α , ( 2 ) repressed by XBP1s , ( 3 ) induced by XBP1u , ( 4 ) stabilized as a consequence of defective ribosome recruitment to the ER , ( 5 ) induced via alternative UPR pathways , ( 6 ) induced as a consequence of oxidative stress or inflammation ( see below ) , or ( 7 ) derived from alternative cell types . To verify which changes in mRNA levels upon Ire1α deletion correlate with protein levels , isolated WT islets were infected in culture with adenoviruses expressing either β-Galactosidase ( Ad-β-Gal ) or the IRE1α K907A RNase mutant ( Ad-ΔR ) , incubated in high glucose ( 18 mM ) , and analyzed by mass spectrometry after 72 h . Expression of the dominant-negative IRE1α caused a 21% reduction in insulin 1 and 2 peptides ( S3 Data ) . The mass spectrometry analysis identified increases and decreases in proteins correlating with the mRNA changes detected by mRNA-Seq ( Fig 3D and S3 Data ) . The GO terms for those mRNAs that were increased by the absence of IRE1α include ROS-generating enzymes , such as inducible nitric oxide synthase ( iNOS/NOS2 ) , and the lysyl-oxidases ( LOXs ) that are involved in oxidation of collagens ( COLs ) that were also increased in the ECM ( Fig 4A , S4C , S5A–S5C Figs ) . Consistent with increased oxidative stress , the KO islets also had significantly higher levels of mRNAs encoding glutathione peroxidases 2 and 3 ( GPX2 and GPX3 ) and protein disulfide isomerases 4 and 5 ( PDIA4 and PDIA5 ) ( Fig 3D , S5A , S5C and S6A Figs ) . Therefore , we directly measured oxidative stress in Ire1α-deleted islets . Upon acute Ad-Cre-mediated Ire1α deletion in Ire1αFe/Fe isolated islets , lipid peroxides ( hydroxyl-octadecadienoic acids [HODEs] ) increased 33% compared to Ad-GFP-infected or Ad-Cre-infected Ire1α+/+ islet control groups ( Fig 4B ) . In addition , increased nitrotyrosine staining was increased in KO pancreas sections after Ire1α deletion by Tam injection in vivo ( Fig 4C ) . Similarly , tenascin C ( TNC ) mRNA and protein were increased in KO islets as measured by mRNA-Seq and mass spectrometry and then confirmed by immunohistochemistry ( S5D Fig ) . Consistent with the increased collagen mRNA expression , Masson’s trichrome stain identified increased collagen staining surrounding the KO islets ( Fig 4D and S5E Fig ) . Since previous studies suggested that antioxidant treatment reduces ER stress in β cells [9 , 49–51] , we tested whether feeding mice chow supplemented with the antioxidant butylated hydroxyanisole ( BHA ) could improve β cell function upon Ire1α deletion . Therefore , at 12 wk post-Tam injection , mice were fed control chow or BHA-supplemented chow for 3 wk ( 12–15 wk post-Tam ) . Notably , feeding mice with BHA-supplemented chow significantly improved glucose homeostasis in mice with β cell-specific Ire1α deletion ( Fig 4E ) that was also reflected by decreased trends of nitrotyrosine , collagen , and TNC staining ( Fig 4C and 4D , S5D and S5E Fig ) . Importantly , these findings indicate that Ire1α deletion causes β cell failure , at least in part , due to oxidative stress . Although the 141 mRNAs that require IRE1α for glucose induction encoded known functions in protein synthesis and the secretory pathway ( Fig 3C , left , and Fig 5A , bottom ) , approximately half of these mRNAs have never been characterized or associated with the IRE1α/XBP1s pathway ( Fig 3D and 3E ( left panels ) , and S3 Data ) . Significantly , the most prominent GO cluster from the IRE1α-dependent and glucose-inducible group of 141 mRNAs included 12 with functions for SRP recruitment to the ER , translocon components , and the catalytic and structural subunits of the signal peptide cleavage complex ( Fig 5A , bottom ) . These 12 mRNAs were induced up to 2 . 5-fold by glucose in an IRE1α-dependent manner , whereas their expression was reduced as much as 2 . 5-fold in the KO islets , i . e . , ~4–6-fold difference during glucose stimulation , suggesting a major bottleneck in the signal peptide-dependent proximal secretory pathway of the β cell when compromised by Ire1α deletion . In order to detect signal peptide cleavage in preproinsulin , which occurs very efficiently , special cellular contexts were utilized . Steady-state labeling demonstrated that , compared to control islets , the KO islets accumulated a slower migrating ~12 kDa species corresponding to the size of preproinsulin ( Fig 5B and S1 Data ) . This steady-state labeling also showed decreased amounts of processed intracellular and secreted insulin and increased proinsulin that is likely due to the prolonged high glucose exposure in vitro ( Fig 5B ) . To extend these findings and focus specifically on the preproinsulin-to-proinsulin processing step , signal peptide cleavage was analyzed in COS-1 cells that do not process proinsulin to insulin . Cells were coinfected with Ad-preproinsulin encoding either WT ( W ) or the Akita misfolded Cys96Tyr mutant ( A ) [52] and coexpressed with Ad-GFP ( G ) or Ad-ΔR ( Δ ) . Ad-preproinsulin infection alone or in combination with Ad-β-Gal produced a single 10 kDa species representing proinsulin ( Fig 5C , lanes 1–3 ) . In contrast , coinfection of Ad-preproinsulin with Ad-ΔR , the IRE1α RNase-dead mutant incapable of splicing Xbp1 mRNA , caused accumulation of a ~12 kDa insulin immune-reactive species , corresponding to preproinsulin , consistent with a defect in signal peptide cleavage when IRE1α-dependent Xbp1 mRNA splicing is compromised ( Fig 5C , lane 4 ) , and this ~12 kDa species accumulated to a greater extent upon expression of the Akita proinsulin mutant ( Fig 5C , lane 6 ) . In addition , adenoviral overexpression of XBP1s was sufficient to induce SEC11C and signal sequence receptor 1 ( SSR1 ) protein levels in COS-1 cells ( Fig 5C ) . Because ribosome-membrane recruitment and translocon mRNAs were heavily Ire1α- and high glucose-dependent , we analyzed ribosome distribution in electron micrographs from Het and KO islets at 2 wk post-Tam injection . The KO had significantly more dispersed ribosomes , a hallmark of disordered polysomes , and more total monosome evidence for defect in polysome formation in the absence of IRE1α ( Fig 5D ) [53 , 54] . Consistent with this observation , Ad-Cre infection of an immortalized Ire1αFe/Fe β cell line increased the monosome/polysome ratio ( S6C Fig ) , further evidence of a translation initiation defect . Finally , we analyzed ribosome recruitment to the ER in the Ire1αFe/Fe β cell line at 48 h after infection with Ad-Cre or controls . Cells were shifted from 12 mM glucose to media containing 4 mM , 12 mM , or 36 mM glucose for 2 h and then subjected to subcellular fractionation . Western blotting for the ribosomal small subunit proteins RPS9 and RPSA in cytosolic and membranous fractions from glucose-stimulated floxed Ire1αFe/Fe insulinoma cells indicated Ire1α deletion in vitro disrupted glucose-stimulated recruitment of the ribosome to the membranous fraction ( Fig 5E and S6D Fig ) . The increased monosomes detected in islets and the IRE1α-deleted insulinoma line ( Fig 5D and S6C Fig ) were accompanied by basally increased RPS9 and RPSA protein , but not increased mRNA levels ( Fig 5E , S6D and S6E Fig ) . We next tested whether chemical inhibition of the IRE1α RNase activity in human islets affected INS mRNA levels after 24 h in low versus high glucose ( S7A Fig ) . The chemical inhibition of IRE1α RNase overnight did not cause INS mRNA levels to accumulate but did block glucose-stimulated induction of proinsulin mRNA , supporting the hypothesis that XBP1s is needed for ER expansion to stimulate proinsulin mRNA translation ( S7A Fig ) . The reduced levels of the XBP1s target gene P58IPK mRNA is consistent with inhibition of XBP1 mRNA splicing ( S7A Fig ) . We then used adenoviral forced expression of XBP1s to induce mRNAs encoding components of the SRP-dependent proximal ER in human islets . Significant expression of GFP was observed at 5 d after infection with Ad-GFP , indicating efficient Ad-mediated expression in human islets ( S7B Fig ) . Interestingly , expression of Ad-Xbp1s , as opposed to its chemical inhibition , had the inverse effect on INS mRNA . Increased XBP1s was sufficient to increase INS mRNA levels , presumably because of increased capacity for mRNA recruitment to the ER and cotranslational translocation ( S7A–S7C Fig ) . We then analyzed the effect of adenoviral expression of IRE1α , its mutants , and XBP1s on proinsulin production in human islets at the protein level . Expression of IRE1α mutants devoid of RNase function ( IRE1α- ΔR or ΔCT ) greatly reduced proinsulin levels compared to controls ( S7D Fig ) . The results support the conclusion that positive regulation of proinsulin production by IRE1α is conserved between mice and humans .
Taken together , these results demonstrate glucose-stimulated IRE1α splicing of Xbp1 mRNA in β cells induces expression of mRNAs encoding proteins important for proinsulin biosynthesis and many other intracellular processes essential for insulin biogenesis that include but are not limited to ribosome recruitment to the ER , cotranslational translocation , and signal peptide cleavage . Inversely , the IRE1α/XBP1s pathway is required to protect the β cell from expression of mRNAs encoding functions related to ER stress , oxidative stress , and inflammation . These results also show that the IRE1α- and glucose-dependent changes in mRNA abundance are important for a myriad of intracellular processes beyond the GO analysis enriched group of mRNAs responsible for expansion of the proximal ER . However , we focused on the most significant functional defect because it is the rate-limiting step in proinsulin synthesis upon glucose stimulation of the β cell ( S8A and S8B Fig ) . Although the UPR was originally characterized as an adaptive response to protein misfolding in the ER [55–57] , to the best of our knowledge to date , there is no evidence that supports that physiological stimuli , such as glucose , cause transcriptional remodeling by IRE1α-mediated splicing of Xbp1 mRNA to increase the demand for ER protein-folding capacity . Our results support the notion that glucose stimulation of β cells requires IRE1α mediated splicing of Xbp1 mRNA to increase entry into and expansion of the SRPR/SSR-dependent secretory pathway’s capacity to accommodate increased preproinsulin synthesis , processing to proinsulin , folding , trafficking , and secretion ( S6A Fig ) . Specifically , in the absence of IRE1α , there was defective glucose-stimulated induction of 12 mRNAs encoding functions in cotranslational translocation , ribosome subunits , and ribosome recruitment to the ER , proinsulin synthesis , and preproinsulin signal peptide cleavage . Because these processes represent the rate-limiting steps in cotranslational translocation and since multiple mRNAs in this functional group were significantly altered , we postulate that in aggregate the reduced expression of these mRNAs combine to cause the majority of the diabetic phenotypes we report for the KO mice . Specifically , we demonstrated at the functional levels that IRE1α is required for four processes critical for insulin biogenesis in mature β cells: ( 1 ) proinsulin mRNA translation , ( 2 ) ribosome recruitment and structure , ( 3 ) signal peptide cleavage , and ( 4 ) suppression of oxidative/inflammatory stress causing mRNAs . Intriguingly , β cells are more sensitive to loss of Ire1α than fibroblasts or hepatocytes [20 , 39 , 40] , possibly because of the rigors of daily periodic postprandial increases in preproinsulin synthesis coupled with basally low levels of antioxidant enzymes [58] . Because mRNA translation is compromised in the KO islets , the source of ROS is likely not uncontrolled protein synthesis , as was previously shown to occur upon elimination of eIF2α phosphorylation in the β cell [59] . In contrast , the Ire1α-KO islets contained higher mRNA levels for oxido-reductases that produce ROS , such as the LOXs , PDIAs , and NOS2 , and that may contribute to the increased ROS within the null islets . Ire1α-deletion also increased TNC mRNA and protein levels . Alternatively , the loss of IRE1α-dependent antioxidant enzymes , such as selenoprotein S ( SELS ) , may increase ROS in the KO islets . Regardless of the source of ROS or lack of protection , glucose tolerance was restored by feeding the KO mice a diet supplemented with the antioxidant BHA , indicating that ROS contribute to the β cell failure upon Ire1α deletion . Previously , it was presumed that tripartite UPR signaling coordinates adaptation through regulation of protein synthesis and gene expression [60] . However , it is becoming evident that each UPR sensor has evolved to fulfill specific requirements in unique cell types . In the β cell , IRE1α-mediated Xbp1 mRNA splicing and PERK-mediated eIF2α phosphorylation [9] are essential to maintain the structural integrity of the ER , preserve glucose responsiveness , and prevent oxidative damage , whereas ATF6α is dispensable [26 , 28 , 61] . Although both IRE1α and PERK are required to expand β cell mass through neonatal development [9 , 25 , 30] , there are significant differences in mature β cells that are void of PERK/eIF2α-P versus IRE1α/XBP1s . Perk deletion or Ser51Ala mutation in eIF2α causes uncontrolled protein synthesis and decreases expression of the β cell-specific Pdx1 , MafA , and Ins1/2 mRNAs [9 , 25] . In contrast , Ire1α deletion reduces expression of mRNAs encoding proteins involved in ribosome recruitment to the ER , mRNA translation , translocation , and signal peptide cleavage without reducing expression of β cell-specific mRNAs . However , disruption of either PERK or IRE1α signaling in the β cell disrupts ER protein folding and trafficking to deplete insulin granules and cause oxidative stress and β cell failure . Whereas the PERK/eIF2α pathway is an important brake for the β cell secretory pathway , the IRE1α/XBP1s is a critical accelerator for increasing proinsulin synthesis in response to higher blood glucose . We propose that IRE1α/XBP1s evolved to expand the capacity for specialized secretory cells . In summary , these findings demonstrate IRE1α/XBP1s is required for β cell function and should be considered in light of new therapeutic approaches that rely on IRE1α inhibition because IRE1α-dependent splicing of Xbp1 mRNA is the only known conserved IRE1α RNase activity to all mammalian cell types [62–65] .
Sanford Burnham Prebys Medical Discovery Institute ( SBPMDI ) follows the “Guide for the Care and Use of Laboratory Animals: Eighth Edition” standards . The Institute’s Animal Care & Use Program is accredited by AAALAC International , and a Multiple Project Assurance A3053-1 is on file in the OLAW , DHHS . Euthanasia is consistent with the recommendations of the 2013 AVMA Guidelines for the Euthanasia of Animals . All animal care and procedures were conducted according to the protocols and guidelines approved by the University of Michigan Committee on the Use and Care of Animals ( UCUCA ) and the SBPMDI , as well as by the Institutional Animal Care and Use Committee ( IACUC ) divisions of the American Association for Laboratory Animal Science ( AALAS ) . IOur AAALAC number is 000710 , and our mouse protocol number is 14–036 . The human islets were sourced from the Clinical Islet Laboratory at the University of Alberta/Alberta Health Sciences Sanford Burnham Prebys Medical Discovery Institute IRB Code: 100894XX . Islet donors’ written consent was given to Clinical Islet Laboratory at the University of Alberta/Alberta Health Sciences . All animal care and procedures were conducted according to the protocols and guidelines approved by the UCUCA and the SBPMDI , as well as by the IACUC division of the AALAS . Mouse and human qRT-PCR primer sequences specific to total levels of mRNAs were obtained from the Harvard Primer Bank: http://pga . mgh . harvard . edu/primerbank/ . See online supporting methods for additional details . The primers used to detect the presence and absence of the Ire1α floxed allele are as follows: FWD-cctacaagagtatgtggagc , REV-ggtctctgtgaacaatgttgagag . Spliced-specific Xbp1 primers are as follows: FWD-gagtccgcagcaggtg , REV-gtgtcagagtccatggga . Unspliced-specific Xbp1 primers are as follows: FWD-ctcagactatgtgcacctct ( within the 26 nt intron ) , REV- catgactgggtccaagttgtccag . The primer sequences for XBP1 flanking PCR are FWD-ccttgtggttgagaaccagg , REV-gtgtcagagtccatggga amplicon + 211 bp ( unspliced ) and 185 bp ( spliced ) . Mice were fasted for 4 h , and then fasted blood glucose measurements were recorded . Glucose tolerance tests ( GTTs ) were performed as previously described by intraperitoneally injecting a glucose solution of 2 g/kg by body weight and recording tail-vein blood measurements over time using a digital glucometer [9] . Insulin and proinsulin content was determined by diluting acid ethanol extracts from the pancreas at 50 mg/ml 1:200 in sample resuspension buffer provided by the ALPCO ELISA kits for insulin ( Cat . 80-INSHU-E01 . 1 ) and proinsulin ( Cat . 80-PINMS-E01 ) Freshly dissected pancreas was fixed in Sorenson’s buffer and processed by the University of Michigan Electron Microscopy Core facility . Blind scoring of insulin granules was performed using Cell Profiler software . Experiments utilizing adenoviruses were performed in triplicate with graphs representing the average of all experiments except the human islets where the individual’s age is stated within the legends . Mouse islets were infected with adenovirus at 24 h post-isolation , and human islets were typically received 3 d postmortem and after one night in media were infected with adenoviruses . For infection of islets and the immortalized Ire1αFe/Fe β cell line , 50 and ten plaque forming units per cell were used , respectively . At 72 h postadenoviral infection , analysis by pulse-chase and measurement for oxidative stress and HODEs was performed as described [9] . The Illumina Genome Analyzer II was utilized to analyze 200 nt long , fragmented mRNA converted to cDNA ( 50 ng/individual ) purified from islets of five individual mice per genotype according to Illumina mRNA-Seq kit ( Part# 1004898 ) . The islet mRNA-Seq data has been deposited to the SRA Study Accession: http://www . ncbi . nlm . nih . gov/sra/ ? term=SRP041246 , and the bioproject website is http://www . ncbi . nlm . nih . gov/bioproject/242958 . Polysome profiles were obtained byt reating cells with 0 . 1 mg/mL cycloheximide ( CHX ) for 10 min at 37°C , washed twice with ice-cold PBS-CHX ( phosphate buffered saline containing 0 . 1 mg/mL CHX ) , and harvested using polysome lysis buffer ( 20 mM Tris-HCl pH 7 . 5 , 100 mM NaCl , 10 mM MgCl2 , 0 . 4% IGEPAL , 50 μg/mL CHX , protease inhibitors , and RNaseIn ) . Lysates were clarified by centrifugation at 13 , 000 × g for 10 min at 4°C . Equal amounts of clarified lysates based on the absorption at 260 nm were layered onto 10%–50% sucrose gradient ( prepared in 20 mM Tris-HCl pH 7 . 5 , 100 mM NaCl , 10 mM MgCl2 , 50 μg/mL CHX ) and centrifuged in an SW41-Ti rotor at 40 , 000 rpm for 2 h at 4°C . Fractions were collected using a Bio-Rad fraction collector , and the amount of total RNA in each fraction was measured using a NanoDrop spectrophotometer [66] . The Pierce kit catalog #78840 was used to isolate subcellular fractions . | One of the most remarkable features of the pancreatic beta cells—those that produce and secrete insulin to regulate glucose homeostasis—is their capacity to increase the synthesis of proinsulin ( the insulin precursor ) up to 10-fold after glucose stimulation . This dramatic increase in the synthesis of proinsulin is a challenge to the proximal secretory pathway and triggers an adaptive stress response , the unfolded protein response , which is coordinated by the IRE1α enzyme and the X-box-binding protein 1 ( XBP1 ) transcription factor . Deletion of IRE1α specifically from the pancreatic beta cells in adult mice resulted in overt diabetic phenotypes such as high blood glucose . mRNA analysis revealed several hundred genes whose expression was coordinately regulated by glucose and IRE1α and whose functions are important for the beta cell secretory pathway . Furthermore , IRE1α also regulates the expression of mRNAs involved in the production of reactive oxygen species ( ROS ) , and we could show that , in fact , oxidative stress is a primary mechanism that causes beta cell failure upon collapse of the secretory pathway . Finally , in experiments with murine and human islets ( the regions of the pancreas where secretory beta cells are located ) , we observed that while IRE1α does not regulate the expression of the gene encoding insulin , it determines final insulin levels by controlling translation of proinsulin mRNA . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | The IRE1α/XBP1s Pathway Is Essential for the Glucose Response and Protection of β Cells |
Development of robust , sensitive , and reproducible diagnostic tests for understanding the epidemiology of neglected tropical diseases is an integral aspect of the success of worldwide control and elimination programs . In the treatment of onchocerciasis , clinical diagnostics that can function in an elimination scenario are non-existent and desperately needed . Due to its sensitivity and quantitative reproducibility , liquid chromatography-mass spectrometry ( LC-MS ) based metabolomics is a powerful approach to this problem . Analysis of an African sample set comprised of 73 serum and plasma samples revealed a set of 14 biomarkers that showed excellent discrimination between Onchocerca volvulus–positive and negative individuals by multivariate statistical analysis . Application of this biomarker set to an additional sample set from onchocerciasis endemic areas where long-term ivermectin treatment has been successful revealed that the biomarker set may also distinguish individuals with worms of compromised viability from those with active infection . Machine learning extended the utility of the biomarker set from a complex multivariate analysis to a binary format applicable for adaptation to a field-based diagnostic , validating the use of complex data mining tools applied to infectious disease biomarker discovery and diagnostic development . An LC-MS metabolomics-based diagnostic has the potential to monitor the progression of onchocerciasis in both endemic and non-endemic geographic areas , as well as provide an essential tool to multinational programs in the ongoing fight against this neglected tropical disease . Ultimately this technology can be expanded for the diagnosis of other filarial and/or neglected tropical diseases .
Onchocerciasis , commonly referred to as “river blindness” is classified by the World Health Organization ( WHO ) as a neglected tropical disease , afflicting approximately 37 million people in Africa , Central and South America and Yemen , with 89 million more at risk [1] . Symptoms of the disease include acute dermatitis and blindness , the result of which is the loss of 1 million disability-adjusted life years ( DALYs ) annually [2] . The causative agent , the filarial nematode Onchocerca volvulus , is transmitted in its larval stage between human hosts through the bite of a Simulium ( sp . ) black fly . Once these parasites have matured into the adult form , they can live for approximately 14 years in subcutaneous nodules within a human host [3] . The drug ivermectin ( Mectizan ) has served as the principal means of onchocerciasis control [4] , however , after initially reducing the number of microfilariae , within a year , the microfilariae return to levels of 20% or higher than that prior to treatment [5] . The combination of the lack of effect of annual ivermectin treatment on adult worm survival and the fecundity of adult females , along with significant fly and human migration patterns has helped to perpetuate the disease . In Africa , where onchocerciasis control programs have been in place since the founding of the Onchocerciasis Control Programme in West Africa ( OCP , 1974–2002 ) and are currently being conducted by the African Programme for Onchocerciasis Control ( APOC , 1995-present ) , diagnosis is an essential aspect of the determination of treatment and distribution of medication . In the Western hemisphere , accurate and robust diagnostics are essential for attaining the goal of disease elimination . Twice yearly dosage of ivermectin , through the efforts of the Onchocerciasis Elimination Program for the Americas ( OEPA , 1992-present ) , has lead to a minimization of infection to 13 foci within six countries in Central and South America . Although mass treatment of onchocerciasis foci in the Western hemisphere is slated to be suspended in 2012 [6] , achieving the goal of elimination is contingent upon continued surveillance of the disease . However , proper surveillance is directly dependent on the availability of robust diagnostic technologies used for infection assessment . This need is further underscored in studies of antibiotic treatments being investigated for targeting Wolbachia endosymbiotic bacteria [7]–[10] as well as reports of sub-optimal response to ivermectin treatment [11] , [12] . In both of these cases an accurate diagnostic is critical for the analysis of drug efficacy and patient drug response . Currently , multinational control and elimination programs primarily rely on various techniques for diagnosis including: entomological studies of Simulian flies , Ov specific antigen tests , antibody tests , analysis of microfilariae in skin snips , nodule palpation and quality of those nodules that can be excised . There are a number of technical concerns with each technique including: a lack of sensitivity and reproducibility , invasiveness , and the inability to distinguish past from present infection or between filarial diseases [13]–[15] . A small molecule/metabolite based test has the potential for reflecting a more accurate picture of infection status , as it is a comprehensive measure of the effects of posttranslational modification and regulation . Furthermore , small molecules are frequently constitutively produced ( e . g . , excretory-secretory products ) , diffuse easily and are inherently non-immunogenic in vivo , thus avoiding some of the technical challenges associated with DNA and protein-based diagnostics . Although adult O . volvulus worms do not reside directly in the blood , the highly vascularized subcutaneous nodules of the human host allow for the potential diffusion of adult parasite-derived compounds into the blood where compounds involved in host response to infection might also be present . Since the microfilariae ( mf ) and third infective larval stage ( L3 ) of the O . volvulus life cycle do come in contact with the vascular system during vector transmission , it is additionally possible that some mf or L3 produced compounds might also be localized to this biological sample . Certainly , as a starting point , the blood matrix serves as an easy to obtain , chemically complex data rich matrix for metabolite analysis [16] , [17] . However , a technical challenge of analyzing a large number of metabolites stems from the shear size and complexity of the resulting data set . Initially devised and applied to the analysis of highly dimensional gene micro-array data , a number of machine learning approaches have been expanded and used for identifying patterns of biomarkers resulting from the multidimensional analysis of genes , proteins , and metabolites that can be linked to early detection [18] , survival prediction [19] , and disease outcomes [20] . Although identification of a single biomarker “smoking gun” is perceived as the ideal scenario , more attention is being focused on the use of multiple markers for improving overall diagnostic accuracy [18] , [21] , [22] and model stability [23] . Herein , we report a liquid chromatography-mass spectrometry ( LC-MS ) based approach to the discovery of a set of molecules that , in combination , provide a statistically relevant characteristic of onchocerciasis infection . An initial untargeted analysis was applied to the profiling of O . volvulus infected and uninfected blood plasma and serum samples representing a variety of geographic regions and disease states , including other tropical diseases . This analysis resulted in a set of statistically significant mass features identified for their potential as onchocerciasis-specific biomarkers . Using multivariate statistics and machine learning algorithms , these metabolic signatures were further evaluated for their ability to discriminate O . volvulus-infected and uninfected individuals , therefore , creating the basis of a small molecule-based diagnostic for onchocerciasis .
The use of human serum and plasma samples in the study was approved by the Scripps Health Human Subjects Committee . Samples with geographic origins outside of the United States of America ( USA ) consisted of pre-existing , unidentifiable diagnostic specimens collected with written informed consent and in cases of illiteracy , a literate witness signed and a thumbprint was made by the participant . These samples were determined by the Scripps Health institutional review board ( IRB ) to be exempt from formal review under 45 CFR 46 101 . O . volvulus negative controls from the USA consisted of serum and plasma samples and were obtained with written informed consent from healthy donors through The Scripps Research Institute Normal Blood Donor Service and approved by the Scripps Health IRB . All patient codes have been removed in this publication . Onchocerciasis positive samples were collected in characterized endemic areas and their status confirmed by either positive skin snip ( mf + ) or nodule palpation ( nodule + ) . Several sample groups used in this analysis were collected during previously published studies including serum from Liberia [24] , [25] and Ghana collected in 2003 [9] . The Ghana sera collected in 1986 and 1991 were obtained from the College of Public Health , University of South Florida . Cameroon samples were obtained as part of a nodulectomy campaign conducted in villages surrounding Kumba , Cameroon in 2006 and consist of plasma from O . volvulus-positive individuals ( nodule + with nodules containing live females ) , O . volvulus-negative individuals ( skin snip - volunteers with no current or prior symptoms of O . volvulus infection ) , and ambiguous samples ( nodules contained either dead , calcified worms or lipomas with no evidence of worms , or for which there were no particular disease symptoms recorded ) . Guatemala sera were obtained as part of a nodulectomy campaign conducted by the Guatemala Ministry of Health and the Centro de Estudios en Salud , Universidad del Valle de Guatemala in several villages within the Guatemalan Central Endemic Zone from 2007–2008 . Nodules were surgically removed from all individuals sampled , and nodule dissection was conducted to assess worm viability on nodules from five of the 21 individuals whose serum was analyzed in this study . Of those dissected , no live worms were found . Leishmaniasis positive , Chagas disease positive , and onchocerciasis negative sera were obtained from the Centro de Estudios en Salud , Universidad del Valle de Guatemala . Indian lymphatic filariasis positive plasma samples were obtained from the Laboratory of Parasitic Diseases , U . S . National Institutes of Health . A detailed summary of the samples analyzed in this study is presented in Table 1 . Solvents used were of high performance ( HPLC ) grade . A methanol precipitation of proteins was conducted by adding 400 µl aliquots of ice cold methanol to 100 µl aliquots of serum and plasma samples . The samples were immediately vortexed for 30 sec and allowed to rest on ice for 20 min . After centrifugation at 13 , 780×g for 5 min , the metabolite containing supernatent was removed from the precipitated protein pellet and transferred to fresh tubes . The supernatent samples were dried in a GeneVac EX-2 Evaporation System ( GeneVac Inc . , Valley Center , New York , USA ) at ambient temperature and then resuspended to a 50 µl volume in water: acetonitrile ( 95∶5 ) , vortexed for 30 sec and then centrifuged again at 13 , 780×g for 5 min . After being transferred to LC vials , samples were stored at 4°C and transferred to the LC-MS thermostated autosampler ( 6°C ) , typically within 48 h of their preparation . In order to minimize instrumental drift , sample sequences were composed of a single injection of each sample in randomized order . To monitor any potential instrument irreproducibility and to confirm the absence of sample carry over within the chromatographic run , a mobile phase blank and an external standard were injected every 24 h throughout the duration of the analysis . Experiments were performed with an electrospray-ionization time-of-flight ( ESI-TOF ) MS ( Agilent 1200 LC , TOF 6210 , Agilent Technologies , Santa Clara , CA , USA ) . Each sample analysis consisted of an 8 µl injection of extracted sample with chromatographic separation across a reverse phase C18 column ( Zorbax 300SB C18 Capillary , 3 . 5 µm , 1 mm×150 mm; Agilent Technologies , Santa Clara , CA , USA ) at a capillary pump flow rate of 75 µl/min . Mobile phase A was composed of water with 0 . 1% formic acid , and mobile phase B was acetonitrile with 0 . 1% formic acid . Each sample was analyzed over a 60 min run time with a gradient consisting of a 45 min linear gradient from 5% to 95% B and 15 min isocratic hold at 95% B . Between sample injections a wash step was used to minimize carry over . It consisted of a saw-tooth linear gradient beginning with a hold at 95% A for 10 min . Then , linear ramping between 5% and 98% B for 5 minute increments throughout the 35 min wash cycle was followed by a 20 minute final re-equilibration of the column with an isocratic hold at 95% A . Consistent mass accuracy ( <2 ppm ) was maintained through the constant infusion ( 2 µl/min ) of reference masses via a second nebulizer . Data were collected in positive electrospray ionization ( ESI ) mode scanning in centroid mode from 75 to 1 , 100 m/z with a scan rate of 1 . 0 spectrum per second in 2 GHz extended dynamic range . The capillary voltage was 3 , 500 V; the nebulizer pressure , drying gas flow and gas temperature were set to 20 psig , 12 l/min and 350°C , respectively . All mass spectral data was collected in . d format and converted to . mzData using the Mass Hunter Qualitative Analysis software version B . 03 . 01 ( Agilent Technologies , Santa Clara , CA , USA ) . XCMS [26] software was used for peak matching , non-linear retention time alignment and quantitation of mass spectral ion intensities across all . mzData mass spectral files . Statistical comparison of the intensity data was conducted using the XCMS built in Welch's t-test . False discovery rate ( FDR ) analysis was conducted with the q-value program [27] in R version 2 . 9 . 0 [28] . Principal Components Analysis ( PCA ) was conducted with Statistica software version 8 . 0 ( StatSoftInc . , Tulsa , OK , USA ) , machine learning algorithms were implemented using Weka Explorer version 3 . 6 . 0 [29] with 10 fold cross-validation settings . The molecular formula assignment made for the 10 selected small molecule biomarkers was conducted through a combination of LC-MS/MS fragmentation using a quadrupole- TOF MS ( QTOF 6510 , Agilent Technologies , Santa Clara , CA , USA ) and sub-2 ppm accurate mass measurements using a Bruker Daltonics Apex II 7 . 0 Tesla Fourier transform ion cyclotron ( FT-ICR ) MS ( Bruker Daltonics . , Billerica , MA , USA ) . For the QTOF analysis chromatographic conditions were identical to those reported for the profiling experiment and serum plasma samples from either the Scripps normal blood or pooled patient samples were used for the analysis . The average m/z and retention times of each of the biomarkers obtained through XCMS analysis , were used for targeted MS/MS analysis with a starting collision-induced dissociation energy of 20eV . Fragmentation patterns were analyzed with the Agilent Mass Hunter Qualitative Analysis software version B . 03 . 01 using the targeted MS/MS and formula generation algorithms and compared with the MS/MS fragment data in the METLIN database [30] . The FTMS system was equipped with a custom machined electrospray source with two nebulizers for dual spray ionization . The main orthogonal nebulizer was used for LC-eluent , while the second nebulizer was used to introduce a calibration mixture containing two compounds ( aminoantipyrine at 204 . 1132 m/z and quinidine 325 . 1911 m/z ) at 3 mM concentration mixed with 1∶10 dilution of Agilent low concentration tune mix . A linear calibration fit was used in the narrow range to internally calibrate individual mass spectra . The chromatographic conditions were identical to those reported for the profiling experiment with an additional analysis using a smaller i . d . column with the same stationary phase composition ( Zorbax 300SB C18 Capillary , 3 . 5 µm , 0 . 3 mm×150 mm; Agilent Technologies , Santa Clara , CA , USA ) at a capillary pump flow rate of 4µl/min . Pooled serum and plasma samples from either the Scripps normal blood or patient samples were used for the analysis .
The most important aspect of any clinical analytical study resides with the quality of the samples used; here representative serum and plasma samples from a variety of subject populations were incorporated to minimize the effects of non-relevant metabolic variation ( e . g . , nutrition , sex , age , race ) and magnify those metabolic differences that are not only statistically significant between specific populations , but relevant in identifying the changes in metabolism that can be directly attributable to infection . One of the analytical limitations with an untargeted LC-MS metabolomics approach is that of inter-sequence reproducibility ( i . e . , sample preparation , instrument drift , column and mass spectral baseline variation ) when comparing samples directly between analytical sequences . Such inter-sequence variability can introduce shifts in ion intensities that can interfere with the accuracy of downstream statistical analysis . Therefore , this study was conducted with single injections of each sample , analyzed in randomized order consecutively within one analytical sequence ( Figure 1 ) . Due to such analytical constraints , small groups of representative samples were selected from various sample classes ( e . g . , O . volvulus-infected and uninfected individuals from various geographic regions and individuals infected with other parasitic diseases ) . XCMS analysis of the sample mass spectral data files ( n = 136 ) resulted in the measurement of a total of 2 , 350 mass features . Testing the overall reproducibility of the analysis , the coefficient of variation ( CV ) was found to be 15 . 9% as calculated from all mass feature intensity values compared across triplicate injections of a single plasma sample analyzed throughout the analytical sequence . This value is comparable to previous studies of analytical variation within plasma and serum analysis by our laboratory and consistent with a number of other LC-MS based metabolomics studies [33] , [34] . Statistical comparison between all onchocerciasis positive samples ( n = 76 ) and all onchocerciasis negative samples ( n = 56 ) , including those infected with other tropical diseases , by Welch's t-test resulted in 194 features with a p<1×10−4; with a false discovery rate FDR of 54% . To reduce the number of potentially erroneous markers and focus on those mass features with the most potential in distinguishing disease , the top 35 mass features ( p<1×10−7 ) were chosen for more stringent analysis through assessment of the quality of the resulting extracted ion chromatograms ( EICs ) ( Figure S1 ) . While XCMS pre-processing software contains a robust retention time correction and peak alignment algorithm , an important aspect of this study is the statistical quantitation of biomarkers , therefore any features with questionable quantitation , observed as imperfect alignment or inconsistent peak boundaries across samples were ruled out of further analysis . Additionally , since several mass features may redundantly describe one chemical metabolite due to the presence of in-source fragments , adducts , or multiply charged species and overlapping retention time . The features were separated into unique peak groups and representative ions with the highest overall abundance were included in a subset of 14 features for further analysis ( Table 2 ) . Interestingly , the majority of these features were detected at lower levels in infected individuals relative to those without onchocerciasis . Analysis of the selected biomarkers with MS/MS and FTMS analysis has provided molecular masses and assigned molecular formulas that could be used to classify the biomarkers into distinct chemical classes; of the 14 markers identified 10 were small molecules and four were protein fragments or small peptides . Beginning with a subset of the larger sample set , the mass spectral data for the top 14 candidate biomarkers were investigated for their ability to discriminate O . volvulus infected individuals ( n = 55 ) from healthy controls ( n = 18 ) from the African serum and plasma samples . PCA of the effect of these 14 biomarkers was used to visualize the variation between these samples groups ( Figure 2A ) . A distinct clustering of the O . volvulus infected versus the healthy individuals was observed across the x-axis of the PCA score plot , implying that principal component 1 ( PC1 ) contained the variance of the data set required to distinguish these two sample groups . The next greatest amount of variation within the data set appeared to have little effect on discriminating infection or even geographic differences , but appears to be more representative of the heterogeneity present among healthy controls . The top 14 candidate biomarkers were also applied to a larger sample set comprised of multiple geographic regions including O . volvulus-infected individuals ( n = 76 ) and healthy and disease controls ( n = 56 ) . PCA of these 14 biomarkers ( Figure 2B ) revealed the inherent complexity encountered when employing a metabolite profiling approach to diagnostic development . As with the initial African samples , there is general clustering of the onchocerciasis positive individuals with the variance contained in PC1 having good discriminatory power . The disease and healthy controls cluster separately from the onchocerciasis positive individuals , however , there is some overlap between one of the Chagas disease and two of the leishmaniasis positive individuals . Interestingly , the lymphatic filariasis samples , infected with the closely related filarial parasite Wuchereria bancrofti , cleanly cluster with the healthy controls . Ideally serum and plasma samples would not be directly compared against each other as the two matrices have distinct chromatographic differences ( Figure S2 ) . However , given the nature of onchocerciasis sample banks that have been collected over the past 20 years , it was important to determine if the resulting biomarker results would be biased to one biological sample type over another . Importantly , our results show that the plasma samples from Cameroon as well as the Indian lymphatic filariasis plasma samples consistently align as expected with the multi-region serum sample set in distinguishing onchocerciasis infected from uninfected individuals . As evidenced in Figure 2C , there is little clustering of the Guatemalan individuals initially classified as onchocerciasis positive; rather there appears to be a continuum of onchocerciasis disease variation within those samples . However , dissections of excised nodules at the time of nodulectomy revealed no live worms , as opposed to the results of the Cameroon samples where infection status was confirmed by the extraction of live O . volvulus worms . Although tools such as PCA provide a graphical means of distinguishing between sample groups , they do not have the ability to provide a quantitative diagnostic assessment as would be needed nor are they intended to be used for field applications of an onchocerciasis diagnostic . Alternatively , machine learning algorithms do provide the necessary binary output , as well as calculate confidence intervals of a given classification . The mass spectral intensity values for the onchocerciasis serum and plasma data set were used as inputs in a collection of machine learning algorithms . The algorithms were chosen to provide a survey of the various types of machine learning algorithms that could be used with mass spectral data in diagnostic assessments , either alone or in combination in more sophisticated algorithms . Results of this analysis are summarized in Table 3 where sensitivity ( true positive rate ) and specificity ( 1–false positive rate ) are displayed . The receiver operating characteristic ( ROC ) areas present a numerical value description of the relationship between sensitivity and specificity for a given diagnostic test [35] , [36] . In the context of a binary classification problem as presented here , a value of 0 . 5 indicates there is no discrimination within the test and shows any result is essentially the same as a random guess , while a value of 1 . 0 indicates a perfect test prediction . Based upon the data , it is clear that the inclusion of the Guatemala samples within the sample analysis dramatically increases the number of reported false positives , compromising the accuracy of the test overall . However , it is important to note that within the context of the Africa sample set , the ROC area approaches , or is equal to , a perfect test prediction in numerous cases , and in the case of the functional trees classification tree algorithm , perfect sensitivity and specificity can be achieved .
Metabolomics , or the measurement of all the metabolites present in an organism , and metabolite profiling , in which a smaller subset of metabolites are measured , have become established as useful tools in the “real-time” measurement of organismal metabolism . For infectious disease , previous metabolomics approaches have included mice challenged with the protozoan parasites Trypanosoma brucei brucei [37] and Plasmodium berghei [38] , trematode parasites Echinostoma caproni [39] and Schistosoma mansoni [40] and some viruses [41] . This study represents the first investigation of a metabolomic approach to the discovery of biomarkers and creation of a diagnostic test for identifying and classifying onchocerciasis infection . Through the use of multivariate statistics and machine learning algorithms , the potential of metabolomic analysis has been demonstrated for uncovering biomarkers for specific determination of not only onchocerciasis infection but holds promise for the diagnosis of other parasitic diseases . Specifically , this was demonstrated by the clustering of the W . bancrofti infected samples with those individuals that were not infected with O . volvulus in the multivariate PCA . This clustering showed the potential specificity of the biomarkers for the discrimination of onchocerciasis from other filarial diseases . Although this analysis consists of only four representative lymphatic filariasis samples , the distinct clustering of these samples with uninfected individuals is noteworthy and argues for future analysis that includes other filarial disease pathogens ( e . g . , Brugia malayi , Loa loa ) . The 14 candidate biomarkers showed excellent performance in the African specific sample set with up to 99–100% sensitivity and specificity when examined with the single machine learning algorithms . With 99% of onchocerciasis disease prevalence in Africa [42] and the presence of multiple regions of ongoing transmission [43] , this is the most clear test of the biomarker strategy . When applied to a multi-region sample set , the multivariate PCA of the biomarker analysis resulted in a wide spread of results across the range of infected and uninfected individuals . This observation raises several questions regarding the unique epidemiological challenges of measuring onchocerciasis in the Americas . In the context of the PCA , the Guatemalan patients did not classify as expected if nodule presence alone is used as an indicator of infection . However , nodule presence as a diagnostic is known to have exceedingly poor sensitivity and specificity . A possible explanation of this data is that the observed heterogeneity is related to microfilarial load . Unfortunately , skin snip samples with mf counts were not collected for the Guatemala sample set . Nonetheless , if this observed spread of data were correlated with variation in the presence of the mf , then in a region such as the Guatemalan Central Endemic Zone ( CEZ ) where biannual dosage of ivermectin reaches high coverage levels [44] , mf should be nearly absent and we would expect to see no spread of the data but rather a distinct cluster with or near the uninfected individuals . Alternatively , the observation that a quarter of the nodules from these infected individuals from the Guatemalan CEZ did not contain living worms , indicates that these biomarkers may be sensitive to not only the presence , but also the viability of the infective worms . The results of this PCA are consistent with an increasing body of evidence that biannual ivermectin treatments , as are received in the Guatemalan CEZ , have an effect on the viability of adult female worms and ultimately on the elimination of parasites [45]–[47] . Since the Guatemalan O . volvulus positive samples do not segregate along clear lines with the clinically confirmed samples from Africa , it is possible that the continuum seen in the PCA plot reflects a range of infection that could be correlated qualitatively or quantitatively to the health of the worms ( e . g . , live healthy , dying , and dead ) in vivo . Given that an individual with dead or dying worms does not need further treatment in the context of ivermectin mass drug administration , this finding is particularly valuable in the context of onchocerciasis elimination progress . Ideally , a biomarker determination study would involve independent sample sets for training , validation , and testing . Due to sample limitations inherent to onchocerciasis and many neglected tropical diseases in general , we have chosen to use an approach that trains on the majority of the sample set , and through the 10-fold cross validation machine learning analyses , conduct tests on small subsets of the full sample set [48] . In this study , we report only those features detected in positive ion mode with the highest statistical significance and the most accurate intensity values by XCMS analysis . Consistent among these 10 small molecule features is that they are all fatty acids and related fatty acid derivatives . Further investigations into the biological roles of these fatty acids and fatty acid sterols in onchocerciasis disease progression and potential interaction with the down-regulated proteins is of distinct interest , not only in the development of a diagnostic but also to more clearly understand the biology of this disease . Almost certainly , other biomarkers could be discovered and validated simply by altering the chromatographic ( e . g . , HILIC ) and/or ionization conditions ( e . g . , negative mode ESI , APCI ) . It is possible that additional markers can be eventually be added to the repertoire of biomarkers used for onchocerciasis detection , further increasing assay specificity . The achievement of the goals of elimination and eradication of onchocerciasis and of the neglected tropical diseases in general , ultimately depends upon the ability to measure and track the progress of disease elimination and recrudescence . Our study highlights advantages of a metabolomics based diagnostic over onchocerciasis diagnostics currently implemented including: sensitivity , reproducibility , invasiveness , and the potential for multiplexing with biomarkers for other filarial and/or neglected tropical diseases . Fine calibration of this test in the Western Hemisphere would require characterized samples from individuals with confirmed active infection . Unfortunately , these samples are rapidly becoming a rarity due to the success that has been achieved by OEPA . Further refinement and validation of this metabolomic based diagnostic approach calls for an expansion of the mass spectral analysis with larger sample sets , while inclusion of a greater demographic representation will allow for further validation of the test in specific populations ( e . g . , children , adults , different genetic backgrounds ) . Eventually , the optimized biomarkers can be ported into field-based technologies ( e . g . , immuno-chromatographic or micro-fluidic-based tests ) for use as a point-of-care diagnostic , a determinant for the distribution and duration of treatment , and ultimately for long-term disease surveillance . | Onchocerciasis , caused by the filarial parasite Onchocerca volvulus , afflicts millions of people , causing such debilitating symptoms as blindness and acute dermatitis . There are no accurate , sensitive means of diagnosing O . volvulus infection . Clinical diagnostics are desperately needed in order to achieve the goals of controlling and eliminating onchocerciasis and neglected tropical diseases in general . In this study , a metabolomics approach is introduced for the discovery of small molecule biomarkers that can be used to diagnose O . volvulus infection . Blood samples from O . volvulus infected and uninfected individuals from different geographic regions were compared using liquid chromatography separation and mass spectrometry identification . Thousands of chromatographic mass features were statistically compared to discover 14 mass features that were significantly different between infected and uninfected individuals . Multivariate statistical analysis and machine learning algorithms demonstrated how these biomarkers could be used to differentiate between infected and uninfected individuals and indicate that the diagnostic may even be sensitive enough to assess the viability of worms . This study suggests a future potential of these biomarkers for use in a field-based onchocerciasis diagnostic and how such an approach could be expanded for the development of diagnostics for other neglected tropical diseases . | [
"Abstract",
"Introduction",
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] | [
"chemical",
"biology/small",
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"chemistry"
] | 2010 | Metabolomics-Based Discovery of Diagnostic Biomarkers for Onchocerciasis |
Snakebite is a major public health problem in many developing countries . Farmers are particularly exposed to snakes , and due to their rural location often experience delays in accessing formal healthcare . The reasons to use traditional healers may include difficulties in accessing formal healthcare , certain beliefs about snakes and snake venom , tradition , and trust in the capacity of traditional healers . Traditional healing , however , may have serious consequences in terms of delays or added complications . There is little in-depth current information about the reasons for its continued use for snakebite . As part of a health services development project to improve health outcomes for snakebite patients , community attitudes to the use of traditional healers were explored in the Mandalay region of Myanmar . With the objective of learning from local communities , information was generated in three communities using participatory appraisal methods with the communities , and focus group discussions with the local healthcare staff . Many snakebite victims in these communities use traditional healing . Reasons include transport difficulties , low cost for traditional healing , inadequacy of anti-snake venom in the formal healthcare sector , and traditional beliefs , as traditional healing practices are rooted in many cultural and traditional factors . The communities reported that even if access to medical care were improved , traditional healing would continue to be used . These findings point to the need for working with traditional healers for prevention , appropriate first aid and timely access to effective treatment for snakebite .
Snakebite is a neglected tropical disease , affecting disempowered rural communities in developing countries . It has been difficult to identify the exact incidence due to inadequate health statistics and the fact that some patients do not seek medical care . In 2008 , global annual incidence was estimated as 1 . 8 million bites [1] , whereas in 1998 Chippaux estimated the incidence as high as 5 . 4 million bites [2] . The global mortality from snakebite likely exceeds 125 , 000 deaths annually . However , a comprehensive community survey indicated that in India alone , the annual mortality from snakebite exceeded 45 , 000 [3] . Considering that many patients may not use health services and die before accessing care , the actual number may be much higher [1 , 3 , 4] . The burden of snakebite is highest in rural areas of the tropics and subtropics of South/ Southeast Asia and sub-Saharan Africa , mainly due to the density and species of venomous snakes present , population density , agriculture base , inadequate public health programs and lack of mechanised farming practices [1 , 5 , 6] . The only specific treatment for snakebite envenoming is antivenom ( AV; “anti-snake-venom” , “ASV” ) [7] . However , with less than adequate health literacy , inadequate access to AV and treatment facilities and other reasons , traditional healing continues to be used by a large number of people . Traditional medicine and healing are based on the communities’ past experiences and observations , passed on through generations verbally or in writing [8] , and is defined as “the sum total of the knowledge , skill , and practices based on the theories , beliefs , and experiences indigenous to different cultures , whether explicable or not , used in the maintenance of health as well as in the prevention , diagnosis , improvement or treatment of physical and mental illness”[9] . Traditional medicine is used , whether alone or in conjunction with biomedicine ( medical care and system based on the principles of Western science ) , by many people in both developing and developed countries; 80% of people in Africa reported to be users , 40% of all health care in China is reported as traditional care , and 38% to 75% of people in developed countries such as Australia , Canada and France are said to access complementary and alternative medicine [10] . Globally , traditional methods such as tattooing and herbal remedies and other methods including electric shock and suction are still used for snakebite [7 , 11 , 12] . The reasons listed in the literature for the continuing use of traditional healing include affordability , availability , and cultural familiarity [13 , 14] . Unfortunately , a significant number of people continue to die after snakebite . This is often due to severe envenoming , made worse in many cases by delays in obtaining effective medical care . These factors may generate a misperception that formal biomedicine ( also known as Western or allopathic medicine ) is ineffective [15 , 16] . A proportion of snakebites by venomous species are ‘dry bites’ , where the bite fails to inject enough venom to cause perceptible clinical effects . Further , many snake species are either non-venomous , or minimally venomous and so unable to cause envenoming . Should the patient seek help from a traditional healer after such a dry bite or non-venomous bite , the patient and the community are likely to mistakenly attribute recovery to the use of traditional medicine . Snakebite incidence is historically high in Myanmar ( 15 . 4/100 , 000/yr ) [17] . 70% of Myanmar’s population resides in rural areas with heavy reliance on subsistence agriculture [18] . Agriculture is a major occupational risk exposing farmers to snakebites . Most venomous bites in Myanmar are attributed to Russell’s Viper , envenoming by which can cause local pain and swelling , coagulopathy , life-threatening hemorrhage , shock , and acute renal failure . Overall , the annual number of snakebite cases , as reported in the national data of snakebite victims who seek care at the government hospitals or health centres fluctuates between 15 , 000 and 20 , 000 [19] . A large proportion of these snakebites occur in six high incidence regions i . e . Mandalay , Sagaing , Bago , Magwe , Ayeyarwady and Yangon . According to these health services data , in 2016 there were 16 , 767 snakebites in Myanmar , out of which 2 , 566 bites were in Mandalay region . These numbers are probably an underestimation of the magnitude of this important public health issue , as they do not include those victims who use traditional healers only and those who die before seeking care at the government health care centres or hospitals . Myanmar Australia Snakebite project for improved health outcomes for snakebite patients worked closely with the main tertiary hospital in Mandalay region . The hospital admission records and clinical information informed that 965 snakebite victims were admitted in 2016; 68 . 5% of these 965 suffered from coagulopathy , 63 . 2% suffered acute kidney injury , 31 . 5% required dialysis and 12 . 4% died . These figures point to the significance of this neglected public health issue . Snakebite treatment according to the biomedical management protocol includes AV , which is essential and the only antidote for envenoming , and supportive treatment such as airway management , treatment of hypotension and shock , treatment of acute kidney injury , management of hemostatic shock and treatment of the bitten site with antibiotics if needed [20] . In Myanmar , government doctors and paramedical staff are trained to provide treatment with AV and supportive treatment . In Myanmar , treatment of snakebite is impaired by problems with the supply of AV , and by shortage of adequately trained staff , particularly in rural areas . These limitations may contribute to the persisting use of traditional methods . Use of the healthcare system in Myanmar is dependent on several factors , including cost , previous experience , fear of surgery , and belief in religious or spiritual healers . Additionally , in some cases AV causes adverse reactions [7 , 21] . Hence , the communities may also harbour fears of biomedical treatment . Many traditional healing methods , such as local incision , herb ingestion , application of snake stones , and tattooing , are ineffective , and in some cases , harmful [7 , 10] . Their use can cause infection , bleeding , gangrene and other problems . In this way , the use of traditional healing may further delay or complicate necessary biomedical treatment . With the continued use of traditional healing practices , it is important to develop a better understanding of the nature of healing practices , the communities’ reasons for and views about its use , and the interface between traditional and biomedical components of the health system . In many snakebite-affected countries , an envenomed victim may need to walk ( or be carried ) for many miles to reach a primary health post . Gutiérrez and colleagues assert that ‘studies of the circumstances that delay the access of people bitten by a snake to health centres are of great value …… [and that the studies] should include in-depth analyses of the cultural characteristics of the communities , the way snakes and snakebites are perceived , the cultural background of local healers… . . ’ [22] . As part of a larger community and health services development project in Myanmar , the aim of this participatory action research was to engage with rural communities to learn from their perspectives , their health knowledge , and reasons for healthcare-seeking practices .
Using participatory methods , traditional healing for snakebite was studied through the lens of community knowledge , experiences and traditions . Gutiérrez et al . note that modern health programmes in rural communities are often culturally biased and paternalistic , lacking participation of the community in question [23] . Participatory methods acknowledge that local communities have valuable stores of knowledge which can guide development [24 , 25] . Learning from communities through participatory approaches is even more important for issues which affect impoverished people . Snakebite is a problem that mainly affects impoverished rural people , and its neglect at the global level is largely due to the fact that the affected populations lack political voice [26] . Participatory rural appraisal sessions ( PRA ) were organised in three communities in villages in Kyaukse and Madaya townships of Mandalay Division . They included the creation of ‘problem walls’ to reflect what the community saw as problems they faced . Focus Group Discussions with three groups of health care providers were conducted in the same settings . The primary care workers , which included Health Assistants , public health staff and midwives , are responsible for basic curative care at community health centres . They provide vaccinations , outreach , public health , preventative and health promotional activities in community and home settings . The three communities and the health care providers in those areas were selected considering representation of various areas of the township , distance to services in the city , access to care , and logistics and feasibility . Three participatory appraisal sessions took place , in 2016 , in public local community meeting places , ensuring an appropriate environment that fostered maximum comfort and interaction between the participants . 135 participants took part in PRA sessions that were held between 10 am and 2 pm . The majority of participants attended for the whole session , whilst others joined late or left early due to obligations such as work and family . This flexibility is part of the participatory and empowerment process , and contributions were respected and considered even if community members were unable to participate for the whole duration of the session . The communities were approached through primary health care workers , who , after permission from village leaders , invited individuals to participate . No community members or primary health care workers refused to take part and no one withdrew from the research . Table 1 informs about the selection process . After introducing the learning aims of the sessions , community members were encouraged to decide among themselves the most important aspects of the snakebite problem and issues with health care for snakebite in their own and surrounding communities . Throughout this process , participants were also encouraged to share their personal and family experiences . Community members discussed , defined and wrote key issues and problems on the flip charts in the shape of bricks ( problems ) . They then discussed the solutions that would be needed to ‘break the wall down’ . In the same villages where the PRA took place , FGDs were conducted at the government rural health centres with 23 primary care workers , with a focus on the extent of snakebite problem in the area , use of traditional and biomedicine health care by the locals and the reasons for such use . These health staff had experiences of snakebite either personally or through their work . In addition to the communities noting their issues on flip charts , a scribe took notes of the discussions . Each of the participatory appraisals and FGDs yielded several pages of raw narrative data . The data were then analysed to identify themes . The thematic analysis consisted of 6 phases [27] , the first step of which was familiarisation with the data through reviews of the detailed notes taken at PRA and FGDs as well as the visual data such as problem walls . The next stages in this analysis involved generating initial codes and interpretative analysis of these codes , leading to searching for themes i . e . important patterns and concepts relevant to the research question about healing methods and the reasons for their use . Themes were then reviewed and refined by carefully considering relevance to the main research questions , whether identified themes were backed by sufficient data , and whether there was clear distinction between the identified themes . The themes were then defined and reported for discussion . This analysis focused on patterns of use , type of traditional methods and the reasons rather than on prevalence . Therefore , such thematic analysis did not include counts or statistical analysis . The research was conducted with ethics approval from the Human Research Ethics Committee at the University of Adelaide and Ethics Committee at the Department of Medical Research at the Ministry of Health in Myanmar .
Kyaukse and Madaya townships are farming communities . All community members who participated in the discussions and appraisals acknowledged that snakebite was a problem in their communities . Working on farms and or walking to or from farms were the activities associated with snakebite , particularly early in the morning and in the evening . People informed that the harvest times in this region , June-July and October-November , were associated with higher incidence of snakebite . The community members considered snakebite a major health issue , and emphasised the need for further inputs for prevention and improved curative care . They informed about the inadequate health awareness and difficulties in accessing transport . They had good knowledge about preventive methods , particularly the need to wear boots when working in the fields and to have a torch while working at night . However , they informed that many do not practice these preventive methods for cost and convenience reasons . For example , it was mentioned that the boots are costly , hot to work in , and get stuck in the mud causing the work to slow down . The solutions by the community members included the need for further health education , access to less costly or subsidised appropriate boots , adequate supplies of AV at health centres closer to their villages , and better access to transport . In fact , groups of local volunteers across many communities facilitate transport for patients from their villages . For example , one of the communities where a PRA session was held had a community car that the locals use to transfer patients to hospitals . However , that car was in need of repair when PRA took place . Healthcare providers considered that with increased use of mechanised farming , the snakebite problem was decreasing . Most bites were by Russell’s vipers , and a few by cobras and a variety of other species . Healthcare providers informed us that access to health services had improved in the last few years as a result of better transport . Firsthand accounts by those who had been bitten by snakes informed us that the use of traditional healing in these communities was either as a stand-alone treatment without using biomedical care , or in conjunction with biomedical care . Those who used traditional healing in conjunction with biomedicine did so before or after the biomedical care . Seven of the PRA participants shared some information about their experience and use of health services and traditional healing . This information is summarised in Table 2 . Two victims informed us that they had visited a traditional healer after presenting to a hospital . Another two said they had been to a hospital but did not use the services of any traditional healer , and the other three said that they had been to a traditional healer but not to a hospital or rural health centre . One woman reported being bitten on her finger while she was picking betel leaves when she was 18 or 19 years old . She went straight to the monk for traditional treatments in the form of herbal medicine and tattooing . She said that she was fully healed after a month of that treatment . One man working in his turmeric plantation at dusk was bitten by a snake and fainted . He was driven to Kyaukse hospital where he was admitted , received AV and discharged after two months . Details of his hospital treatment were not discussed as the focus of discussions was traditional healing . After discharge from the hospital he went to see the monk for further treatment . With regards to types of traditional healing , the community members did not distinguish between traditional healing as a practice and traditional healers as the practitioners . Traditional healing was seen both as a profession and a tradition . However , in reference to the methods being used , community members made a clear distinction between monks and other traditional healers as two separate types of traditional healer . This distinction appeared to be due to the spiritual healing aspect of the care provided by the monks . A range of methods of healing were used by both monks and other traditional healers . Both practiced physical and spiritual methods . The physical techniques included asking the patient to chew the root of a particular plant to diagnose what type of snake had bitten the patient . The diagnosis was based on taste; whether it was bitter or not . Other practices included making incisions with a razor blade , tattooing with either ink or herbal medicines , use of a syringe to suck out venom , and rolling a heated glass bottle on the bite site to draw snake venom out . Tying a rope or a piece of cloth above the bite site as a tourniquet is practiced by many snakebite victims and the community as a first aid method , noted through observation of patients at the Project’s site hospitals . Communities did not discuss the tourniquet at the PRA sessions as a traditional method; probably because of the fact that health care providers at health centres and hospitals now advise communities not to use a tourniquet ( a recommended first aid method for snakebite about a decade ago ) . Faith-based spiritual techniques used by the monks and by the other healers included use of holy water , chants , prayers and astrology . Community members mentioned that “herbal concoctions” are used by both monks and other traditional healers . The following is a description , as reported by a community member , of one of the methods used by a monk: “Using a razor blade , the monk makes 10 parallel surface cuts around the wound . He then takes a 20cc plastic syringe and cuts off the top , placing it on the bite site . Using a second syringe and a thin tube he draws out the poison , which can be seen being removed in thick clots ( Fig 1 ) . After the poison is removed , blood starts to come out of the syringe . If the blood is not clotting , the monk knows [that he needs] to refer patients to hospital . The monk uses a bowl of bottled water to flush out the syringe throughout the procedure , and has the patient consume some traditional medicine [recipe unknown to the community members] to increase urine output . If the patient urinates 3 times [after the treatment] , they are said to be cured” . How the traditions are passed on , and the methods used , was elaborated on by a 58 year old local traditional healer who lives in the village and participated in the participatory discussions and analysis: The staff who participated in FGDs were also aware of these treatment types , including tattooing , incision , and using a glass bottle to remove venom . The quality of traditional healing was perceived to be good and was trusted by the participants . The community members reported that visiting a monk or other traditional healer was common and most of the snakebite victims in these communities access such services at some point following the bite . Many participants were of the view that even if the local health centres were adequately stocked with AV and if the treatment was available close to the village ( s ) , people would still choose to see a traditional healer in addition to using biomedical care . In contrast to the community members’ views , the healthcare providers said that if there was enough medicine available at the centres , most people would report straight there and not to a traditional practitioner , despite strong community belief in traditional healing for snakebite . According to community members , one reason for trust in and use of traditional healers is that deaths from snakebite after treatment by traditional healers are rarely seen by the communities . They reported success rates of up to 95% among those who use the local traditional treatments . Communities also held the monks in high regard due to the spiritual and community service aspects of the healing , and lacked access to formal biomedical care because of high cost , distance from health care facilities , and difficulties with transport . For community members , the cost of care was a major factor . Most pointed out that the cost of hospital treatment for snakebite , including car hire , food for carers , accommodation for carers and some out of pocket medicine was around 300 , 000 Kyat ( about 220USD ) . In contrast , treatment from a monk or traditional healer was often free , for a voluntary donation , or minimal fee . One person informed that they paid approximately 30 , 000 Kyat ( 22USD ) for their visit to the monk . One village had no car and for any emergency the villagers needed to rent a car at a high cost . In the two villages that did have community cars , one had broken down and the community lacked the funds to repair it . Another reason community members gave for using traditional healing first was the desire to avoid visiting hospitals , with some speaking unfavourably of the treatment in hospital settings . They cited factors such as unkind treatment and being afraid of the staff . In contrast , others said that they liked the care provided at the hospital . In fact , one of the community members bitten was very satisfied with the treatment he received at hospital , and as a result , did not seek any traditional treatment . The misconception that snakebite victims cannot be treated at a hospital or health centre without bringing the snake involved with them was voiced by two of the community members who had been bitten , one of whom had tried to catch the snake after being bitten . It is true that accurate identification of the offending snake can be useful , particularly if the snake is a non-venomous or minimally venomous species , thus allowing the snake-bitten person to be reassured and discharged from treatment in most cases . At some FGD sessions , staff noted that if the snake was clearly a non-venomous or minimally venomous species , they could then discharge the patient without need for referral on to a larger health facility . These staff appeared confident in their ability to identify such non-risk snakes . The healthcare staff mentioned transportation , low cost of traditional healing and strong traditional beliefs as reasons for the continued use of traditional healers . Some staff emphasised that traditional beliefs , not cost , were the major reason . The healthcare staff themselves did not appear to believe in traditional healing , and stressed that they had no contact with the traditional healers or monks about the treatment of snakebite patients . The healthcare staff also considered inadequate staffing and AV supplies at the health centres as contributing to the use of traditional healing . At one of the sessions , the staff said that the local monk only treated people because he wanted what was best for the community and that he knew that the local rural health centres were not adequately stocked with AV . Adding to this , some staff suggested that monks and other traditional healers could possibly be incorporated into the modern healthcare system .
Traditional beliefs , proximity to care , low cost , and perceived or actual inadequacies of the formal biomedical healthcare system emerged as the main factors associated with the local communities’ use of traditional healers . Spirituality also plays a key role in influencing decisions about treatment , and services by monks are seen as associating care with spirituality and community service . These beliefs appear to have a major influence on the use of traditional service despite awareness among community members that treatment is available in the formal biomedical sector , that the formal care is heavily subsidised and that many patients indeed have used formal biomedical care . The trust in and use of traditional care for snakebite is continuing despite expansion and better access to and use of biomedical care [28] . In fact , around the globe , the overall use of traditional and alternate medicine has increased in the last decade within the context of escalating costs of care and an increasing emphasis on patient-centred care [29] . Traditional healing plays a positive role in terms of social connectedness , harmony , services and support for fellow community members . However , there exists a knowledge gap which needs to be addressed to facilitate better health and wellbeing . Results of this participatory assessment indicated that one of the reasons communities trust the capacity of traditional healers is a perceived better success rate of the treatment in the traditional sector . In fact , a significant number of snake bites are either dry bites by venomous snake species with no systemic envenoming , or bites by one of the many species of non-venomous or minimally venomous snakes . Many who visit traditional healers as their first point of care therefore may view their traditional treatment as having been successful after suffering only from a dry bite or non-venomous bite . Additionally , as traditional healers often refer patients to hospital only when the symptoms are severe , hospitals end up seeing more patients with clinical complications , many of whom deteriorate and die [30] . These factors combine to generate a misperception about success of traditional healing and ineffectiveness of services in the hospitals . This suggests a need for health education for communities to bring about informed choice . Another misconception that community health education programs should address is that of the need to bring the dead snake to the hospital for identification , in order for diagnosis and treatment of the patient . Efforts by the patient or community members to kill and bring the snake could lead to further harm , either by the snake , or through further delays in necessary AV treatment . However , as noted earlier , identification of the snake can be beneficial in some circumstances . The increasing availability of mobile phones with inbuilt cameras , even in rural communities , might provide a future avenue for snake identification without a need to capture/kill the snake . Some felt that the traditional health practitioners simply want what is best for their patients , and would happily forego their practice if formal services were more available . In fact , they informed that healers and monks do already refer severe snakebite cases to formal biomedical services . At the same time , we received the impression that some monks and traditional healers relied on snakebite treatment as a form of income , which would mean that foregoing treatment of snakebite victims could affect their livelihood . One strategy could be to integrate traditional medicine into the national health systems and define how it might support disease prevention , promotion and treatment [29] . This research informed us about the communities’ trust in the service provided by the traditional healers , and for that reason we believe that the traditional healers could be engaged to provide community health education and appropriate first aid and facilitate early transfer of patients to nearby healthcare facilities . As some of the traditional healers provide care on a fee for service basis , identifying mechanisms to financially compensate traditional healers could facilitate their engagement for community health education , appropriate first aid and timely referrals . The role of traditional practitioners in working with skilled care providers and facilitating referrals to hospital is well investigated for other highly important public health issues such as safe motherhood services . For economic , access or cultural reasons , many women receive care from traditional birth attendants . As deliveries are safer if conducted by a skilled attendant or at a facility providing quality care , integration of traditional birth attendants with the formal system has been promoted . One of the barriers to this integration is the potential financial implication for the traditional care providers [31] . Research , however , has highlighted that the traditional care providers are willing to work with the formal health services [32] . As the use of services provided by traditional healers is common among snakebite victims , it is important that efforts are made to engage the traditional healers; and it is anticipated that they could be willing . However , as provision of care to snakebite victims is a source of income for traditional healers , a strategy that does not address financial concerns may fail to create an effective linkage between snakebite traditional healers and the formal health sector . While it could be argued that some traditional healing practices should , at the very least , be discouraged as being harmful and delaying referral to medical care , this participatory analysis of the local situation suggested that ‘phasing out’ of the whole concept of traditional healing for snakebite would not be an easy or advisable option . Policies that intend to force such moves may be challenging due to deeply held beliefs and a myriad of factors influencing peoples’ attitudes and practices , but more importantly , may also create tension and conflict between the formal health sector , communities , and traditional healers . Conversely , the concomitant use of biomedical care and traditional care by these communities offers an opportunity to facilitate the involvement of traditional healers for improved preventative practices , correct first aid procedures , and timely access to AV and other biomedical care as needed . In Nepal , for example , it was found that the traditional healers could be successfully trained to perform critical roles in primary prevention , first aid , and referrals [13] . Though disregarding traditional healthcare altogether may seem logical from a clinical point of view , it fails to take into account complex societal and health system factors . Even worse , such an approach tends to undermine cultural and traditional beliefs , and could cause further alienation and disempowerment . Within a context of deeply embedded beliefs , more credible are those suggestions which promote a dialogue between traditional healers and modern medical practitioners [8 , 2 , 33] . A plan to combat the snakebite problem needs to acknowledge traditional healers as an important stakeholder with potential to act as partners in prevention , appropriate first aid and prompt referral to effective treatment at health facilities . With monks providing traditional healing to the communities in Myanmar , they could be engaged in a similar way to the Maw Phra , or Doctor Monk , program which was implemented by Dr Prawase Wasi during the 1970s and early 80s . The program involved Buddhist monks , who are highly respected in Myanmar , and hold a strong association between Sadha , education and care for patients . It provided refresher courses in herbal medicine , as well as some basic skills of biomedicine [34] . Limitations: This research was able to yield some valuable data , particularly about the methods of and reasons for the use of services by traditional healers . Nevertheless , it has some important limitations . First , while local staff were actively involved in facilitating these sessions , interpreters were required for the researchers who didn’t understand Myanmar language . The local staff interpreted and translated; however , some interpretations may have been lost in translation . Secondly , this research was limited to three communities only , and the perspectives that we have gained about cost , transport , effectiveness of biomedical care could be area specific . Thirdly , the participatory sessions took place during the middle of the day when some young farmers had to be working in the fields . Since they are a demographic at such high risk of snakebite their participation would have been valuable . Fourthly , some of the healthcare staff had interacted separately with the project team members as a part of the wider project , and their views , particularly about the need for AV to treat snakebite patients , might have been influenced by that interaction . Despite these limitations , the purpose of this research was to highlight the snakebite phenomenon from the communities’ point of view . Public health and health system managers should take into account the valuable perspective that was gained , and would stand to benefit from discussions with their local communities on how the biomedical and traditional systems might operate side by side . | Snakebite is a major health issue that affects many people , particularly young poor farmers in developing countries in the tropics . Many patients suffer poor outcomes due to inadequate or delayed access to effective treatment . A large number still use traditional healers . Often patients visit traditional healers before accessing formal health care; thus incurring delays in receiving antivenom ( AV ) needed to treat envenoming . In other cases , traditional healing methods may themselves cause complications . In Myanmar , while most patients now access formal medical care , many also use traditional healers . We consulted communities in three rural areas in the Mandalay region and found that the reasons for using traditional healers include difficulties with transportation , cost , inadequacy of AV in the formal health sector , and trust in traditional healing within the context of longstanding tradition . These findings point to the need for working with the traditional healers as they could be effective agents to encourage prompt use of formal healthcare . | [
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"he... | 2018 | Why snakebite patients in Myanmar seek traditional healers despite availability of biomedical care at hospitals? Community perspectives on reasons |
Trypanosoma cruzi ( T . cruzi ) is an intracellular protozoan parasite and the etiological agent of Chagas disease , a chronic infectious illness that affects millions of people worldwide . Although the role of TLR and Nod1 in the control of T . cruzi infection is well-established , the involvement of inflammasomes remains to be elucidated . Herein , we demonstrate for the first time that T . cruzi infection induces IL-1β production in an NLRP3- and caspase-1-dependent manner . Cathepsin B appears to be required for NLRP3 activation in response to infection with T . cruzi , as pharmacological inhibition of cathepsin B abrogates IL-1β secretion . NLRP3−/− and caspase1−/− mice exhibited high numbers of T . cruzi parasites , with a magnitude of peak parasitemia comparable to MyD88−/− and iNOS−/− mice ( which are susceptible models for T . cruzi infection ) , indicating the involvement of NLRP3 inflammasome in the control of the acute phase of T . cruzi infection . Although the inflammatory cytokines IL-6 and IFN-γ were found in spleen cells from NLRP3−/− and caspase1−/− mice infected with T . cruzi , these mice exhibited severe defects in nitric oxide ( NO ) production and an impairment in macrophage-mediated parasite killing . Interestingly , neutralization of IL-1β and IL-18 , and IL-1R genetic deficiency demonstrate that these cytokines have a minor effect on NO secretion and the capacity of macrophages to control T . cruzi infection . In contrast , inhibition of caspase-1 with z-YVAD-fmk abrogated NO production by WT and MyD88−/− macrophages and rendered them as susceptible to T . cruzi infection as NLRP3−/− and caspase-1−/− macrophages . Taken together , our results demonstrate a role for the NLRP3 inflammasome in the control of T . cruzi infection and identify NLRP3-mediated , caspase-1-dependent and IL-1R-independent NO production as a novel effector mechanism for these innate receptors .
Trypanosoma cruzi is an intracellular trypanosomatid protozoan that is transmitted to the human host by blood-feeding Reduviidae bugs from the subfamily Triatominae . T . cruzi is the causative agent of Chagas disease and American trypanosomiasis , a chronic infectious disease . While Chagas disease is endemic in Latin America , a significant increase in confirmed cases of Chagas disease has recently been reported in the USA , Canada , Japan , Australia and Europe , indicating that it is an emerging disease [1] [2] [3] . Due to increasing immigration from endemic countries and a lack of regular screening in blood banks and hospitals ( with a few exceptions ) , T . cruzi infection is a potential public health issue in the USA and Europe . The control of T . cruzi by the immune system depends on both innate and adaptive responses . Innate immune cells are responsible for the initial recognition of the parasite , as well as the initiation and coordination of adaptive responses [4] . The transmembrane Toll-like Receptor ( TLR ) family of pattern recognition receptors ( PRRs ) plays a central role in the recognition of T . cruzi by the immune system [5] . TLR4 [6] , TLR2 [7] [8] [9] , TLR9 [10] and TLR7 [11] are responsible for sensing glycoinositolphospholipid-containing ceramides ( GIPLs ) , glycosylphosphatidylinositol ( GPI ) anchors from the trypomastigote form of the parasite ( t-GPI mucin ) , T . cruzi DNA and T . cruzi RNA , respectively . These receptors initiate a signaling cascade that is dependent on the adaptor molecule MyD88 and culminates in the activation of pro-inflammatory genes that are crucial for resistance to T . cruzi infection , including IL-12 [12] [13] [14] [15] , IFN-γ [16] and the microbicidal molecule nitric oxide ( NO ) [17] [18] . MyD88−/− mice are highly susceptible to T . cruzi infection , possibly because of defects in the production of pro-inflammatory cytokines [19] . In addition to TLR , NOD1 , a member of the cytosolic NOD-like receptor ( NLR ) family , plays a role in controlling T . cruzi infection [20] . NOD1−/− macrophages exhibit impaired production of pro-inflammatory cytokines and NO , and NOD1−/− mice succumb to the acute phase of T . cruzi infection . Despite evidence for the critical role of NOD1 in controlling T . cruzi , the agonist responsible for NOD1 activation remains unknown . In addition to NOD1 and NOD2 , the cytosolic PRR family includes inflammasomes , which are multiprotein complexes that contain a sensor protein from the NLR or pyrin and HIN domain-containing protein ( PYHIN ) families . The signaling cascade initiated by inflammasomes includes the recruitment of the apoptosis-associated speck-like ( ASC ) protein ( which contains a caspase activating and recruitment domain ( CARD ) and pro-caspase-1 or pro-caspase-11 ( pro-caspase-4 in humans ) , culminating in the activation of these proteases ( reviewed in [21] [22] [23] ) . Active caspase-1 mediates the maturation and secretion of the IL-1β and IL-18 pro-inflammatory cytokines and , along with caspase-11 , induces the pyroptosis cell death program . NLRP3 , NLRC4 and AIM2 are the best-characterized inflammasome complexes . NLRC4 is activated in response to bacterial proteins such as flagellin [24] or the inner rod component of the bacterial type III secretion system ( T3SS ) [25] , whereas AIM2 senses cytosolic bacterial [26] and viral DNA [27] . NLRP3 is activated by several different classes of molecules , including pathogen-associated molecular patterns ( PAMPs ) from viruses [28] , bacteria [29] [30] [31] , fungi [32] and protozoa [33]; signals from endogenous damage-associated molecular patterns ( DAMPS ) such as uric acid and ATP [34] [35]; and crystalline or particulate structures such as alum [36] [37] and silica [38] . NLRP3 activation by these molecules , which are not structurally related , appears to be influenced by ROS production , lysosomal rupture or potassium efflux , which are believed to result in distinct activation signals [39] [40] [41] . Inflammasomes have been implicated in the host response to several pathogens . NLRP3 has been identified as an effector in the control of Mycobacterium [42] , Cytrobacterium [43] , Salmonella [44] , Brucella [45] , Influenza A virus [46] [47] [48] , Candida [32] and other pathogens . However , the in vivo role of the NLRP3 inflammasome in the host defense against T . cruzi has not been elucidated . Here , we demonstrate that the NLRP3 inflammasome controls T . cruzi parasitemia by inducing NO production via a caspase-1-dependent , IL-1R-independent pathway .
This study was carried out 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 ( http://www . cobea . org . br/ ) . The protocol was approved by the Committee on the Ethics of Animal Experiments of the Institutional Animal Care and Use Committee at the Federal University of Sao Paulo ( Id # CEP 0162/11 ) . C57BL/6 wild type ( WT ) , caspase-1−/− , NLRP3−/− , MyD88−/− , IL-1R−/− , iNOS−/− and IFN-γ−/− mice were bred in our animal facilities at the Federal University of São Paulo . The caspase-1−/− mice were kindly provided by Dr . Richard Flavell ( Yale University , USA ) , and the NLRP3−/− mice were kindly provided by Dr . Vishva Dixit ( Genentech , USA ) . Eight-week-old female mice were infected subcutaneously ( s . c . ) with 103 blood-derived trypomastigotes from the Y strain of T . cruzi , which was obtained from the Dante Pazzanese Institute . Parasitemia was monitored by counting the number of bloodstream trypomastigotes in 5 µL of fresh blood collected from the tail vein . Spleens were removed 10 days after infection . All animal procedures were developed with all efforts to minimize suffering and were approved by the Ethics Committee for Animal Care of the Federal University of São Paulo UNIFESP ( Id # CEP 0162/11 ) . Peritoneal macrophages ( PMs ) were obtained by peritoneal lavage 4 days after intraperitoneal injection of a 1% starch solution ( Sigma Aldrich ) . Peritoneal cells were cultured overnight in RPMI 1640 medium supplemented with 3% heat-inactivated FCS and antibiotics . Non-adherent cells were removed by washing with warm RPMI medium . PMs ( 1 , 5×106 cells/300 µl ) cultured in RPMI medium were infected with trypomastigotes from T . cruzi strain Y ( 3 parasites/cell ) purified from a monkey epithelial cells ( LLCMK2 ) . The cells were treated with a caspase-1 inhibitor ( z-YVAD-fmk ) ( 10 µM ) ( MBL International Corporation ) , an iNOS inhibitor ( 1 mM ) ( Aminoguanidin , AG ) , a cathepsin B inhibitor ( Ca-074-Me ) ( Sigma Aldrich ) , a K+ channel inhibitor ( Glybenclamide , GLB ) ( InVivogen ) , an ROS inhibitor ( Apocynin , APO ) ( 25 µM to 100 µM ) ( Sigma Aldrich ) or neutralizing antibodies against IL-1β and IL-18 ( 0 , 1 , 1 and 10 ng/mL ) ( BD Pharmingen ) . Supernatants were collected 24 h after infection to detect IL-1β , IFN-γ and IL-6 , and 48 h after infection to measure the NO concentration . PMs ( 3×105 ) from C57BL/6 wild type ( WT ) , caspase-1−/− , NLRP3−/− , MyD88−/− , iNOS−/− and IL-1R−/− , mice were infected with trypomastigotes from T . cruzi Y ( 1∶5 ) for 2–4 h . Extracellular parasites were removed by washing , and after 48 hours the chambers were fixed with methanol and stained with DAPI ( 4′ , 6-Diamidino-2-phenylindole ) ( Sigma Aldrich ) . The frequency of infected macrophages and the number of amastigotes inside the macrophages were evaluated by fluorescence microscopy ( 600× ) . At least 500 macrophages were counted for each representative experiment . Supernatants from in vitro-infected PMs ( 1 , 5×106 cells/300 µl ) or cultured spleen cells from infected mice ( 3×106 cells/300 µl ) were collected after 24 h . IL-1β and IL-6 were measured using enzyme-linked immunosorbent assay ( ELISA ) kits from BD Pharmingen ( OptEIA ) according to the manufacturer's instructions . The levels of IL-1β were determined using a standard curve generated with the 17 KDa mature form of recombinant mouse IL-1β , as previously described [49] . IFN-γ was detected by sandwich ELISA using a purified anti-IFN-γ capture antibody ( BD Pharmingen ) and a biotin rat anti-mouse IFN-γ antibody ( BD Pharmingen ) . IFN-γ levels were calculated based on a standard curve generated using recombinant cytokine ( BD Pharmingen ) . The nitrite concentration was determined by the Griess reaction . Briefly , 50 µl of Griess reagent solution ( 1% sulfanilamide , 0 . 1% naphthylene diamine dihydrochloride , 2% H3PO4 ) was added to 50 µl samples , and the absorbances were measured at 540 nm . Experiments were performed in duplicate or triplicate , and at least three independent experiments were performed . Data are presented as the mean ± S . D . Statistical analysis of the data was carried out using one-way or two-way ANOVA and Tukey's post-test . Differences between the experimental groups were considered significant as follows: p<0 . 05 ( * ) , p<0 . 01 ( ** ) and p<0 , 001 ( *** ) .
The NLRP3 inflammasome is activated by a variety of stimuli , including viral , bacterial , fungal and protozoan pathogens . However , the activation of NLRP3 by T . cruzi has not been previously reported . To determine the ability of T . cruzi to activate NLRP3 , we evaluated the secretion of IL-1β by T . cruzi-infected peritoneal macrophages ( PMs ) . T . cruzi infection induced the secretion of IL-1β by PMs isolated from WT mice , but not from PMs isolated from NLRP3−/− and caspase-1−/− mice ( Figure 1A ) . Lysosomal cathepsins , ROS and/or potassium efflux have been reported to influence NLRP3 activation in response to diverse stimuli [35] [36] [37] . To investigate the role of these molecules in T . cruzi-induced NLRP3 activation , PMs from WT mice were infected with T . cruzi in the presence of pharmacological inhibitors of cathepsin B ( Ca-074ME ) , NADPH oxidase ( apocynin , APO ) and potassium channels ( glybenclamide , GLB ) . IL-1β secretion by infected macrophages was inhibited by Ca-074ME in a dose-dependent manner , but was not inhibited by APO or GLB ( Figure 1B ) . Taken together , these results indicate that T . cruzi is able to induce NLRP3 activation by a mechanism that involves cathepsin B . Next , we evaluated the role of NLRP3 in host resistance to T . cruzi infection by assessing parasitemia and mortality in NLRP3−/− and caspase-1−/− mice infected with T . cruzi Y . A significantly greater number of T . cruzi parasites were found in the blood of both NLRP3−/− and caspase-1−/− mice compared to WT mice ( Figure 2A ) . Interestingly , the degree of parasitemia exhibited by the NLRP3−/− and caspase-1−/− mice was comparable to that exhibited by the MyD88−/− and iNOS−/− mice , which are considered susceptible models for T . cruzi infection [18] [19] ( Figure 2A and Table S1 ) . The greatest parasite burden was found in IFN-γ−/− mice ( Figure 2A and Table S1 ) . To evaluate the pro-inflammatory responses of NLRP3−/− and caspase-1−/− mice during the acute phase of T . cruzi infection , we assessed the level of cytokines and NO in the supernatants of cultured spleen cells isolated 10 days after infection . MyD88−/− mice were used as a positive control for susceptibility to infection . Unlike MyD88−/− spleen cells , the spleen cells isolated from NLRP3−/− and caspase-1−/− mice produced considerable levels of IL-6 ( Figure 2B ) and IFN-γ ( Figure 2C ) , although they did not produce as much IFN-γ as the WT cells . As expected , IL-1β was produced by splenocytes isolated from WT mice , but not by splenocytes isolated from NLRP3−/− , caspase-1−/− and MyD88−/− mice ( Figure 2D ) . In agreement with a previous report [19] , the production of NO by spleen cells from infected MyD88−/− mice was significantly reduced compared to spleen cells isolated from WT mice ( Figure 2E ) . Surprisingly , however , the levels of NO produced by splenocytes from NLRP3−/− and especially caspase-1−/− mice were even lower than the levels of NO produced by splenocytes isolated from MyD88−/− mice ( Figure 2E ) . The defect in NO production by spleen cells isolated from the NLRP3−/− and caspase-1−/− mice could explain the high levels of parasitemia observed in the iNOS−/− , NLRP3−/− and caspase-1−/− mice ( Figure 2A ) . However , unlike the MyD88−/− and IFN-γ−/− mice , which succumbed to infection , the NLRP3−/− and caspase-1−/− mice survived infection ( Figure 2F ) . These data suggest that NLRP3 and caspase-1 are required for NO production and parasite control during the acute phase of T . cruzi infection . Because macrophages are the major source of NO during T . cruzi infection , the deficiency in NO production exhibited by the caspase-1−/− and NLRP3−/− mice could reflect an impairment in macrophage function . To test this hypothesis , PMs from WT , MyD88−/− , NLRP3−/− , and caspase-1−/− mice were infected in vitro with T . cruzi trypomastigotes . After 48 hs , the DAPI-stained slides were analyzed by fluorescence microscopy . The NLRP3−/− and caspase-1−/− macrophages demonstrated a marked deficiency in the control of infection , as the number of amastigotes ( Figure 3A ) and the frequency of infected cells ( Figure 3B ) were even higher than in the susceptible MyD88−/− macrophages . Notably , the difference in the amastigotes numbers of the macrophages derived from the knockouts and the WT mice was not related to the invasion rate of T . cruzi , as similar numbers of amastigotes were observed in the macrophages from WT , NLRP3−/− and caspase-1−/− mice after 2 h of infection ( Figure S1A ) . In contrast , 48 h after infection , the number of amastigotes present in the WT macrophages only was significantly reduced ( Figure S1A ) . Therefore , in the absence of NLRP3 and caspase-1 , macrophages were more permissive to parasite replicative or less efficient to kill them , or both . Importantly , pyroptosis ( macrophage death induced by inflammasome activation ) was not observed during T . cruzi infection . The majority of the T . cruzi-infected macrophages isolated from WT mice stained with the vital dye acridine orange and did not incorporate ethidium bromide , in contrast to macrophages stimulated with cytosolic flagellin , a potent pyroptotic stimulus [49] ( Text S1 and Figure S1B ) . As observed in the spleen cells from infected mice ( Figure 2 ) , unlike MyD88−/− macrophages , macrophages isolated from NLRP3−/− and caspase-1−/− mice secreted the pro-inflammatory cytokine IL-6 ( Figure 3C ) but did not produce NO ( Figure 3D ) . Furthermore , NLRP3-dependent NO production in response to T . cruzi infection appeared to be regulated by cathepsin B , as Ca-074ME , but not APO or GLB , significantly reduced the levels of NO produced by macrophages from WT mice ( Figure 3E ) . These results correlate well with our observations of IL-1β production ( Figure 1B ) . Moreover , treatment with Ca-074ME significantly increased the frequency of infection in macrophages isolated from WT and MyD88−/− mice ( Figure 3F ) , as well as the number of amastigotes found inside these cells ( data not shown ) . In contrast , Ca-074ME treatment had no effect in PMs from NLRP3−/− and even resulted in a small increase in levels of infection in PMs from caspase-1−/− mice ( Figure 3F ) . Taken together , these results suggest that the NLRP3 inflammasome mediates NO production in response to T . cruzi infection by a mechanism that involves cathepsin B . To confirm the role of NLRP3-dependent NO secretion in the control of T . cruzi infection , macrophages were infected in the presence of AG , a selective iNOS inhibitor . Treatment with AG increased the number of intracellular parasites found in WT and MyD88−/− macrophages by 100% and 25% , respectively ( Figure 4A ) , indicating that the NO produced by these macrophages contributes to the control of T . cruzi infection . Conversely , no significant effect was observed in NLRP3−/− macrophages treated with AG ( Figure 4A ) , supporting the idea that NLRP3-mediated NO secretion is involved in the control of parasite replication . Because NLRP3 activation leads to caspase-1 activation and consequent IL-1β secretion , we next asked whether caspase-1 and IL-1β are required for NLRP3-dependent NO production in response to T . cruzi infection , and what role these molecules play in the control of infection by macrophages . In contrast to the caspase-1−/− macrophages , we observed only a minor increase in the number of amastigotes in macrophages isolated from IL-1R−/− mice compared to macrophages isolated from WT mice ( Figure 4B ) . These results correlate with the ability of the IL-1R−/− macrophages to produce NO ( Figure 4C; white bars ) . Moreover , in the presence of the caspase-1 inhibitor ( z-YVAD-fmk ) or AG , the NO production ( Figure 4C ) and the capacity to control of T . cruzi infection ( Figure 4D ) of the WT and IL-1R−/− macrophages were abrogated . Importantly , treatment with z-YVAD-fmk or AG had no significant effect on NO production ( Figure 4C ) and amastigote numbers ( Figure 4D ) in macrophages from caspase-1−/− and iNOS−/− mice . This finding indicates that caspase-1 plays a major role in the induction of NLRP3-dependent NO production and the control of T . cruzi infection by macrophages . As demonstrated in Figure 3D , MyD88−/− macrophages secreted NO , although at reduced levels compared to WT macrophages . Because MyD88 participates in the IL-1R and IL-18R signaling pathways , these results confirm that NO production in response to T . cruzi infection is mediated by a signaling pathway that involves NLRP3/caspase-1 and is independent of IL-1β and IL-18 . In fact , the neutralization of IL-18 or IL-1β plus IL-18 reduced NO production by T . cruzi-infected macrophages from WT mice by only 15–20% ( Figure 5A ) . Notably , treatment with z-YVAD-fmk abrogated the secretion of NO by MyD88−/− macrophages ( Figure 5B ) and rendered them highly susceptible to T . cruzi replication ( Figure 5C ) . Together , these results suggest that MyD88 and caspase-1 , as well as IL-1β and IL-18 to a lesser extent , cooperate to clear T . cruzi infection by inducing NO production .
Cells from the innate immune system play a key role in sensing and controlling the acute phase of T . cruzi infection . While TLR2 , TLR4 , TLR7 , TLR9 and NOD1 play well-established roles in host resistance to T . cruzi infection , it is unknown whether inflammasomes , multiprotein platforms that are emerging as central regulators in many infections and inflammatory pathologies , are involved . Herein , we show that T . cruzi activates NLRP3 inflammasomes by a mechanism that involves lysosomal cathepsins . NLRP3 and the effector protease caspase-1 appear to participate in controlling the acute phase of T . cruzi infection , as NLRP3−/− and caspase-1−/− mice exhibit a very prominent peak parasitemia , despite their ability to secrete IL-6 and IFN-γ . However , in the absence of NLRP3 and caspase-1 , NO production is significantly impaired , indicating the involvement of NLRP3 inflammasomes in inducing NO secretion . The deficiency in NO secretion reflects in the inability of macrophages to control intracellular amastigotes . Interestingly , NLRP3-dependent NO secretion is completely abrogated in the absence of caspase-1 , but not in the absence of IL-1R , which could explain the ability of IL-1R−/− macrophages , but not caspase-1−/− macrophages , to prevent T . cruzi replication . In fact , macrophages isolated from MyD88−/− mice , which are unable to respond to TLRs agonists ( except TLR3 ) , IL-1 and IL-18 , secrete NO , although at low levels compared to WT cells . Moreover , the inhibition of caspase-1 in MyD88−/− macrophages renders them susceptible to T . cruzi infection as NLRP3−/− and caspase-1−/− macrophages . Collectively , our data demonstrate the existence of a novel NO induction pathway that involves NLRP3 inflammasomes ( Figure 6 ) . Recently , it has been reported that common pathways involving the production of ROS , disruption of lysosomes and/or potassium efflux influence NLRP3 activation , as they are activated by several PAMPs and DAMPs that are not structurally related [39] [40] [41] . T . cruzi infection induced IL-1β secretion by spleen cells as well as by macrophages isolated from WT mice , but not by cells isolated from NLRP3−/− and caspase-1−/− mice , which suggests that T . cruzi activates NLRP3 inflammasomes . Importantly , IL-1β production was also reduced in MyD88−/− cells , which is expected since pro-IL-1β is transcriptionally regulated by signals dependent on TLR [50] . Interestingly , the pharmacological inhibition of cathepsin B abrogated T . cruzi-induced IL-1β secretion , suggesting that lysosomal pathways are involved in NLRP3 activation in response to T . cruzi infection . In fact , T . cruzi invades a variety of cell types by recruiting lysosomes to the plasma membrane , which they fuse with to form a steady parasitophorous vacuole [51] . T . cruzi requires the acidic environment of the lysosome to exit the parasitophorous vacuole and replicate in cell cytosol . We suggest that the lysosomes are disrupted by this process , leading to liberation of cathepsins which then mediate NLRP3 activation by an as yet unknown mechanism . NLRP3 inflammasomes appear to control T . cruzi replication during the acute phase of infection , as NLRP3−/− and caspase-1−/− mice exhibit a very prominent peak parasitemia 11 d after infection . While NLRP3 is involved in many inflammatory disorders , little is known about the role of NLRP3 in controlling infections . Recently , we demonstrated that NLRP3 and AIM2 inflammasomes are involved in the control of Brucella abortus infection through an IL-1β- and IL-18-dependent mechanism [45] . NLRP3-induced IL-1β secretion also mediates host resistance to C . rodentium , [43] , M . kansasii [42] , S . typhimurium [44] , Influenza A virus [46] and C . albicans [32] [52] . However , NLRP3 does not appear to play a role in the control of the protozoan parasite Plasmodium [33] [53] . Conversely , the absence of NLRP3 or IL-1β resulted in increased survival of the malaria strain P . chabaudi adami DS , indicating that NLRP3 promotes malaria pathology . The first demonstration of the involvement of inflammasomes in the control of a protozoan infection was recently described in collaboration with our group [54] . In this article , we demonstrated that the NLRP3 inflammasome is involved in the control of L . amazonensis , L . braziliensis and L . infantum chagasi through a mechanism that involves IL-1R-dependent NO production . Interestingly , deficiencies in NLRP3 and caspase-1 result in high levels of parasites in the blood and inside macrophages , which is similar to results from MyD88−/− and iNOS−/− mice . However , iNOS−/− , NLRP3−/− and caspase-1−/− mice do not succumb to infection , unlike MyD88−/− mice . The pronounced deficiency in IFN-γ production exhibited by MyD88−/− cells , but not by NLRP3−/− and caspase-1−/− cells , could explain this difference , as IFN-γ−/− mice also succumb early to infection . It is well-established that the deficiency in IFN-γ secretion by MyD88−/− mice infected with T . cruzi reflects an impairment , not only in the microbicidal capacity of the macrophages but also in DC and TCD8 cell functions , as well an antibody switch to IgG2a and FCγR induction [55] . This could also explain why the highest number of parasites was observed in IFN-γ−/− mice compared to all the other immunodeficient mouse strains studied here . Despite the ability to secrete pro-inflammatory cytokines , spleen cells and macrophages from NLRP3−/− and caspase-1−/− mice were not able to secrete NO in response to T . cruzi infection . The role of NO in the control of T . cruzi infection is well established [56] . Mice that are deficient in iNOS [18] or have been treated with pharmacological iNOS inhibitors [57] [58] exhibit high levels of parasites in the blood and can succumb to the acute phase of infection , depending on the parasite and mouse strain . In our model , the NLRP3−/− and caspase-1−/− mice behaved in a manner identical to the iNOS−/− mice , with a higher magnitude of peak parasitemia compared to WT mice; however , most of the NLRP3−/− , caspase-1−/− and iNOS−/− mice survived infection . Macrophages isolated from the NLRP3−/− and caspase-1−/− mice failed to kill amastigotes and exhibited an infection index that was even higher than that observed in MyD88−/− macrophages . The incapacity to control T . cruzi infection demonstrated by NLRP3−/− and caspase-1−/− macrophages correlated with a deficiency in NO secretion . Moreover , adding AG to T . cruzi-infected macrophages increased the parasite burden in WT and MyD88−/− macrophages . Conversely , no effect of AG was observed in NLRP3−/− cells , indicating that this inflammasome is required for NO production in response to T . cruzi infection . The maturation and secretion of the pro-inflammatory cytokines IL-1β and IL-18 and the induction of pyroptosis , a specialized form of inflammatory cell death , are the major effector mechanisms induced by activated caspase-1 [21] [23] . While pyroptosis is known to pay a role in controlling bacterial infections [59] , this specialized cell death process did not appear to occur during T . cruzi infection , as the majority of the macrophages were viable 48 hs after infection . Recently , we described a caspase-1-dependent pathway for iNOS activation in response to cytosolic flagellin that is mediated by NAIP5/NLRC4-inflammasomes [60] . Caspase-1-dependent iNOS activation is involved in the control of Legionella pneumophilla [60] and Salmonella typhimurium ( unpublished data from our group ) by macrophages . Interestingly , MyD88 , IL-1β and IL-18 are dispensable for iNOS activation in response to cytosolic flagellin . IL-1β and IL-18 also appear to play a minor role in the induction of NO production and the capacity of macrophages to control T . cruzi infection , as a genetic defect in IL-1R , as well as the neutralization of IL-1β and IL-18 , have little influence on these activities . Conversely , NO secretion was not observed in caspase-1−/− macrophages or in macrophages treated with z-YVAD-fmk , confirming the existence of a pathway of NLRP3-mediated NO secretion in response to T . cruzi infection that is dependent of caspase-1 but independent of IL-1β and IL-18 , the best studied caspase-1 substrates . However , the mechanism by which caspase-1 mediates NO secretion remains unclear . Recently , it was demonstrated that a chromatin-associated multifunctional enzyme PARP1 ( also called ARTD1 ) negatively regulates the transcription of NF-κB-dependent target genes by a pathway that requires NLRP3/caspase-1/caspase-7 activation [61] . Mutating the PARP1 cleavage site D214 rendered PARP-1 uncleavable and inhibited PARP1 release from chromatin and chromatin decondensation , thereby inhibiting the expression of cleavage-dependent NF-κB target genes such as il-6 , cfs2 and lif , but not ip-10 [61] . Earlier studies showed that PARP1 cleavage is essential for stimulus-dependent transcriptional activation of transiently transfected reporter plasmids containing the inos and mip2 NF-κB sites in mouse lung fibroblasts ( MLFs ) [62] [63] . Additionally , inos expression is significantly reduced in the presence of noncleavable PARP1 ( D214N ) , compared to cleavable PARP1 ( WT or enzymatically inactive ) [64] . It was recently demonstrated that treatment with Olaparib ( a pharmacological inhibitor of PARP-1 enzymatic activity ) reduced T . cruzi replication in VERO cells [65] . However , the enzymatic activity of PARP-1 is not related to the role of cleaved PARP-1 in the regulation of chromatin condensation and access to transcription factors [60] . Therefore , further studies are required to understand the molecular machinery involved in the regulation of iNOS activation by the caspase-1/PARP-1 axis . TLR pathways [66] , IFN-γ [67] and IFN-γ-related cytokines such as IL-12 [15] [68] are known to be the major inducers of NO production in response to T . cruzi infection . However , we demonstrate here that pharmacological inhibition of caspase-1 abrogates NO secretion by spleen cells and macrophages from WT and MyD88−/− mice and renders their macrophages as susceptible to T . cruzi infection as NLRP3−/− , caspase-1−/− and iNOS−/− cells . This indicates that the requirement of NLRP3 inflammasomes for NO secretion bypasses the need for MyD88 , and identifies a novel NLRP3-dependent pathway that stimulates NO production in response to T . cruzi infection . Despite the minor effect of IL-1R and IL-18 on NO secretion in response to T . cruzi infection , the amount of NO produced by macrophages and cultured spleen cells from MyD88−/− mice is significantly lower than that produced by WT cultures . The fact that caspase-1 inhibition in MyD88−/− cells abrogates NO production and renders macrophages more susceptible to T . cruzi infection suggests that the NLRP3 and MyD88 pathways cooperate to induce NO secretion and promote host resistance to T . cruzi . Taken together , our data describe a novel effector mechanism mediated by NLRP3 inflammasomes that contributes to the control of T . cruzi infection . | Inflammasomes are cytosolic innate receptors that are emerging as central effectors in the control of infections and inflammatory pathologies . NLRP3 is the most studied member of inflammasomes with established role in the control of bacterial and viral infections . This manuscript describes original studies on the involvement of NLRP3 inflammasome in the control of Trypanosoma cruzi , the etiological agent of Chagas disease , a chronic infectious illness that affects millions of people in the world . T . cruzi activates NLRP3 inflammasome by a mechanism involving cathepsin B . NLRP3−/− and caspase1−/− mice display high parasitemia during acute phase of T . cruzi infection , which could be explained by a severe defect in the production of nitric oxide ( NO ) and in the impairment of their macrophages to control intracellular parasites . Interestingly , inhibition of caspase-1 , but not the neutralization of IL-1β and IL-18 , the best-studied caspase-1 substrates , abrogated NO production by WT and MyD88−/− macrophages and rendered them as susceptible to T . cruzi infection as NLRP3−/− macrophages . Together , our results indicate a caspase-1-dependent and IL-1β and IL-18-independent pathway for NO production as a new effector mechanism played by NLRP3 to control T . cruzi infection . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | NLRP3 Controls Trypanosoma cruzi Infection through a Caspase-1-Dependent IL-1R-Independent NO Production |
Echolocating bats use the echoes from their echolocation calls to perceive their surroundings . The ability to use these continuously emitted calls , whose main function is not communication , for recognition of individual conspecifics might facilitate many of the social behaviours observed in bats . Several studies of individual-specific information in echolocation calls found some evidence for its existence but did not quantify or explain it . We used a direct paradigm to show that greater mouse-eared bats ( Myotis myotis ) can easily discriminate between individuals based on their echolocation calls and that they can generalize their knowledge to discriminate new individuals that they were not trained to recognize . We conclude that , despite their high variability , broadband bat-echolocation calls contain individual-specific information that is sufficient for recognition . An analysis of the call spectra showed that formant-related features are suitable cues for individual recognition . As a model for the bat's decision strategy , we trained nonlinear statistical classifiers to reproduce the behaviour of the bats , namely to repeat correct and incorrect decisions of the bats . The comparison of the bats with the model strongly implies that the bats are using a prototype classification approach: they learn the average call characteristics of individuals and use them as a reference for classification .
Voice is defined as the entirety of all acoustic signals produced by the vocal organs of an organism and its ability to produce them . Vocalizations are mostly used for communication . They can contain information about identity , gender , maturity , health , behavioural context , etc [1]–[3] . Specific properties of the sound production and articulation apparatus are responsible for the individual-specific spectral properties of vocalizations . The human voice , for instance , reveals the identity of individuals and lately it has been shown that other animals can also recognize individuals according to their social vocalizations [4]–[10] . Social vocalizations constitute an important part of the vocal repertoire of bats . These vocalizations have been characterized for many species and contexts and were shown to contain individual signatures [11]–[23] . In addition to social vocalizations , microchiropteran bats constantly emit echolocation calls and use the returning echoes to perceive their surroundings [24] . These echolocation calls are tonal signals that exhibit a structured change in frequency over time that is normally less variable than that of the social vocalizations . The ability to recognize individuals based on echolocation calls might explain many of the social behaviours observed in bats [e . g . , 16] . Several studies tried to find individual-specific cues in bat echolocation calls [2] , [25]–[28] . Recently , the response of bats to the echolocation calls of different individuals has been tested and the results suggested that they could recognize individuals according to their echolocation calls [29] . The echolocation calls of the greater mouse-eared bats ( Myotis myotis ) used in this study are ∼3 ms long frequency-modulated ( FM ) down-sweeps ranging from ∼100 kHz to ∼30 kHz . The exact spectral-temporal structure of the calls changed depending on the task . We hypothesize that , despite this variability , the echolocation signals might contain individual-specific characteristics , generated by the bats' vocal apparatus , which are sufficient for individual recognition . We first tested whether bats can distinguish between individuals according to their echolocation calls using the most direct approach used until today: training greater mouse-eared bats to classify echolocation calls of other individuals played back to them in a two alternative forced choice ( 2-AFC ) experiment . After showing that the bats can clearly recognize their conspecifics , we used a statistical approach , new in this field , to train statistical classifiers to reproduce the bats' behaviour , namely to make similar correct and incorrect decisions as the bats . Our approach offers two main advantages in comparison to former unsuccessful attempts to statistically identify individual bats according to their echolocation calls [30] . First , our method is almost unlimited in the number of parameters that can be fed into it . This enabled us to use the raw representations of the calls and not to limit ourselves to a set of parameters as was always the case before . Second , we used a large data set containing ca . 800 calls per bat . Such a large data set enables us to create a good model of the individual's call despite its large variability . We used the statistical classifier as a model of the bat's underlying decision process to show how classification is statistically possible and to understand how the bats might be able to recognize other individuals .
All bats emitted calls typical for flying in confined spaces with a very characteristic spectral-temporal structure . Despite this repeating pattern , the spectral content of the calls varied largely among individuals for both behavioral and technical reasons ( see Materials and Methods and Figure 1 ) . There was also some intra-individual variability of the sweep rate ( Table 1 ) depicting the differences in the time structure of the calls . Finally , it is worth emphasizing that the SNR of the calls varied dramatically ( Table 1 ) as a result of the varying distance from the microphone . The bats required 15–24 days before they were able to stably correctly recognize the individuals in more than 75% of the trials . The learning curves ( Figure 2 ) fluctuated between days . After training , all bats were able to recognize S+ ( a single call of the bat they learned to recognize ) with much higher accuracy than chance level ( Table 2 ) . Bats were able to generalize from the learned task to recognize S+ or avoid S− ( a single call of the bat that they learned to avoid ) when presented with calls of new bats that were never heard during training ( Table 2 ) . Most of the bats showed both a preference for S+ and an avoidance of S− . The higher percentage of approaching S+ when presented with S0 ( a single call of a bat that they did not encounter during training ) can be a result of the fact that the S+ calls in these experiments were taken from the training set and thus - the bats might have already heard them during training . The lower avoidance of S− when presented with S0 could result from the fact that they were familiar to the bats and the bats were even rewarded when approaching them during the test phase . A linear classifier ( Support Vector Machine – SVM ) learned to classify the calls with high accuracy ( correct decision rates of 81–90% ) . This was the case for both types of representations of the calls , i . e . the temporal-spectral spectrograms and the spectral power spectrum densities ( PSD , Table 3 ) although in the case of the PSDs the performance was a bit lower ( 77–84% ) . This indicates that individual-specific information is abundant in the calls . The overall performance of the linear machines was similar to that of the bats . Our main goal was to model the behavior of the bats . Therefore , more than the overall performance , we were interested to find a classifier that behaves like the bat in the sense that it makes more errors in trials that the model considers to be more difficult and vice versa . We assessed the similarity between the bat and its model by measuring the correlation between the performance of the bat and the performance of the model on the same test set ( see Materials and Methods ) . The performance of the model was indirectly measured by calculating the distances between the pairs of calls in the test set . This reflects the metric of the model . A high correlation between the two indicates that the bat made more errors in trials that are considered to be difficult by the machine and vice versa . Except for a single case ( using the PSD for the classification task of bat 6 vs . bat 1 ) the metrics ( distances to the hyperplane ) of the linear classifiers are actually negatively correlated with the error rate of the bats , implying that they were using different features than the model to classify the calls ( Table 3 ) . We were , however , able to train non-linear SVMs that correlated with the bat's behavior in each of the classification tasks . This was true both for the spectrograms and the PSDs , although the correlation seems a bit less salient in the case of the PSDs ( Figure 3 ) . The overall performance of the non-linear SVMs behaving most similarly to the bats was very close to that of the bats , when using the spectrograms and was a bit lower when using the PSDs ( Table 3 ) . In one case ( classification of bat 5 vs . bat 2 ) the performance when using the PSDs was much lower . To eliminate the possibility that a single simple cue was sufficient for classification we analyzed the commonly used call parameters ( starting/terminal/maximum energy frequencies , bandwidth and call duration , Table 1 ) and tested the performance when relying solely on each of them . We used exactly the same pairs of calls that were presented to the bats in the testing phase and measured the percent of correct decisions if the bat would rely on one of the above parameters , ( e . g . always go to the call with a lower or higher terminal frequency ) . In almost all cases , relying on any single cueresulted in a performance at chance level ( 45–55% ) . For the classification task of bat 2 vs . bat 5 , using two single cues ( the bandwidth or the initial frequency ) was sufficient to correctly classify 60–65% of the calls - higher than chance but much lower than the observed performance .
This last similarity implies that the decisions of the bats can be modeled as a prototype classifier [31] in the sense that the bat learns the mean calls of the bat pair as a prototype for the two classes ( S+/S− ) . To test this hypothesis we applied a simple prototype classifier to our data . We used the nearest mean-of class prototype classifier , in which each class is represented by its mean and each call is assigned to the class whose mean PSD is closer to its PSD using the Euclidean distance . The means were calculated from the training data exclusively . Since the bats heard two calls in each trial , we calculated the sum of distances between the PSDs of these calls and the mean PSDs for both the correct and the incorrect assignments . We considered any case for which the correct sum of distances was smaller than the incorrect sum of distances as a correct decision of the classifier . We repeated this for the spectrograms as well . Despite its simplicity , the prototype classifier achieved a classification performance significantly higher than chance level for both the PSDs and the spectrograms ( Table 4 ) . The lower performance compared to the non-linear SVM is not surprising due to the simplicity of this classifier . The overall performance however , is less important in our case . It could probably be increased by a more sophisticated prototype classifier , for instance one that only learns the means of features that have a large inter-bat variability . Much more important is the very high correlation between the distance metric of this classifier ( sum of prototype distances ) and the bat performance , meaning that the bats tend to make more errors when the calls presented to them are farther from the mean calls ( Figure 5A ) . An interpretation of the SVM decision rule regarding the spectrograms is not easy due to their high dimensionality , but the above analysis suggests a prototype classifier as well ( Figure . 5 and Table 4 ) . To validate this idea we ranked the spectrograms of the presented call pairs of Bat 1 and Bat 3 according to distances between them ( based on the non-linear SVM metric ) . The closer the two spectrograms are to each other , the more difficult they should be to classify . To test the prototype hypothesis we next measured how similar each spectrogram pair is to the pair created by the two class means . We calculated the linear correlation between a ) the difference between the pairs and b ) the difference between the mean spectrograms . We found a strong positive correlation between the two . which shows that the more similar the difference between two spectrograms is to the mean difference , the easier it is to classify by the trained SVM . As this SVM was trained to imitate the bat's behavior , this once again supports the hypothesis that the bats are using some sort of a prototype classifier ( Figure 5B ) . In summary , for both PSDs and spectrograms , we found evidence that the bats use a prototype classifier in which they evaluate the mean difference between the calls of the bat couple as a reference to which they compare the difference between any new pair of calls they hear . This hypothesis is strengthened by the results of the generalization experiments , which suggest that the bats are using both S+ and S− to classify ( Table 3 ) . We did not observe the exact PSDs of all classification tasks , mainly because the amount of errors for the other tasks was very small . The application of a prototype classifier ( Table 4 and Figure 5A ) however , implies that all of them were using a sort of a prototype classifier . Researchers were always fascinated by the social behaviors exhibited by bats . There are , for instance , some reports of bats leaving the roost and flying to and between foraging sites in groups of between two and six individuals [16] , [22] . Little is known about how bats might perform the strenuous task of remaining in a group when flying at high speeds in darkness , or about how they avoid interference between each others' echolocation calls . The finding that bats can recognize their conspecifics based on their echolocation calls might have some significant implications in this context . Despite their stereotyped spectrograms , echolocation calls show a large task-dependent variability that obscures possible features in the calls that might facilitate the recognition of individual bats [30] . For this reason , we had to use statistical classifiers as a new method of analysis in a context that requires a minimal set of restrictive assumptions on candidate discriminative features . The results pointed strongly towards a prototype strategy . This now enables us to design additional behavioral experiments to test this hypothesis . To test the prototype hypothesis one could , for instance , divide the calls of one of the bats into 2 subgroups that are selected such that their prototype ( mean ) is very different . The tested bat should then be trained using calls from one subgroup and tested using calls from the other . If the prototype hypothesis holds , the bat would be expected to have a very high error rate . An alternative approach could be to use the hyperplane learnt by the SVM to simulate artificial calls at known distances from the hyperplane and therefore known difficulty [see 32 for more details] . Comparing the performance of the tested classifiers on the PSDs or on the spectrograms reveals that the performance when using the PSDs does not drop as we would expect from taking into account the drop of information ( Table 3 ) . This implies that most of the information necessary for classification already exists in the frequency domain . Along with the above analysis of PSDs , this suggests that the filtering properties of the vocal tracts of the individuals , which reflect vocal tract resonances ( formants ) provide sufficient acoustic cues for individual recognition . These findings are in line with some recent evidence supporting the presence of formants in animal calls [8]–[10] , [33]–[35] . It is quite probable that for the classification of the complete repertoire of M . myotis calls , including calls emitted in different behavioral situations that show a much higher variation of temporal-spectral relations , the PSDs might even be advantageous compared to the spectrograms since they provide a time-independent set of cues .
We conducted the experiments using five adult male M . myotis ( Borkhausen , 1797 ) , captured in Bulgaria ( license from the Ministry of Environment and Waters , 34/04 . 07 . 2005 , Sofia , Bulgaria ) and housed under standardized conditions ( 16∶8 h light∶ dark cycle , 24±2°C and 65±5% humidity ) . Bats were fed on mealworms ( larvae of Tenebrio molitor ) only during training and experimental sessions . The diet was supplemented with minerals ( Korvimin® , WDT ) and vitamins ( Nutrical© , Albrecht ) and freshwater was accessible all the time . The animals used in the experiments were kept together for a few months in a flight cage that enabled them to fly regularly . Five bats were recorded separately while freely flying in a flight room ( 3 . 6×6 . 0×2 . 8 m ) covered with acoustic foam to reduce echoes from the walls and floor . The flight behavior consisted of two patterns: The animals either circled in the room ca . 2 m above ground , or they flew to one of the walls and hung on it . In the latter case we encouraged them to fly again by clapping the hands or gently poking them with a butterfly net . The sound recordings were performed with custom-made equipment ( Universität Tübingen , Germany ) including an ultrasonic microphone ( flat response ±3 dB between 18 and 200 kHz ) in a stationary position pointing 45° upwards at one end of the room and a digital recorder ( PCTape ) , with a sampling rate of 480 kHz . The order of the animals was selected using the Latin squares method [36] to mitigate undesired effects caused by the order or time of the day . The recordings lasted 20 minutes in total , collected on two consecutive days . This procedure provided us with a large data set of over 2000 calls per bat . The characteristics of the calls varied greatly within each individual even though they were emitted under the same conditions . This variability had at least two causes: 1 ) Behavioral - the bats were constantly changing their distance from the walls , especially when approaching them to land and adjusted their echolocation accordingly [37] , [38] . 2 ) Acoustical - the calls were recorded when the bats were at different distances from the microphone and with different aspect angles to it . This resulted in substantial changes in the signal to noise ratio ( SNR: see Results for more details ) . We discarded all calls that were shorter than 2 ms since they were severely affected by the directionality of the microphone ( i . e . calls with a strong attenuation at high frequencies ) . This procedure left us with approximately 800 calls for each bat . In the behavioral experiments each bat was trained to distinguish between two other specific bats in a 2-AFC paradigm . Each experimental bat was assigned two other bats between whose calls it had to distinguish . We will refer to the bat it had to approach as S+ and to the other one as S− . The bats had to sit on a Y-shaped platform and crawl to the side where the calls of S+ were played . The stimuli consisted of alternately playing a single call of S+ on one side of the platform and a single call of S− on the other side with a 0 . 5 s pause between them until the bat made a decision . All calls were normalized in the time domain to have the same maximum amplitude . We used custom-made equipment ( Universität Tübingen , Germany ) to play back the calls with a sampling rate of 480 kHz . The loudspeakers ( Thiel Diamond Driver D2 20-6 ) were positioned 1 . 35 m from the platform and 1 . 35 m apart from each other , forming an equilateral triangle together with the platform . The side on which S+ was presented varied randomly between the trials . The experiments were divided into a training phase and a testing phase . In the training phase the bats were trained to perform the task using a subset of the data composed of 80% of the calls ( the training set ) chosen randomly . During training , when the bat crawled to S+ , it was rewarded with a mealworm . The bats needed ∼4 days of training to get used to sitting on the Y-platform ( they were fed on it ) . They needed another ∼3 days to learn to crawl to one of the sides of the Y-platform to get the reward . To do this , we placed the bat in the starting arm and played back S+ from one side and S− from the other one , showing the mealworm at the end of the correct arm and rewarding the bat for crawling towards it . The next step ( the training phase ) consisted of the training on the task . S+ and S− were played back as described above and the bats were rewarded for crawling to the correct side . When they made an error the trial would be repeated up to 3 times . If the bat continued misclassifying we moved to the next pair of calls . Once a bat made more than 75% correct decisions\3 days in a row it was transfered into the testing phase . The training phase lasted ∼20 days on average so that each bat performed ∼25 trials per day so that in total the bats heard ∼500 calls of each bat before starting the testing phase . In the testing phase , we used the remaining 20% of the calls that had never been heard by the bats before . Each pair of calls was played back during a single trial . The decision of the bats was always rewarded , so that the experimenter could not give the bats a hint about the correct answer ( a double blind paradigm ) . The assignment of bat pairs ( S+ vs . S− ) were as following: bat1–bat2 vs . bat6 , bat3–bat6 vs . bat1 , bat4–bat5 vs . bat2 and bat5–bat3 vs . bat1 . We used four different pair of bats ( rather than testing all bats on the same task ) assuming that all tasks were more or less equally hard and thus a high performance in all of them would imply high performance for any chosen pair of bats . We recorded the calls that were played back by the speakers to validate that the system was working properly with the same recording equipment mentioned above . To test the ability of the bats to generalize and to estimate whether they learned to recognize S+ or to avoid S− we conducted another set of control experiments . Here S+ or S− were presented on one side and S0 , which consisted of a call of one of two novel bats never played back to that animal before , on the other side . The S+/S− calls were randomly selected from the training set , since the bats recently heard all of the testing calls and were not exposed to training calls for at least 2 weeks . The order of presentation of S+ or S− and S0 was random as well as the side on which they were played . The rest of the procedure was the same as in the testing session . We used Support Vector Machines [SVM] , [39 , 40] , a well-known classification algorithm in the field of machine learning , to classify the calls of the different bats . This method is suitable for dealing with multi-dimensional data and uses the raw data in order to learn the best features for classification , with minimal prior assumptions on the data distribution . We tested the performance of the classifier using two different representations of the calls: spectrograms and power spectral densities ( PSD ) . The spectrograms are a time-frequency decomposition of the calls and therefore represent both types of information the bats possess after the basic filtering in the ear [41] . The spectrograms were calculated using a Hann FFT window of 240 points with 0 . 9 overlap between consecutive windows , providing a frequency resolution of 2 kHz and a time resolution of 0 . 5 ms . The part of the spectrogram containing the call was segmented from the background noise using Otsu's method [42] . This was done for each spectrogram separately and provided us with the call segments that were clearly above noise . We should emphasize that this was done for the machine classification only . The bats had to face noisy calls with a large variability of background noise . We restricted the spectrograms to the frequency range between 21–140 kHz , which contains the entire frequency range of the calls . This left us with very high-dimensional data ( 4200 dimensions: 60 frequencies times 70 time points ) . We aligned all spectrograms in the time axis such that in all calls the maximal energy at 30 kHz was at the same time instant of the spectrogram . We used Principal Component Analysis ( PCA ) to reduce the dimensionality of the data . Each data point ( representing a single call ) was projected on the 300 eigenvectors with the highest eigenvalues . This reduced the dimensionality of the data to 300 dimensions . In a spectrogram of a frequency-modulated M . myotis call most of the values of each spectrogram contain background noise . Reducing the dimensionality in a way that preserves the directions of the greatest variance ( using PCA ) should therefore get rid of a large amount of noise . In every experiment , the eigenvectors were exclusively calculated from the covariance matrix of the training set ( see below ) . The PSD contains only the frequency information of the calls , leading to a classification that is independent of temporal information ( e . g . , call duration , sweep rate ) which tends to vary widely in nature . Throughout the paper they will sometimes be referred to as spectra . The PSDs were calculated with Welch's method with a 2 ms window with 0 . 5 overlap . We then under-sampled the PSDs so that their frequency resolution was identical to that of the spectrograms , ensuring that they contained the same spectral information as the spectrograms but no temporal information . All data points ( spectrograms after PCA and PSDs ) were normalized ( divided by the maximum ) so that each of them had a maximum of 1 before they were used for classification . SVMs are state-of-the-art learning algorithms based on statistical learning theory . A linear SVM uses a training data set to learn a hyperplane ( a multidimensional decision boundary ) that divides the data set into two classes . It does so by minimizing the classification error and at the same time by maximizing the distance between the hyperplane and the data points that are closest to it . A non-linear SVM is used when the data cannot be separated linearly . It first transforms the data non-linearly into a higher-dimensional space ( feature space ) and then finds a hyperplane that divides the data into the two classes in this space . In both cases the hyperplane is simply a geometrical multidimensional plane either in the original or in the feature space . Since in many cases a perfect separation of the data into two classes is not possible , the learning algorithm is adjusted to enable a certain amount of misclassification . This is controlled by a constant ( C ) that defines the penalty for misclassified points . This constant is known as the free parameter of the SVM . We applied SVM classifiers on both types of data ( i . e . , spectrograms and PSDs ) . We used the same training set of calls that was used to train the bats in order to train the classification machines and the same test set to test them . We tested both linear and non-linear SVMs . For the non-linear SVMs , we trained non-linear machines using the radial basis Gaussian kernel [RBF] , [39] , [40 , 43] to transform the data nonlinearly before computing the separating hyperplane . This is a standard choice in machine learning that usually performs well in a wide range of applications . The use of the RBF kernel introduces a second parameter ( σ ) that sets the width of the Gaussian . In order to optimize the classifier to perform like the bat ( see below ) we tested 8 different values for each of the two parameters ( 0 . 1 , 1 , 10 , 50 , 100 , 500 , 1000 , 10000 ) and trained linear SVMs with all possible C values and non-linear SVMs with all possible combinations of the two in order to find a classifier with a performance that is most similar to that of the bats . There are several possibilities to optimize the model such that it behaves like a bat . The overall performance ( error rate ) is not a sufficient criterion since it does not provide any information about the classification strategy - e . g . , the bat and model could do the exact opposite right and wrong decisions but still have the same error rate . An exact comparison between the decisions of the bat and the decisions of the model ( percent of identical right/wrong decisions ) is a better criterion , but it is also limited since it divides the trials into identical decisions and non-identical decisions but provides no information about how difficult each decision was . We therefore chose a different criterion , one which is , to our understanding , more informative . For each model ( linear/non-linear SVM ) we computed the distances between the pairs of test calls the bat had to classify according to the model . This can be done by computing the distance of each call from the hyperplane . The distance from the hyperplane can be thought of as an estimation of how difficult the call is to classify . The closer a call is to the hyperplane , the more difficult it is to classify , since it is closer to the boundary between the two classes . We refer to this measure as the metric of the model and it reflects how difficult/easy each trial is considered to be according to the model . We assumed that if the machine captured the features used by the bats for classification , the distance between the calls should positively correlate with the performance of the bats , meaning that the farther apart the two calls presented to the bat were , the easier it should be for the bats to classify them correctly . In practice we divided the entire distance range into 4 distance classes , each containing an equal number of calls and plotted the error rate of the bats for each of these distance ranges . We then calculated the correlation between the performance of the bat and the difficulty of the trials it performed , represented by the average distances of the group of trials . We searched for the parameters that yielded a classifier that maximizes this correlation . To choose the best parameters we divided the test set into 3 equally sized sub-sets of data . We then used only two thirds of the test set to choose the best model ( this set is called the validation set ) and we measured the results on the un-used third . This process was repeated three times and ensures that the test set did not influence our decision . This procedure also provided us with an estimation of the variance of the model's performance . We implemented the SVM classifier using the free “spider” software ( http://www . kyb . mpg . de/bs/people/spider ) . For more details about the application of SVMs on a data set of spectrograms see Yovel et al [31] . | Animals must recognize each other in order to engage in social behaviour . Vocal communication signals could be helpful for recognizing individuals , especially in nocturnal organisms such as bats . Echolocating bats continuously emit special vocalizations , known as echolocation calls , and perceive their surroundings by analyzing the returning echoes . In this work we show that bats can use these vocalizations for the recognition of individuals , despite the fact that their main function is not communication . We used a statistical approach to analyze how the bats could do so . We created a computer model that reproduces the recognition behaviour of the bats . Our model suggests that the bats learn the average calls of other individuals and recognize individuals by comparing their calls with the learnt average representations . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"neuroscience/behavioral",
"neuroscience",
"neuroscience/cognitive",
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] | 2009 | The Voice of Bats: How Greater Mouse-eared Bats Recognize Individuals
Based on Their Echolocation Calls |
Up to 40% of the world's population is at risk for Plasmodium vivax malaria , a disease that imposes a major public health and economic burden on endemic countries . Because P . vivax produces latent liver forms , eradication of P . vivax malaria is more challenging than it is for P . falciparum . Genetic analysis of P . vivax is exceptionally difficult due to limitations of in vitro culture . To overcome the barriers to traditional molecular biology in P . vivax , we examined parasite transcriptional changes in samples from infected patients and mosquitoes in order to characterize gene function , define regulatory sequences and reveal new potential vaccine candidate genes . We observed dramatic changes in transcript levels for various genes at different lifecycle stages , indicating that development is partially regulated through modulation of mRNA levels . Our data show that genes involved in common biological processes or molecular machinery are co-expressed . We identified DNA sequence motifs upstream of co-expressed genes that are conserved across Plasmodium species that are likely binding sites of proteins that regulate stage-specific transcription . Despite their capacity to form hypnozoites we found that P . vivax sporozoites show stage-specific expression of the same genes needed for hepatocyte invasion and liver stage development in other Plasmodium species . We show that many of the predicted exported proteins and members of multigene families show highly coordinated transcription as well . We conclude that high-quality gene expression data can be readily obtained directly from patient samples and that many of the same uncharacterized genes that are upregulated in different P . vivax lifecycle stages are also upregulated in similar stages in other Plasmodium species . We also provide numerous examples of how systems biology is a powerful method for determining the likely function of genes in pathogens that are neglected due to experimental intractability .
Renewed efforts to combat malaria have focused on the goal of total eradication . While most attention is on the more deadly Plasmodium falciparum malaria , Plasmodium vivax is the most geographically widespread human malaria parasite causing an estimated 80–250 million cases of vivax malaria each year [1] . P . vivax malaria has traditionally been found outside of tropical areas and was endemic throughout North America and Europe until the introduction of DDT . Despite the large burden of disease caused by P . vivax , it is overlooked and left in the shadow of the enormous public health burden caused by P . falciparum in sub-Saharan Africa . The widely held misperception of P . vivax as being relatively infrequent , benign , and easily treated explains it's nearly complete neglected across the range of biological and clinical research . In fact , P . vivax malaria seriously threatens more people than has been historically appreciated . Recent reports [2]–[4] provide abundant evidence that challenge the paradigm that P . vivax infection causes benign disease: P . vivax malaria may result in severe symptoms similar to P . falciparum . Selective pressure for resistance to malaria has had a great influence on the human genome and most Africans are immune to P . vivax malaria due to mutations in the Duffy receptor that the parasites use to invade the red cell . A fundamental difference between P . vivax and P . falciparum is the formation of dormant liver stage parasites called hypnozoites that are resistant to schizonticidal drugs that kill erythrocytic stage parasites . Despite schizonticidal drug therapy , the patient may experience multiple relapses months or years following the primary infection . Attempts to eradicate malaria will depend on having effective and non-toxic drugs that target P . vivax liver stage hypnozoites . Hypnozoite biology is poorly understood but is likely related to persistence of the parasite in locales where mosquito populations vary seasonally . Little is known about what triggers a relapse but some strains form different numbers of hypnozoites and have different relapse frequencies , which may be correlated with latitude[5] . There is controversy about whether hypnozoites represent a different lifecycle stage or merely represent an arrested early exo-erythrocytic phase . Almost nothing is known about metabolic activity in the hypnozoite , thwarting all efforts at rationale drug design . The mechanism of the only drug known to eradicate hypnozoites , the 8-aminoquinoline drug , primaquine , is still not completely understood . While rodent models have furthered our understanding of Plasmodium liver stage development , they do not form hypnozoites . The only other available model for studying hypnozoite biology is the closely related P . cynomolgi species that must be studied in the rhesus macaque . Studies of P . vivax biology still depend on obtaining erythrocyte stage parasites or sporozoites from infected humans or non-human primates or their mosquito vectors , respectively . Drug sensitivity testing remains difficult . In an earlier era compounds were tested for activity against hypnozoites in prisoners who had been given malaria [6] . Drug testing in vitro is limited by current difficulties in culture techniques . Only a few genetic manipulations of P . vivax have been successful [7] . These impediments have discouraged many researchers from working on P . vivax , and limited the potential for molecular genetic analysis of this elusive liver stage . Given that forward and reverse genetic methods , which have been powerful in P . falciparum , are not readily available for investigating the genome of P . vivax , comparative genomics and virtual genetic methods offer the best opportunities to elucidate P . vivax gene function . Previous gene expression profiling of P . falciparum [8] , [9] P . yoelii [10] throughout many developmental life cycle stages in the mammalian host and insect vector , and in P . vivax blood stages [11] , [12] has provided fundamental insight into Plasmodium biology and illustrated how gene function can be predicted based on gene expression patterns throughout development . The power of this method is to assure that there is sufficient diversity in different stage parasites to allow for the delineation of distinct patterns of gene expression . Here we used a systems biology approach to characterize the P . vivax transcriptome . Using a custom high-density tiling microarray , we obtained a diverse set of gene expression data from human and mosquito stages including sporozoites , gametes , zygotes and ookinetes , and in vivo asexual blood stages obtained from infected patients in the Peruvian Amazon . These data are combined with published short term in vitro culture data [11] . Using a guilt-by-association approach we create hypotheses about the function of many uncharacterized genes . Comparison to datasets of P . falciparum and P . yoelii reveals conserved and species-specific patterns . This analysis provides new insights into the metabolic state of parasites growing within humans . It shows that many of the orthologs of P . falciparum transcripts needed for exo-erythrocytic development are present in the P . vivax sporozoites suggesting that slight modifications in exo-erythrocytic development may allow hypnozoite formation .
The protocol used to collect human blood samples for this work was approved by the Human Subjects Protection Program of The Scripps Research Institute , and the University of California , San Diego and by the Ethical Committees of Universidad Peruana Cayetano Heredia and Asociacion Benefica PRISMA , Iquitos , Peru . Written informed consent was obtained from each subject or the parent , in the case of minors . The consent form states in English and Spanish that samples may be used for any scientific purpose involving this or any other project , now or in the future and that the samples may be shared with other researchers . Patients who presented to local health clinics in the Peruvian Amazon region of Iquitos with typical signs and symptoms of malaria were evaluated by light microscopy examination of Giemsa-stained blood smears to have P . vivax parasitemia . After informed consent , 20 ml of blood was drawn into heparinized vaccutainer tubes , placed in a portable incubator maintained between 37 and 39°C , and transported to laboratory facilities within 30 minutes . Blood was centrifuged at 900×g for 5 minutes and plasma was removed . Red cells were resuspended in 2 volumes of suspended animation ( SA ) solution ( 10 mM Tris , 170 mM NaCl , and 10 mM glucose pH 7 . 4 ) and passed through a cellulose column ( CF-11 powder , Whatman Ltd ) placed in a 37°C incubator , to remove white blood cells . Asexual parasites were enriched by gradient purification . Following filtration , cells were washed twice in SA solution at 900 g for 5 minutes and then resuspended in a 1∶3 v/v ratio in SA solution . For the production of gametes/zygotes and ookinetes in vitro , The red cells containing parasites were resuspended in exflagellation medium ( 10 mM Tris , 170 mM NaCl , 10 mM glucose , 25 mM NaCO3 , 10% AB+ human serum , 50 mM xanthurenic acid ) to induce parasite emergence from gametocytes , exflagellation and fertilization . The cell suspension was then layered over a discontinuous gradient of 38% , 42% and 50% Percoll ( Sigma , USA ) in RPMI 1640 medium ( Invitrogen USA ) and centrifuged at 600 g for 15 minutes . Female gametocytes at the 38–42% interface were removed from the gradient . Red cells containing asexual parasites which sediment below the gradient were collected and washed separately in SA solution at 900 g for 10 minutes . Zygotes and ookinetes and untransformed gametes at the 11%–16% interface were collected , washed in PBS and resuspended in Trizol and stored at −70°C . Microscopic analysis of cellular morphology confirmed the enrichment of sexual stages . Small aliquots of purified cells were stained and 60 fields were examined to determine the relative percentage of asexual and gametocyte cells ( Table 1 ) . Red cells from asexual enrichments were subjected to lysis by a 0 . 1% Saponin solution in PBS for 15 minutes at 4°C . Parasites were pelleted at 1200 g for 5 minutes and washed three times in PBS before resuspension in Trizol and storage at −70°C for shipment to the Scripps Research Institute . We designed a Affymetrix custom P . vivax whole-genome tiling microarray with 4 . 2 million 25-bp probes covering both strands at six base pair spacing , based on the genome assembly of 2809 contigs ( PlasmoDB Ver . 5 . 4 ) . This microarray includes 1 . 7 and 2 . 3 million probes uniquely mapped to coding regions and non-coding regions , respectively . Altogether 5419 P . vivax genes are represented on the array , 4676 of which have P . falciparum orthologs . This array will be made available for purchase from Affymetrix , part number PvivaxLi520507 . RNA was purified by Chloroform extraction and isopropanol precipitation and purified by RNeasy Mini Kit ( Qiagen ) following the manufacturer's instructions . RNA was quantified by spectrophotometer and qualitatively analyzed by BioRad Experion RNA StdSens Analysis kit ( BioRad ) . 1 ug of total RNA from asexual blood stage parasites was used to produce cDNA in the Two-cycle cDNA synthesis kit ( Affymetrix ) and amplified to produce labeled cRNA in the IVT Labeling kit ( Affymetrix ) , and purified using the Genechip Sample Cleanup Module ( Affymetrix ) , according to manufacturer's instructions . For the sporozoite sample , 100 ng of total RNA was used to produce cDNA in the Two-cycle cDNA synthesis kit ( Affymetrix ) and amplified to produce labeled cRNA in the IVT T7 MEGAScript kit ( Affymetrix ) . Following the first IVT reaction , the cRNA was split into two reactions , containing approximately 500 ng each for the second round of cDNA synthesis and amplified to produce labeled cRNA in the IVT Labeling kit ( Affymetrix ) , and purified using the Genechip Sample Cleanup Module ( Affymetrix ) , according to manufacturer's instructions . 20 ug of amplified cRNA was hybridized to the P . vivax tiling microarray for 14 hours . The genechips were washed on Affymetrix Wash Station using standard Affymetrix protocol FlexGE-WS450_00001 and scanned on the Affymetrix scanner . The Affymetrix CEL files microarray data are available for download from our companion website ( http://carrier . gnf . org/publications/Pv ) . Gene expression data are also visible in PlasmoDB on the P . vivax gene info pages . For more information on gene expression interpretation from our custom whole genome tiling array please see the website . All gene expression values were subjected to a probe-level two-way ANOVA test to determine variability of expression across all samples [13] . A total of 4 , 326 genes were identified as differentially expressed with pANOVA <0 . 05 and FC >2 ( Table S1 ) and were subjected to further OPI clustering analysis . All data can be downloaded from the companion web site . All Plasmodium gene descriptions and name aliases were downloaded from PlasmoDB ( version 6 . 1 ) . Function annotation data for P . falciparum , P . yoelii , P . vivax , P . berghei , P . chaubaudi and P . knowlesi were obtained from PlasmoDB , then merged with P . falciparum function annotation data from Gene Ontology ( November 2009 release ) . We had previously collected gene co-citation data through Google Scholar and NCBI [10] , this dataset is appended with co-citation data found in “gene notes” section of the PlasmoDB annotation files . We also considered previously published data such as P . falciparum protein complexes [14] , P . falciparum cell cycle k-means clusters [9] , and our own literature-based annotations [10] . For each gene list , we further recruited additional gene members that are homologs or orthologs among Plasmodium species according to the latest OrthoMCL database ( version 3 ) . The final annotation database contains 3 , 732 gene lists that contains at least one P . vivax gene , including 2 , 538 GO groups , 1 , 066 literatures , 84 custom GNF lists , 29 complexes and 15 cell cycle clusters . From these 2 , 002 lists contains at least two differentially expressed P . vivax genes , therefore were used to create clusters of co-regulated genes that share gene ontology annotation using the ontology-based pattern identification ( OPI ) algorithm described previously [15]-[17] . To weight samples correctly , replicate samples were weighted 50% each and effected counted as one sample . When our dataset were merged with Bodzech data , the overall weighting of samples in each data set was adjusted so that two data sets contributed equally in the clustering analysis . Visualization of the OPI expression patterns in Figure 1 used the OPI query pattern data as described previously [17] . Briefly , the query pattern is the best representative expression pattern of a given cluster , and was typically derived from common pattern shared by most of the known GO members of the cluster . Each query profile is normalized by subtracting the mean and divided by the standard deviation , a standard practice used for heat map plotting . The query patterns representing the 192 gene functions were hierarchically clustered so that related biological processes are close to each other . For each of the 192 statistically significant P . vivax OPI clusters , we identified the P . falciparum and P . yoelii orthologs of its cluster members . We then calculated the average pair-wise Pearson correlation coefficient of the P . falciparum orthologs in a previously published cell cycle dataset of 2 , 235 genes . The correlation coefficients are almost all positive with a median at 0 . 45 . We also calculated using a similar approach the average correlation coefficients of the orthologs in a combined P . yoelii and P . falciparum life cycle dataset . All results are available in Table S2 . For each piece of P . vivax gene function prediction , we checked if its P . falciparum or P . yoelii orthologs were also predicted to be in the same function category based on the previous P . yoelii and P . falciparum life cycle dataset . Both previously published OPI clusters [10] and new OPI clusters based on the latest gene annotation database were used in this cross validation analyses . Notice here an orthlog was considered to be associated with a GO group as long as it appeared in the group or one of its descendant groups . Support was found for 89 of the 192 OPI clusters . We first projected the previously published P . falciparum yeast two-hybrid protein interaction database into P . vivax by introducing interactions to all the corresponding P . vivax ortholog pairs using OrthoMCL version 3 . We then constructed a protein network for each OPI cluster and recorded the size of the network . To evaluate the statistical significance of the resultant network , we replaced the cluster members through random sampling and repeated the network construction process 1000 times . The p-value of the network was estimated based on the probability of an equal or more complex network to occur by chance . The whole process was carried out by either only considering direct protein-protein interactions or including indirect protein-protein interactions and whichever led to a better p-value was selected for presentation . An indirect protein-protein interaction refers to two proteins interact via another protein . A total of 63 networks were determined to have p-values <0 . 05 . It was previously shown that genes co-cited in a literature tend to be more correlated in their expression profile compared to gene members of the same GO group , probably due to the careful review process involved in the publication [10] . Therefore , it could be worthwhile introducing virtual protein-protein interactions among genes co-cited . With these virtual interactions appended to the yeast two-hybrid dataset , we repeated the above network evaluation processes for non-literature derived OPI clusters and identified 11 additional networks . Protein networks were visualized using Cytoscape ( Version 6 . 1 ) , where nodes can be color coded according to the level of confidence in their function predictions , and edges can be color coded to reflect the data sources . Statistical enrichment of motifs in upstream regions was determined using GeneSpring 7 . 3 software , using the “search for regulatory sequences” with parameters of 0 to 1000 bases , motif sizes of 5 to 9 bps and allowing for up to two N's in the central region . Only cutoffs with a corrected p-value of less than 0 . 05 are reported except for very small clusters . P . vivax sporozoites were obtained from Sanaria , Inc . from mosquitoes fed on P . vivax infected chimpanzees infected with India VII strain P . vivax [18] . Sporozoites were dissected from mosquito salivary glands and purified . Approximately 150 , 000 sporozoite cells were used for RNA preparation . Similarly purified sporozoites of P . falciparum strain 3D7 isolated from mosquitoes infected with in vitro cultured parasites were also obtained from Sanaria , Inc . P . falciparum salivary gland sporozoites were obtained from Sanaria , Inc . P . falciparum sporozoite RNA was isolated and amplified using Affymetrix kits as described for P . vivax sporozoite samples . P . falciparum 3D7 strain RNA from in vitro synchronized trophozoite stage parasites was isolated and amplified as described for P . vivax samples . Amplified cRNA was hybridized to the Pftiling array described previously [19] . To validate the expression comparison of genes that are differentially expressed in sporozoites of P . vivax and P . falciparum , we performed quantitative reverse transcriptase polymerase chain reaction ( qRT-PCR ) on 22 genes with orthologs in both species , and two P . vivax specific genes . Primers used are listed below . All primer sets were optimized using genomic DNA from 3D7 strain P . falciparum and Salvador I strain P . vivax at three dilutions of 10 ng/ul , 1 ng/ul and 0 . 1 ng/ul to ensure that the amplification threshold values accurately reflected the difference in DNA template concentration . Primer sets for the two different species produced similar threshold Ct values ( what does Ct stands for ) ( +/− 1 . 5 Ct ) for all primer sets for both species . An additional aliquot of 150 , 000 sporozoites for both P . falciparum and P . vivax from Sanaria , Inc , were used to isolate total RNA using Trizol as described previously . This total RNA sample was split into equally into three reactions to produce single stranded cDNA using reverse transcriptase and a T7-Oligo dT primer from the cDNA synthesis kit ( Affymetrix ) according to manufacturer's instructions . The single stranded cDNA was used as template for QRT-PCR reactions using the primer sets presented . To account for variability in the input cDNA between different cDNA reactions from the same species , we normalized the threshold Ct values by the average difference between reactions across all genes . We cannot know the identity of any one gene , which is expressed at the exact same level in both species that can be used to control for variation . However , when comparing P . vivax and P . falciparum threshold Ct values , we found that the highest expressed gene ( CSP ) and lowest expressed gene ( Pv117045 zinc finger ) in both species showed very similar threshold Ct values within 1 . 5 Ct cycles , which was the within the error observed by DNA optimization . Our gene expression data from multiples species , including P . yoelii , as well as proteomic data and conventional experiments show CSP is often one of the most abundant proteins in plasmodium sporozoites . It seems safe to use this in normalization . qRT-PCR reactions were prepared using SYBR GREEN PCR Master Mix ( Applied Biosystems ) according to manufacturer's instructions , and were run on Applied Biosystems TaqMan machine using SDS 2 . 2 . 1 software . Threshold Ct values were determined using default settings and automatic threshold determination . All amplification results were manually inspected to ensure that threshold levels were determined within the logarithmic amplification phase of the reaction for accurate determination of Ct values . Fold difference between P . falciparum and P . vivax qRT-PCR determined expression values are equal to 2 raised to the power of the difference in Ct values between the two species . qRT-PCR results and primers used are listed in Supplemental Table S2 . The list of P . vivax sporozoite-specific genes used to seed the OPI cluster includes: S13 , MAC/Perforin ( PVX_000810 , PFD0430c ) ; SIAP-1 ( PVX_000815 , PFD0425w ) ; pf52 protein ( PVX_001015 , PVX_001020 , PFD0215c ) ; ECP1 , cysteine protease ( PVX_003790 , PFB0325c ) ; asparagine-rich antigen Pfa35-2 ( PVX_081485 , PFA0280w ) ; S24 , hypothetical protein ( PVX_081555 , PFA0205w ) ; TRSP ( PVX_081560 , PFA0200w ) ; TRAP ( PVX_082735 , PF13_0201 ) ; S14 , hypothetical protein ( PVX_084410 , PFL0370w ) ; S25 , kinesin-related protein ( PVX_084580 , PFL0545w ) ; MAEBL ( PVX_092975 , PF11_0486 ) ; S1 , hypothetical protein ( PVX_094625 , PF10_0083 ) ; kinesin-related protein ( PVX_094710 , PFL0545w ) ; conserved hypothetical protein ( PVX_097795 , PFE0230w ) ; hypothetical protein ( PVX_118360 , PF14_0404 ) ; circumsporozoite ( CS ) protein ( PVX_119355 , PFC0210c ) ; early transcribed membrane protein 13 , ETRAMP13 ( PVX_121950 , PF13_0012 ) ; S23 , conserved hypothetical protein ( PVX_123155 , PF08_0088 ) ; S4 , conserved hypothetical protein ( PVX_123510 , PFL0800c ) ; conserved hypothetical protein ( PVX_123750 , PFL1075w ) . To infer gene expression of un-annotated genes in P . vivax , we performed a BLAST search of all P . falciparum and P . knowlesi annotated genes against the P . vivax genome to identify all putative orthologous genes that may not be annotated in P . vivax . The BLAST similarity coordinates were used to define the coding region in P . vivax . We have not validated the coding sequence for proper gene translation nor have we defined intron-exon boundaries for these genes . These gene boundary definitions were used to pick probes to evaluate the level of gene expression from these regions in the same way as all other annotated P . vivax genes . These genes were originally named using the GeneID numbers of their P . falciparum and P . knowlesi orthologs . We have included these gene expression values for these putative genes in Table S1 . We also performed an analysis of all P . vivax RNA microarray hybridization data to identify highly transcribed regions of 50 bp that do not overlap with existing gene annotations . We found a few of these regions , but they appeared to correspond to additional exons , intronic regions , or 5′ or 3′ untranslated regions of existing genes . One additional gene identified by this method is the Pv_PF11_0140 gene . We provide putative P . vivax Gene ID numbers for these genes based on their position relative to existing flanking genes . We provide a list of these new putative gene coordinates in Supplemental Table S3 .
Blood samples were collected from eight different P . vivax-infected patients with uncomplicated malaria in Iquitos , Peru . Human leukocytes were removed by filtration and P . vivax gametocytes were separated from asexual stage parasites by gradient centrifugation . There was only a small proportion of gametocytes ( 0–15% ) in the final sample ( Table 1 ) , so we hereafter refer to these samples as asexual profiles . By microscopy the asexual cells in the patient blood samples appeared to be rings and early trophozoite stages , with no late trophozoite or schizont stages , likely as the result of natural synchronization in the Peruvian patients . For one sample , we put the isolated gametocyte stages into in vitro culture and induced sexual stage development to obtain a mixed gamete/zygote stage sample and a mostly pure ookinete stage sample from the patient isolate ( Table 1 ) . Additionally , we isolated salivary gland sporozoites from dissected mosquitoes fed on an experimentally infected chimpanzee . We assayed gene expression using a custom P . vivax whole genome tiling microarray with 4 . 2 million 25-base pair probes covering both strands at six base pair spacing . A semi-quantitative estimate of transcript abundance for each gene could be obtained with this microarray design because the 5 , 419 P . vivax genes were probed by hundreds of independent oligonucleotides . While this array can be used to find noncoding RNAs , including antisense RNAs , the labeled cRNA for hybridization was prepared using a polyA reverse transcriptase priming method that would not give accurate descriptions of noncoding RNAs and thus we limited our analysis to predicted coding regions . In order to calculate an approximate gene expression level , E , we used the MOID algorithm , which ranks the probe intensity values for the 20 probes at the 3′ end of the transcript having similar GC values . It then assigns the expression value , E , to difference between the background ( computed from thousands of probes with a similar GC content not predicted to be in the P . vivax or human genome ) and the probe at the 70th percentile ( Table S1 ) . Because the varying GC content of different P . vivax genes could give rise to spurious apparent expression levels , extensive optimization of probe selection was undertaken to ensure robust measurements of gene expression across all samples . We show that MOID expression values are not changed by selection of the independent 13th or 14th of 20 ranked probes ( Methods S1 Figure IIIA ) . Additional optimization for different GC contents ( Methods S1 ) shows that we can select probes from a whole-genome tiling array to accurately detect gene expression . To verify reproducibility of our analysis , we analyzed three samples , two asexual and one sporozoite in two technical replicates each , and obtained Pearson correlation coefficients of 0 . 986 to 0 . 996 , indicating excellent reproducibility , whereas lower correlation is observed between samples from divergent asexual groups ( Methods S1 Figure IIIB-D ) . Results were also confirmed by qRT-PCR , and compared to expressed sequence tags ( ESTs ) , as described in Methods S1 . We first sought to address our hypothesis that genes involved in similar processes would be co-expressed . We identified 4 , 326 differentially expressed genes using the cutoff of ANOVA p-value <0 . 05 and fold change ( FC ) >2 using the individual probe intensity values for each gene using just our data . Differentially expressed genes were clustered using ontology-based pattern identification ( OPI ) , an algorithm previously used with P . falciparum and P . yoelii expression datasets [10] ( Figure 1A ) . This algorithm begins with 2 , 002 lists of genes sharing a common gene ontology ( GO ) annotation , literature co-citation , or other annotated parasite-specific process , e . g . , there are 38 genes known to be involved in DNA replication . For each group a representative expression profile vector is computed using E values from all conditions for all genes in the group . Then all of the 4 , 326 differentially expressed genes are ranked by the correlation coefficient calculated between the gene's expression vector and the representative expression profile vector . The algorithm then uses a correlation coefficient optimization routine and creates expression clusters that contain the largest number of genes with common annotation and high correlation . In the case of the GO process “DNA replication” a group of 60 genes is created which contains 11 of 38 annotated DNA replication genes . The probability of this distribution occurring by chance is less than 10−13 . Similarly , there is less than 10−8 probability of identifying 5 of 15 genes with an annotation of glycolysis in a cluster of 21 genes . In addition to using gene ontologies we also used other groupings of genes . In a previous analysis of P . falciparum lifecycle stages [9] we had identified 106 genes upregulated in sporozoites , which corresponds to 66 P . vivax orthologs in our dataset ( GO:CCYCL01 ) . Of these 35 were found in a group of 366 genes with a probability of enrichment by chance of 10−20 . Overall these data showed that patterns of gene expression were nonrandom and that genes with similar functions showed much greater cohesion than would be expected by chance . Altogether , 121 clusters of highly correlated genes with shared annotation were identified ( see companion web site: http://carrier . gnf . org/publications/Pv ) . Previous analysis of P . vivax blood stage gene expression for parasites taken into synchronous short term culture has been performed [11] and we compared our results to these . Because this study involved two color microarrays and did not produce expression levels , direct comparisons between expression levels are not possible . However , we could still apply the same OPI clustering algorithm to the Bozdech data . Clustering of the Bozdech data ( see companion web site ) gave less information about sporozoites and sexual stages but revealed highly significant functional enrichments , especially within the area of protein biosynthesis and ribosome function , which is expected because of the higher sampling throughout the erythrocytic cycle . For example , 48 or the 58 annotated genes with a predicted role in cytosolic ribosome ( GO:0022626 ) were found in a cluster of 126 genes , with a probability of enrichment by chances of 10−63 . The data showed that in many cases the same genes that cluster with “cytosolic ribosome” in the Bozdech data also cluster with “small ribosomal subunit” in our data . The gene , PVX_084645 , co-clusters with ribosomal genes in both cases and is listed as hypothetical but its P . falciparum ortholog , PF14_0360 , is listed as an eukaryotic translation initiation factor 2A protein and thus its association with ribosomes is not surprising . PVX_101135 , a hypothetical , clusters with ribosomal proteins in both cases . BLASTP ( p = 1 . 3×10−35 ) shows a strong match to the yeast protein YOR091W , a protein of unknown function that associates with ribosomes that interacts with GTPase Rbg1p [20] . We also co-clustered Bozdech data with our data to generate more accurate predictions of gene function creating a set of 192 different clusters containing between 2 and 493 genes and p-values between 10−3 and 10−52 ( Figure 1 , Table S2 ) . Many of our functional predictions can be cross-validated with previously published data sets . In particular we checked if the same function prediction can be made based on combined P . falciparum and P . yoelii data set , using either previously published OPI clusters [10] or an updated cluster set using the latest gene annotations . For each P . vivax cluster we ran permutation tests to see their P . falciparum orthologs form denser protein networks than what would be expected by chance using both published two hybrid data [21] and literature co-citation data [10] . In total , 75 of the 192 OPI clusters led to protein networks with a p-value less than 0 . 05 based on 1000 permutation simulations . For example , PVX_123920 , a putative ubiquitin-activating enzyme e1 clusters with genes involved in the proteosome regulatory particle in both P . vivax and in P . falciparum and has two-hybrid support as well [21] . While there are numerous examples that can be derived from well-studied processes , the greatest value of this data is in supporting predictions for genes that may not be found in other model organisms . PVX_092415 and PVX_113830 cluster with genes involved in merozoite development in P . falciparum and in P . vivax ( GO:GNF0218 ) and furthermore , are supported by two-hybrid interaction studies from P . falciparum ( Figure 2 ) . Likewise , PVX_000945 shows a similar pattern . The Toxoplasma gondii homolog of this protein has been isolated from rhoptries [22] as has , the Toxoplasma ortholog of PVX_113800 , which also clusters with genes involved in merozoite development in P . falciparum . There are numerous examples from pre-erythrocytic stages as well . Of course , some caution must be used in evaluating the data because genes involved in two different processes may be co-expressed ( e . g . DNA replication is occurring during gamete production ) and yet be involved in relatively different processes . Nevertheless this clustering exercise gives functional predictions for the many uncharacterized genes found in an OPI cluster . Having established the overall quality of the data we examined the groups of genes that were differentially expressed in any one stage . While our initial expectation was that patient-derived blood samples would be relatively homogenous , unexpectedly , we observed large differences in the expression profiles of asexual samples with the most pronounced differences observed in genes involved in glycolysis . While we found little or no differential expression of the first three enzymes of the pathway: hexokinase ( PVX_114315 ) , glucose-6-phosphate isomerase ( PVX_084735 ) and 6-phophofructokinase ( PVX_099200 ) , the eight remaining glycolytic enzymes: fructose 1 , 6-bisphosphate aldolase ( PVX_118255 ) , triosephosphate isomerase ( PVX_118495 ) , glyceraldehyde-3-phosphate dehydrogenase ( PVX_117321 ) , phosphoglycerate kinase ( PVX_099535 ) , phosphoglycerate mutase ( PVX_091640 ) , enolase ( PVX_095015 ) , lactate dehydrogenase ( PVX_116630 ) and pyruvate kinase ( PVX_114445 ) all showed very strong differential expression between samples with some genes showing up to a 100-fold higher expression in some asexual samples relative to others ( Figure 3 ) . These last eight enzymes are among the top 1% when ranked by transcript abundance in vitro trophozoites of P . falciparum and are among the top 5% of P . vivax genes in some samples but not others ( Figure 3 ) . These differences are unlikely to occur by chance ( p = 1 . 9×10−7 comparing P . vivax group 1 and group 2 defined in Figure 3 with a paired t-test ) . While the high glycolysis samples were the minority ( two of eight ) , they were more similar to that described in Cui et al . Here 22 , 236 ESTs from P . vivax-infected Thai blood samples [12] were sequenced . Our data showed good Spearman rank correlation with the Thai EST numbers and showed that those samples with high glycolysis gene E values were most similar ( r = 0 . 53 ) ( Table 1 and Methods S1 ) to the Thai strain . While it may be that these expression differences are due to contamination with gametocytes [23] the presence of 0–10% contaminating gametocytes cannot mathematically explain why lactate dehydrogenase is present at ∼3 , 300 units in two asexual samples and as low as 75 ( almost indistinguishable from background ) in others [24] . Furthermore , genes typically associated with gametocytogenesis ( e . g . Pvs25 , PVX_111175 ) are higher in the high glycolysis samples ( CM12 and CM13 ) . An alternative is that although morphologies looked similar , a substantially different proportion of early and late cell cycle stages were contained in high and low glycolysis samples . Genes typically associated with schizogony and invasion such as myosin motor proteins and reticulocyte binding proteins , transcribed later in the Bozdech erythrocytic cycle data , were expressed at higher levels in the low glycolysis samples ( Figure 1 ) . Despite this , glycolysis transcripts generally do not show 50–100 fold changes in expression levels throughout the in vitro erythrocytic cycle in P . falciparum cultured in vitro [8] , [9] nor are such large fold changes observed with P . vivax cultured in vitro [11] . In addition , such a hypothesis would require the patient samples to have been tightly synchronized . While more samples will be need to be examined , the data raise an intriguing possibility that there may be differential regulation of metabolism in a subset of patient-derived samples as observed in P . falciparum patient–derived samples ( Figure 3 ) [24] . The molecular determinants regulating hypnozoite formation and relapse are unknown . One hypothesis is that the hypnozoite is an early stage exo-erythrocytic form ( EEF ) that is arrested in development . If this hypothesis is correct , we would expect the transcriptional profile of P . vivax sporozoites to be similar to species that do not form hypnozoites . Thus , we compared our P . vivax sporozoite expression profile to a P . falciparum sporozoite sample and three P . yoelii sporozoite samples analyzed previously [25] , [26] . Sporozoite transcriptome comparisons showed that P . vivax sporozoites are generally similar to P . yoelii and P . falciparum sporozoites with positively correlated expression of highly expressed genes ( r = 0 . 5 ) . Despite some species-specific differences , we observed similar expression for many P . vivax genes whose P . falciparum and P . yoelii orthologs were previously shown to be upregulated in sporozoites , including some of the most highly expressed genes in the P . vivax sample ( Table 2 ) : In addition to genes known to be upregulated in sporozoites such as the initiation factor UIS1 , and the serine threonine phosphatase UIS2 , the list contains a number of known candidates for pre-erythrocytic subunit vaccines including the apical membrane antigen 1 ( AMA1 ) gene and the circumsporozoite protein ( CSP ) . In addition there are genes whose disruption has led to genetically attenuated sporozoites that may eventually be used in whole organism vaccines , including the ortholog of P . falciparum etramp10 . 3 or UIS4 in P . yoelii [27] , [28] , UIS3 [29] and P52 [30] . From the set of genes upregulated in multiple species ( Table S4 ) we identified a set of sporozoite conserved orthologous transcripts ( SCOT ) that were upregulated in multiple species but not yet annotated that may provide a list of possible candidates antigens for P . vivax pre-erythrocytic vaccines , or which when disrupted may yield genetically attenuated sporozoites . There are also , of course , genes which are not found in P . falciparum that are upregulated in P . vivax sporozoites and may play some unique role in the biology of P . vivax or other hypnozoite-forming parasites . Examples from the sporozoite specific combined OPI cluster ( GO:GNF0006 ) include up to 63 genes without P . falciparum orthologs . There are also some genes previously shown to be upregulated in P . falciparum sporozoites , but whose orthologs were downregulated or barely increased in P . vivax sporozoites or vice versa ( Table S4 ) . One ApiAP2 gene ( PVX_090110 ) and two zinc finger proteins ( PVX_099045 , PVX_099045 , PVX_081725 ) , which often function as transcription factors , were downregulated as were several RNA binding proteins ( PVX_098995 , PVX_098995 , PVX_100715 ) and several kinases including two serine/threonine protein kinases ( PVX_081395 , PVX_081395 , PVX_002805 ) , a FIKK calcium-dependent protein kinase ( PVX_091755 ) and a MAP kinase ( PVX_084965 ) . Although some of these differences could be due to strain specific or sporozoite collection variation we were able to confirm these species-specific differences in gene expression for 19 genes using qRT-PCR ( Table S5 ) . The process of sexual development and meiosis are poorly characterized in many species . An expression profiles from the mix of macrogametes and zygotes ( Table 1 ) derived from in vitro cultivation of fertilized patient-derived gametocytes showed upregulation of only a few characterized genes include those with roles in DNA replication , meiosis and chromatin structure ( Table 3 ) . The majority of strongly differentially expressed genes are uncharacterized , although many had been shown to be upregulated during induction of in vitro gametocytogenesis in P . falciparum[15] . As predicted , high expression of the P . vivax ortholog of Pvs25 ( PVX_111175 ) , the ookinete surface protein , is found in this stage . Another membrane protein gene , the ortholog of the Toxoplasma gondii PhIL1 ( photosensitized INA-labeled protein 1 ) ( PVX_081335 ) , was among the most highly upregulated ( >20-fold ) in P . vivax gametes/zygotes . This protein associates with the cytoskeleton and is localized to the apical end of the plasma membrane [31] where it may function as an ookinete-specific surface protein for midgut invasion . The transcription factor high mobility group protein , HMGB2 ( PVX_089520 ) is found among the top 20 genes expressed in this sample ( See Table S1 ) . It was shown to be a critical regulator of oocyst development and appears to activate genes that are most highly transcribed in gametocytes , but then stored and translated in ookinetes [32] . Interestingly , some of these uncharacterized proteins are also upregulated during meiosis in humans , such as the human orthologs of PVX_117890 [33] . An exceptionally large proportion of the genes upregulated in zygotes ( Table 3 ) did not fall into clusters with enrichments of known genes , highlighting the problem of using “guilt by association” when the existing knowledge base is sparse . Gene ontology-based OPI clustering revealed few annotated gene functions to be upregulated in ookinetes . This finding likely reflects the difficulty of obtaining sufficient quantities of this developmental stage for in vitro study . We found high expression of genes involved in chromatin ( e . g . histones ) and translation , potentially reflecting the fact that once the parasite reaches the midgut and forms an oocyst , thousands of rounds of DNA replication will likely commence . Genes specifically upregulated in oocysts were mostly uncharacterized . Ookinete surface protein genes such as the P . vivax ortholog of the CSP and TRAP-related protein ( PVX_095475 ) , previously shown to be essential for ookinete invasion [34] , [35] , the transmission-blocking target antigen Pfs230 ( PVX_003905 ) , a sexual stage antigen s48/45 domain containing protein ( PVX_003900 ) , the transmission blocking target antigen precursor Pfs48/45 ( PVX_083235 ) , showed modest levels of upregulation ( <3-fold ) in ookinetes relative to any other stage . In P . falciparum , transcripts for many of these peak in early stage gametocytogenesis [15] , and thus high expression would not necessarily be expected in P . vivax ookinetes , even if protein is detected in present . On the other hand there were a number of uncharacterized genes that showed substantial upregulation in ookinetes ( >3-fold ) . These may encode the proteins that are needed for early oocyst formation . Some examples ( see Table S1 for details ) include a possible calcium dependent kinase , ( PVX_083525 ) , and 5-aminolevulinic acid synthase ( PVX_101195 ) , a key enzyme in the porphyrin synthesis pathway that leads to heme synthesis . Proteases and other degradative enzymes may be needed to exit the blood meal , penetrate the peritrophic matrix or form the oocyst on the mosquito midgut . A number of proteases were unregulated in ookinetes , including Ulp1 ( ubiquitin-like protein-specific protease , PVX_100650 ) , which is specifically required for cell cycle progression in other species . While in many cases drug resistance in malaria parasites is conferred by single nucleotide polymorphisms [36]–[38] , in some cases transcript amplification events in a target or in a pump [39] confer greater tolerance to drugs . We therefore compared levels of drug resistance gene transcripts to determine if anything interesting might be found . Most genes involved in drug resistance such as pvcrt ( chloroquine resistance transporter , PVX_087980 ) , dhps ( dihydropteroate synthase , PVX_123230 ) , gtpch ( GTP cyclohydrolase , PVX_123830 ) or mdr1 ( multidrug resistance gene 1 , PVX_080100 ) showed similar patterns in P . vivax and P . falciparum . An exception was dihydrofolate reductase ( dhfr , PVX_089950 ) , the target of the widely used antimalarial antifolate drug , pyrimethamine . Remarkably , dhfr expression was 50-fold higher in the India VII P . vivax sporozoites ( confirmed by qRT-PCR , see Table S6 ) and 10-fold to 30-fold higher in P . vivax asexual stages compared to P . falciparum , where it was near background in all stages . Interestingly , while P . falciparum is sensitive to pyrimethamine , P . vivax is considered intrinsically resistant [40] , although in vitro drug sensitivity data is not available for most P . vivax strains . Both dhfr mutations [41] , [42] and amplifications [43] have been shown to confer resistance in P . falciparum . Thus , the higher dhfr expression in P . vivax relative to P . falciparum may confer higher tolerance to pyrimethamine . While much of our data shows that patterns of gene expression are conserved across Plasmodium species the data also reveals possible roles for information about the many uncharacterized genes which are specific to P . vivax including members of multigene families . The largest paralogous gene family of P . vivax is the diverse superfamily of variant surface protein genes ( vir ) found in subtelomeric regions [44] and may be related to the rif genes of P . falciparum and yir genes of P . yoelii [45] . The highly variable surface protein var gene family in P . falciparum has no orthologs in P . vivax . Expression levels for most of the 274 vir genes on the array were lower than for other genes . The average for vir genes was 210 units ( approximately the 35th percentile ) using the highest value in any one stage ( MaxExp in Supplemental Table 1 ) , versus 572 units ( approximately the 80th percentile ) on average for other genes . In addition 39 of the 109 genes in the genome , which were not detected as differentially expressed in any sample , were vir genes . Only 27 of the 274 probed vir genes showed strong differential regulation ( >5-fold change , pANOVA<0 . 001 ) versus 1 , 044 of the remaining 5 , 420 genes . Interestingly , four of the highest expressed vir genes ( PVX_086860 , PVX_096970 , PVX_096980 , PVX_096985 ) were among the highest expressed genes in the two high glycolysis asexual samples , indicating possible alternate mechanisms of expression control . Three of these highly expressed vir genes are adjacent to one another suggesting coordinated regulation of the entire region . Cui et al . also found these three vir genes and another gene in the same region ( PVX_096975 ) had the highest numbers of ESTs in their patient samples [12] . Malaria parasites are known to be able to decorate the surface of infected erythrocytes with proteins that play roles in sequestration and immune evasion . Many of the exported proteins contain sequences , called PEXEL or VPS motifs that direct them out of the parasitophorous vacuole and to the surface [46] , [47] and many are members of multigene families , presumably because enhanced levels of recombination between members or epigenetic transcriptional switching between members allows the parasite to evade the host immune responses to these exposed proteins . Many of the exported genes in P . falciparum are transcribed at specific , mid-trophozoite stages of the parasite cell cycle [47] . The number of predicted exported proteins in P . vivax is not as large as in P . falciparum most likely due to problems in recognizing PEXEL/VPS motifs in this species . For example , only 160 of the 346 vir proteins contain predicted export motifs [48] . In addition to vir genes the 20 members of the P . vivax PHIST ( Plasmodia helical interspersed subtelomeric ) exported gene family ( Pv-fam-b ) [49] on the array are expected to be exported as well as some members of the Pv-fam-h and Pv-fam-e families . Remarkably many of the members of the multigene families as well as most predicted exported proteins that show strong differential transcription show peak expression in just one of our blood stage samples , CM115 ( Figure 4 ) . Five of the eight exported PHIST genes , six of the ten of Pv-fam-e family of RAD GTPases ( some exported ) , nine of the 11 , Pv-fam-d , and 9 or the 11 Pv-fam-a genes that show strong ( >5X ) differential expression again peak mostly in CM115 . While vir genes show lower levels of expression overall , many of those that are differentially expressed also peak in CM115 . Notably , 17 of the 38 expressed above 300 units show peak transcript levels in sample CM115 . Many of the genes showing dramatic upregulation ( up to 100 fold ) in sample CM115 are abundantly transcribed . The Pv-fam-d family with 16 genes of unknown function has 2 genes in the top 1% of all genes ranked by maximum expression as does the Pv-fam-a family of tryptophan rich antigens ( PvTRAg , average max expression in any one sample = 1 , 969 units ) . One P . vivax tryptophan-rich antigen , PvTRAg ( PVX_090265 ) , has shown a very high seropositivity rate for the presence of antibodies in P . vivax malaria patients [50] . Another highly immunogenic antigen in this multigene family is PvATRAg74 ( PVX_101510 ) , recombinant versions of which showed erythrocyte binding activity and were recognized by all P . vivax patient sera tested [51] . This gene also ranks in the top 1% of transcripts in CM115 . Many of the exported genes in P . falciparum are transcribed at specific , mid-trophozoite stages of the parasite cell cycle [47] and it seems likely that most of the parasites in asexual sample CM115 are at this export permissive stage . Thus many of the genes that are specifically upregulated in CM115 may play a role in immune evasion . While genes involved in DNA replication are also upregulated in CM115 , these same genes are also upregulated in zygotes , while those encoding exported proteins may not be . These data nicely illustrate how the random collections of gene expression data that may be obtained from a neglected parasite can be used to create high quality predictions if enough random and yet diverse data is available . The data also illustrate that an advantage of using P . vivax patient samples is that a high level of synchrony may exist , that is confirmed by the Bozdech data ( Figure 4 ) . Of course members of multigene families that share sequence similarity may have different cellular roles depending on their expression in the parasite lifecycle . An example is the group of cysteine protease genes referred to as serine repeat antigens ( SERAs ) . In P . falciparum , PFB0325c , one of the seven SERA genes is expressed in sporozoites , while the others are expressed in blood stages . Disruption of this SERA ortholog , called ECP1 in P . berghei , results in parasites that are unable to migrate out of the oocyst[52] . Although P . vivax contains 13 SERA cysteine protease paralogs , only the ECP1 ortholog ( PVX_003790 ) was dramatically upregulated in sporozoites ( 105X ) . Two others showed substantial upregulation in sample CM12 . None of the SERAs show maximal expression in sample CM115 , nor do any members of the Pv-fam-c , homologous to the SURFIN gene family in P . falciparum . These are not appreciably expressed in any of our samples and may be functioning in other life stages . In many organisms , including Plasmodium species , genes that are co-expressed share common sequence motifs in the regions upstream of their translational start sites [16] . To determine whether this would be true in P . vivax we looked for enriched motifs in various OPI groupings created with our data or the combined larger dataset . Here all possible 6–9 mers are counted within a group of co-expressed genes and these numbers are compared to the number found in all upstream regions . A corrected p-value that accounts for the multiple testing hypothesis is then computed . In general we find that transcriptional control is conserved and that many of the same motifs , which appear to control gene expression in P . falciparum also appear to control gene expression in P . vivax . For example an unbiased search for overrepresented sequences upstream of the 70 sporozoite-specific genes with promoter sequences ( GO:GNF0006 ) relative to upstream regions in the whole genome found a motif similar to one associated with P . falciparum sporozoite genes . This putative regulatory sequence , TGCATG , is found upstream of 44 of the 70 P . vivax sporozoite-specific OPI cluster genes . The corrected probability of enrichment by chance in this set relative to the rest of the upstream regions in the genome is 0 . 02 . Additionally , 48 of 65 genes in the list of SCOT genes contained the TGCATG motif ( Table S6 , p = 9 . 16×10−7 ) . This is similar to the PfM24 . 1 sporozoite-specific regulatory motif CATGCAG identified in P . falciparum [16] , sharing the core CATGC sequence and identical to the sequence ( Table S6 ) bound by the P . falciparum ApiAP2 transcription factor PF14_0633 [53] . The P . yoelii ortholog of this transcription factor ( PY00247 ) is among the highest expressed genes in midgut sporozoites [10] , supporting the hypothesis that this protein functions as a specific activator of sporozoite transcription [25] . A rich variety of other promoter motifs could be found by searching the various other OPI clusters as well . The sequence TGTAnnTACA was found enriched in the 1000 bases upstream of 383 genes from an OPI cluster containing 12 of the 19 genes mentioned in a an analysis of P . falciparum sexual development ( GO:PM16005087 , 92/364 genes , p = 1 . 69×10−26 ) . This motif ( Figure 5 ) is identical to the motif that is found in 65 of the 246 genes upregulated during sexual development in P . falciparum [15] . A cluster of genes enriched for ones with a role in gliding motility gave the motif TGTnnACA ( 86 or 155 genes , p = 0 . 00224 ) . A search of a cluster with many genes predicted to be involved in merozoite development ( GO:GNF0218 ) produced the motif GTGCA in 392 of 443 genes with a probability of enrichment by chance of 4 . 31×10−13 . Protein binding microarrays have shown that the P . falciparum AP2 transcription factor , PFF0200c binds this motif [53] . It is also found upstream of P . falciparum genes transcribed during schizogony [16] . As with P . falciparum [54] the sequence CACAC was enriched in a cluster of genes containing an abundance of DNA replication genes ( GO:0030894 , 192 of 228 promoter regions , p = 4 . 7×10−6 ) . Novel motifs were also found . A cluster enriched for genes identified in a male gametes ( GO:PM16115694 [55] ) yielded the motif CGTACA in 35 of 66 genes ( p = 0 . 0645 ) and the sequence GCTATGC was found upstream of 36 of the 105 genes with promoter sequences in the cluster containing many of the structural constituents of the ribosome ( GO:0003735 , p = 0 . 007 ) . This motif is similar to binding site for the AP2-O transcription factor ( TAGCTA ) that functions as a positive regulator of ookinete gene expression [56] . While these motifs may not be the same , expression of ribosomal proteins is upregulated in ookinete stages and one must keep in mind that transcription factor binding is likely to be combinatorial and a given gene may have multiple regulatory sites contained within its upstream region .
Analysis of the transcriptome of blood stages and sporozoites of P . vivax shows that the general mechanisms of growth , development , metabolism , and host-parasite interactions are shared by all Plasmodium species . Some of the expression differences that exist between species may provide insight into the molecular and genetic basis of biological differences that distinguish the species . The most significant differences include the formation of hypnozoites by P . vivax , pathogenic processes of sequestration and antigenic variation , and the wider geographical distribution of P . vivax into temperate regions . However , examination of a much larger number of different strains will probably be need to determine which differences of the differences we find are likely to be due to speciation and which are due to strain or experiment variability . Because of the difficulties of working with P . vivax , transcriptional and proteomic studies represent one of the most effective ways to find candidate genes for vaccines or other processes . Many of the genes that cluster with proven antigens for pre-erythrocytic vaccines such as CSP may be worth investigating especially if sera drawn from individuals living in P . vivax endemic regions shows cross reactivity to their cognate protein [57] . A high-throughput analysis P . falciparum proteomic data [58] revealed one exceptionally abundant sporozoite protein named Ag2 in P . falciparum [59] or CelTos in P . berghei [60] , which was more immunogenic than previously identified antigens such as CSP . In P . falciparum this is the third most abundant transcript in sporozoites . Ag2 one of the few antigens that is able to provide cross-species immunity and indeed we also find it highly expressed in our sporozoite sample . Given that many of the SCOT genes show strong expression only in sporozoite stages , their disruption may lead to genetically attenuated sporozoites that cannot develop in the liver , but which nevertheless provide immunity . The gamete/zygote and ookinete expression data provides insight into mosquito midgut biology of a second human-infecting malaria parasite , and confirms common stage-specific gene expression shared by multiple Plasmodium species . Processes of sexual development , meiosis and DNA replication were evident in the gamete/zygote transcriptome . Some ookinete surface protein genes show highest expression in the gamete/zygote stage . Ookinetes produce stage-specific surface proteins , secreted proteases and invasion-related gliding motility proteins . While we observe upregulation of many cell-surface genes involved in invasion in our gametes/zygotes and ookinetes relative to asexual stages , this conclusion should be considered in light of the fact that our asexual stage gene expression represented ring and trophozoite stages , but no schizont and merozoite stage gene expression , thus biasing the analysis against the invasive blood stage parasites . The function of the vir gene family is still unknown , but published data seems to indicate its function is fundamentally different from var genes . While P . falciparum var genes display mutually exclusive expression only during the mature stages [61] , different vir [11] , yir [62] and rif [63] genes are expressed in different intra-erythrocytic stages and gametocytes [64] . Previous studies showed that vir expression is not clonal , and multiple vir subfamilies are expressed in individual parasites from infected patients [65] . This is supported by our analysis of vir expression in vivo , with high-level expression of a small subset of vir genes in the high glycolysis samples along with other genes such as the PvTRAG genes that are likely involved in antigenic variation and immune evasion . However , examination of our data will also show that expression levels for many vir genes was very low or not detectable , indicated that they may be silenced . It is formally possibly that none of the parasites that we collected were at a stage where vir genes are being actively transcribed , potentially because of sequestration . The overall low levels could also be attributed to having mixed stage parasites in our samples , however many other cell-cycle regulated genes such as histones showed strong ( >50 ) fold differential expression in some of our blood stage samples . Finally , vir gene genetic differences between the genome reference strain , Salvador I , and the Peruvian samples [66] could contribute to reduced expression levels , although high expression was certainly found for other members of variable gene families . Excepting these caveats , our data are consistent with a model in which some vir genes are transcriptionally-silenced . The OPI analysis here provides functional predictions for a large numbers of genes . However , its limitation is that it uses existing knowledge and it is likely that interesting clusters of genes may be of mixed function , which can be estimated by examining the false positive and true positive rates in each cluster in Supplemental Table 2 . In some cases the functional enrichment may be misleading and thus caution should be used in interpreting the labels . Many of the exported protein and blood stage antigens are found in an OPI cluster named “ribonuclease activity” reflecting the fact that this is the only well-annotated cellular process going on at this time . Finally , 501 of the differentially expressed genes were not contained in any of the OPI clusters . This may be because they are playing a role as ookinete , oocyst or hypnozoite function . Traditional hierarchical or k-means clustering is likely the best way to find out the function of these genes ( see companion web site ) . Finally , 296 were not considered differentially expressed . Some of these may be upregulated in liver or oocyst sporozoite stages or they may be silenced . One of the more remarkable things about the gene expression analysis is its robustness and insensitivity to sample contamination or admixture . Our group contained few samples focused on early sexual stage parasites and only a single zygote sample and yet we were able to extract groups of genes from which we could extract a sexual development transcription factor motif . Of course , the fact that we had no oocyst salivary gland sporozoite or liver stages means that this dataset is not comprehensive and more work will need to be done . While P . vivax is by no means a model organisms the gene expression data described here may be more useful for discovering motifs involved in regulating transcription as the lower AT content may be less problematic . It should also be noted that successes were not dependent on having the larger structured Bozdech dataset as many of the motifs could be extracted from the OPI clusters created with our data alone . A group of genes enriched for those with roles in sexual development ( GO:PM16005087 ) could be extracted from our data when clustered independently ( 7 of the 19 genes in a group of 172 , p = 10−5 ) and could be used in motif finding . However no groups of genes enriched for ones with roles in sexual development were found when the Bozdech data was analyzed independently ( See Supplemental Table 2 ) . However , when the datasets were combined , the quality of the cluster improved ( 12 of the 19 genes in a group of 246 , p = 10−8 ) even though the Bozdech samples were not predicted to contain sexual stage parasites . Thus , more data and diversity is better . These gene expression data for in vivo patient infections , gametes/zygotes , ookinetes and sporozoites of the P . vivax parasite provide an important foundation and reference for future studies . Numerous differences in gene expression suggest many hypotheses to be tested by researchers in the laboratory and in the field , and may be used to guide drug treatment and vaccine development for P . vivax . Further studies may provide correlations between in vivo parasite gene expression variability between patient samples and phenotypes of disease severity and drug resistance . In combination with accurate drug treatment outcomes and patient data , we will begin to identify the key determinants of the host-parasite interactions in this important pathogen . | Most of the 250 million malaria cases outside of Africa are caused by the parasite Plasmodium vivax . Although drugs can be used to treat P . vivax malaria , drug resistance is spreading and there is no available vaccine . Because this species cannot be readily grown in the laboratory there are added challenges to understanding the function of the many hypothetical genes in the genome . We isolated transcriptional messages from parasites growing in human blood and in mosquitoes , labeled the messages and measured how their levels for different parasite growth conditions . The data for 5 , 419 parasite genes shows extensive changes as the parasite moves between human and mosquito and reveals highly expressed genes whose proteins might represent new therapeutic targets for experimental vaccines . We discover sets of genes that are likely to play a role in the earliest stages of hepatocyte infection . We find intriguing differences in the expression patterns of different blood stage parasites that may be related to host-response status . | [
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"b... | 2010 | A Systems-Based Analysis of Plasmodium vivax Lifecycle Transcription from Human to Mosquito |
Human influenza viruses replicate almost exclusively in the respiratory tract , yet infected individuals may also develop gastrointestinal symptoms , such as vomiting and diarrhea . However , the molecular mechanisms remain incompletely defined . Using an influenza mouse model , we found that influenza pulmonary infection can significantly alter the intestinal microbiota profile through a mechanism dependent on type I interferons ( IFN-Is ) . Notably , influenza-induced IFN-Is produced in the lungs promote the depletion of obligate anaerobic bacteria and the enrichment of Proteobacteria in the gut , leading to a “dysbiotic” microenvironment . Additionally , we provide evidence that IFN-Is induced in the lungs during influenza pulmonary infection inhibit the antimicrobial and inflammatory responses in the gut during Salmonella-induced colitis , further enhancing Salmonella intestinal colonization and systemic dissemination . Thus , our studies demonstrate a systemic role for IFN-Is in regulating the host immune response in the gut during Salmonella-induced colitis and in altering the intestinal microbial balance after influenza infection .
Influenza is a highly contagious viral infection that has a substantial impact on global health . Notably , outbreaks of influenza infection are usually associated with an increased incidence or severity of secondary bacterial infections responsible for high levels of morbidity during seasonal influenza episodes . We and others have previously shown that IFN-Is play a critical role in the development of secondary bacterial pneumonia after influenza infection [1] . Since their discovery in 1957 , IFN-Is have been recognized as the central antiviral cytokines in vertebrates [2] . The type I IFN family mainly consists of numerous subtypes of IFNα and a single IFNβ , whose induction appears to be ubiquitous in most cell types . Toll-like receptor ( TLR ) -mediated IFN-I induction plays a key role in facilitating antiviral responses [3] . IFN-Is bind to a common heterodimeric receptor , IFN-α/β receptor ( IFNAR ) , composed of two subunits , IFNAR1 and IFNAR2 . Binding activates the JAK/STAT pathway , which induces pro-inflammatory genes that inhibit viral replication and boost adaptive immunity [4] , and regulates the transcription of multiple interferon-stimulated genes ( ISGs ) [5] . A recent study has shown that influenza infection can alter the composition of the intestinal flora , resulting in immunological dysregulation that may promote inflammatory gut disorders [6] . The mammalian gut harbors a complex microbiota that plays a key role in host health through its contribution to nutritional , immunological , and physiological functions . Intestinal commensals are required for maintaining gut homeostasis through dynamic interactions with the host’s immune system [7] . Resident microbiota promote gut immune homeostasis by regulating T regulatory cells ( Tregs ) [8] and T helper 17 cells ( Th17 ) [9] . The gut microbiota can inhibit infection through direct microbial antagonism or by stimulating several host effectors and injury responses [10 , 11] . However , the intestinal commensals also pose an enormous challenge to the host that needs to remain “ignorant” to a selection of microbial antigens and keep the bacterial load anatomically contained , while remaining responsive to its dissemination [12] . Reciprocal interactions between gut microbiota and the host immune system shape the microbial community and influence imbalances that can lead to disease [13] . These changes are often characterized by a reduction of obligate anaerobic bacteria , and a proliferation of facultative anaerobic Enterobacteriaceae [14] . Furthermore , some people with pulmonary influenza infections also experience symptoms of gastrointestinal disorders , especially children [15] . Influenza RNA is rarely recovered from their stool [15] , so it is unclear whether the symptoms develop from swallowed respiratory secretions or from active infection of the gastrointestinal tract . In order to investigate the role of IFN-Is induced during influenza infection in modulating the endogenous intestinal microbiota , we established a model of influenza pulmonary infection using genetically modified animals with defective IFNAR signaling ( Ifnar1–/–mice ) . Remarkably , we found that influenza infection alters the intestinal microbial community supporting gut Proteobacteria pathobionts through a mechanism dependent on IFN-Is . While the importance of IFN-Is in antiviral defense is well established , their role during bacterial infection is more ambiguous . Moreover , we wanted to test whether primary influenza infection can predispose the host to secondary intestinal bacterial infections . We therefore developed a model of sequential influenza pulmonary infection followed by secondary Salmonella-induced colitis using Ifnar1–/–mice to investigate the effects of IFN-Is induced during influenza infection on intestinal host defense against Salmonella . Interestingly , we found that lung induced IFN-Is enhanced the growth of Salmonella in the inflamed gut and increased its systemic dissemination to secondary sites . Furthermore , we found that influenza pulmonary infection resulted in a profound inhibitory effect on the intestinal antibacterial and inflammatory responses against Salmonella infection in a IFN-I dependent manner .
Previously , it was shown that influenza infection causes intestinal injury through microbiota-dependent inflammation [6] . Considering that IFN-Is are essential components of the host antiviral response , we hypothesized that these molecules might also mediate changes in the intestinal microbiota during viral influenza infection . To study this , we infected wild-type ( WT ) and Ifnar1 knockout ( Ifnar1-/- ) mice by non-surgical intratracheal instillation [16 , 17] with a sublethal dose ( 200 infectious units ) of influenza A/Puerto Rico/8/34 ( PR8 ) . Mice were monitored daily for 17 days after infection . We assessed the microbiota composition in the fecal content of WT and Ifnar1-/- mice before PR8 or mock infection and at 9 day post infection ( dpi ) ( Fig 1A ) since the peak weight loss was observed at 9 dpi in WT and Ifnar1−/− mice . PR8 viral load was quantified after non-surgical intratracheal instillation at 1 dpi , and we detected live virus only in the lungs , neither in the colon content nor in the cecum tissue ( S1A and S1B Fig ) . MiSeq Illumina analysis of microbial DNA extracted from fecal samples confirmed observations reported by others [18] that the mouse intestinal microbiota , independent of the genotype , consists of two major bacterial phyla , the Bacteroidetes and the Firmicutes ( Fig 1B ) , with the most relevant classes being Bacteroidia and Clostridia ( Fig 1C ) . No statistical differences were found in the fecal microbiota composition between WT and Ifnar1-/- mice , either before infection at day 0 or after mock infection at day 9 . Moreover , we observed low abundance of Proteobacteria in the intestinal microbiota of the uninfected and mock-infected mice , previously reported by others [19] , independent of the mouse genotype ( Fig 1B ) . Furthermore , at day 9 post PR8 infection , Bacteroidetes and Firmicutes were still the most dominant colonizers in both mouse genotypes ( Fig 1B ) . Our findings , however , uncovered a significant blooming of Proteobacteria at day 9 after PR8 infection only in the WT mice , whereas no significant increase was noted in the Ifnar1-/- mice , irrespective of the infection ( Fig 1B ) . Indeed , while Proteobacteria represented 1% on average in uninfected and mock-infected mice , regardless of the genotype , they comprised approximately 15% of the total fecal microbiota in the PR8-infected WT mice ( p = 0 . 0340 One-Way ANOVA after Bonferroni correction ) ( Fig 1B ) . The most striking change in the fecal microbial community of WT mice after PR8 infection was the increased abundance of the genus Escherichia , being however mostly undetectable in uninfected and mock-infected mice of both genotypes ( p = 0 . 0011 One-Way ANOVA after Bonferroni correction ) ( Fig 1C ) . Overall , greater Proteobacteria colonization levels after influenza infection in WT mice were not caused by differences in Proteobacteria abundance between WT and Ifnar1-/- mice prior to PR8 infection . Moreover , the thriving of Proteobacteria after PR8 infection in the WT but not Ifnar1-/- mice supports our hypothesis that influenza virus is able to alter the intestinal microbiota , and that this action is dependent on IFN-Is . In addition , using 16S quantitative PCR ( qPCR ) analysis we confirmed a significant increase in Enterobacteriaceae in the stool samples of the PR8-infected WT mice , but not in the PR8-infected Ifnar1-/- mice ( Fig 1D ) , however no significant difference was found between WT and Ifnar1-/- mice at day 0 prior to infection ( Fig 1D ) . Furthermore , we detected a significant lower level of Segmented Filamentous Bacteria ( SFB ) in the stool samples of the PR8-infected WT mice compared to the uninfected WT mice ( S1C Fig ) . SFB are Clostridia-correlated bacteria closely attached to the intestinal epithelium , which are able to activate a range of host defenses , including the production of antimicrobials , development of Th17 cells and increased colonization resistance to the intestinal pathogen Citrobacter rodentium [9] . However , uninfected WT and Ifnar1-/- mice were found similarly colonized with SFB; furthermore , the SFB abundance did not significantly change in the Ifnar1-/- mice , despite PR8 infection ( S1C Fig ) . In summary , our findings indicate that differences in the fecal microbiota between WT and Ifnar1-/- mice prior to influenza infection are insufficient to explain the PR8-mediated changes in specific endogenous bacterial population in WT mice . Similar results , as observed with influenza , were obtained when synthetic stimulators of IFN-Is such as poly I:C ( pIC ) [20 , 21] were administered to WT and Ifnar1-/- mice by non-surgical intratracheal instillation at day 0 and at day 2 ( S1D Fig ) . Using 16S qPCR analysis we found a significant increase in Enterobacteriaceae at day 4 and day 5 in the fecal samples of the pIC-treated WT mice , but not in the pIC-treated Ifnar1-/- mice ( S1E Fig ) . However , lower level of SFB was found at day 4 only in pIC-treated WT mice , but not in the pIC-treated Ifnar1-/- mice ( S1F Fig ) . Collectively , our findings highlight a critical role of type I IFN-mediated signaling induced in the lungs during pulmonary influenza infection in predisposing the host to dysbiosis . Our analysis specifically demonstrates a flourishing of resident bacteria belonging to Proteobacteria pathobionts , and a depletion of a subset of indigenous SFB . Since we demonstrated that IFNAR1-mediated signaling increased the abundance of endogenous Enterobacteriaceae during influenza infection , we aimed to test whether they could similarly affect the growth of Salmonella Typhimurium ( S . Typhimurium ) , a leading cause of acute gastroenteritis and inflammatory diarrhea , using a mouse model of acute colitis . One of the hallmarks of S . Typhimurium virulence in mice is its systemic manifestations resembling typhoid fever; in the typhoid model no intestinal inflammation is observed , and subsequently Salmonella numbers in the colon content are low and extremely variable [22 , 23] . To achieve colitis , S . Typhimurium must be administered to mice pretreated with the antibiotic streptomycin , this results in its effective colonization of the intestinal lumen , followed by high density growth and mucosal inflammation [22] . In our colitis model , WT and Ifnar1-/- mice were treated with streptomycin 1 day prior to S . Typhimurium infection in order to achieve acute inflammation of the cecal mucosa . On day 0 , mice were first infected with a sublethal dose of PR8 virus or PBS by non-surgical intratracheal instillation , then secondarily infected by oral gavage with 107 CFU of S . Typhimurium or given LB medium alone at 5 dpi ( Fig 2A ) . Mice were monitored daily until euthanasia at 8 dpi . At that time point , WT mice infected with PR8 , followed by S . Typhimurium , referred to as “secondarily infected” , were noted to have significantly more weight loss than those infected only with S . Typhimurium ( Fig 2B ) , and no difference in weight loss between the two groups of Ifnar1-/- mice was detected at 8 dpi . The extent of the weight loss seen in secondarily infected WT mice at 8 dpi was also greater than in secondarily infected Ifnar1-/- group ( Fig 2B ) . Lung viral load measured by plaque assay ( Fig 2C ) and qPCR ( S2A Fig ) revealed no difference between the WT and Ifnar1-/- groups , implying that the influenza infection was not the direct cause of the weight loss . However , at 7 and 8 dpi , corresponding to 48 and 72 hours ( h ) post S . Typhimurium infection , respectively , we found a significant increase ( 12-fold and 18-fold , respectively ) in the S . Typhimurium burden in the colons of the WT mice previously infected with PR8 ( Fig 2D and 2E ) . In contrast , infection with PR8 did not enhance S . Typhimurium gut colonization in the Ifnar1-/- mice ( Fig 2D and 2E ) . Using 16S qPCR analysis , we also detected a significant increase in the Salmonella copy number in the colon content of secondarily infected WT mice at 8 dpi compared to the S . Typhimurium-only infected WT mice . No difference was detected in the Salmonella gene copies between secondarily infected Ifnar1-/- and S . Typhimurium-only infected Ifnar1-/- mice at 8 dpi ( Fig 2F ) . These results were confirmed by 16S-Denaturing Gradient Gel Electrophoresis ( DGGE ) analysis performed from the microbial DNA extracted from the colon content 8 dpi ( S2B Fig ) . Likewise , at 8 dpi , a significant increase was noted in the total Enterobacteriaceae copy number only in the colon content of secondarily infected WT mice , compared to the S . Typhimurium-only infected WT mice ( Fig 2I ) . We interpret this rise in total Enterobacteriaceae gene copies to represent an increase in the population of Salmonella , since a rise in other Enterobacteriaceae was not detected . This is in accordance with previous observations [24] showing no overgrowth of commensal Enterobacteriaceae , despite high levels of inflammation , in mice infected with S . Typhimurium . Finally , we enumerated S . Typhimurium in the mesenteric lymph nodes ( MLN ) and lungs of WT and Ifnar1-/- mice at 8 dpi . Similar to our findings regarding the colonic burden , prior infection with PR8 enhanced the ability of S . Typhimurium to disseminate to the MLN and the lungs in WT but not Ifnar1-/- mice ( Fig 2G and 2H ) . Indeed there was significantly less dissemination of S . Typhimurium overall in the Ifnar1-/- mice compared to WT mice ( Fig 2G and 2H ) . Similar results in S . Typhimurium intestinal colonization were obtained when mice were infected at day 5 with 103 CFU of S . Typhimurium , without streptomycin pretreatment ( typhoid model ) ( S2C and S2D Fig ) . Indeed , as expected , S . Typhimurium numbers in the colon content were low and highly variable in S . Typhimurium-only infected WT and Ifnar1-/- mice , whereas prior infection with PR8 was still able to increase S . Typhimurium gut colonization in WT , but not in Ifnar1-/- mice ( S2D Fig ) . Overall , these results further support our hypothesis that PR8 infection predisposes mice to secondary Salmonella infection in a IFN-I-dependent manner . We further tested whether pIC could elicit similar effects in our acute colitis mouse model as shown with influenza . WT and Ifnar1-/- mice were intraperitoneally ( i . p . ) injected with pIC or saline 1 day prior and 2 days after the oral gavage administration of 107 CFU of S . Typhimurium or LB alone ( S3A Fig ) . Alternatively , WT and Ifnar1-/- mice were treated with pIC through non-surgical intratracheal instillation 1 day prior and 1 day after the oral gavage administration of 107 CFU of S . Typhimurium ( S4A Fig ) . S . Typhimurium CFUs were measured in the colon content , MLN , lungs and spleen at 72 h post bacterial infection . We found that pIC treatment significantly increased the S . Typhimurium burden in the luminal colon of the WT mice , but not Ifnar1-/- mice ( S3B Fig and S4B Fig ) . Moreover , we found that pIC enhanced S . Typhimurium dissemination in the WT but not in the Ifnar1-/- group ( S3C–S3E Fig and S4C–S4E Fig ) . These findings imply that the pro-bacterial effects induced by pIC during S . Typhimurium infection are largely mediated by IFN-Is , and that these can potently enhance S . Typhimurium pathogenicity . In summary , our studies have consistently shown that IFN-Is confer a fitness advantage to Salmonella in colonizing the intestine and disseminating to systemic sites . We next investigated whether IFN-Is might augment Salmonella intestinal colonization and dissemination through the suppression of specific well-characterized antimicrobial genes . To this end , we analyzed the expression of the following: Ifnγ , the gene that is of pivotal importance in host defense against intramacrophage pathogens [25] , in Salmonella-induced colitis [26–28] and in the systemic control of Salmonella infections [29 , 30]; S100A9 , the gene that encodes one of the two subunits of calprotectin , an antimicrobial heterodimer that acts as a metal-sequestering protein , which can starve Salmonella and many other microorganisms of critical nutrients , such as zinc and manganese [24 , 31]; Lcn2 , the gene that encodes the antimicrobial peptide lipocalin-2 , which sequesters iron-laden siderophores to inhibit Enterobacteriaceae growth [32] . We and others had already noted that transcript levels of Ifnγ , S100A9 , and Lcn2 were increased in the ceca of WT streptomycin-pretreated mice during S . Typhimurium infection [24 , 33] . We next chose to compare transcript levels in WT and Ifnar1-/- mice , which were both previously PR8- or mock-infected , and secondarily infected with S . Typhimurium , following the infection model depicted in Fig 2A . At 8 dpi , cecal tissue was excised from the large intestines of WT and Ifnar1-/- mice , and transcription of the candidate antimicrobial genes was measured by qPCR . Although basal transcription of Ifnγ , S100A9 and Lcn2 was similar between mock-infected WT and mock-infected Ifnar1-/- mice ( Fig 3A , 3C and 3E ) , the transcription of Ifnγ and Lcn2 was significantly higher in the ceca of the Ifnar1-/- group , compared to the WT group , after infection with S . Typhimurium alone ( Fig 3A and 3E ) . Moreover , PR8 infection alone did not change the induction level of these genes in the ceca of WT and Ifnar1-/- mice ( Fig 3A , 3C and 3E ) . However , WT mice secondarily infected with S . Typhimurium had a significant reduction in the transcription levels of all three genes , especially S100A9 , compared to those only infected with S . Typhimurium ( Fig 3A , 3C and 3E ) . By contrast , Ifnar1-/- mice showed no difference in the transcript levels of these genes when comparing secondarily S . Typhimurium-infected mice with S . Typhimurium-only infected mice ( Fig 3A , 3C and 3E ) . Differences were also confirmed in WT mice at the protein level; Western Blot revealed drastically reduced production of S100A9 and Lcn2 in the ceca of the secondarily S . Typhimurium-infected mice compared to the S . Typhimurium-only infected mice ( Fig 3B and 3F ) . As expected , Ifnar1-/- groups showed no difference in the expression of these antimicrobial peptides when comparing the two infection groups ( Fig 3D and 3H ) . The concentration of Ifnγ in the serum of either WT and Ifnar1-/-mice infected only with PR8 or PBS did not rise above basal levels ( 36 pg/ml for WT-PR8 and 66 pg/ml for Ifnar1-/--PR8-infected groups; Fig 3G ) . Yet , the serum level of Ifnγ was found drastically reduced in the secondarily infected WT group , in comparison with the S . Typhimurium-only infected WT group; by contrast , no disparity was detected between the same groups in the Ifnar1-/- cohort ( Fig 3G ) . Similar effects were observed when the i . p . pIC model instead of the PR8 infection was employed ( S5 Fig ) . S100A9 and Lcn2 were both strongly inhibited at the transcript ( S5A and S5B Fig ) and protein level ( S5E and S5F Fig ) in the ceca of WT but not Ifnar1-/- mice following pIC treatment then S . Typhimurium infection . Cecal Ifnγ transcription levels were also reduced by pIC treatment in the WT mice infected with S . Typhimurium , but not in the S . Typhimurium-infected Ifnar1-/- mice ( S5C Fig ) . Moreover , pIC treatment dramatically lowered the Ifnγ serum level in the S . Typhimurium-infected WT mice , but not in the S . Typhimurium-infected Ifnar1-/- mice ( S5D Fig ) . All together , our findings demonstrate that IFNAR1-mediated signaling can inhibit the host antimicrobial response to Salmonella infection . To broaden our study of the effects IFN-Is have on the level of cytokine expression in an inflammatory setting such as Salmonella-induced colitis , we examined the expression of the pro-inflammatory genes Il6 and Cxcl2 , and the anti-inflammatory genes Il10 and Muc2 . IL-6 and CXCL2 play important roles in macrophage activation and neutrophil function and recruitment . IL-10 and MUC2 are considered essential immunoregulators in the intestinal tract . IL-10 mainly functions to dampen excessive inflammatory responses that risk damaging the host [34 , 35] . MUC2 is critical for colon protection , as Muc-2 deficient mice spontaneously develop colitis [36 , 37] . Their transcript level in the cecum was assessed in both WT and Ifnar1-/- mice at 8 dpi after PR8 or mock infection , and following a secondary S . Typhimurium infection . After S . Typhimurium infection , both pro-inflammatory genes were induced in the cecum of WT and Ifnar1-/- mice ( Fig 4A and 4C ) . Significantly higher transcription of Il6 was observed in S . Typhimurium-only infected Ifnar1-/- mice compared to S . Typhimurium-only infected WT mice ( Fig 4A ) . Interestingly , in WT but not Ifnar1-/- mice , the secondarily infected S . Typhimurium group showed significantly lower levels of pro-inflammatory gene transcription than the S . Typhimurium-only infected group ( Fig 4A and 4C ) . By contrast , PR8 infection enhanced cecal Il10 levels in an IFNAR1-dependent manner ( Fig 4B ) . Basal transcript expression of Il10 was overall similar in mock-infected WT and Ifnar1-/- mice . However , we observed a lower level of induction of Il10 in S . Typhimurium-only infected Ifnar1-/- mice compared to S . Typhimurium-only infected WT mice ( Fig 4B ) . We did not detect upregulation of Muc2 in S . Typhimurium-only infected WT or Ifnar1-/- mice , although our samples showed a high level of variability ( Fig 4D ) . However , in WT but not Ifnar1-/- mice , PR8 infection enhanced cecal Muc2 levels . Furthermore , we observed an approximately 10-fold upregulation of Muc2 in the secondarily infected WT group compared to the S . Typhimurium-only infected WT group ( Fig 4D ) . To further study the contribution of IFN-Is in the modulation of the intestinal host response during Salmonella infection , we examined the transcription of Ifnβ and Ifnα4 in both the lungs and cecum of mice infected with PR8 . We found their induction in the lungs but not in the cecum . We also examined the transcription level of Cxcl10 and Mx1 , ISGs strongly induced by IFN-Is . Strikingly , we observed approximately 10-fold upregulation of Cxcl10 in the cecum of PR8-infected WT mice compared to mock-infected WT mice . In contrast , no difference was detected between Ifnar1-/- groups ( Fig 4E ) . Moreover , in WT but not Ifnar1-/- mice secondarily-infected with S . Typhimurium , the level of induction of Cxcl10 in the cecum was approximately 4-fold higher than S . Typhimurium-only infected mice ( Fig 4E ) . Similarly , the cecal expression of Mx1 was largely upregulated after PR8 infection in the WT mice , but not in the Ifnar1-/- mice , and as anticipated the level of induction of Mx1 tended to be lower overall in the Ifnar1-/- mice compared to WT mice ( Fig 4F ) . Remarkably , we observed a 12-fold upregulation of Mx1 in the secondarily infected WT mice compared to the secondarily infected Ifnar1-/- mice ( Fig 4F ) . In conclusion , the dissimilar regulation of ISGs in the cecum of WT and Ifnar1-/- mice after PR8 infection suggest that type I IFN-mediated signaling significantly contributes to the host response against S . Typhimurium infection . Histopathology from the cecum extracted at 8 dpi illustrates that WT and Ifnar1-/- mice develop severe inflammation 72 h after infection with S . Typhimurium , whereas no abnormalities are seen after PR8 or mock infection . However , WT mice infected with PR8 followed by S . Typhimurium infection had reduced inflammation compared to WT mice infected with S . Typhimurium alone ( Fig 5A ) . Indeed , the mucosal integrity ( assessed by cryptitis and epithelial erosions ) , inflammatory cell infiltration and submucosal edema were exacerbated in the S . Typhimurium-only infected WT group compared to secondarily infected WT group ( Fig 5B and 5C ) . However , no difference in the histopathology was noted between S . Typhimurium-only infected Ifnar1-/- group compared to secondarily infected Ifnar1-/-group ( Fig 5A , 5C and 5D ) . Similar differences in the cecal inflammatory score detected by histopathology were noted when pretreating WT mice with pIC before S . Typhimurium infection ( S6A–S6C Fig and S7A–S7C Fig ) . However , pIC did not reduce the inflammatory score in the cecum of S . Typhimurium-infected Ifnar1-/- mice ( S6A , S6C and S6D Fig; S7A , S7C and S7D Fig ) . To summarize , during S . Typhimurium-induced colitis , prior infection with influenza alters gut immune response promoting anti-inflammatory cytokines and reducing pro-inflammatory cytokines through an IFNAR1-dependent mechanism . The end result is a decrease , mediated by IFN-Is , of the intestinal tissue damage in mice that were first infected with influenza prior to S . Typhimurium infection .
IFN-Is are primarily considered to be antiviral and immunomodulatory cytokines [38]; their effects during bacterial infection are still controversial . Although IFN-Is have been shown to protect against and limit infection with certain bacterial pathogens [39 , 40] , they can also impair the clearance of others [41 , 42] . This suggests a complex and bacterium-specific mechanism of action . Influenza virus is a major cause of respiratory illness in humans , with the potential to cause lung damage and sensitize the host to secondary pulmonary infections [1] . Although gastroenteritis symptoms have been reported during infection , the mechanism through which influenza virus would affect the gut is not completely clear . Our studies have shown that influenza pulmonary infection has an effect on the mouse fecal microbiota and promotes secondary infection with the intestinal pathogen , S . Typhimurium . Importantly , we have shown that these effects are dependent on IFN-Is , which are induced in the lungs during influenza pulmonary infection . These in turn can alter the gut microbial composition and suppress host immunity to a secondary Salmonella intestinal infection . In line with a previous study [6] , we found that PR8 infection and poly I:C treatment increased the relative abundance of Enterobacteriaceae and decreased the number of SFB in the fecal content of WT mice . However , we found that the relative abundance of these bacterial groups remained unchanged after PR8 infection or poly I:C treatment in mice deficient in IFN-I signaling , demonstrating that IFN-Is play an essential role in regulating their numbers . Based on these results and consistent with previous observations [43] , we speculate that IFN-Is might regulate the populations of the major bacterial phyla within the intestinal tract . Moreover , our analysis showed that only particular members of the fecal microbiota were affected by PR8-induced IFN-Is . While anaerobes , such as Bacteroidetes and Firmicutes , make up the majority of the healthy microbiota , IFN-Is released during influenza infection promote the blooming of indigenous Proteobacteria pathobionts to the detriment of restricted anaerobic commensals , leading to significant intestinal disbyosis . This is in accordance with previous findings that show that bacterial imbalance characterized by an enrichment of Proteobacteria is observed during intestinal inflammatory disorders in humans , including Crohn's disease [44] and enteropathy in human immunodeficiency virus ( HIV ) -infected subjects [45] . In addition , we show that influenza- and poly I:C-induced IFN-Is promote the intestinal overgrowth of the bacterial pathogen S . Typhimurium , which is an important component of Enterobacteriaceae and clinically associated with severe gastroenteritis and inflammatory diarrhea in humans [46] . Furthermore , we showed that Ifnar1-/- mice were more resistant to weight loss during S . Typhimurium secondary infection than WT mice , and allowed less translocation of S . Typhimurium across the intestinal wall . Not surprisingly , we also reported an increased resistance in Ifnar1-/- mice against single S . Typhimurium infection , as indicated by reduced weight loss and bacterial dissemination , as well as higher induction of specific antibacterial genes , compared to S . Typhimurium-only infected WT mice . This is in agreement with previous reports where S . Typhimurium has been shown to induce the expression of IFN-Is by macrophages [47] , along with improved host survival and enhanced control of S . Typhimurium in Ifnar1-/- mice in a necroptosis-dependent mechanism [48] . While there were differences between S . Typhimurium-only infected WT and Ifnar1-/- mice , these differences were strongly exacerbated when mice were previously infected with PR8 , indicating the contribution of IFN-Is produced during influenza infection to secondary S . Typhimurium infection . Considering that the number of S . Typhimurium that disseminated systemically was greatly increased following influenza infection in WT , but not in Ifnar1-/- mice , this suggests that IFN-Is may potentially relax the intestinal barrier to allow for Salmonella systemic dissemination . In support of this , we found that the host inflammatory and antimicrobial responses against S . Typhimurium were reduced in the intestine after influenza infection in a mechanism dependent on IFNAR . Host inflammatory responses are essential to keep Salmonella localized to the gut and to limit the systemic spread of the pathogen . The immunosuppression mediated by IFN-Is would diminish local host surveillance , resulting in poor control of Salmonella in the gut and enhanced bacterial dissemination . We demonstrated that the induction of IFN-γ was decreased at the transcriptional level in the inflamed cecum and at the protein level systemically in an IFN-I-dependent manner . This is important given the key role IFN-γ plays in intestinal antibacterial immunity , host survival and resolution of Salmonella infection [49] . The host inflammatory response against Salmonella in the gut [50] includes the synthesis of antimicrobial peptides , some of which may possess a secondary function as regulatory molecules [51] . Two critical components of the mammalian nutritional immune response against S . Typhimurium are lipocalin-2 and calprotectin , which are both highly induced by this pathogen in the inflamed gut . Lipocalin-2 and calprotectin sequester essential nutrients from microorganisms and exert an antimicrobial effect against several bacteria , including intestinal commensals . Both antimicrobials were dramatically suppressed in the inflamed gut after PR8 infection in a mechanism that depended on IFNAR signaling . Paradoxically , we know that the metal deficiency induced by high levels of both antimicrobials can be evaded by Salmonella through expression of high-affinity metal transporters [24 , 52] . However , metal starvation may still have a critical defensive role against Salmonella by forcing infected host cells in the local microenvironment to undergo apoptosis [53 , 54] , thereby destroying the internalized Salmonella . Furthermore , both antimicrobial peptides act as crucial paracrine chemoattractants to recruit neutrophils [55 , 56] , which play a major role in preventing systemic dissemination of S . Typhimurium , as suggested by clinical and experimental data [57] . Additionally , the induction in the gut of anti-inflammatory Il10 after influenza infection might , not only inhibit the bactericidal response of macrophages , but also cause infected macrophages to function as hosts for bacterial replication , as previously shown [58] . IL-10 has been shown to be important in the pathogenesis of Salmonella infection and regulation of subsequent host immune responses . IL-10 levels are elevated in susceptible strains of mice [59] suggesting that those strains producing IL-10 at high levels cannot adequately control Salmonella infection . Moreover , the relevance of the anti-inflammatory role mediated by IFN-Is in the gut during Salmonella infection was confirmed by histopathology , which underscores the concept of the double-edged sword IFN-Is represent during secondary intestinal infections . Although IFN-I-mediated effects promote Salmonella intestinal colonization and systemic dissemination , they also limit the damage triggered by exacerbated inflammation induced by Salmonella infection . Furthermore , several clinical trials could not completely define the therapeutic effects of IFN-Is in patients with ulcerative colitis , an IBD with a complex etiology that includes genetic and environmental factors leading to chronic inflammatory responses against the gut microbiota [60 , 61] . This raises important questions about the potential mechanisms of action of IFN-Is in IBDs such as ulcerative colitis . Notably , IFNβ therapy markedly attenuates the course and severity of disorders such as Multiple Sclerosis ( MS ) . Indeed , in vitro studies previously showed that IFNβ induced the release of the anti-inflammatory cytokine IL-10 from lymphocytes acquired from patients with MS [62 , 63] , which might indicate that IFNβ could eventually induce an anti-inflammatory response in the colonic mucosa as well . Many investigators have reported a variety of both beneficial and detrimental immune functions for IFN-Is during bacterial infections , and this clearly expands the old misguided notion that IFN-Is serve “only” as antiviral cytokines . Specifically , we have examined the effects of IFN-Is induced by pulmonary influenza infection in intestinal bacterial homeostasis and during secondary enteric infection . We propose that during influenza infection , this family of cytokines alters the intestinal microbiota composition , leading to an overgrowth of pathobionts which puts the host at risk to develop intestinal bacterial disorders . This is particularly significant as IFN-Is are currently being used as anti-inflammatory therapies for several immunological disorders such as IBD and MS . Furthermore , we propose that influenza-induced IFN-Is enhance susceptibility to Salmonella intestinal colonization and dissemination during secondary Salmonella-induced colitis through suppression of host intestinal immunity . Our work highlights the critical importance of further studies that clarify the roles and effects IFN-Is play in balancing host susceptibility to bacterial infection and inflammatory control , as well as the potential risk associated with influenza infection in predisponing the host to Salmonella infections and intestinal disorders .
IR715 is a fully virulent , nalidixic acid-resistant derivative of Salmonella enterica serotype Typhimurium WT isolate ATCC 14028 [64] . The strain was cultured aerobically in Luria Bertani ( LB ) broth at 37°C . Bacterial strains and plasmids used in this study are listed in S1 Table . Carbenicillin was added to final 100 mg/L as needed . To render all strains equally resistant to streptomycin , pHP45omega plasmid [65] was introduced by electroporation . 7–9 week old mice with a C57BL/6J genetic background were used in all experiments . Ifnar1−/−mice were generated as previously reported [42] . The same number of males and females were used in each treatment group . Colonies of Ifnar1−/− and WT mice were maintained and housed in the same pathogen-free facilities at UCLA . The mouse studies described in this manuscript were performed under the written approval of the UCLA Animal Research Committee ( ARC ) in accordance to all federal , state , and local guidelines . All studies were carried out under strict accordance to the guidelines in The Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and the accreditation and guidelines of the Association for the Assessment and Accreditation of Laboratory Animal Care ( AALAC ) International under UCLA OARC Protocol Number 2009-012-21 . Mice were infected with a sublethal dose ( 200 PFU ) of the mouse-adapted influenza A/Puerto Rico/8/34 ( PR8 ) virus strain by non-surgical intratracheal instillation [66] . Briefly , mice were anesthetized with a mixture of ketamine and xylazine ( 100 mg/10 mg/kg ) , suspended by an incisor wire on an angled stand , and then a fixed volume containing 200 PFU of PR8 in pharmaceutical grade PBS was instilled inside the trachea [66] . Following aspiration of the inoculum into the lungs , mice were maintained on warming pads and monitored until completely ambulatory . Mice were monitored daily and weight was recorded . On day 1 , 8 or 17 after PR8 ( or mock ) infection , mice were euthanized by CO2 asphyxiation . Lungs were excised , lobes were separated and placed in a 2 mL FastPrep homogenization tube containing lysing matrix D . Sterile DPBS was added to give a final 20% w/v suspension and homogenized using a FastPrep-24 Instrument , 6 m/s , 45 s ( MP Biomedicals , Santa Ana , CA ) . Lung homogenate was diluted 1:10 in TRIzol Reagent ( Life Technologies , Grand Island , NY ) for analysis of gene expression , or serially diluted with DPBS for quantification of bacterial burden by CFU analysis and viral titer by plaque assay . Viral titer was determined by plating a monolayer of MDCK cells in 6-well tissue culture treated plates , then incubating with lung homogenate , serially diluted in virus dilution buffer ( PBS with 1% Pencillin/Streptomycin , 0 . 2% BSA , 0 . 005% DEAE Dextran , 1X CaCl2/MgCl2 ) , for 1 h at 37°C . Extracellular virus was removed by gently washing the monolayer , then an overlay containing 2% low melting point agarose in virus growth medium ( MEM containing BME vitamins , 10 mM HEPES , 1% Pencillin/Streptomycin , 0 . 15% NaHCO3 , 0 . 2% BSA , 0 . 0015% DEAE Dextran , 0 . 7 mg/ml TPCK-treated Trypsin ) was applied and plates were incubated for 2 days at 37°C . The overlay was gently aspirated , then the plates were incubated with 0 . 3% crystal violet in 20% ethanol and plaque forming units ( PFU ) were enumerated . Mice were administered 200 PFU of PR8 virus or PBS through non-surgical intratracheal instillation [66] . Mice were monitored daily at the same time until day 17 , when all the mice were euthanized . Fecal samples were collected from WT and Ifnar1−/− mice before PR8 or mock infection on day 0 and at 9 dpi , then snap frozen in liquid nitrogen . The fecal DNA was subsequently extracted using the QIAamp DNA stool kit ( Qiagen ) , according to the manufacturer’s instructions . The fecal microbial DNA was used for 16S quantitative real-time PCR ( qPCR ) analysis and for Illumina MiSeq analysis . Two μl of extracted fecal bacterial DNA was used as a template for 16S qPCR reaction with the primer pairs previous developed and presented in S2 Table . The 16S gene copy numbers per μl of DNA from each sample ( one fecal pellet collected from each mouse ) was determined using the plasmids described in S1 Table . For MiSeq analysis , bacterial DNA was amplified by a two-step PCR enrichment of the 16S rDNA ( V4 region ) encoding sequences from each sample with primers 515F and 806R modified by addition of barcodes for multiplexing . Libraries were sequenced using an Illumina MiSeq system . Following quality filtering , the sequences were demultiplexed and trimmed before performing sequence alignments , identification of operational taxonomic units ( OTU ) , clustering , and phylogenetic analysis using QIIME open-source software ( http://qiime . org ) . Mice were infected on day 0 with 200 PFU of PR8 influenza strain or PBS through non-surgical intratracheal instillation [66] . WT and Ifnar1-/- mice were orally gavaged with 0 . 1 ml of a 200 mg/ml streptomycin/sterile water solution on day 4 , prior to mock infection in LB or oral infection with 1×107 CFU of S . Typhimurium in LB on day 5 . Colon content was collected at 48 h postbacterial infection , weighed , homogenized in 1 ml of sterile PBS , serial diluted and plated on LB agar containing appropriate antibiotics . At 72 h post-bacterial infection the cecum was harvested for mRNA , protein , and histopathology . The colon contents , spleen , lungs and mesenteric lymph nodes ( MLN ) were collected , serially diluted , and plated on appropriate antibiotic LB agar plates to determine bacterial counts . Lungs were harvested for mRNA isolation and plaque assay . Blood was collected by cardiac puncture , allowed to clot at room temperature; serum was isolated by centrifugation , transferred to a sterile tube and stored at -80°C until ELISA cytokine analysis . Groups of 4–6 mice were used for each experiment . Mouse weight was taken daily until euthanasia . For the typhoid model , WT and Ifnar1-/- mice were infected on day 0 with 200 PFU of PR8 influenza strain or PBS through non-surgical intratracheal instillation [66] . Mice were gavaged with 1×103 CFU of S . Typhimurium in LB , without streptomycin pre-treatment , on day 5 . The colon contents were collected at 72h post bacterial infection , serially diluted , and plated on appropriate antibiotic LB agar plates to determine bacterial counts . Polyinosinic polycytidylic acid ( #tlrl-pic ) was purchased from Invivogen ( San Diego , CA ) . Mice were administered 50 μg of pIC or saline through non-surgical intratracheal instillation [66] on day 0 and on day 2 . Fecal samples were collected from WT and Ifnar1−/− mice on day 0 before treatment and on day 4 and day 5 , then snap frozen in liquid nitrogen . The fecal DNA was subsequently extracted using the QIAamp DNA stool kit ( Qiagen ) , according to the manufacturer’s instructions . The fecal microbial DNA was used for 16S qPCR analysis , as described above , and the copy numbers’ fold increase from each mock and pIC-treated sample ( one fecal pellet collected from each mouse ) on day 4 and day 5 over the baseline before treatment on day 0 were calculated . In the i . p . pIC model , WT and Ifnar1-/- mice were intraperitoneal injected with 150 μg of pIC or saline on day -1 , and intragastrically treated with streptomycin ( 0 . 1 ml of a 200 mg/ml solution in sterile water ) [22] on day -1 . Alternatively , in the non-surgical intratracheal instillation pIC model , WT and Ifnar1-/- mice were injected with 50 μg of pIC or saline on day -1 , and intragastrically treated with streptomycin ( 0 . 1 ml of a 200 mg/ml solution in sterile water ) [22] on day -1 . In both models , mice were then orally gavaged with a dose of 107 CFU of S . Typhimurium in 0 . 1 ml of LB on day 0 or mock-infected . A booster dose of 100 μg or 50 μg of pIC was administered on day 2 or on day 1 in the i . p pIC or in the non-surgical intratracheal instillation pIC models , respectively . On day 3 , corresponding to 72 h post S . Typhimurium infection , mice were euthanized; the cecum was collected for mRNA and protein isolation and also for histopathological analysis . Serum was separated from blood and collected for ELISA cytokine detection . CFU was enumerated from homogenates of colon content , MLN , lungs and spleen serially diluted and plated on agar plates containing the appropriate antibiotic selection . Total RNA was extracted from mouse cecal tissue with TRIzol Reagent ( Life Technologies ) . Reverse transcription of 1 μg of total RNA was performed with the iScript cDNA Synthesis kit ( Bio-Rad ) . qPCR was performed using iTaq Universal Sybr Green Supermix ( Bio-Rad ) . For analysis , target gene expression of each sample was normalized to the respective level of L32 mRNA . Fold changes in gene expression values were then calculated using the mean from the control samples as a baseline and determined using the ΔΔ Ct method . A list of qPCR primers used in this study is provided in S2 Table . See also S1 References . Mouse IFNγ ELISA Ready-SET-Go ! was purchased from eBioscience . Cytokine serum levels were measured according to the manufacturer’s instructions . Total protein was extracted from mouse cecum tissue using TRIzol Reagent ( Life Technologies , Grand Island , NY ) . 15 μg of total protein was resolved using 12 . 5% SDS-PAGE gels and transferred to PVDF membranes . The membranes were blocked with 2% nonfat dried milk and incubated at 4°C with primary antibodies . Detection of mouse HSP90 α/β was performed with primary rabbit polyclonal antibodies ( Santa Cruz Biotechnology ) , while detection of S100A9 was performed with polyclonal goat anti-mouse S100A9 ( R&D Systems ) . Lcn-2 was detected by polyclonal goat anti-mouse Lcn2 ( R&D Systems ) . After overnight incubation at 4°C , the blots were washed and then incubated for 1 h at room temperature with secondary goat anti-rabbit and donkey anti-goat antibodies conjugate to horseradish peroxidase ( HRP ) ( Southern Biotech and Santa Cruz Biotechnology , respectively ) . After washing , bands were developed using the SuperSignal West Pico Chemiluminescent Sustrate ( Thermo Scientific ) per manufacturer’s instructions and visualized using Gel Doc ( BioRad ) . Total genomic bacterial DNA was isolated using the MasterPure DNA purification kit ( Epicentre ) . DNA quality and quantity were determined with a Spectronic Genesys UV spectrophotometer at 260 nm and 280 nm ( Spectronic Instruments , Inc . Rochester , NY ) . Amplification of bacterial 16S rDNA was carried out by PCR as described previously [67] . Briefly , the universal primer set Bac1 and Bac2 [68] ( S2 Table ) was used to amplify an approximately 300-base-pair internal fragment of the 16S rDNA . Each 50 μl PCR contained 100 ng purified genomic DNA , 40 pmole each primer , 200 μM of each dNTP , 4 . 0 mM MgCl2 , 5 μl 10 X PCR buffer , and 2 . 5 U Taq DNA polymerase ( Invitrogen ) . Cycling conditions were 94°C for 3 min , followed by 30 cycles of 94°C for 1 min , 56°C for 1 min and 72°C for 30 s , with a final extension period of 5 min at 72°C . The resulting PCR products were evaluated by electrophoresis through 1 . 0% agarose . DGGE was performed by use of the Bio-Rad DCode System ( Hercules , CA , USA ) . A 40% to 60% linear DNA denaturing gradient ( 100% denaturant is equivalent to 7 M urea and 40% de-ionized formamide ) was formed in 8% ( w/v ) polyacrylamide gels . Approximately 300 ng PCR product was applied per lane . The gels were submerged in 1 X TAE buffer ( 40 mM Tris base , 40 mM glacial acetic acid , 1 mM EDTA ) and the PCR products were separated by electrophoresis for 17 h at 58°C using a fixed voltage of 60 V . After electrophoresis , the DNA bands were stained with 0 . 5 μg/ml ethidium bromide and DGGE profile images were digitally recorded using the Molecular Imager Gel Documentation system ( Bio-Rad ) . DIVERSITY DATABASE Software ( Bio-Rad ) was used to assess the change in the relative intensity of bands corresponding to bacterial species of interest . The DNA bands of interest were excised from the DGGE gels and transferred to a 1 . 5-ml microfuge tube containing 20 μl sterile ddH2O . Tubes were incubated at 4°C overnight before the recovered DNA samples were re-amplified with the universal primer set Bac1 and Bac2 . The PCR products were purified using the QIAquick PCR purification kit ( Qiagen ) and sequenced at the UCLA Core DNA Sequencing Facility . The sequences obtained were subjected to nucleotide BLAST searches against the NCBI ( http://blast . ncbi . nlm . nih . gov/ ) and Human Oral Microbiome ( http://www . homd . org/index . php ) databases . The differences between treatment groups were analyzed by non-parametric 2-tailed Mann-Whitney U test , non-parametric Kruskal-Wallis test ( Dunn's Multiple Comparison Test ) and One-Way Analysis of variance ( ANOVA ) with Bonferroni correction , as specified in each Fig legend . Data were expressed as the geometric mean or mean ± SEM , as indicated in each Fig legend , and the results were considered statistically significant when the p value was < 0 . 05 . All calculations were performed using GraphPad Software , unless indicated otherwise . Tissue samples were fixed in formalin for 24 h , processed according to standard procedures for paraffin embedding , sectioned at 4 mm , and stained with hematoxylin and eosin . The pathology score of cecal samples was determined by blinded examinations of cecal sections by a board-certified pathologist using previously published methods [22] . Each section was evaluated for the presence of neutrophils , mononuclear infiltrate , submucosal edema , epithelial erosions and cryptitis . Inflammatory changes were scored from 0 to 4 according to the following scale: 0 = none; 1 = low; 2 = moderate; 3 = high; 4 = extreme . The inflammation score was calculated as a sum of each parameter score and interpreted as follows: 0–3 = within normal limits; 4–8 = mild; 9–14 = moderate; 15–20 = severe . The mouse studies described in this manuscript were performed under the written approval of the UCLA Animal Research Committee ( ARC ) in accordance to all federal , state , and local guidelines . All studies were carried out under strict accordance to the guidelines in The Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and the accreditation and guidelines of the Association for the Assessment and Accreditation of Laboratory Animal Care ( AALAC ) International under UCLA OARC Protocol Number 2009-012-21 . Influenza infections were performed under ketamine/xylazine anesthesia and all efforts were made to minimize animal pain and discomfort . | Influenza is a respiratory illness . Symptoms of flu include fever , headache , extreme tiredness , dry cough , sore throat , runny or stuffy nose , and muscle aches . Some people , especially children , can have additional gastrointestinal symptoms , such as nausea , vomiting , and diarrhea . In humans , there is no evidence that the influenza virus replicates in the intestine . Using an influenza mouse model , we found that influenza infection alters the intestinal microbial community through a mechanism dependent on type I interferons induced in the pulmonary tract . Futhermore , we demonstrate that influenza-induced type I interferons increase the host susceptibility to Salmonella intestinal colonization and dissemination during secondary Salmonella-induced colitis through suppression of host intestinal immunity . | [
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"enterobacteriace... | 2016 | Influenza Virus Affects Intestinal Microbiota and Secondary Salmonella Infection in the Gut through Type I Interferons |
Chronic Chagas cardiomyopathy caused by Trypanosoma cruzi is the result of a pathologic process starting during the acute phase of parasite infection . Among different factors , the specific recognition of glycan structures by glycan-binding proteins from the parasite or from the mammalian host cells may play a critical role in the evolution of the infection . Here we investigated the contribution of galectin–1 ( Gal–1 ) , an endogenous glycan-binding protein abundantly expressed in human and mouse heart , to the pathophysiology of T . cruzi infection , particularly in the context of cardiac pathology . We found that exposure of HL–1 cardiac cells to Gal–1 reduced the percentage of infection by two different T . cruzi strains , Tulahuén ( TcVI ) and Brazil ( TcI ) . In addition , Gal–1 prevented exposure of phosphatidylserine and early events in the apoptotic program by parasite infection on HL–1 cells . These effects were not mediated by direct interaction with the parasite surface , suggesting that Gal–1 may act through binding to host cells . Moreover , we also observed that T . cruzi infection altered the glycophenotype of cardiac cells , reducing binding of exogenous Gal–1 to the cell surface . Consistent with these data , Gal–1 deficient ( Lgals1-/- ) mice showed increased parasitemia , reduced signs of inflammation in heart and skeletal muscle tissues , and lower survival rates as compared to wild-type ( WT ) mice in response to intraperitoneal infection with T . cruzi Tulahuén strain . Our results indicate that Gal–1 modulates T . cruzi infection of cardiac cells , highlighting the relevance of galectins and their ligands as regulators of host-parasite interactions .
Chagas disease , caused by infection with the protozoan parasite Trypanosoma cruzi , represents the main cause of infectious heart disease in Latin America . It is estimated that about 8 to 10 million people worldwide are infected with T . cruzi , mostly in Central and South America where Chagas disease is endemic [1 , 2] . In the last decade , an increased number of cases has been well documented in North America , Europe and Western Pacific , mostly because of the influx of immigrants from endemic countries [3–5] . In humans , the acute phase usually occurs with mild signs and symptoms that are not unique to this disease . However , being the cardiac muscle one of the most heavily parasitized tissues , myocarditis characterized by pericarditis , ventricular enlargement , conduction abnormalities and congestive heart failure is consistently observed in acutely infected patients , with an estimated mortality rate of 0 . 25 to 0 . 5% , often in children [6 , 7] . Myocarditis is found during symptomatic acute parasite infection , but also by histopathological examination of heart biopsies in patients with no apparent signs of cardiac disease [8] . In fact , following acute infection , patients enter an asymptomatic phase , which lasts throughout life in the majority of infected subjects . The remaining 30–40% of chronically infected individuals develop cardiac or digestive disorders ( megaoesophagus and megacolon ) , or both , during their lifetime [6] . The cardiac form is the most common and severe manifestation of Chagas disease , causing congestive heart failure , arrhythmias and conduction abnormalities , which often results in sudden death [9–11] . The mechanisms linking the acute and chronic myocardial progression have not yet been clarified . Currently , it is well accepted that the etiology of chagasic cardiomyopathy is multifactorial , suggesting multiple complex interactions between the host and the parasite [12 , 13] . Several studies revealed that the development of cardiac symptoms is associated with T . cruzi persistence and its genetic variability , and these effects are controlled by the host immune response , which involves activated T and B lymphocytes , myeloid cells , pro-inflammatory cytokines , cross-reactive antibodies and endogenous lectins [14–17] . Galectin–1 , a proto-type member of the galectin family , has the ability to recognize N-acetyllactosamine ( LacNAc ) residues present in N- and O-glycans [18 , 19] . This lectin plays different roles governed not only by its relative concentrations but also by its subcellular compartmentalization [19] . While intracellular Gal–1 controls signaling pathways via protein-protein or protein-glycan interactions , extracellular Gal–1 plays key roles in cell aggregation , cell adhesion to the extracellular matrix and regulation of cell survival , inflammation , immunity and angiogenesis [20–25] . Recently , Seropian and colleagues demonstrated that Gal–1 expression is up-regulated in cardiac cells exposed to hypoxic microenvironments or proinflammatory cytokines , as well as in peri-infarcted area of the mouse heart after experimental acute myocardial infarction ( AMI ) and in human cardiac tissue from patients with end-stage chronic failure [26] . Furthermore , hearts from mice lacking the Gal–1 gene ( Lgals1-/- ) which underwent experimental AMI , showed a higher number of inflammatory cells together with a lower number of regulatory T ( Treg ) cells compared with their wild-type ( WT ) counterpart . Overall , these findings suggest a potential role of Gal–1 in controlling the inflammatory response in cardiac tissue exposed to internal or external insults [26] . With regards to T . cruzi infection , Gal–1 has been found to be up-regulated in cardiac tissue from patients with severe chronic Chagas cardiomyopathy . Moreover , an increase frequency of anti-Gal–1 autoantibodies was found to be associated with the severity of cardiac damage during the course of the disease [27] . Whereas low concentrations of Gal–1 increased the number of trypomastigotes ( Tulahuén strain ) in infected macrophages by diminishing IL–12 production , high concentrations of this lectin promoted macrophages apoptosis and inhibited parasite replication [28] . However , the role of Gal–1 during T . cruzi infection of cardiac cells has not been yet elucidated . Here we undertook this study to investigate the expression and function of Gal–1 in the adult murine cardiac cell line HL–1 infected with two different phylogenetic discrete typing units ( DTUs ) of T . cruzi , namely the Brazil and Tulahuén strains , belonging to TcI and TcVI DTUs , respectively . In addition , we analyzed the impact of endogenous Gal–1 during the course of experimental T . cruzi infection using the above mentioned T . cruzi strains , focusing on parasitemia , survival rates and heart alterations . Our findings identify a protective role of Gal–1 on T . cruzi infection of cardiac cells and demonstrate how parasite infection reprograms expression of cell surface glycans , shifting the balance toward a Gal-1-non-permissive glycophenotype .
Clinical research protocols followed the tenets of the Declaration of Helsinki . The protocols used in this study were approved by the Medical Ethics Committee of Fernandez Hospital ( Buenos Aires , Argentina ) . All patients gave written informed consent before blood collection and after the nature of the study were explained . Animal studies were conducted in accordance with the Guide for the Care and Use of Laboratory Animals , 8th Edition ( 2011 ) . The protocols used were approved by Animal Care Committee of the Instituto Nacional de Parasitología “Dr . Mario Fatala Chaben” , Administración Nacional de Laboratorios e Institutos de Salud “Dr . Carlos G . Malbrán” ( Buenos Aires , Argentina ) . Patient selection was conducted at the Cardiovascular Division of Fernandez Hospital . Positive serology for Chagas disease was determined by two or more tests ( indirect immunofluorescence , enzyme-linked immunosorbent assay [ELISA] , indirect hemagglutination , or complement fixation ) and those patients who had at least two of three reactive serological tests were considered T . cruzi infected . Patients underwent a complete clinical and cardiologic examination that included medical history , physical examination , electrocardiogram ( ECG ) at rest , laboratory and chest X-ray examinations , and Doppler echocardiography evolution . The exclusion criteria considered the presence of systemic arterial hypertension , diabetes mellitus , thyroid dysfunction , renal insufficiency , chronic obstructive pulmonary disease , hydroelectrolytic disorders , alcoholism , history suggesting coronary artery obstruction and rheumatic disease , and the impossibility of undergoing clinical examination . The study population consisted of 28 patients who completed the screening protocol and were in the chronic phase of the infection , 19 patients with cardiac symptoms and 9 patients in the asymptomatic phase . Forty-two non-infected individuals , within the same age range ( 30–70 years old ) which showed negative serological tests for Chagas disease , were included as control group . Recombinant Gal–1 ( rGal–1 ) was produced and purified essentially as described previously [23 , 29] . LPS content of the purified samples ( <60 ng/mg ) was tested using a gel-clot Limulus test ( Associates of Cape Cod , Falmouth , MA ) . Trypomastigotes of the Brazil and Tulahuén ( stock Tul–2 ) strains [30 , 31] were obtained from the extracellular medium of infected monolayers of Vero cells . After separation of Vero cells and cellular debris by centrifugation at 500 x g for 5 min , trypomastigotes were collected by centrifugation at 2 , 200 x g for 10 min and resuspended in RPMI medium containing 10% FCS . Parasites were counted using a Neubauer chamber and used for in vitro infection experiments as described below . Bloodstream trypomastigotes of both strains were maintained in vivo by serial passages of blood-form trypomastigotes in BALB/c mice . HL–1 , an immortalized adult murine cardiac cell line , was plated onto gelatin/fibronectin pre-coated vessels and cultured in Claycomb medium ( Sigma-Aldrich , St . Louis , MO , USA ) supplemented with 10% FCS , 100 U/ml penicillin , 100 μg/ml streptomycin and 2 mM L-glutamine as previously described [32] , under water jacketed incubator at 37°C , 5% CO2 . pcDNA3-Gal–1 expression vector was cloned as previously described [33] . Briefly , HL–1 cells were transiently transfected in a 24-well tissue plate with pcDNA3-Gal–1 plasmid or empty vector ( 0 . 45 μg DNA/well ) using the Lipofectamine 2000 reagent ( Invitrogen Co . , Carlsbad , USA ) , according to the manufacturer’s instructions . Cells were selected for resistance to Geneticine ( Life Technologies , Foster City , CA ) . Transfection efficiency was tested by determining Gal–1 concentration in the supernatants of HL–1 cells by ELISA . HL–1 cells seeded on 12 mm cover-slips in 24-well tissue plates ( 2 x 104 cells/well ) , were incubated with rGal–1 ( 10 or 50 μg/ml ) for 24 h , in the presence or absence of 100 mM lactose . Then , cardiac cells were infected with trypomastigotes of the Brazil or Tulahuén strains using two different protocols: a ) cells were infected with a parasite-to-host cell ratio of 5:1 for 18 h; unattached parasites were then removed by washing with PBS and cells were kept in culture with fresh medium for additional 30 h or , b ) cells were infected with a parasite-to-host cell ratio of 10:1 for 4 h; unattached parasites were removed by washing with PBS and cells were kept in culture with fresh medium for 4 days . In experiments with pcDNA3-Gal-1-transfected HL–1 cells , we directly infected the cells with trypomastigotes according to protocol a ) . Of note , no differences were found in the percentage of infected cells between wild-type HL–1 cells and those transfected with the plasmid alone ( mock ) ( S1 Fig ) . At the indicated time , cells were washed with PBS , fixed in 2% ( w/v ) paraformaldehyde/PBS overnight , rinsed and kept for 5 min in 0 . 02 M glycine/ PBS pH 7 . 4 to quench reactive groups of the fixative . Each manipulation was preceded by washing the cells three times in PBS . After cell permeabilization with PBS-Triton X–100 1% for 5 min and blocking with PBS-BSA 2% , intracellular parasite were identified by indirect immunofluorescence with sera from T . cruzi infected mice ( dilution 1:1 , 000 ) as primary antibody ( Ab ) and Alexa Fluor 488 conjugated goat anti-mouse IgG ( Life Technologies , Foster City , CA ) as secondary Ab . Dishes were mounted in Vectashield medium ( Vector Labs , UK ) containing DAPI and visualized using a fluorescence microscope at a magnification of 200X . Images were acquired using an Olympus DP71 digital camera . The number of cells was determined by using Cell Profiler software ( version 2 ) . The percentage of infected cells was determined by counting an average of 3 , 500 cells in each slide on 3–5 distinct coverslips in randomly selected fields; each sample was tested in three to five replicates , in at least two independent experiments . A cell was considered infected when contained at least one intracellular amastigote . Age- and gender-matched mice with genetic deletion of the gene encoding Gal–1 ( Lgals1-/- ) and WT mice with equivalent genetic background ( C57BL/6 ) were kindly provided by Dr . Francoise Poirier ( Jacques Monod Institute , Paris , France ) . Eight-week-old mice were inoculated intraperitoneally with 2 , 500 bloodstream trypomastigotes of the Brazil or Tulahuén strains resuspended in 200 μl RPMI media . Non-infected Lgals1-/- and WT mice ( n = 5–15 ) injected only with RPMI media were used as controls . Parasitemia was determined twice a week from the second to the fifth week of infection by counting parasites in a 5 μl drop of tail vein blood . Results were expressed as the number of trypomastigotes per ml of blood . Survival was recorded daily until 95 dpi . For histopathological studies , infected and control mice were lightly anesthetized with Avertin ( tribromoethanol ) before sacrifice by cervical dislocation at 120 and 19 dpi with Brazil and Tulahuén strains , respectively . Hearts and skeletal muscle samples were harvested , fixed in 10% neutral buffered formalin , processed routinely and embedded in paraffin . Five micron thickness sections were stained with hematoxylin and eosin ( H&E ) and examined at an Olympus DP71 light microscope . The number of parasitized cells per section and the extent of inflammation were recorded on a single blind basis as described previously [34 , 35] . Briefly , different areas of the heart ( atria , ventricular walls and septum ) and skeletal muscle sections were evaluated qualitatively and scored according to the distribution ( focal , confluent or diffuse ) and the extent of inflammation as follows: normal ( 0 ) ; focal or a single inflammatory foci ( 1 ) ; multifocal , non-confluent inflammatory infiltrates ( 2 ) ; confluent inflammation with partial section involvement ( 3 ) ; diffuse inflammation extended through the section ( 4 ) . HL–1 cells ( 1 x 104 cells/well ) seeded in a 48-well tissue plate were cultured in the presence of rGal–1 ( 10 or 50 μg/ml ) for 18 h and then infected with 5 x 104 trypomastigotes of the Brazil or Tulahuén strains . After 18 h , cells were washed to remove unattached parasites and incubated for 48 h . Samples were finally processed for phophatidylserine exposure by using the FITC-Annexin V Apoptosis Detection Kit ( BD Pharmingen , Chicago , IL , USA ) according to the manufacturer’s instructions . Cells fixed with 2% ( w/v ) paraformaldehyde/PBS , were acquired using a FACSAria flow cytometer . Data were analyzed with WinMdi software . T . cruzi-infected and non-infected HL–1 cells were harvested at 5 dpi and counted in a Neubauer chamber . Approximately 1 x 105 cells were washed twice with 0 . 5 ml of cold PBS-BSA 1% and resuspended in lectin buffer-BSA 1% , containing the following biotin-conjugated lectins: PNA , SNA , HPA , LEL , PHA-L , MAL II ( S2 Fig and S1 Table ) at the appropriate concentration for 1 h at room temperature , and then incubated with FITC conjugated streptavidin ( BD Pharmingen , Chicago , IL , USA ) in lectin buffer-BSA 1% . After 30 min at room temperature , cells were fixed with 2% ( w/v ) paraformaldehyde/PBS and a minimum of 10 , 000 events were acquired on a FACSAria flow cytometer . Non-specific binding was determined with FITC conjugated streptavidin alone . Data analyses were carried out with WinMdi software . Results were expressed as the percentage of positive cells or as the relative specific fluorescence index ( SFI ) for each lectin . The relative SFI was expressed by the ratio of the SFI of infected cells samples to that of non-infected cells samples; SFI was calculated by dividing mean fluorescence recorded with the specific biotinylated lectin and FITC-streptavidin by the fluorescence intensity obtained with FITC-streptavidin only . Similar procedure was performed to determine Gal–1 binding to HL–1 infected cells and non-infected cells . In this case , cells were incubated with rGal–1 at three different concentrations ( 5 , 10 and 50 μg/ml ) in the absence or presence of lactose ( 100 mM ) and further revealed using a FITC-labeled anti-mouse Gal–1 Ab ( dilution 1:200 in lectin buffer-BSA 1% ) . To confirm the glycophenotype , paraformaldehyde-fixed infected and non-infected cells seeded in 12 mm cover slides were incubated with LEL and PHA-L and FITC conjugated streptavidin as described above . Coverslips were visualized using a fluorescence microscope at a magnification of 200X . Images were acquired using an Olympus DP71 digital camera . Soluble Gal–1 was determined using an in-house ELISA . Briefly , high binding 96-well microplates ( NuncMaxisorb ) were coated with capture Ab ( 2 μg/ml purified rabbit anti-Gal–1 polyclonal IgG ) in 0 . 1 M sodium carbonate pH 9 . 5 . After incubation for 18 h at 4°C , wells were rinsed three times with wash buffer ( 0 . 05% Tween–20 in PBS ) and incubated for 1 h with blocking solution ( 2% BSA in PBS ) . Samples and standards ( 100 μl ) were diluted in PBS-BSA 1% and incubated for 18 h at 4°C . Plates were then washed and incubated with 100 ng/ml biotinylated detection Ab ( purified rabbit anti-Gal–1 polyclonal IgG ) for 1 h . Plates were rinsed three times before adding horseradish peroxidase-labeled streptavidin ( 0 . 33 μg/ml; Sigma-Aldrich , St . Louis , MO , USA ) for 30 min at room temperature . After washing , 100 μl of TMB solution ( 0 . 1 mg/ml tetramethylbenzidine and 0 . 06% H2O2 in citrate-phosphate buffer pH 5 . 0 ) was added to the plates . The reaction was stopped by adding 2N HCl and Optical density ( OD ) was determined at 450 nm in a Versamax microplate reader ( Molecular Devices ) . All samples were tested in duplicate , in two independent experiments . A standard curve ranging from 2 . 5 to 320 ng/ml of rGal–1 was run in parallel . Results are expressed as means of duplicates , in ng/ml . Infected and non-infected HL–1 cells were washed with PBS , harvested and homogenized in ice-cold lysis buffer ( 10 mM HEPES , 2 mM EDTA , 150 mM NaCl 150 , 0 . 1% NP40 ) in the presence of a protease inhibitor kit ( Complete Mini EDTA-free , Roche , Germany ) . After protein quantification by Bradford reagent ( Sigma-Aldrich , St . Louis , MO , USA ) , equal amount of protein ( 60 μg per lane ) was resolved on a 15% SDS-PAGE , transferred to nitrocellulose membranes and then immunoblotted with a rabbit anti-Gal–1 polyclonal Ab ( dilution 1:3 , 000 ) or a mouse monoclonal Ab for β-actin ( BD Pharmingen , Chicago , IL , USA ) as a loading control . Blots were then incubated with horseradish peroxidase-conjugated anti-rabbit IgG ( Vector Labs , UK ) or horseradish peroxidase-conjugated anti-mouse IgG ( Sigma-Aldrich , St . Louis , MO , USA ) . Immunoblots were visualized with the Immobilon chemiluminescent horseradish peroxidase substrate ( Millipore , Billerica , MA ) according to manufacturer’s instructions . The bands were scanned and quantified using ImageJ software ( version 1 . 410 ) . Total RNA was extracted using Trizol reagent ( Gibco/BRL , Grand Island , USA ) . Reverse transcription was performed using oligodT and Superscript Reverse II transcriptase ( Life Technologies , Foster City , CA ) , according to the manufacturer’s instructions . Real time RT-PCR was done in Rotor-Gene 6000 ( Corbett , UK ) device using SYBR Green PCR master mix ( Life Technologies , Foster City , CA ) . Data were analyzed using the relative standard curve method and results were normalized with respect to glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) mRNA levels . The following primers were used: mouse Gal–1 , forward 5′-TGAACCTGGGAAAAGACAGC–3′ and reverse 5′-TCAGCCTGGTCAAAGGTGAT–3′; mouse GAPDH forward 5′- ACTCCCACTCTTCCACCT -3′ and reverse 5′- TCCACCACCCTGTTGCT -3′ . Expression was calculated from the standard curves and then expressed in arbitrary units of Gal–1 relative to GAPDH . Unless otherwise indicated , values are expressed as means ± SEM of at least three independent experiments . Statistical comparisons were performed with one-way ANOVA followed by Tukey test for multiple-group comparisons , except for glycophenotype analysis where Dunnett’s test was used to compare every mean with the control mean . For Gal–1 expression determined by Western-blot , Student’s t test was used . Concentration of Gal–1 measured by ELISA was evaluated by using non-parametric Kruskal-Wallis test followed by Dunn’s multiple comparison test , while parasitemia and histological findings were analyzed by using the Mann Whitney U test . Log-rank test was performed for statistical comparison of animal survival curves . All tests were performed using GraphPad Prism ( GraphPad Software Inc . , CA , USA ) . p<0 . 05 was considered statistically significant .
Because Gal–1 expression was higher in heart tissue from patients with chronic Chagas cardiomyopathy who underwent cardiac transplantation [26] , we first examined Gal–1 levels in sera from patients during the chronic phase of disease . Results showed that Gal–1 was increased in sera from chronic chagasic patients compared with non-infected subjects ( Fig 1 ) . However , no difference was observed between Gal–1 levels in sera from cardiac patients compared with those from asymptomatic individuals . Thus , elevated Gal–1 levels delineate chronic Chagas Disease irrespective of cardiac pathology . Given the higher levels of Gal–1 in sera and cardiac tissue in response to T . cruzi infection , we analyzed the expression of this lectin in infected and non-infected cardiac cells . We infected the murine cardiac cell line HL–1 with trypomastigotes belonging to two different DTUs , Tulahuén ( TcVI ) or Brazil ( TcI ) strains . The presence of Gal–1 was assessed after 2 and 5 days post infection ( dpi ) at the protein and mRNA levels . HL–1 cells infected with T . cruzi Tulahuén strain did not show any difference in the expression of either Gal–1 mRNA or protein compared with non-infected cells at both times analyzed ( Fig 2A and 2B ) . On the contrary , cells infected with T . cruzi Brazil strain showed a slight reduction in Gal–1 protein levels only after 2 dpi while no significant differences were achieved at mRNA level at any of the times tested ( Fig 2A and 2B ) . However , when we analyzed the secretion of Gal–1 , we found increased amount of this lectin in supernatants of HL–1 cells infected with T . cruzi ( Tulahuén and Brazil strains ) at day 5 post-infection ( Fig 2C ) . To evaluate whether enhanced Gal–1 secretion correlated with cellular lysis , we assessed the release of the cytoplasmatic enzyme lactate dehydrogenase ( LDH ) into the culture media of infected HL–1 cells . Results showed that LDH activity was considerably greater in supernatants of infected HL–1 cells after 5 dpi using trypomastigotes of both strains ( Fig 2D ) . These data showed an association between Gal–1 and LDH release , suggesting that increased lysis of cardiac cells might contribute to greater Gal–1 secretion . To investigate the impact of Gal–1 on T . cruzi infectivity of cardiac cells , HL–1 cells were incubated with different concentrations of recombinant Gal–1 ( rGal–1 ) during 24 h and then infected with trypomastigotes of both strains , Tulahuén and Brazil . Of note , we used rGal–1 doses which did not affect cell viability as evaluated by annexin V-FITC staining ( S3 Fig ) . After 4 dpi with trypomastigotes of the Tulahuén strain , the percentage of infected cells significantly diminished in the presence of increasing concentration of rGal–1 ( Fig 3A and 3B ) . Similar results were obtained when HL–1 cells were infected with the Brazil strain , although a non-significant trend toward a decrease in T . cruzi infectivity was observed at 10 μg/ml of rGal–1 ( Fig 3D and 3E ) . These data indicate that rGal–1 alters the infection of cardiac cells by T . cruzi , independently of the parasite lineage ( TcI or TcVI ) . Because at 4 dpi , the percentage of infected cells could be affected by multiple rounds of infection with T . cruzi , we evaluated the effect of rGal–1 at early time periods of the infection cycle . After 2 dpi , rGal–1 at 50 μg/ml diminished infection of cardiac cells by both T . cruzi lineages; this effect was prevented in the presence of lactose ( Fig 3C and 3F ) , suggesting that the carbohydrate recognition domain ( CRD ) of Gal–1 may be involved in Gal–1 modulation of parasite infection . Similar results were observed when Gal–1 transfected HL–1 cells were infected with both T . cruzi strains ( Fig 3G ) , indicating that both exogenous and endogenous Gal–1 control parasite infectivity . To further analyze the mechanistic bases of this effect , we evaluated whether rGal–1 binds to trypomastigote forms of T . cruzi , either from Tulahuén or from Brazil strain . Notably , no specific binding of rGal–1 was observed with any of the parasite strains ( Fig 4 ) , either by fluorescence staining or by flow cytometry , suggesting that direct binding of Gal–1 to the parasite does not account for the regulatory effects of this lectin . The ability of T . cruzi trypomastigotes to control apoptotic programs in cardiac cells [36 , 37] and the ability of Gal–1 to regulate viability of different cell types [18] , prompted us to investigate the effect of this lectin in parasite induced phosphatidylserine ( PS ) exposure , an early apoptotic event , on plasma membrane of HL–1 cardiac cells . Thus , we pre-incubated HL–1 cells with rGal–1 ( 10 and 50 μg/ml ) and , following infection with trypomastigotes of the Tulahuén or Brazil strain , annexin V staining was performed . While trypomastigotes of both strains induced considerably exposure of phosphatidylserine residues in HL–1 cardiac cells , the percentage of annexin V-positive cells significantly diminished when cells were pre-incubated with 50 μg/ml rGal–1 before parasite infection ( Fig 5 ) . These data suggest that Gal–1 , not only reduces T . cruzi infectivity , but also protects cardiac cells from T . cruzi driven-phosphatidylserine exposure . To test if T . cruzi infection induces changes in the glycophenotype of cardiac cells , we analyzed binding of a panel of biotinylated lectins that recognize specific glycan structures ( S2 Fig and S1 Table ) to HL–1 cells . Infection with trypomastigotes of the Tulahuén strain led to reduced binding of Lycopersicon Esculentum agglutinin ( LEL ) , a lectin that recognizes poly-LacNAc-enriched glycans and phytohemagglutinin-L ( PHA-L ) , a lectin that binds to β1 , 6-N-acetylglucosamine-branched complex N-glycans ( Fig 6A ) . Analysis by fluorescence microscopy confirmed these findings ( Fig 6C ) , indicating that poly-LacNAc and complex branched N-glycans , which are key saccharide ligands required for Gal–1 binding , are hindered in response to T . cruzi infection . Next , we analyzed reactivity for Sambucus nigra agglutinin ( SNA ) , a lectin that recognizes α2 , 6-linked sialic acid . Results showed that the number of SNA+ HL–1 cells was significantly higher after infection with trypomastigotes of the Tulahuén strain compared to non-infected cells ( Fig 6B ) . Gal–1 preferentially recognizes poly-LacNAc units present on the branches of N- and O-glycans , but does not bind to α2 , 6-sialylated LacNAc residues [18] . Hence , we hypothesized that increased α2 , 6-linked sialic acid together with reduced poly-LacNAc and β1 , 6-branched N-glycans may limit Gal–1 binding to the surface of infected cardiac cells . To address this question , we analyzed binding of rGal–1 to HL–1 cells infected with T . cruzi Tulahuén strain , in the absence or presence of the galectin-specific disaccharide lactose . Consistent with the surface glycophenotype of these cells , we detected lower binding of Gal–1 to infected versus non-infected cardiac cells; an effect which was dose- and saccharide-dependent ( Fig 6D ) . However , infection of HL–1 cells with trypomastigotes of the Brazil strain led only to reduction of PHA-L reactive complex N-glycans , although no changes were detected in the percentage of cells exposing α2-6-linked sialic acid on N-glycans ( Fig 6A , 6B and 6C ) . Thus , infection with T . cruzi Tulahuén strain selectively controls the glycosylation signature of cardiac cells . This strain-dependent regulatory effect may control Gal–1 binding , parasite infection and cardiomyocyte function . To investigate the relevance of Gal–1 during T . cruzi infection in vivo , Lgals1-/- and WT female and male mice were examined for parasitemia , survival and histopathology , following intraperitoneal inoculation of the parasite . Although mice were challenged with the same parasite inoculum size , the course of the infection was different according to the T . cruzi strain used . The peak of parasitemia in Tulahuén strain-infected mice ( occurring at 19–22 dpi ) was higher than that recorded in mice infected with the Brazil strain ( at 26–28 dpi; p<0 . 05 ) , suggesting strain-dependent differences in the in vivo infectivity of T . cruzi trypomastigotes ( Fig 7A and S4 Fig ) . Interestingly , Lgals1-/- mice infected with trypomastigote of the Tulahuén strain had significantly higher parasitemias compared to WT mice ( p<0 . 05 ) , regardless of the gender of the animals ( Fig 7A ) . On the contrary , levels of parasitemia were slightly higher in Lgals1-/- male mice infected with the Brazil strain compared to that of WT mice at 28 dpi , whereas no differences were found in female mice ( S4 Fig ) . The percentage of female Lgals1-/- mice that survived acute infection with T . cruzi Tulahuén strain was significantly lower ( P = 0 . 0008; Fig 7B ) and the mean survival time was shorter ( P = 0 . 0127; S2 Table ) compared to their WT counterpart . There were no differences in mortality rates and survival time between male Lgals1-/- and WT animals infected with trypomastigotes of the Tulahuén strain . On the other hand , mortality rates and survival time of Lgals1-/- mice infected with the Brazil strain were similar to that of their WT mice counterparts , regardless of mice gender ( S4 Fig and S2 Table ) . Differences in the survival rates and parasitemia in mice infected with T . cruzi Tulahuén strain , prompted us to analyze the histopathology of heart and skeletal muscles . The density of parasitized cells was significantly higher in the heart , but not in the skeletal muscle of Lgals1-/- mice infected with T . cruzi Tulahuén strain at the peak of parasitemia , compared with WT mice; this effect was independent of the gender of the mice ( p<0 . 05; Fig 8A and 8B ) . However , the inflammation score in Lgals1-/- mice was lower in the heart of females and in the skeletal muscle of male animals , compared with their WT counterparts ( Fig 8A and 8C ) . Because the susceptibility or resistance to infection relies not only on the host but also on parasite genetics , we believe that , in our hands , Brazil strain had less infectivity for C57BL/6 mice and , probably higher inoculum of Brazil strain would recapitulate the results obtained with a more virulent T . cruzi strain such as Tulahuén . Overall , our data indicate strain-dependent differences in Gal-1-mediated protection of T . cruzi infection in vivo .
The interaction between parasite and host cells is crucial for T . cruzi survival involving the recognition of a large number of ligands and/or receptors on the surface of both the parasite and the host cells [37–41] . With regards to cardiomyocytes , carbohydrate residues of membrane glycoconjugates , like galactosyl , mannosyl , and sialyl residues , together with mannose receptors not only participate in parasite entry but are also regulated by the infection itself [41] . Intracellularly , the parasite takes control of host cells , including cardiomyocytes which respond to infection with the production of cytokines , chemokines , metalloproteinases , and glycan-binding proteins [41] . Galectins , a family of glycan-binding proteins , have recently emerged as novel regulators of host-parasite interactions [42 , 43] . With regards to T . cruzi infection , Pineda et al . recently showed the differential recognition by human Gal–1 , -3 , -4 , -7 and -8 of fourteen different strains of T . cruzi corresponding to the six lineages representing the genetic diversity of the parasite , suggesting strain-dependent glycosylation of the parasite surface [44] . Interestingly , Gal–3 , the best studied galectin in the context of T . cruzi infection , is recruited during parasite invasion of host cells and influences intracellular trafficking of amastigotes [45] . Moreover , expression of Gal–3 in the thymus of T . cruzi infected mice has been shown to determine the premature exit of immature T cells by modulating thymocyte-extracellular matrix interactions [46] . This ‘chimera-type’ galectin has been found to be required for T . cruzi adhesion to human coronary artery smooth muscle cells and for B cell function following infection with the parasite [47–49] . In addition , mice lacking Gal–3 showed increased blood parasitemia and impaired cytokine production during T . cruzi infection [50] . Although the role of other members of the galectin family during T . cruzi infection still needs to be addressed , these data highlight the multifunctional role of these lectins in host-parasite communication . Here , we aimed at dissecting the role of Gal–1 and its specific glycans in the infection of cardiac cells with T . cruzi trypomastigotes ( lineages Tcl and TcVI ) . We demonstrated that Gal–1 not only reduced infection by T . cruzi but also diminished phosphatidylserine exposure , an early apoptotic event driven by the parasite on HL–1 cells . This effect was also reflected by -in vivo experiments showing that Lgals1-/- mice intraperitoneally inoculated with T . cruzi Tulahuén strain had higher parasitemia and lower survival rates than WT animals . Moreover , our data show that T . cruzi infection can reprogram the glycophenotype of cardiac cells toward a Gal–1 resistant profile , thus highlighting a potential parasite strategy to avoid the beneficial inhibitory effects of Gal–1 in host cells . This Gal–1 restrictive glycophenotype is similar to that observed in T helper ( Th ) -2 polarized cells [23] , in M2-type microglia [51] and in tumor-associated endothelial cells [25] . Regarding the mechanisms underlying the effects of Gal–1 on cardiac cells , it is well known that galectins can act by blocking or stimulating pathogen attachment and infection through binding to host or microbial glycans , or by interfering with molecular interactions required for microbial entry to host cells [42] . In this study , we found , both by flow cytometry and fluorescence staining , that Gal–1 did not bind to T . cruzi surface glycans from any of the parasite strains analyzed . Therefore , we hypothesized that Gal–1 released via an autocrine or paracrine pathway might recognize specific glycans on the surface of HL–1 cardiac cells that are necessary for T . cruzi attachment and/or invasion . However , Gal–1 could also act directly facilitating cytokine release by cardiac cells [42] . To determine whether cardiac cells are a major source of Gal–1 production , we evaluated Gal–1 mRNA and protein expression in infected HL–1 cells . Even tough , T . cruzi infection did not induce Gal–1 expression in HL–1 cells , we observed increased amount of this lectin in culture media of infected cells , probably due to cellular damage generated by parasite release . This result might explain the up-regulation of Gal–1 in heart tissue from patients with chronic Chagas cardiomyopathy as reported by Giordanengo et al . [27] . In addition , we found that Gal–1 levels were greater in sera from patients with chronic Chagas disease , irrespective of cardiac alterations . Interestingly , T . cruzi infection up-regulated Gal–1 expression and secretion in different immune cells , including B cells and macrophages [28 , 52] . In vivo studies confirmed the protective effect of Gal–1 on T . cruzi infection observed in the in vitro assays . Lgals1-/- mice infected with T . cruzi Tulahuén strain showed higher parasitemia together with lower survival rate , which was more evident in female than male animals . In addition , histopathological analysis revealed a major number of parasites in the heart of those animals , but surprisingly Lgals1-/- mice showed a slightly decrease in the inflammatory response as compared to their WT counterparts . Based on previous reports [53] , we would expect that a strong inflammatory response will be accompanied by an increased parasite burden in the heart or skeletal muscle of Lgals1-/- mice . However , similar findings were reported in mice with genetic or acquired deficiencies of the immune system [35 , 54 , 55] , supporting the notion that the lower inflammation observed in hearts of Lgals1-/- mice may be the result of a differential regulation of the immune responses in these knock-out animals . In addition , differences observed between genders are not unexpected; the immune response to some microorganisms and the subsequent clinical outcome of the infection are linked to host hormonal pathways [56 , 57] . Interestingly , substantial disparities in male and female individuals have been clearly documented in T . cruzi infection [58 , 59] . Our results highlight the role of Gal–1 in the complex parasite-driven immune-endocrine networks since important discrepancies in parasitemia and survival rates were observed in male and female animals lacking the Lgals1 gene . Moreover , in a very recent study , Poncini and colleagues demonstrated that Lgals1-/- mice infected by intradermoplantar inoculation with T . cruzi RA strain , displayed lower mortality and parasite burden in muscle tissue than WT mice [60] . The discrepancy with our data could be associated with different administration routes as the presence of different phagocytic cell types at sites of inoculation and the local immune response triggered by T . cruzi infection may dictate not only changes in parasite load but also susceptibility or resistance to infection [61–63] . Altogether , these findings suggest that galectin-glycan interactions may influence the outcome of the infection depending on the strain of the parasite ( Tulahuén , Brazil or RA ) , the route of infection ( intraperitoneal or intradermoplantar inoculation ) and the subtle differences in the immune responses triggered by each T . cruzi strain [61–63] . Furthermore , similar strain-specific divergences have been previously reported by Toscano et al . , showing that endogenous Gal–3 can differentially regulate the outcome of experimental malaria , when three distinct strains of rodent malaria parasites , Plasmodium yoelii 17XNL , Plasmodium berghei ANKA and Plasmodium chabaudi AS were inoculated in mice lacking Gal–3 [64] . Finally , our data suggest that the parasite may display evasive mechanisms to counteract the effect of Gal–1 . In fact , T . cruzi infection altered the glycophenotype and decreased the availability of galectin-binding sites on cardiac cells as evidenced by increased α2-6-sialylation which prevented Gal–1 recognition of poly-LacNAc structures . Changes in the glycophenotype were not as evident in HL–1 cells infected with parasites belonging to the Brazil strain , which could explain , at least in part , the differential infectivity of this strain compared with the Tulahuén strain . In line with these findings , Vray et al . described that changes in glycosylation structures of infected dendritic cells rendered a profile that was more reactive with Gal–3 , affecting not only the infectivity , but also the migratory capacity of these cells in the context of Chagas disease [65] . In this regard , it has been demonstrated that electric communication in the heart is modulated by regulated glycosylation , particularly by sialylation of the cardiac voltage-gated Na+ channels ( Nav ) and Kv [66–68] . Our results suggest that parasite-induced remodeling of the host cell glycome might contribute to mechanical and functional alterations in the heart of infected host . In conclusion , our findings demonstrate that: a ) Gal–1 inhibits T . cruzi infection of cardiac cells , and b ) parasite infection alters the surface glycophenotype of cardiac cells , restricting Gal–1 and possibly limiting its inhibitory activity . Importantly , these effects were dependent on multiple parameters including parasite inoculum and strain , route of entry of the parasite and gender of the host . Thus , modulation of Gal-1-glycan interactions in cardiac cells may influence parasite-induced heart injury . Further studies are warranted to clarify the potential clinical relevance of our findings . | Galectins are a family of endogenous lectins defined by a well-conserved carbohydrate recognition domain ( CRD ) that recognizes β-galactoside-related glycans presented by several glycoconjugates . Up to now , fifteen galectins have been identified in a variety of cells and tissues and proposed to be crucial in diverse biological processes . Galectin–1 ( Gal–1 ) , a prototype member of the galectin family , plays key roles in pathogen recognition and in the modulation of innate and adaptive host immune responses . Following infection with the intracellular parasite Trypanosoma cruzi , the etiological agent of Chagas disease , Gal–1 was found to be up-regulated in cardiac tissue from patients with chronic Chagas cardiomyopathy . In the present study , we identified a protective role of Gal–1 in T . cruzi infection of cardiac cells , highlighting the ability of this parasite to control the glycophenotype of these cells . Our data also disclose the relevance of parasite strain-dependent differences in Gal-1-mediated control of T . cruzi infection in vivo . The findings presented here will contribute to delineate the role of Gal-1-glycan interactions during T . cruzi infection , particularly in the context of heart tissue injury , with critical implications in Chagas disease . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Galectin-1 Prevents Infection and Damage Induced by Trypanosoma cruzi on Cardiac Cells |
Klebsiella pneumoniae is an important cause of sepsis . The common Toll-like receptor adapter myeloid differentiation primary response gene ( MyD ) 88 is crucial for host defense against Klebsiella . Here we investigated the role of MyD88 in myeloid and endothelial cells during Klebsiella pneumosepsis . Mice deficient for MyD88 in myeloid ( LysM-Myd88−/− ) and myeloid plus endothelial ( Tie2-Myd88−/− ) cells showed enhanced lethality and bacterial growth . Tie2-Myd88−/− mice reconstituted with control bone marrow , representing mice with a selective MyD88 deficiency in endothelial cells , showed an unremarkable antibacterial defense . Myeloid or endothelial cell MyD88 deficiency did not impact on lung pathology or distant organ injury during late stage sepsis , while LysM-Myd88−/− mice demonstrated a strongly attenuated inflammatory response in the airways early after infection . These data suggest that myeloid but not endothelial MyD88 is important for host defense during gram-negative pneumonia derived sepsis .
Globally , lower respiratory tract infections are in the top ten causes of death , both in high- and low-income countries [1] . Pneumonia is the most common cause of sepsis and frequently caused by gram-negative pathogens from the family of Enterobacteriaceae , including Klebsiella ( K . ) pneumoniae [2]–[4] . Increasing rates of extended-spectrum β-lactamases producing Enterobacteriaceae are a major health concern and make the development of new therapies urgent , since infection with such pathogens is associated with increased mortality [5]–[7] . Infection is detected by sensors of the innate immune system collectively called pattern recognition receptors [8] , [9] . Toll-like receptors ( TLRs ) prominently feature herein , able to detect a variety of conserved microbial patterns as well as “danger signals” released from host cells as a consequence of injurious inflammation . As such , TLRs play an important role in the initiation and amplification of the host response [8] , [9] . The universal adaptor for all TLRs except TLR3 is myeloid differentiation primary response gene ( MyD ) 88 , that propagates the signal of activated TLRs intracellularly , leading to NFκB and MAP kinase activation . In addition , MyD88 mediates IL-1β and IL-18 receptor signaling [10] . We and others recently demonstrated the importance of MyD88 dependent signaling for survival and antibacterial defense during K . pneumoniae infection [3] , [11] , [12] . During respiratory tract infection different MyD88 expressing cells may contribute to host defense , including innate immune cells , such as alveolar macrophages , intraepithelial dendritic cells and migrated leukocytes , and parenchymal cells , such as lung epithelium and endothelium [13]–[15] . By creating chimeric mice using bone marrow ( BM ) transplantation , we reported the importance of MyD88 in both radiosensitive ( hematopoietic ) cells and radioresistant ( parenchymal ) cells for antibacterial defense and survival during Klebsiella pneumonia derived sepsis [12] . Whereas the role of hematopoietic cells in host defense against bacteria is undisputed , there are only few reports about the specific contribution of the vascular endothelium to the pathophysiology of infection and sepsis . Some evidence points to an attenuation of tissue and organ injury during polymicrobial sepsis when endothelial NFκB signaling was specifically targeted , without an effect on bacterial clearance [16]–[19] . However on the other hand the specific expression of endothelial TLR4 was reported to be sufficient for adequate bacterial clearance in a model of gram-negative infection [20] . Therefore , we here aimed to study the role of MyD88 dependent signaling in myeloid and endothelial cells during K . pneumoniae pneumosepsis by using mice with cell-specific targeted deletion of Myd88 and BM transfer . We demonstrate that myeloid , but nor endothelial cell MyD88 is important for host defense during pneumonia derived sepsis caused by Klebsiella .
To investigate the relative contribution of MyD88 dependent signaling in myeloid and endothelial cells to protective immunity during gram-negative pneumosepsis we crossed mice homozygous for the conditional Myd88 flox allele ( Myd88fl/fl mice ) [21] with mice expressing Cre under control of the myeloid cell LysM promoter ( to generate LysM-Myd88−/− mice ) [22] or the myeloid plus endothelial cell Tie2 promoter ( to generate Tie2-Myd88−/− mice ) [23] . To determine the efficiency of Cre-induced Myd88 deletion in specific cell types , we performed qPCR to quantify the remaining Myd88fl/fl in blood total leukocytes , granulocytes , monocytes and lymphocytes , in alveolar and peritoneal macrophages , in splenocytes and in lung endothelial and epithelial cells ( figure 1A ) . As expected , the deletion efficiency of Cre in LysM-Myd88−/− was very high in the myeloid compartment , especially in macrophages , granulocytes and to a lesser extent monocytes; lymphocytes and endothelial cells were unaffected . As anticipated , the Myd88fl/fl allele was almost completely absent in endothelial cells of Tie2-Myd88−/− mice . In addition , excision of the Myd88fl/fl allele was also virtually complete in all hematopoietic cell types of Tie2-Myd88−/− mice , as well as in lymphocytes and ( accordingly ) in splenocytes . Next , to determine the functional consequences of these Cre-mediated cell-specific Myd88 deletions , we incubated whole blood leukocytes , alveolar and peritoneal macrophages and splenocytes obtained from LysM-Myd88−/− , Tie2-Myd88−/− and control mice with K . pneumoniae LPS or heat-killed K . pneumoniae , using TNFα release as readout; we focused on these cell types since they confer protective functions during infection and sepsis [24]–[26] . In agreement with the genetic characterization of cells from LysM-Myd88−/− and Tie2-Myd88−/− mice , whole blood leukocytes from both genotypes showed a clearly reduced responsiveness to Klebsiella and Klebsiella LPS , with Tie2-Myd88−/− leukocytes showing the largest defect ( figure 1B ) . In addition , LysM-Myd88−/− and Tie2-Myd88−/− alveolar and peritoneal macrophages displayed strongly reduced TNFα release upon stimulation ( figure 1C , D ) , while the strongest defect in splenocyte responsiveness was seen in Tie2-Myd88−/− cell cultures ( figure 1E ) . Together these results indicate that Tie2-Myd88−/− mice are MyD88 deficient in hematopoietic , lymphoid and endothelial cells , while in LysM-Myd88−/− mice MyD88 deficiency is restricted to hematopoietic cells . Next , we infected LysM-Myd88−/− , Tie2-Myd88−/− and Myd88fl/fl Cre negative control mice with K . pneumoniae via the airways and monitored mortality during a 5-day follow up ( figure 2A ) . LysM-Myd88−/− and Tie2-Myd88−/− mice displayed massive mortality within the first 2 days after infection with median survival times of 1 . 8 and 1 . 5 days respectively , while control mice had a median survival time of 2 . 9 days ( both p<0 . 001 versus control mice ) . Notably , Tie2-Myd88−/− mice showed an accelerated mortality relative to LysM-Myd88−/− mice ( p<0 . 01 for the difference between groups ) . To obtain insight in the cause of early lethality of LysM-Myd88−/− and Tie2-Myd88−/− mice we next infected mice with Klebsiella in a separate experiment and harvested lungs , blood , spleen and liver for quantitative cultures 24 hours post infection ( i . e . shortly before the first deaths were expected to occur ) , seeking to collect data representative for host defense at the primary site of infection and bacterial dissemination . At this time point , both LysM-Myd88−/− and Tie2-Myd88−/− mice had ≥2-log more bacteria in their lungs relative to control mice ( p<0 . 01 and 0 . 001 respectively compared to controls , figure 2B ) . Moreover , bacterial counts were significantly higher in blood and spleen of LysM-Myd88−/− and Tie2-Myd88−/− mice ( both p<0 . 05 compared to control mice , figure 2C and D ) . In addition , Tie2-Myd88−/− mice had significantly higher amounts of bacteria in their livers ( p<0 . 01 compared to control mice , figure 2E ) . Tie2-Myd88−/− mice had higher bacterial counts when compared with LysM-Myd88−/− mice in all body sites , although these differences did not reach statistical significance . Together these data indicate that LysM-Myd88−/− and Tie2-Myd88−/− mice demonstrate a strongly enhanced bacterial growth and dissemination during gram-negative pneumonia derived sepsis , resulting in accelerated mortality . To obtain insight in local inflammation at the primary site of infection we harvested lungs from LysM-Myd88−/− , Tie2-Myd88−/− and control mice 24 hours post infection for semi-quantitative histopathology , focusing on key histological features characteristic for severe pneumonia ( figure 3 ) . The extent of lung pathology did not differ between groups . LysM-Myd88−/− mice had lower myeloperoxidase ( MPO ) concentrations in whole lung homogenates , indicative of a reduced neutrophil content . In accordance , the number of Ly6+ cells was lower in LysM-Myd88−/− mice relative to controls . To obtain further insight in the role of MyD88 in cells targeted by LysM- and Tie2-driven Cre recombinase in lung inflammation during Klebsiella pneumonia , we measured the levels of the proinflammatory cytokines IL-1β , TNF-α , IL-6 , the anti-inflammatory cytokine IL-10 and the neutrophil attracting chemokines CXCL-1 and CXCL-2 in lung homogenates ( table 1 ) . The pulmonary concentrations of all mediators were similar in LysM-Myd88−/− , Tie2-Myd88−/− and control mice , with the exception of TNF-α levels which were significantly lower in Tie2-Myd88−/− mice ( p<0 . 01 compared to controls ) . Plasma IL-6 levels were significantly increased in LysM-Myd88−/− and Tie2-Myd88−/− mice ( p<0 . 05 to 0 . 01 respectively compared to controls , table 1 ) likely as a result of higher bacterial loads . In addition , we determined E-selectin levels in both lung homogenates and plasma as a reflection of endothelial cell activation [27] and observed that lung levels of E-selectin were significantly increased in Tie2-Myd88−/− mice , probably as a result of the higher bacterial burden ( p<0 . 05 compared to control mice ) ( figure S1 ) . The model of Klebsiella pneumonia and sepsis used here is associated with rises in the plasma concentrations of LDH ( indicative for cellular injury in general ) and AST ( reflecting hepatocellular injury ) in the late stage of infection [28] . To study if the absence of MyD88 in myeloid and/or endothelial cells affected the degree of liver and cellular injury we determined the plasma levels of these parameters but observed no differences ( figure S2 ) . Together these data suggest that the increased mortality in LysM-Myd88−/− and Tie2-Myd88−/− mice occurred as a result of overwhelming bacterial growth rather than as a result of pulmonary or distant organ injury . Considering that Tie2-Myd88−/− mice have strongly impaired MyD88 signaling in hematopoietic and endothelial cells , we decided to restore the hematopoietic compartment of Tie2-Myd88−/− mice with BM of Myd88fl/fl control mice after lethal irradiation , thereby creating mice with a more exclusive MyD88 deficiency in endothelial cells . In order to adequately estimate the effect size , we created two control groups: Tie2-Myd88−/− mice transplanted with Tie2-Myd88−/− BM and control mice transplanted with control BM . After 6 weeks of recovery , we infected mice with K . pneumoniae intranasally and sacrificed them 24 hours later . In addition , to check the efficiency of the BM transplantation to restore the responsiveness of relevant cell types from Tie2-Myd88−/− mice to Klebsiella , we euthanized 2–3 uninfected mice of each recipient group and repeated cell stimulation experiments as described above . These experiments revealed that transfer of control BM in Tie2-Myd88−/− mice fully restored the capacity of blood leukocytes , and alveolar and peritoneal macrophages , and partially that of splenocytes , to produce TNFα upon exposure to Klebsiella in vitro ( figure 4A–D ) . The response of cells obtained from the two control groups transplanted with isogenic BM ( control mice+control BM and Tie2-Myd88−/− mice+Tie2-Myd88−/− BM ) replicated the impaired response of untransplanted Tie2-Myd88−/− mice relative to control mice . Importantly , after 24 hours of infection , lung bacterial loads of Tie2-Myd88−/− +control BM mice were indistinguishable from control+control BM mice , while the difference between Tie2-Myd88−/−+Tie2-Myd88−/− BM mice and control+control BM mice phenocopied the difference between Tie2-Myd88−/− and control mice observed in untransplanted mice ( p<0 . 001 , figure 5A ) . In line , lung bacterial levels were significantly lower in Tie2-Myd88−/−+control BM mice compared to Tie2-Myd88−/−+Tie2-Myd88−/− BM mice ( p<0 . 01 ) . Bacterial numbers in blood and spleen confirmed the protective effect of reconstitution of the hematopoietic compartment of Tie2-Myd88−/− mice with MyD88 sufficient BM ( p<0 . 05 versus Tie2-Myd88−/−+Tie2-Myd88−/− BM mice , figure 5B , C ) . The extent of lung pathology , lung MPO levels and the number of Ly6+ cells in lung tissue were not different between groups ( figure S3 ) . Moreover , lung and plasma cytokine/chemokine and E selectin concentrations were not affected by the selective absence of endothelial MyD88 in Tie2-Myd88−/−+control BM mice , except for slightly lower lung levels of IL-10 compared to control+control BM mice ( table S1; figure S4 ) ) . Also , the plasma levels of AST and LDH did not differ between groups ( figure S4 ) . Together , these data indicate that endothelial cell MyD88 has no role in antibacterial defense or in lung or distant organ injury after infection with Klebsiella via the airways . Mice with a complete MyD88 deficiency show a strongly impaired antibacterial defense after infection with Klebsiella via the airways caused by a mitigated neutrophil recruitment into the airways associated with strongly reduced local levels of neutrophil attracting mediators [11] . We wished to determine whether a similar mechanism is at play in LysM-Myd88−/− mice . Thus , LysM-Myd88−/− and control mice were infected with K . pneumoniae intranasally and lungs and bronchoalveolar lavage ( BAL ) fluid was harvested 6 hours later . LysM-Myd88−/− mice showed higher bacterial loads in whole lung homogenates , but not in BAL fluid ( figure 6A ) . Importantly , LysM-Myd88−/− mice displayed a strongly attenuated influx of neutrophils into the bronchoalveolar compartment ( figure 6B ) , which was associated with markedly reduced levels of TNFα , CXCL-1 and CXCL-2 in BAL fluid; IL-6 concentrations in BAL fluid did not differ between groups ( figure 6D ) . Hence , these data suggest that LysM-Myd88−/− mice replicate the phenotype of Myd88−/− mice with regard to impaired neutrophil influx in the airways during early Klebsiella pneumonia at least in part caused by a reduced chemotactic gradient due to impaired chemoattractant production .
Several MyD88 dependent TLRs are known to be important for the innate immune response to respiratory tract infection with K . pneumoniae , particularly TLR4 and TLR9 , and during late stage infection or in the presence of high bacterial numbers , TLR2 [29]–[32] . Since TLRs and other innate immune sensors are widely distributed among different cell types in the airways , comprising both hematopoietic and non-hematopoietic cells , our laboratory engaged in several studies seeking to dissect the cell-specific contribution of TLR and MyD88 signaling in host defense during Klebsiella pneumonia derived sepsis [12] , [32] . Using BM chimeras we reported that TLR2 and TLR4 expression in hematopoietic cells are crucial for antibacterial defense , while MyD88 in hematopoietic and parenchymal cells is equally important [12] , [32] . BM transplantation can introduce artefacts caused by the irradiation and/or incomplete replacement of recipient hematopoietic cells , and cannot provide detailed information about the specific cell type that is affected [33] . In the present study we used the Cre-lox system combined with BM transfer to study the role of myeloid and endothelial cell specific MyD88 signalling in the host response during Klebsiella induced pneumosepsis . We demonstrate that while myeloid MyD88 contributes significantly to host defense , endothelial cell MyD88 has no role herein . Endothelial cells are resident cells implicated in sepsis pathogenesis and the induction of organ injury [34] . Earlier investigations examined the contribution of TLR and NFκB signaling within the vascular endothelium to the host response during experimental sepsis . Inhibition of endothelial NFκB signaling by overexpression of a degradation-resistant form of the NF-κB inhibitor I-κBα under the control of the endothelial cell specific VE-cadherin-5 promoter attenuated tissue inflammation and organ injury during endotoxemia and abdominal sepsis [16]–[19] . In addition , these mice displayed strongly reduced coagulation activation upon administration of endotoxin [19] . Endothelial cell specific NFκB inhibition did not influence the clearance of Listeria monocytogenesis , Streptococcus pneumoniae or Salmonella enterica after intravenous infection [16]–[19] . However , transgenic Tie2 driven expression of TLR4 in Tlr4−/− mice , resulting in mice with TLR4 expression restricted to endothelial cells , was sufficient for adequate bacterial clearance after intraperitoneal infection with Escherichia coli [20] . We used mice with Tie2 driven expression of Cre recombinase to delete hematopoietic and endothelial MyD88 in Myd88fl/fl mice and observed a strongly impaired host defense as reflected by very high bacterial loads and increased mortality . Previous studies support Tie2 expression in hematopoietic cells and the lack of specificity for endothelial cells [23] , [35] . Similarly , the VE-cadherin-5 promoter is reported to drive Cre recombinase gene expression not only in endothelial cells but also in a subset of hematopoietic cells [36] . As such , the Cre-lox system seems less suitable to specifically study the function of genes in endothelial cells . Therefore , to generate mice with endothelial cell specific MyD88 deficiency , we reconstituted Tie2-Myd88−/− mice with BM of control mice and confirmed functional recovery of their hematopoietic cells with regard to responsiveness to Klebsiella . These mice were indistinguishable from control mice with regard to antibacterial defense , inflammation and distant organ injury , strongly suggesting that endothelial cell MyD88 does not play an important role in the host response during Klebsiella induced pneumosepsis . Although this “negative” finding may seem to contrast with previous studies on the role of endothelial cells in severe infection [16]–[20] , our approach clearly differs from these earlier investigations , both with regard to the target of genetic manipulation ( deletion of MyD88 versus inhibition of NFκB [16]–[19] and endothelial cell TLR4 expression on an otherwise TLR4 deficient background [20] ) and the sepsis model used ( pneumonia versus abdominal or intravenous infection [16]–[20] ) . Importantly , mice with TLR4 exclusively on endothelial cells were unable to recruit neutrophils into the lungs upon intratracheal LPS administration [20] and , similarly , studies in TLR4 BM chimeras have indicated that neutrophil influx after airway exposure to LPS occurs by mechanisms that do not rely on TLR4 expression by radioresistant ( including endothelial ) cells [37] , which is completely consistent with our present data . Of note , findings in TLR4 BM chimeras have suggested that neutrophil accumulation in lungs upon intravenous LPS challenge does largely dependent on TLR4 in radioresistant cells [38] , indicating that the role of cell-specific TLR signaling in neutrophil recruitment likely depends on the route by which the bacterial stimulus is administered . LysM-Cre mediated deletion of the floxed Myd88 allele resulted in MyD88 deficiency especially in macrophages and neutrophils , and to a lesser extent monocytes [22] , [39] . Clearly , these myeloid cell MyD88 deficient mice showed a strongly compromised host defense after infection with Klebsiella , as reflected by enhanced mortality , increased bacterial numbers at the primary site of infection and an impaired early neutrophil influx and cytokine/chemokine release in the airways . Thus , MyD88 expressed by alveolar macrophages and neutrophils is essential for initiation of an adequate early innate immune response in the lung after infection with Klebsiella via the airways and the absence thereof results in uncontrolled bacterial growth and death . The phenotype of LysM-Myd88−/− mice was very similar to the previously documented phenotype of Myd88−/− mice during Klebsiella pneumonia [3] , [11] , [12] , underlining the importance of myeloid cell MyD88 during respiratory tract infection . The Klebsiella strain used here cannot be killed by macrophages or neutrophils in vitro , illustrating its high virulence and precluding analysis of a possible direct role of MyD88 in killing . Previous studies have reported a role for MyD88 in killing of commensal and attenuated pathogenic Gram-negative bacteria [40] , but not in killing of Listeria by macrophages [41] . While innate immunity is important for antibacterial defense , it can also cause harm by hyperinflammation induced organ injury [42] , [43] . Deficiency of MyD88 has been shown to be protective in polymicrobial sepsis , in which especially liver injury was found to be associated with MyD88 dependent signaling [43] , [44] . A recent study demonstrated that mice with selective expression of MyD88 in myeloid cells displayed enhanced hepatocellular injury during abdominal sepsis induced by cecal ligation and puncture [45] . Here we found no evidence for a role of either myeloid or endothelial cell MyD88 in hepatocellular damage during pneumonia derived sepsis caused by K . pneumoniae . Hence , although MyD88 may contribute to organ injury during sepsis , its role likely depends on the type and primary source of the infection . Using MyD88 BM chimeras , we recently reported a role for both hematopoietic and parenchymal MyD88 in host defense in this model [12] . Since we could not demonstrate a role for endothelial cell MyD88 in the present investigation , MyD88 expressed in the respiratory epithelium may be involved . Indeed , lung epithelial cells have been implicated in host defense during respiratory tract infection [15] . The importance of MyD88 dependent signaling in lung epithelial cells was recently elegantly demonstrated in a model of Pseudomonas pneumonia in epithelial specific MyD88 knock-in mice [46] , [47] . Selective expression of MyD88 in the airway epithelium was sufficient for neutrophil recruitment to the site of infection and bacterial clearance [47] . In addition , transgenic overexpression of IκB-α in alveolar and bronchial epithelium in mice resulted in a reduced neutrophil influx into BAL fluid upon intrapulmonary delivery of LPS [48] , [49] and an increased growth of the gram-positive pathogen Streptococcus pneumoniae upon intratracheal infection [50] . Studies using mice in which Myd88 is deleted specifically in respiratory epithelium are warranted to establish the role of epithelial MyD88 in host defense against Klebsiella pneumonia derived sepsis . However , our first preliminary results with mice generated from intercrossings of Myd88fl/fl mice and mice with Cre recombinase controlled by the surfactant protein C promoter [51] , resulting in mice with a targeted deletion of Myd88 in distal airway epithelium , suggest that epithelial cell MyD88 does not contribute to protective immunity during Klebsiella pneumonia . Therefore , our earlier data using MyD88 BM chimeras [12] may have been confounded by incomplete replacement of recipient ( MyD88 sufficient ) hematopoietic cells . LysM-Myd88−/− mice showed a strongly impaired neutrophil influx into the bronchoalveolar space 6 hours after infection with Klebsiella , together with markedly reduced local concentrations of neutrophil attracting mediators such as TNF-α , CXCL1 and CXCL2 . Notably , global MyD88 deficiency similarly results in an early impairment of neutrophil chemoattractant release and neutrophil migration into the airways in mouse models of pneumonia caused by a variety of bacterial and viral species [11] , [52]–[57] , as well as during sterile lung inflammation [58] , [59] . These data suggest that hematopoietic and global MyD88 deficiency impair host defense during pneumonia by a largely similar mechanism that involves an inability to produce a chemotactic gradient that would normally attract neutrophils to the site of the infection . Notably , MyD88 deficient mice showed extensive lung inflammation , including high E-selectin levels , at 24 hours after infection , suggesting that these late responses can be induced by Klebsiella via MyD88-independent mechanisms ( e . g . , via the TRIF pathway ) in the presence of the ( by then ) very high bacterial loads . Similarly , global Myd88−/− mice were previously reported to show profound lung inflammation during late stage bacterial pneumonia in the presence of high bacterial loads [54] , [56] , [60] . E-selectin , while implicated in the rolling of neutrophils along the vascular endothelium [61] , does not seem to play a role in neutrophil recruitment to the lungs elicited by bacterial stimuli [62] , [63] . In conclusion , to our knowledge , we here report for the first time on the role of MyD88 in myeloid and endothelial cells in severe bacterial infection , using a clinically relevant model of gram-negative pneumonia derived sepsis characterized by gradual growth of bacteria at the primary site of infection followed by dissemination , tissue injury and death . While myeloid MyD88 was crucial for protective immunity , endothelial MyD88 played no role herein . Our results suggest that myeloid MyD88 deficiency results in enhanced lethality during Klebsiella pneumonia by a mechanism that involves a strongly attenuated early inflammatory response at the primary site of infection and as a consequence thereof uncontrolled bacterial growth . These data provide new insights in the pathophysiology of gram-negative sepsis and may be helpful for the development of therapeutics aimed at specific cell types .
Experiments were carried out in accordance with the Dutch Experiment on Animals Act and approved by the Animal Care and Use Committee of the University of Amsterdam ( Permit number: DIX 100121 , sub-protocols DIX102300 and DIX101613 ) . Homozygous Myd88fl/fl mice [21] were crossed with LysM-Cre [22] or Tie2-Cre mice [23] , both obtained from the Jackson Laboratory ( Bar Harbor , Maine ) , to generate myeloid ( LysM-Myd88−/− ) and myeloid plus endothelial cell ( Tie2-Myd88−/ ) specific MyD88 deficient mice . Myd88fl/fl Cre negative littermates were used as controls . All mice were backrossed at least 8 times to a C57Bl/6 background and age- and sex matched when used in experiments . Peritoneal lavage was performed with 5 ml sterile PBS under isoflurane anesthesia and lavage fluid was collected in PBS containing a final concentration of 10% FBS , 1% antibiotics ( penicillin- streptomycin- amphotericin B ( Gibco , Paisley , United Kingdom ) ; heart puncture was performed and blood was collected in EDTA or heparin containing tubes; BAL was performed with 10 ml PBS in portions to obtain alveolar macrophages and spleens were harvested . For whole blood stimulation , 100 µl of heparinized blood was pipetted in a 96 wells U-bottom cell culture plate ( Greiner bio-one , Alphen a/d Rijn , Netherlands ) . Spleens were crushed through a 40 µm mesh and after lysis of erythrocytes with an ammoniumchloride containing lysis buffer , splenocytes were seeded in RPMI complete ( containing 10% FBS , 1% antibiotics , 10 mM L-glutamine , Gibco ) at a density of 500 . 000 cells per well in 96 wells U-bottom culture plate ( Greiner bio-one ) . Peritoneal and alveolar macrophages were seeded in flat bottom 96 wells cell culture plates ( Greiner Bio-one ) at a density of approximately 50 . 000 and 30 . 000 respectively per well in RPMI complete and left to adhere overnight . Cells were stimulated for 20 hours with the indicated concentrations of heat-killed K . pneumoniae or LPS derived from Klebsiella pneumoniae ( Sigma ) diluted in RPMI complete medium in a final volume of 200 microliter . Whole blood leukocyte genomic DNA was isolated from fresh EDTA blood and primary cells using the Nucleospin Blood Kit ( Machery Nagel , Düren , Germany ) and in addition , from FACS purified monocytes , neutrophils and lymphocytes . For this , erythrocyte lysis of EDTA blood with ammoniumchloride containing lysis buffer was performed and cells were stained for cell surface molecules using FITC-conjugated anti-mouse Ly6-C &Ly6-G ( Gr-1 ) , PE-conjugated anti-mouse CD11b ( BD Biosciences ) and biotinylated anti-mouse CD115 ( eBioscience ) , secondary staining was performed with streptavidin-APC ( BD Biosciences ) . Monocytes were identified as Gr-1dim/CD-115+ and neutrophils as Gr-1high/Cd115− within the fraction of CD11b+ cells , the fraction of Cd11b− cells with a low Side Scatter and Forward Scatter pattern were identified as lymphoid cells [64] . Total RNA was reverse transcribed using oligo ( dT ) primer and Moloney murine leukemia virus reverse transcriptase ( Invitrogen , Breda , The Netherlands ) . We quantified the residual amount of the “floxed” region of MyD88 in LysM-Myd88−/− and Tie2-Myd88−/− mice in blood and particular cell types using the primer sequences 5′-ACGCCGGAACTTTTCGAT-3′ ( forward ) ; 5′-TTTTCTCAATTAGCTCGCTGG-3′ relative to the unaffected Socs-3 gene with primer sequences 5′- ACCTTTCTTATCCGCGACAG- 3′ ( forward ) and 5′- TGCACCAGCTTGAGTACACAG-3′ ( reverse ) in a SybrGreen reaction on an LightCycler system ( LC480 , Roche Applied Science , Mannheim , Germany ) . The amount of remaining “floxed” MyD88 region in LysM-MyD88−/− and Tie2-MyD88−/− mice was calculated using the 2-deltaCt ( ΔΔCt ) method using the amount of genomic DNA from Myd88fl/fl mice for the no-deletion control [21] . The deletion efficiency was calculated as ( 1 - residual Myd88fl ) ×100 . Pneumonia was induced by intranasal inoculation with ∼6×103 colony forming units ( CFU ) of K . pneumoniae serotype 2 ( ATCC 43816; American Type Culture Collection , Manassas , VA ) and survival was monitored or in separate experiments mice were euthanized after 6 or 24 hours of infection when organs were harvested and processed exactly as described [12] , [32] . Lung ( and cell supernatant ) levels of IL-1β , TNF-α , IL-6 , IL-10 , CXCL-1 and CXCL-2 were measured by ELISA ( R&D Systems , Minneapolis , MN ) . Plasma levels of TNF-α , IL-6 , and IL-10 were measured by using a cytometric bead array multiplex assay ( BD Biosciences ) . MPO was measured by ELISA from HyCult Biotechnology ( Uden , the Netherlands ) . Lactate dehydrogenase ( LDH ) and aspartate aminotransferase ( AST were measured using kits from Sigma and a Hittachi analyzer ( Boehringer Mannheim ) . Histologic examination of lungs was performed exactly as described [32] . For granulocyte immunohistochemic stainings lung tissue slides were deparaffinized and rehydrated . Endogenous peroxidase activity was quenched by a solution of 0 . 3% H2O2 ( Merck ) . Slides were then digested by a solution of pepsin 0 . 025% ( Sigma , St . Louis , MO , USA ) in 0 . 1 M HCl . After being rinsed , the sections were incubated in Ultra V Block ( Thermo Scientific , Fremont , CA ) and then exposed to a FITC-labeled anti-mouse Ly6-G and Ly6-C monoclonal antibody ( BD Pharmingen , San Diego , CA ) . After washes , slides were incubated with a rabbit anti-FITC antibody ( Nuclilab , Ede , The Netherlands ) followed by further incubation with Brightvision poly-horseradish peroxidase anti Rabbit IgG ( Immunologic , Duiven , The Netherlands ) , rinsed again and developed using Bright DAB ( Immunologic , Duiven , the Netherlands ) . The sections were counterstained with methyl green and mounted in Pertex mounting medium ( Histolab , Gothenburg , Sweden ) . The Ly-6G and Ly-6C+ percentage of total lung surface was determined with imageJ software ( Rasband , W . S . , ImageJ , U . S . National Institutes of Health , Bethesda , Maryland , USA , http://rsb . info . nih . gov/ij/ , 1997–2011 ) . BM transplantation was done as described previously [12] . Three groups were generated: Tie2-Myd88−/− ( recipient ) +control BM ( donor ) , Tie2-Myd88−/−+Tie2-Myd88−/− BM and control+control BM mice . Myd88fl/fl mice and BM were used as control . Data are expressed as box-and-whisker diagrams depicting the smallest observation , lower quartile , median , upper quartile , and largest observation ( in vivo experiments ) or as means ± standard error of the mean ( tables , cell stimulation experiments ) ; Comparison of these data was done by Mann Whitney U test . Differences in the proportion of positive cultures were analyzed by Fisher's exact test . Survival curves are depicted as Kaplan-Meier plots and compared using log-rank test . These analyses were done using GraphPad Prism ( San Diego , CA ) . P<0 . 05 was considered statistically significant . | Klebsiella pneumoniae is an important causative pathogen in hospital acquired or health care associated pneumonia and sepsis . Toll-like receptors recognize conserved motifs expressed by pathogens and thereby initiate the innate immune response . Myeloid differentiation primary response gene ( MyD ) 88 is a common adapter for multiple Toll-like receptors that is important for protective immunity during Klebsiella infections . The contribution of different cell types to MyD88 mediated protection is not known . We used a model of Klebsiella pneumonia and secondary sepsis in mice that were selectively deficient for MyD88 in specific cell-types that are implicated to be important for host defense mechanisms by use of a tissue specific gene recombination system and bone marrow transfer . We demonstrate that MyD88 in myeloid cells , but not in endothelial cells , is important for host defense during pneumonia derived sepsis caused by Klebsiella pneumoniae . | [
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] | 2014 | Hematopoietic but Not Endothelial Cell MyD88 Contributes to Host Defense during Gram-negative Pneumonia Derived Sepsis |
Schistosome infection persists for decades . Parasites are in close contact with host peripheral blood immune cells , yet little is known about the regulatory interactions between parasites and these immune cells . Here , we report that extracellular vesicles ( EVs ) released from Schistosoma japonicum are taken up primarily by macrophages and other host peripheral blood immune cells and their miRNA cargo transferred into recipient cells . Uptake of S . japonicum EV miR-125b and bantam miRNAs into host cells increased macrophage proliferation and TNF-α production by regulating the corresponding targets including Pros1 , Fam212b , and Clmp . Mice infected with S . japonicum exhibit an increased population of monocytes and elevated levels of TNF-α . Reduction of host monocytes and TNF-α level in S . japonicum infected mice led to a significant reduction in worm and egg burden and pathology . Overall , we demonstrate that S . japonicum EV miRNAs can regulate host macrophages illustrating parasite modulation of the host immune response to facilitate parasite survival . Our findings provide valuable insights into the schistosome-host interaction which may help to develop novel intervention strategies against schistosomiasis .
Schistosomes are parasitic flatworms belonging to the genus Schistosoma and are the causative agents of schistosomiasis , a neglected tropical disease [1] . Schistosome infection persists for decades suggesting a complex pathogen-host interaction that likely modulates the host immune response . Schistosomes also utilize host factors for parasite development . For example , host hormones ( insulin ) [2] , growth factors ( TGF-β ) [3–6] and inflammatory factors [7] have been shown to influence schistosome gene expression , development [8] and metabolism . On the other hand , molecules secreted by the parasites regulate host immunological cell apoptosis [9 , 10] . Although several studies have evaluated the effect of excreted/secreted products from schistosomes on the host immune response [11–15] , the mechanisms that schistosomes use to modulate the functions of host peripheral immune cells remain to be poorly characterized . Extracellular vesicles ( EVs ) , small membrane-bounded secreted vesicles , are involved in the regulation of many biological processes [16] . Recent studies have described the isolation and characterization of EVs from helminth parasites . For example , proteomic analysis of EVs from Schistosoma mansoni indicated that EV proteins are potential vaccine candidates [12 , 17]; exosomes from Schistosoma japonicum induce M1-type immune-activity in macrophages in vitro [18] , EVs isolated from the trematodes Echinostoma caproni and Fasciola hepatica are internalized into intestinal host cells [19] , and miRNAs are associated with F . hepatica EVs [20] . However , the regulatory roles of EVs and their miRNA cargo remain poorly characterized . In the nematode Heligmosomoides polygyrus , Buck and coworkers identified secreted EVs that contain miRNAs and Y RNA that suppress gene expression in host cells [21] , suggesting that helminth EVs as well as their miRNA cargo may play important regulatory roles in parasite-host interactions [22 , 23] . Adult schistosomes reside in the veins of vertebrate hosts in contact with host peripheral blood immune cells for extended periods . To determine if schistosome EVs regulate the functions of host immune cells , we first isolated SjEVs from adult S . japonicum and determined their miRNA contents . Using in vitro and in vivo experiments we then demonstrated that SjEV miRNAs are taken up by host peripheral blood immune cells , particularly by host monocytes . RNA-seq analyses on host cells exposed to SjEVs indicate that SjEV miRNAs may regulate TLR , TNF and other signaling pathways in recipient cells . SjEV miRNA cargo miR-125b and bantam led to increased monocyte proliferation and TNF-α production by down regulating Pros1 , Fam212b and Clmp . Increased monocyte levels induced by SjEVs may be important to the parasites as experiments decreasing monocytes in S . japonicum infected mice led to markedly reduced worm burden and egg production . Overall , our findings reveal a key role of SjEV miRNA cargo in modulation of host-pathogen interaction that facilitates parasite survival in the definitive host .
Using a protocol for schistosome EV isolation we previously developed [11 , 24] , we further characterized the isolated SjEVs . qNano analysis indicated that SjEVs ranged in size from 100 nm to 400 nm ( Fig 1A ) . Our previous proteomic studies indicated that the SjEV preparations consist primarily of S . japonicum proteins [24] . Immunoblotting here indicated that the isolated SjEVs contain several known exosomal markers including GAPDH , HSP70 , and HSP90 ( Fig 1B ) . The isolated SjEVs/NCTC EVs were not contaminated with endotoxin ( S1 Fig ) . Next , using high-throughput sequencing we determined the population of small RNAs associated with SjEVs ( S1 Table ) . Bioinformatic analyses indicated that a relatively large percentage of small RNAs in SjEVs were miRNAs ( 32% ) ( Fig 1C ) including miR-125b , miR-61 , miR-3505 , and a helminth specific bantam miRNA ( S1 Table ) . SjEV miR-125b was consistently and highly enriched in SjEVs , accounting for 64 . 21% of the total miRNA reads in three biological replicates ( Fig 1D and S2 Fig ) . Moreover , using RT-qPCR we verified the relative abundance of several SjEV miRNAs in independently prepared SjEVs ( Fig 1E ) . Adult schistosomes can reside in the host mesenteric veins for several decades in close contact with host peripheral immune cells . To determine whether SjEVs are taken up and regulate host peripheral immune cells ( Fig 2A ) , we incubated in vitro PKH67-labeled SjEVs with murine peripheral immune cells . The peripheral immune cells were then sorted using flow cytometry to distinguish CD3e T cells , B220 B cells , CD11b+ Ly6C+ monocytes , and NK1 . 1 cells and define the uptake of the labeled EVs into different immune cells . SjEVs were identified predominantly in monocytes , followed by T cells , B cells , and NK cells ( Fig 2B and 2C ) . Experiments using mice injected through the tail vein with PKH67 labeled SjEVs demonstrated that SjEVs were also taken up by peripheral immune cells in vivo ( Fig 2D ) . As shown in Fig 2E and 2F and S3 Fig , although uptake appears not as selective in vivo , PKH67-labeled SjEVs were highest in monocytes of mice at 4 h and 12 h of post injection consistent with the in vitro results . Analysis of peripheral blood immune cells from S . japonicum infected mice demonstrated that the miRNA cargo of SjEVs , miRNA cargo miR-125b , bantam , miR-61 , miR-277b , were also taken up and present in peripheral immune cells during a natural infection ( Fig 2G ) . Notably , these S . japonicum miRNAs are markedly more abundant in monocytes compared to NK cells that take up fewer SjEVs ( Fig 2H ) . Overall , these results indicate that S . japonicum EVs are taken up primarily by host monocytes with the transfer of their cargo miRNAs into the recipient cells . To initiate experiments to determine if SjEV miRNAs can functionally modulate RNAs within host recipient cells , we first labeled SjEV RNAs using the Exo-Glow exosome labeling kit , incubated the labeled SjEVs with a murine macrophage cell line ( RAW264 . 7 cells ) , used fluorescence microscopy to examine SjEV uptake into cells , and then determined if SjEV miRNAs were transferred into the recipient cells . As shown in Fig 3A , the labeled SjEVs were taken up by RAW264 . 7 cells . RT-qPCR analysis indicated that SjEV miRNAs ( miR-125b , miR-61 , bantam , and miR-277b ) were readily detected in RAW264 . 7 cells incubated with SjEVs ( Fig 3B ) , indicating that the SjEV miRNAs were transferred to the recipient cells . A prerequisite for miRNAs to perform their functions is to be bound by an Argonaute protein , a key component of the RNA-inducing silencing complex ( RISC ) . We used a mammalian Argonaute antibody in a pull-down experiment using RAW264 . 7 cells treated with SjEVs ( Fig 3C ) followed by RT-qPCR to determine whether SjEV miRNAs in RAW264 . 7 cells are loaded into host cell Argonaute . SjEV miRNAs were significantly enriched in Argonaute pull downs ( Fig 3D ) . These results indicate that SjEV miRNAs are transferred to host recipient cells and associate with host cell Argonaute . This suggests that SjEV miRNAs may be incorporated into a functional RISC complex that could regulate host gene expression in the recipient host cells . To examine the potential regulatory roles of SjEV taken up by recipient cells , we used RNA-seq to identify changes in steady state levels of mRNAs in cells treated with S . japonicum EVs . We identified 2 , 569 affected mRNAs , 1 , 211 upregulated and 1 , 358 downregulated , in the three biological replicates of SjEVs treated cells ( Fig 4A and 4B and S2 Table ) . KEGG analysis suggested that altered mRNAs were mainly involved in the TNF and Toll-like receptor ( TLR ) signaling pathways , cytokine-cytokine receptor interaction , Rap1 signaling pathway and other signaling pathways ( S3 Table ) . To gain further insight into potential regulatory roles of SjEV miRNA cargo in recipient cells , we selected the abundant miR-125b ( 64 . 21% of the miRNA reads ) and bantam that is a helminth specific SjEV miRNA for further analysis . We first used TargetScan and miRanda to predict putative targets for these two miRNAs . These programs co-predicted 257 putative mRNA targets for miR-125b and 12 putative mRNA targets for bantam ( S5 Table ) . We then selected several predicted mRNA targets and used RT-qPCR to corroborate that these mRNA target levels were altered in SjEV treated cells . Consistent with the RNA-seq results , RT-qPCR demonstrated a reduction of these mRNAs in independent experiment of RAW264 . 7 cells treated SjEVs ( Fig 4C ) . We determined the expressions of several targets in THP-1 monocytes cells treated with SjEVs using RT-qPCR . Similar results were obtained ( S4 Fig ) . In addition , we also examined mRNA levels of several proteins associated with TNF and TLR signaling pathways which we observed to be elevated in the RNA-seq analysis . RT-qPCR analysis confirmed that were increased in cells treated with SjEVs ( S5 Fig ) Among the predicted targets of miR-125b , protein S1 ( Pros1 ) and F11r are known to inhibit the TLR-triggered inflammatory responses and regulate immune cell functions and cytokine production , respectively . To determine whether Pros1 and F11r mRNAs could be targeted by SjEV miR-125b , the mRNA 3’ UTR predicted miRNA-binding sites ( predicted using TargetScan and mRanda ) were cloned into the 3’ UTR of a luciferase reporter ( pGlu-CMV ) for evaluation of target repression . The recombinant plasmids as well as the corresponding miRNA mimics and anti-sense were transfected into RAW264 . 7 cells and luciferase assays carried out . As shown in Fig 5A , transfection of miR-125b mimics resulted in a reduction of luciferase activity compared to a scrambled miRNA control , indicating that miR-125b can downregulate the expression of an mRNA with the cognate target regions in mammalian cells . Importantly , co-transfection with antisense miR-125b mimics in these experiments led to de-repression of luciferase . These results suggest that miR-125b can downregulate the expression of the corresponding Pros1 and F11r mRNA target regions in RAW264 . 7 cells . To determine whether miR-125b can regulate the predicted endogenous cellular targets , we transfected miR-125b mimics into cultured RAW264 . 7 cells and examined the impact on the abundance of their target mRNAs . Transfection of miR-125b mimics significantly downregulated Pros1 and F11r mRNA ( Fig 5B ) . Notably , we also observed that both Pros1 and F11r mRNAs were significantly reduced in peripheral blood monocytes isolated from mice infected with S . japonicum compared to uninfected mice ( Fig 5C ) . Furthermore , transfection of miR-125b mimics into RAW264 . 7 cells also led to an increase in TLR signaling mRNAs including p38 , Traf5 , Irf7 , IL-1β , IL-6 and TNF-α ( Fig 5D ) . In addition , transfection of miR-125b mimics into RAW247 . 1 cells resulted in significantly elevated levels of TNF-α in the culture medium ( Fig 5E ) . To further shown that Pros1 and F11r down-regulation impacts TLR signaling , we designed and optimized siRNAs that effectively silence Pros1 ( siRNA-425 ) or F11r ( siRNA-716 ) ( Fig 5F and S6 Fig ) . Treatment of RAW247 . 1 cells with the Pros1 siRNA to inhibit Pros1 led to increased expression of molecules involved in TLR signaling , including p38 , IL-1β , IL-6 , Tlr1 , Tollip , Traf5 and TNF-α ( Fig 5G ) . However , F11r silencing did not affect TLR signaling ( S7 Fig ) . Pros1 silencing also led to a significantly increased concentration of TNF-α in the cell culture medium , while no significant effect was observed in F11r-silenced cells ( Fig 5H and S7 Fig ) . Overall , these results indicate that SjEV miR-125b can downregulate Pros1 to influence the expression of molecules involved in TLR receptor signaling as well as the expression of TNF-α . Among the predicted host cell targets of the SjEV bantam miRNA , we selected three including Fam212b , Clmp and Prtg for further analysis , and then cloned the region of each target containing the potential miRNA-binding sites into the 3’ UTR of a luciferase reporter . We found that transfection of bantam miRNA mimics resulted in a reduction of luciferase activity of synthetic targets compared to transfection with scrambled miRNA mimics , indicating that bantam miRNA can down-regulate the expression of these host target mRNAs ( Fig 6A ) . To determine whether S . japonicum bantam miRNA can regulate the predicted cellular targets , we transfected bantam miRNA mimics into RAW264 . 7 cells and then examined mRNA levels of the predicted RNA targets in the TNF signal pathway . Bantam miRNA mimic transfection led to the downregulation of the predicted targets ( Fig 6B ) and up-regulated of molecules involved in TNF signaling pathway ( Fig 6C ) . The levels of TNF-α were also significantly increased in the culture medium of cells transfected with bantam miRNA mimic ( Fig 6D ) . We also observed that the expression of Fam212b , Clmp , and Prtg were significantly reduced in peripheral blood monocytes isolated from mice infected with S . japonicum ( Fig 6E ) . Overall , these data suggest that SjEV bantam miRNA can target Fam212b , Clmp , and Prtg in macrophages to influence TNF-α production . To further demonstrate that reduction of Fam212b , Clmp , and/or Prtg mRNAs can affect TNF-α production , we designed and optimized siRNA duplexes for each target ( S8 Fig ) . Introduction of these siRNAs led to reduced levels of Clmp and Fam212b ( but not Prtg ) and increased expression of TNF-α in macrophages ( Fig 6F and S9 Fig ) . These data support that SjEV bantam miRNA downregulation of Clmp and Fam212b can influence TNF-α expression in macrophages . Since SjEV miR-125b and bantam can increase TNF-α production in macrophages and TNF-α is an autocrine growth regulator for macrophage differentiation , we examined the effect of SjEV miRNA cargo on macrophage proliferation both in vitro and in vivo . RAW264 . 7 cells transfected with SjEV miR-125b and bantam mimics demonstrated significantly increased cell proliferation in vitro ( Fig 7A ) . The effects were further confirmed using flow cytometry analysis ( Fig 7B and 7C ) . RAW264 . 7 cells treated with SjEVs led to increased cell proliferation , whereas the effect was not observed in control treatments such as PBS , NCTC clone 1469 cell EVs and heat-inactivated SjEVs ( S10A , S10B and S10C Fig ) . Further analysis of transcript levels of several M1 and M2 markers such as iNOS , IL-12 , TNF-α , IL-10 , Arg-1 in RAW264 . 7 cells treated with SjEVs and protein concentrations of TNF-α , IL-13 and IL-10 in culture media of treated cells indicated that RAW264 . 7 cells treatment resulted in the mixture of M1- and M2-polarized macrophages ( S11 Fig ) . To evaluate the effect of SjEVs on macrophage proliferation in mice , we used tail vein injection to deliver SjEVs into monocytes of mice . Flow cytometry analysis indicated that administration of SjEVs significantly increased the population of monocytes , while T-cells decreased , in SjEV treated mice compared to controls ( Fig 7D ) . Next , we determined whether an increased population of peripheral blood monocytes was present in mice infected with S . japonicum . We found that peripheral blood monocytes were significantly increased ( Fig 7E ) and the levels of TNF-α were also significantly augmented in serum isolated from S . japonicum infected mice ( Fig 7F ) at 14 , 22 , 28 , 32 days of post infection , respectively . Overall , these data demonstrate that SjEVs with their miRNA cargo can increase population of peripheral blood monocytes that may play a critical role in the modulation of host-pathogen interaction . Since an increased population of monocytes and elevated concentrations of TNF-α are associated with mice infected with schistosomes , we evaluated whether host peripheral blood monocyte levels can influence worm burden and egg deposition . Mice were treated with clodronate at day 14 of infection to reduce monocytes ( Fig 8A ) and at 26 days post-infection , blood samples were collected , and the populations of peripheral blood immune cells were determined by flow cytometry . Monocytes were depleted by approximately 80% in mice administered clodronate liposomes compared to control mice ( Fig 8B ) , and the concentrations of TNF-α were also significantly decreased compared to control mice ( Fig 8C ) . RT-qPCR analysis indicated that the abundance of SjEV miR-125b and bantam was significantly reduced in monocytes isolated from clodronate liposome–treated mice ( S12 Fig ) . More importantly , worm burden and egg deposition in the liver was also significantly decreased in mice administered clodronate liposomes ( Fig 8D ) . Egg induced inflammation in the livers was lessened in mice administered clodronate liposomes , compared to the egg-induced granuloma formation in control mice ( Fig 8E ) . Overall , these results suggest that an increased population of peripheral blood monocytes induced by schistosome secreted EVs may play a key role in the parasite survival and egg production .
Adult schistosomes survive in their mammalian host for extended periods in close contact with host peripheral blood immune cells . Understanding the mechanisms through which the parasites manipulate the host immune response for their survival is important to our understanding of the intricate parasite-host interaction and may contribute to the development of novel strategies against schistosomiasis . Here , we demonstrated that SjEVs released from adult S . japonicum are mainly taken up by host peripheral blood monocytes and that their cargo miRNAs , miR-125b and bantam , can target host monocyte genes to regulate cell proliferation and TNF-α production . S . japonicum infected mice exhibit increased levels of monocytes and SjEVs introduced into mice increase host monocytes . SjEVs induction of proliferation of host monocytes and increased TNF-α appears to contribute to schistosome survival . Overall , these findings reveal critical roles of SjEV miRNAs in the modulation of the host immune response that contributes to parasite survival . In the present study , we isolated extracellular vesicles from S . japonicum based on an improved protocol as developed in a previous study [24] . Analysis of the size of isolated SjEVs by qNano indicates the particles are 100-400nM in size . The improved protocol may enable isolation of larger EVs such as microvesicles and/or the qNano provides a broader size distribution than our previous EM analysis . Flow cytometry analysis indicated that SjEVs were predominantly taken up by monocytes ( Ly6C+ and CD11b+ ) both in vivo and in vitro studies . Ly6C is an inflammatory marker of monocytes[25] . The inflammatory monocytes typically stay in circulation for short periods ( about 1–2 days ) and then traffic to the sites of inflammation of tissues and organs . The monocytes can then develop into macrophages that contribute to local and systemic inflammation[26] . Consequently , we used a macrophage cell line ( RAW264 . 7 ) for our in vitro studies to gain insight into the roles of SjEV miRNA cargo on macrophages . We demonstrated that SjEVs are taken up by RAW264 . 7 cells and alter RNA expression profiles . These changes could be due to protein , small RNAs , lipids , or other material in the SjEVs . Among the S . japonicum small RNAs in the SjEVs , a large proportion appear to be siRNAs corresponding to repeats followed by miRNAs . A recent study identified repeat associated secondary siRNAs with a nematode Argonaute in the EVs of Heligmosomodies bakeri that modulate host genes [27] . In the current study , we focused on SjEV miRNAs including miR-125b and bantam . We predicted targets of these miRNAs in host cells and demonstrated through transfection of miRNA mimics that a number of host mRNAs can be regulated by the mimics . SjmiR-125b can target Pros1 and F11r and bantam can target Clmp , Fam212b and Prtg . Previous studies suggested Pros1 and F11r may be involved in the regulation of immune cell functions and cytokine production [28–31] . In addition , Fam212b , Clmp , and Prtg regulate cell adhesion [32] , migration [33] , or differentiation [34] . Transfection of SjEV miR-125b and bantam miRNA mimics into RAW264 . 7 cells led to elevated level of TNF-α and significantly increased expression of molecules involved in TLR and TNF signal pathways . siRNA reduction of Pros1 , Fam212b , and Clmp in RAW264 . 7 cells led to increased levels of TNF-α in the culture medium . Taken together , these results suggest that SjEV miR-125b and bantam miRNA can regulate TNF-α production by reducing levels of Pros1 , Fam212b , and Clmp , and alter macrophage immune cell function . TNF-α mainly produced by macrophages can increase macrophage proliferation and influence gene expression [35 , 36] . We show here that mice infected with S . japonicum have increased levels of monocytes and TNF-α . We further show that RAW264 . 7 cells transfected with miRNA mimics or treated with SjEVs and SjEV injection into mice leads to proliferation of monocytes and increased TNF-α production . TNF-α has been shown to positively influence parasite development and egg laying [37 , 38] . We found that reduction of host monocytes using clodronate liposomes introduced into S . japonicum infected mice led to a significant reduction of worm burden , egg deposition , and TNF-α level ( Fig 8C and 8D ) . Overall , these data suggest that elevated host levels of monocytes and TNF-α may be important for worm development and survival . Consistent with these data , host TNF-α has been shown to induce differential gene expression [39] and protein phosphorylation [7] in schistosomes . In mammalian cells , TNF-α may activate some signaling cascades-caspase cascade with subsequent apoptosis , NF-kB activating cascade and JNK cascade by binding TNF-α receptors [40] . It remains to be determined how increased monocytes and TNF-α influence parasite survival . Taken together , our results demonstrate that SjEV miRNA cargo can regulate the function of host peripheral monocytes modulating the host immune response to facilitate parasite survival . The findings provide new insights into the role of SjEV miRNAs in schistosome-host interaction and may contribute to develop novel intervention strategies against schistosomiasis .
All experiments involving mice and rabbits were carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Ministry of Science and Technology of the People’s Republic of China . All efforts were made to minimize animal suffering . All animal procedures were approved by the Animal Management Committee and the Animal Care and Use Committee of the Shanghai Science and Technology Commission of the Shanghai municipal government for the Shanghai Veterinary Research Institute , Chinese Academy of Agricultural Sciences , China ( Permit number: SYXK 2016–0010 ) . The life cycle of S . japonicum ( Anhui isolate ) was maintained using New Zealand rabbits ( Shanghai Ling Chang Biological Technology Co . , Ltd , Shanghai , China ) and BALB/c mice ( Shanghai SLAC Laboratory Animal Co . , Ltd , Shanghai , China ) and the intermediate snail host Oncomelania hupensis ( Center of National Institute of Parasitic Disease , Chinese Center for Disease Control and Prevention , Shanghai , China ) . Mice were challenged with 80–150 normal S . japonicum cercariae via abdominal skin penetration . New Zealand rabbits were percutaneously infected with approximately 1 , 500 S . japonicum cercariae ( Anhui isolate , China ) . Schistosomes were collected at 25–28 days post-infection ( dpi ) and washed with PBS . The S . japonicum EVs were isolated as described previously [24] . Briefly , parasites were maintained in preheated RPMI-1640 culture medium for 2 hours at 37°C and 5% CO2 . The culture media were collected , followed by centrifugation as described previously [24] . A total exosome isolation kit ( ThermoFisher Scientific , Carlsbad , CA , USA ) was used for EV isolation according to the manufacturer’s instructions with minor modifications as described previously [24] . The size of isolated EVs was determined using Zetasizer Nano ( Malvern , United Kingdom ) . The endotoxin activity of isolated EVs was determined using ToxinSensor Chromogenic LAL Endotoxin Assay Kit according to the manufacturer’s instructions ( GenScript , Nanjiang , China ) . Total RNA was extracted from S . japonicum EVs using TRIzol LS reagent ( ThermoFisher Scientific ) , and RNA quality was analyzed using an Agilent 2100 system ( Agilent Technologies , Santa Clara , CA , USA ) . RNA was size-selected using 15% denaturing PAGE , and libraries were prepared from the 18–30 nt fraction using an TruSeq Small RNA Library Preparation Kit ( Illumina , San Diego , CA , USA ) . The small RNA libraries were subjected to sequencing using Illumina 50bp single end sequencing performed on an Illumina HiSeq 2000 machine at the Beijing Genomics Institute ( Shenzhen , China ) . The raw sequencing data were deposited with the NCBI SRA accession: PRJNA508449 . Reads were mapped to the draft S . japonicum genome sequences ( sjr2_scaffold . fasta , downloaded from ftp://lifecenter . sgst . cn:2121/nucleotide/corenucleotide ) using the Short Oligonucleotide Alignment Program ( http://soap . genomics . org . cn ) . Reads were also mapped to the rabbit genome ( http://asia . ensembl . org/Oryctolagus_cuniculus/ , version 81 ) to identify host sequences associated with the S . japonicum EV library . Small RNAs from each library were sequentially mapped to the following databases for their classification: ( 1 ) Sj rRNAs and tRNAs; ( 2 ) Sj miRNAs; ( 3 ) Sj repeats; ( 4 ) Sj mRNAs; ( 5 ) Sj genome; ( 6 ) Rabbit rRNAs and tRNAs; ( 7 ) Rabbit miRNAs and other miRNAs from miRBase ( v21 ) ; and ( 8 ) Rabbit genome . The results were derived using bowtie [40] mapping allowing one mismatch . We also compared mapping results that allows no mismatch as well as 2 or 3 mismatches . The number of the reads that can be mapped increase as the criteria for matching becomes less stringent , but the overall conclusion is the same . This is consistent with sequence polymorphisms derived from different individuals as compared to the reference genome . The isolated SjEVs were lysed using lysis buffer , separated by 10% SDS-PAGE , and immunoblotting was performed as described previously [25] using primary antibodies against HSP70 ( Catalog number: D154145-0025 ) , GAPDH ( Catalog number: D110016-0100 ) ( Sangon Biotech , Shanghai , China ) and HSP90 that produced by immunizing purified recombinant SjHSP90 protein ( 1:2000 dilutions ) . Murine macrophage ( RAW264 . 7 ) cells were obtained from the American Type Culture Collection ( ATCC ) and grown using DMEM ( ThermoFisher Scientific ) medium supplemented with 10% fetal bovine serum ( FBS , ThermoFisher Scientific ) . Murine liver cells ( NCTC clone 1469 cells ) were obtained from the ATCC and grown using DMEM ( ThermoFisher Scientific ) medium supplemented with 10% horse serum ( ThermoFisher Scientific ) . EVs were isolated from the supernatant of NCTC clone 1469 cells . Briefly , cell supernatants were harvested , centrifuged at 2 , 000 × g for 30 min to eliminate cells , and then centrifuged at 12 , 000 × g for 20 min to eliminate cellular debris and larger vesicles . EVs were isolated as described above . The protein concentration of EVs was determined using a BCA protein assay kit ( Sangon Biotech , Shanghai , China ) . To verify the internalization of S . japonicum EVs by RAW264 . 7 cells , cells were seeded in 12-well plates ( approximate 2×105 cells per well ) and cultured with advanced DMEM serum-free media ( ThermoFisher Scientific ) for 4 h . The EVs from S . japonicum ( 5 μg ) or NCTC clone 1469 cells ( 5 μg ) were labeled using a Exo-Glow exosome labeling kit ( System Biosciences , Palo Alto , CA , USA ) according to the instructions as described by Peterson et al . ( 2015 ) [41] , in which the RNAs associated with EVs are labeled red . The labeled SjEVs were incubated with RAW264 . 7 cells for 4 hours . Then , cells were fixed by 4% paraformaldehyde , washed with PBS , and stained with 4' , 6-Diamidino-2-Phenylindole ( 1μg/mL ) ( ThermoFisher Scientific ) , the cells examined by fluorescent microscopy and the images captured ( Olympus , Tokyo , Japan ) . Antibodies directed against the following markers were used to stain peripheral blood immune cells for flow cytometry analysis: Ly-6C-APC ( Clone: HK1 . 4 , Catalog number: 17-5932-80 ) , CD11b-APC-eFluor780 ( Clone:M1/70 , Catalog number: 47-0112-80 ) , CD45R ( B220 ) -eFluor450 ( Clone: RA3-6B2 , Catalog number: 48-0452-80 ) , NK1 . 1 PE-eFluor 610 ( Clone: PK136 , Catalog number: 61-5941-80 ) , and CD3e PE ( Clone: 145-2C11 , Catalog number: 12-0031-81 ) . All antibodies were obtained from eBioscience ( San Diego , CA , USA ) . To determine SjEV uptake into peripheral blood immune cells , 200 μL of blood was collected from mice with anticoagulant sodium citrate solution , red cells lysed using RBC lysis buffer ( Biolegend , San Diego , USA ) , the remaining cells incubated with 2 . 5 μg PKH67 labeled SjEV for 4 h , stained with antibodies ( 1:200 dilution ) at 4 °C for 30 min . , and then the cells were washed with 1% BSA wash solution . The cells were suspended in 200 μL of wash solution and then sorted and analyzed using a BD FACSAria II system ( BD Biosciences , Mountain View , CA , USA ) . Each measurement contained 1×105 cells . Data were analyzed using FACSDiva software ( BD Biosciences ) . To determine the distribution of SjEVs in mice peripheral blood immune cells , mice were administered PKH67-labeled SjEVs ( 5 μg/mice ) by tail vein , and four hours and twelve hours post-injection , blood was collected and peripheral blood immune cells were isolated , analyzed , and sorted by flow cytometry as described above . The percentage of cells positive for PKH67-labeled SjEVs was determined for each cell population . To characterize peripheral blood immune cells in mice , 200 μL of blood was collected from control mice and mice infected with S . japonicum and then incubated with the antibodies described above and CD45 monoclonal antibody-FITC/eFluor506 ( Clone: 30-F11 , Catalog number: 11-0451-81 ) ( eBioscience ) . The cells were treated with red cell lysis buffer ( Biolegend ) as described above . For some experiments , FACS-sorted monocytes , NK cells , T cells and B cells were collected and total RNAs were isolated and RT-qPCR performed as described below . Total RNA was extracted from samples using TRIzol reagent ( ThermoFisher Scientific ) according to the manufacturer’s protocol . RNA was quantified using a Nanodrop ND-1000 spectrophotometer . For miRNA analysis , the miScript II RT Kit ( Qiagen , Hilden , Germany ) was used to reverse-transcribe RNA to cDNA . Real-time PCR was performed using a miScript SYBR Green PCR Kit ( Qiagen ) in an Eppendorf Realplex2 Detection System Mastercycler ep realplex ( Eppendorf , Hamburg , Germany ) . The PCRs were performed at 95 °C for 15 min , followed by 45 cycles of 94°C for 15 s , 55°C for 30 s , and 70°C for 30 s . The miScript primers for miR-125b , bantam , miR-61 , miR-3505 and miR-277b are the property of Qiagen . For determining the abundance of miRNAs in SjEVs , spike-in control of C . elegans miR-39 miRNA mimic provided from miRNeasy Serum/Plasma Spike-In Control ( Qiagen ) was used as the control . For determining the abundance of SjEV miRNAs in the RAW264 . 7 cells , Glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) was used as the internal control ( forward primer: CAT GGC CTT CCG TGT TCC TA; reverse primer: CCT GCT TCA CCA CCT TCT TGA T ) . The 2−ΔCt method was used to calculate relative miRNA abundance [42] . For mRNA expression analysis in RAW264 . 7 cells or peripheral blood monocytes cells , cDNAs were transcribed from total RNA with random primers combined with oligo dT primer using a PrimeScript RT reagent Kit ( TaKaRa , China ) , and RT-qPCR analysis was performed using the SYBR Premix ExTaq kit ( TaKaRa ) according to the manufacturer’s instructions . Primer sequences are shown in S4 Table . The murine GAPDH gene was used as the internal control . The 2−ΔCt method was used to calculate relative mRNA abundance as described above . For mRNA expression analysis in THP-1 cells , THP-1 cells were kindly provided by Stem Cell Bank of Chinese Academy of Sciences and were cultured with RPMI Medium 1640 containing 10% of heat inactivated fetal bovine serum ( ThermoFisher Scientific ) and supplemented with 10 mM Hepes ( ThermoFisher Scientific ) and 50 pM β-mercaptoethanol ( ThermoFisher Scientific ) . The SjEVs and NCTC EVs were incubated with THP-1 cells for 4 h . The total RNA isolation , cDNA preparation and qPCR as described above . The β-Actin gene was used as the internal control . Primers sequences are shown in S4 Table . RAW264 . 7 cells were seeded in 6-well plates ( 2×105 cells per well ) and cultured overnight . The following day , cells were treated with SjEVs ( 5 μg/well ) or NCTC clone 1469 cell EVs ( 5 μg/well ) . Cell lysates were prepared in 500 μL of lysis buffer containing 25 mM Tris ( pH 7 . 4 ) , 150 mM KCl , 0 . 5% NP-40 , 2 mM ethylenediaminetetraacetic acid ( EDTA ) , 1 M NaF , 0 . 5 M dithiothreitol , and protease inhibitors and then centrifuged at 10 , 000×g for 10 min at 4 °C . The pull-down assay was performed using a Dynabeads Protein G Immunoprecipitation Kit ( ThermoFisher Scientific ) according to the manufacturer’s instructions with Anti-AGO2 antibody ( Abcam , Catalog number: ab186733 ) . In brief , approximately 10 μL of Anti-AGO2 antibody was coupled with 1 . 5 mg of Dynabeads . Then , 200 μL of the protein lysate ( 100 μg of protein ) was incubated with the antibody-treated beads for 20 min at room temperature , and the Dynabeads were washed three times in 200 μL of the wash buffer provided with the kit . Finally , target antigens were eluted with elution buffer , the elutes separated by SDS-PAGE , and then analyzed by immunoblotting using Anti-AGO2 antibody and tubulin antibody ( Catalog number: T6074 , Sigma ) . RNA was also isolated from the immunoprecipitates and qPCR was used to examine SjEV miR-125b and bantam levels . RAW264 . 7 cells were grown in DMEM as described above and seeded into 24-well plates at approximately 20 , 000 cells per well . The following day , cells were incubated with S . japonicum EVs ( 5 μg total EV protein per well ) for 4 h and then washed twice with PBS . Total RNA was extracted using TRIzol reagent ( ThermoFisher Scientific ) according to the manufacturer’s protocol . The quality of RNA was determined using an Agilent 2100 system ( Agilent Technologies ) . Library preparation and sequencing were carried out at Shanghai Personal Biotechnology Co . , Ltd . ( Shanghai , China ) . Polyadenylated mRNAs were isolated from the total RNA using beads with oligo ( dT ) ( ThermoFisher Scientific ) , the mRNA fragmented , and cDNA prepared using random hexamer primers . A cDNA library was then prepared using a SureSelect Strand Specific RNA Library kit ( Agilent Technologies ) according to the manufacturer’s specifications . Paired-end reads with an average length of 101 bp were generated by sequencing cDNA libraries on an Illumina HiSeq 2000 sequencer ( Illumina ) . Raw data were filtered to remove low-quality reads and the reads mapped to reference sequences ( http://asia . ensembl . org/Mus_musculus/info/index ) using the Short Oligonucleotide Alignment Program ( http://soap . genomics . org . cn ) . Raw data files have been deposited at the NCBI under project number PRJNA471019 . In silico prediction of potential miRNA target genes was carried out by miRanda ( http://www . microna . org/microrna/getDownloads . do ) and TargetScan ( http://www . targetscan . org/mmu_71/ ) . Target genes predicted by miRanda and TargetScan that were also observed to be downregulated in S . japonicum EVs treated RAW264 . 7 cells were selected for further analysis . Target 3’ UTR mRNA fragments corresponding to SjEV miR-125b targets derived from miRanda and TargetScan were PCR-amplified from S . japonicum cDNA , and the PCR products were cloned into the pCMV-Glu vector 3’ UTR ( Targeting Systems , El Cajon , CA , USA ) using standard molecular cloning methods . The primer pairs used for PCR amplification and the restriction enzyme sites are listed in S6 Table . The recombinant plasmids were confirmed by sequence analysis . Recombinant and control plasmids were transfected at 80 ng per well ( approximately 2×104 cells ) into cultured HEK293/RAW264 . 7 cells using Lipofectamine 2000 ( ThermoFisher Scientific ) along with pGL3 ( 40 ng ) for normalization . At 24 h post-transfection , the cells were further transfected with a miRNA mimic , anti-miRNA , or control scrambled miRNA ( 40 nM ) ( S7 Table ) at the indicated concentrations using Lipofectamine 2000 . The miRNA mimics , anti-sense miRNAs or scrambled miRNAs used were modified by 2`-O-methyl or 2`-O-methyl and phosphorothioate . The transfected cells were incubated for the indicated time prior to collection for dual luciferase assay as described below . Transfections were carried out in triplicate three times using two independent plasmid preparations . At 24–48 h post-transfection , luciferase assays were carried out using the Dual Glo Luciferase assay system ( Promega , Madison , WI , USA ) according to the manufacturer’s instructions , and luminescence was measured by a luminometer ( Berthold , Germany ) . The relative reporter activity for transfected cells was obtained by normalization to co-transfected firefly luciferase activity ( pGl3 ) or protein concentration using the Pierce BCA protein assay kit combined with the Compat-Able protein assay preparation reagent set ( ThermoFisher Scientific ) . RAW264 . 7 cells were incubated with SjEVs , PBS , NCTC clone 1469 cell EVs , or transfected with miRNA mimics or control miRNA ( S7 Table ) . At the indicated times , cell proliferation was analyzed using a Cell Titer-Lumi Luminescent cell viability assay kit ( Beyotime Biotechnology , Jiangsu , China ) according to the manufacturer’s protocol . The luciferase activity was normalized to protein concentration using the Pierce BCA protein assay kit combined with the Compat-Able protein assay preparation reagent . For flow cytometry analysis , a Click-iT EdU Alexa Fluor 647 Flow Cytometry Assay Kit ( ThermoFisher Scientific ) was used to determine cell proliferation according to the manufacturer’s protocol . Briefly , RAW264 . 7 cells were seeded in 24-well plates ( approximate 2×104 cells per well ) and cultured overnight . The following day , cells were treated with SjEVs , NCTC clone 1469 cell EVs , or PBS or transfected with miR-125b and bantam miRNA mimics for 5 hours . EdU was then added to the culture medium at a final concentration of 10 μM and incubated for 2 h . The cells were then washed with 1% BSA , fixed using Click-iT Fixative and incubated for 15 min at room temperature . The fixed cells were washed twice with 1% BSA and resuspended in 100 μL of 1X Click-iT saponin-based permeabilization and wash reagent . Click-iTreaction cocktail ( 0 . 5 ml ) was added to each tube , mixed well and incubated for 30 min at room temperature . The cells were washed with 3 mL of 1X Click-iT saponin-based permeabilization and wash reagent and then resuspended in 500 μL of 1X Click-iT saponin-based permeabilization and wash reagent for flow cytometry analysis . To determine macrophage polarization upon SjEV treatment , RAW264 . 7 cells were treated with 1 μg/ml LPS ( Sigma-Aldrich , St . Louis , MO , USA ) , 10 ng/ml IL-4 ( R&D Systems , Minneapolis , MN , USA ) , SjEVs , NCTC EVs , PBS ( blank control ) . At 12 h of post treatment , total RNAs were isolated from treated cells and prepared cDNA was used for qPCR analysis as described above . The culture media of cells treated with SjEVs , NCTC EVs and PBS were analyzed for TNF-α , IL-10 , and IL-13 concentrations as described below . RAW264 . 7 cells were seeded in 24-well plates ( approximate 2×104 cells per well ) and cultured as described above . On the following day , siRNAs targeting Pros1 , F11r , Prtg , Fam212b , Clmp and control siRNA were transfected into cultured cells ( S8 Table ) . At 24–48 hours post-transfection , the cells were collected for RT-qPCR or protein analysis . The qPCR primers are listed in S4 Table . Commercially available antibodies for Pros1 and F11r were used for immunoblotting as described above , with the primary antibodies against Pros1 ( Catalog number: D121412-0025 ) , F11r ( Catalog number: D154077-0025 ) ( Sangon Biotech , Shanghai , China ) , and tubulin ( Catalog number: T6074 , Sigma ) antibodies at a 1:800 dilution . To quantify TNF-α concentration , cell culture media from different treatments or murine sera isolated from controls and schistosome infected mice at different times of post-infection were analyzed by the TNF-α Mouse ELISA Kit ( ThermoFisher Scientific ) according to the manufacturer’s protocol . Briefly , 50-μL samples ( medium or sera ) and diluted standards were added into wells , and then , 50 μL of biotin conjugate was added into each well . The plates were covered with adhesive film and incubated at room temperature for 2 hours on a microplate shaker . Then , the wells were washed six times with wash buffer . Next , 100 μL of diluted streptavidin-HRP was added to wells and then incubated at room temperature for 1 hour . After six washes , 100 μL of TMB substrate solution was added into each well , incubated at room temperature for 30 min , and then stop solution was added . The optical density in each well was measured by a TECAN microplate reader ( TECAN , Switzerland ) . In addition , aliquots from cell culture supernatants were used to analyze TNF-α , IL-10 , and IL-13 using ELISA kits , according to the manufacturer’s instructions ( eBioscience , USA ) . Cytokine concentrations were calculated by referring to standard curves . Four- to six-week-old male BALB/c mice ( mean weight 25 ± 2 g ) were purchased from the Shanghai SLAC Laboratory Animal Co . , Ltd . Twelve mice were randomly divided into two groups . Each mouse was challenged with 150 ± 5 normal S . japonicum cercariae via abdominal skin penetration . At 14 days post-infection , mice in each group were injected with 200 μL of clodronate liposomes ( from Vrije Universiteit Amsterdam ) or control liposomes ( from Vrije Universiteit Amsterdam ) diluted in 800 μL of PBS via the tail vein . Five additional injections were performed at 16 , 18 , 20 , 22 and 24 days post-infection . At 26 days post-infection , blood samples were collected from each mouse , and peripheral blood immune cells were sorted by flow cytometry as described above . At 28 days post-infection , the parasites were perfused , and worm burden and egg production in the liver were counted as described elsewhere [43] . The abundance of SjEV miR-125b and bantam in monocytes were determined by RT-qPCR as described above . The concentration of TNF-α in the serum of mice was measured by ELISA as described above . For SjEVs tail vein injection experiments , mice were administered with either SjEVs ( 5 μg/mouse ) in 100 μl PBS , NCTC clone 1469 cell EVs ( 5 μg/mouse ) in 100 μl PBS , or 100 μl PBS . At 4–24 hours of post-injection , blood was collected , and peripheral blood immune cells were sorted by flow cytometry as described above . Mouse livers collected from the monocyte depletion experiments were cut into approximately 1 . 0 cm×1 . 0 cm×0 . 3 cm pieces , washed in PBS , and fixed in 10% formalin . The formalin-fixed samples were dehydrated in ethanol , cleared with xylene and embedded in paraffin wax . Sections ( 6 μm thick ) were prepared and stained with hematoxylin and eosin . Results were analyzed using SPSS software ( version 17 ) . Comparisons between groups were made using Student’s t tests / one way ANOVA . Differences were considered significant when P ≤ 0 . 05 . | Schistosomes that cause schistosomiasis infection persist for decades despite a host immune response . Therefore , elucidating the mechanism of schistosome survival will not only contribute to the understanding of host-parasite interaction but also lead to the development of novel strategies against schistosomiasis . Extracellular vesicles ( EVs ) and their miRNA cargo have been shown to be mediators of intercellular communication involved in the regulation of many biological processes . Here , we demonstrated that EVs released from Schistosoma japonicum ( SjEVs ) are taken up primarily by macrophages and other host peripheral blood immune cells and their miRNA cargo transferred into recipient cells . Uptake of S . japonicum EV miR-125b and bantam miRNAs into host cells increased macrophage proliferation and TNF-α production that contributes to parasite survival . Our findings reveal key roles of SjEV miRNAs for facilitating parasitism in schistosomes . | [
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"analysi... | 2019 | Schistosoma japonicum extracellular vesicle miRNA cargo regulates host macrophage functions facilitating parasitism |
In embryonic stem ( ES ) cells , bivalent chromatin domains with overlapping repressive ( H3 lysine 27 tri-methylation ) and activating ( H3 lysine 4 tri-methylation ) histone modifications mark the promoters of more than 2 , 000 genes . To gain insight into the structure and function of bivalent domains , we mapped key histone modifications and subunits of Polycomb-repressive complexes 1 and 2 ( PRC1 and PRC2 ) genomewide in human and mouse ES cells by chromatin immunoprecipitation , followed by ultra high-throughput sequencing . We find that bivalent domains can be segregated into two classes—the first occupied by both PRC2 and PRC1 ( PRC1-positive ) and the second specifically bound by PRC2 ( PRC2-only ) . PRC1-positive bivalent domains appear functionally distinct as they more efficiently retain lysine 27 tri-methylation upon differentiation , show stringent conservation of chromatin state , and associate with an overwhelming number of developmental regulator gene promoters . We also used computational genomics to search for sequence determinants of Polycomb binding . This analysis revealed that the genomewide locations of PRC2 and PRC1 can be largely predicted from the locations , sizes , and underlying motif contents of CpG islands . We propose that large CpG islands depleted of activating motifs confer epigenetic memory by recruiting the full repertoire of Polycomb complexes in pluripotent cells .
Increasing evidence suggests that Polycomb- ( PcG ) and trithorax-group ( trxG ) proteins and associated histone modifications are critical for the plasticity of the pluripotent state , for the dynamic changes in gene expression that accompany ES cell differentiation , and for subsequent maintenance of lineage-specific gene expression programs [1]–[4] . PcG proteins are transcriptional repressors that function by modulating chromatin structure [2]–[4] . They reside in two main complexes , termed Polycomb repressive complexes 1 and 2 ( PRC1 and PRC2 ) . PRC2 contains Ezh2 , which catalyzes histone H3 lysine 27 tri-methylation ( H3K27me3 ) , as well as Eed and Suz12 . PRC1 contains Ring1 , an E3 ubiquitin ligase that mono-ubiquitinylates histone H2A at lysine 119 ( H2Aub1 ) [5] , [6] . Other PRC1 components include Bmi1 , Mel-18 , and Cbx family proteins with affinity for H3K27me3 [2] , [3] . Interplay between PcG complexes and modified histones has been proposed to mediate stable transcriptional repression [2] , [3] . In the prevailing model , PRC2 is recruited to specific genomic locations where it catalyzes H3K27me3 . The modified histones in turn recruit PRC1 , which catalyzes H2Aub1 and thereby impedes RNA polymerase II elongation [7] , [8] . PRC1 may also affect PRC2 function through as yet undefined mechanisms [2] , [3] . Several groups have combined chromatin immunoprecipitation ( ChIP ) with microarrays to examine the genomic localizations of individual PcG subunits [9]–[13] . Lee et al used tiling arrays to map the PRC2 subunit Suz12 in human ES cells , identifying nearly 2000 gene targets . Boyer et al used promoter arrays to identify 512 genes co-occupied by PRC2 and PRC1 components in mouse ES cells . In both studies , the implicated gene sets were highly enriched for developmental transcription factors ( TFs ) , many of which become de-repressed upon ES cell differentiation or in a PRC2-deficient background . Concurrent studies of histone methylation in ES cells led to the unexpected finding that virtually all sites of PcG activity not only carry the repressive H3K27me3 modification , but are also strongly enriched for the activating , trxG-associated H3 lysine 4 tri-methylation ( H3K4me3 ) mark [14] , [15] . Genomic regions with the two opposing modifications were termed ‘bivalent domains’ and proposed to silence developmental regulators while keeping them ‘poised’ for alternate fates . Upon ES cell differentiation , most bivalent promoters resolve to a ‘univalent’ state . Induced genes become further enriched for H3K4me3 and lose H3K27me3 , while many non-induced genes retain H3K27me3 but lose H3K4me3 [15] , [16] . Despite this progress , our understanding of PcG regulation and bivalent domains remains limited . In the current study we sought to address two outstanding issues . The first relates to whether all bivalent domains have the same regulatory structure . The recent observation that human and mouse ES cells show overlapping H3K27me3 and H3K4me3 at over 2000 promoters , only a portion of which have developmental functions , suggests that bivalent domains may reflect multiple , distinct regulatory entities [16]–[18] . The second relates to the mechanisms that underlie the targeting of PcG complexes and the establishment of bivalent domains in ES cells . In Drosophila , PcG complexes are recruited to DNA elements termed Polycomb response elements ( PREs ) . However , mammalian equivalents of these elements have yet to be identified [4] . We addressed these outstanding issues through genomewide analysis of PcG complex localization in mouse and human ES cells . We used the newly developed ‘ChIP-Seq’ method , which leverages ultra high-throughput sequencing to generate uniquely comprehensive maps of protein-DNA interactions [16] , [19] . The data reveal two classes of bivalent domains with distinct regulatory properties . The first class corresponds to bivalent domains with both PRC2 and PRC1 . These ‘PRC1-positive’ bivalent domains show striking evolutionary conservation , correspond to large H3K27me3 regions in ES cells that are significantly more likely to retain H3K27me3 upon differentiation , and account for a vast majority of implicated developmental regulator genes . By contrast , PRC1-negative bivalent domains , which are exclusively bound by PRC2 , are weakly conserved , poorly retain H3K27me3 , and largely correspond to membrane proteins or genes with unknown functions . Remarkably , computational genomic analysis of the ChIP-Seq data suggests a simple genomic code in which the locations , sizes and motif contents of CpG islands may predict the genomewide localizations of PRC2 , PRC1 and bivalent domains in ES cells . Based on these data , we propose a model in which large CpG islands depleted of activating transcription factor motifs confer epigenetic memory elements through mammalian development by recruiting PRC2 and PRC1 during early embryogenesis .
To gain insight into the structure , function and conservation of bivalent chromatin , we used ChIP-Seq to acquire genomewide maps of PcG complex components and related histone modifications in ES cells ( Table S1 ) . Chromatin from mouse v6 . 5 ES cells or human H9 ES cells was immunoprecipitated using antibodies against Ezh2 , Suz12 , Ring1B , H3K4me3 , H3K27me3 or H3K36me3 ( Materials and Methods ) . We also used biotin-streptavidin interaction ( bioChIP ) to purify chromatin from a transgenic mouse ES line in which endogenous Ring1B is fused to biotin ligase recognition peptide . DNA isolated in each ChIP experiment was sequenced to high depth using the Illumina Genome Analyzer . Aligned reads were integrated into maps that indicate enrichment of a given epitope as a function of genome position . In total , we created eight genomewide maps that each reflects two to eleven million aligned reads and together represent over 2 Gb of sequence . All data are publicly available at http://www . broad . mit . edu/seq_platform/chip/ . The availability of genomewide data for mouse and human ES cells acquired using identical antibodies and methodologies provides an opportunity to study the conservation of chromatin state in pluripotent cells . We systematically compared chromatin state at 13 , 200 orthologous promoters , identifying striking similarities at orthologous genomic loci ( Figure 1A , Figure S1; Table S2 , S3 , and S4 ) . In both mouse and human ES cells , roughly three-quarters of gene promoters are marked by H3K4me3 . There is strong correspondence between species as >94% of promoters with H3K4me3 in mouse also carry H3K4me3 in human . Roughly one fifth of H3K4me3 promoters also carry H3K27me3 , and thus are bivalent ( mouse: n = 2978; human: n = 2529 ) ( Figure S1C ) . There is again strong conservation , with more than half of bivalent mouse promoters also carrying bivalent chromatin in human ES cells ( Figure 1B and Figure S1A ) . As shown previously , many bivalent mouse promoters correspond to homeobox TFs or other developmental regulators [14] , [15] . These gene categories show particularly strong conservation of chromatin state , with roughly 70% correspondence between mouse and human . Still , there are numerous developmental regulators whose chromatin state differs between species ( Figure S3 ) . Closer inspection of these genes reveals a number of interesting cases that appear to reflect biological differences between the two pluripotency models: Thus , comparative analysis of human and mouse ES cells suggests extensive conservation of the pluripotent chromatin state while also illuminating divergent chromatin regulation associated with signaling pathways and transcriptional programs known to vary between the studied cell models ( see also Figure S3 ) . The strong conservation of bivalent domains seen here contrasts with the surprisingly weak correspondence observed previously for Oct4 and Nanog targets between mouse and human ES cells [26] . Consistent with prior studies , our data suggest that global patterns of H3K27me3 and H3K4me3 are intimately tied to transcriptional programs and cellular state , and that the bivalent combination is a conserved mark of silent developmental regulators in pluripotent cells . The identification of a distinct set of bivalent promoters targeted by Ring1B prompted us to investigate the functional significance of PRC1 occupancy . We made several striking observations relevant to chromatin regulation , epigenetic memory , development and differentiation: We next studied the chromatin maps to gain insight into another fundamental unanswered question – namely , the mechanisms that underlie the initial recruitment of PcG complexes and the formation of bivalent domains in ES cells . The extensive epigenetic reprogramming that precedes the pluripotent state suggests that elements in the genomic sequence itself must play central roles in this process [1] , [27] , [28] . Yet the identity of these PcG-determining sequence elements has remained elusive .
We have applied ChIP-Seq and computational genomic analysis to study the genomewide distributions of key histone modifications and PcG subunits in mouse and human ES cells , thereby gaining insight into the structure , function and establishment of bivalent domains . The ChIP-Seq data reveal two distinct sets of bivalent domains in ES cells . One set , defined based on co-occupancy by both PRC1 and PRC2 , shows special epigenetic properties , including higher evolutionary conservation of chromatin state and robust retention of repressive chromatin through differentiation . This set is exquisitely enriched for developmental targets in that over one third of the corresponding genes encode TFs , morphogens or cytokines . In striking contrast , a second set of bivalent domains , occupied by PRC2 only , is actually under-represented for TF genes relative to the genome average , and shows weak conservation and retention of the PcG-associated chromatin marks . We suggest that the complete repertoire of PcG machinery is needed for full functionality of bivalent domains and associated chromatin in the epigenetic regulation of key developmental genes . The data also suggest a potential model for understanding the initial recruitment of PcG complexes for the coordinated establishment of bivalent chromatin . In particular , we find that PRC2 association in ES cells is entirely restricted to sequences with high CpG content , the vast majority being annotated CpG islands . The status of a given CpG island – whether it carries PRC2 and bivalent H3K4me3/H3K27me3 chromatin or only H3K4me3 – correlates with underlying motif content . CpG islands with PRC2 show a striking depletion of transcriptional activator motifs and a modest enrichment of repressor motifs . Thus , PRC2 appears to localize to CpG islands that are transcriptionally silent in ES cells because they lack activating DNA sequence motifs . CpG islands have been extensively correlated with trxG complexes and H3K4me3; recruitment of the former likely involves CXXC proteins with affinity for un-methylated CpG dinucleotides [15] , [52] , [53] . We propose that CpG islands by default similarly mediate PcG recruitment and catalysis of H3K27me3 in mammalian ES cells , except when the default is over-ridden by transcriptional activity . In this model , the extent of PcG/H3K27me3 and trxG/H3K4me3 at any given CpG island is determined by its baseline transcriptional status which is dictated by underlying motif content . The view that transcriptional status is upstream of PcG status in ES cells is consistent with the subtle transcriptional changes evident in PcG-deficient ES cells [9] , [54] . Although our analyses do not shed light on the underlying mechanisms , PRC2 recruitment may also involve proteins with affinity for un-methylated CpGs or may be mediated indirectly through recognition of other histone modifications such as H3K4me3 . In either case , active transcription within a locus would preclude stable PRC2 association and thereby restrict it to inactive CpG islands . Large PRC2-positive CpG islands tend to also carry PRC1 . The expansive regions of H3K27me3 associated with these islands may contribute to PRC1 recruitment via chromodomain proteins [2] , [3] . As discussed above , bivalent domains that carry both PRC2 and PRC1 appear to have unique epigenetic regulatory properties . We therefore propose that large CpG islands depleted of activating motifs confer epigenetic regulation by recruiting both key PcG complexes in pluripotent cells . Such islands may thereby reflect mammalian memory elements analogous to Polycomb response elements in flies . The tight correspondence between DNA sequence and PcG localization may have implications for important cellular processes , such as development and epigenetic reprogramming . Induced pluripotent stem ( iPS ) cells and ES cells exhibit nearly identical chromatin patterns , including the locations of bivalent domains [55] , [56] . The sequences described above may function as templates for the robust assembly and appropriate positioning of PcG complexes and bivalent domains during pre-implantation development or the artificial reprogramming of somatic cells to iPS cells [1] , [28] . What then might be the purpose of an initial chromatin state fully encoded by genetic sequence and an associated transcriptional program ? Based on existing evidence , we suggest that PcG complexes and associated chromatin buffer the pluripotent ground state by reinforcing the repression of factors that induce differentiation . The initial chromatin architecture also appears poised for the dynamic expression changes that accompany differentiation and for the subsequent engagement of epigenetic controls to maintain lineage-specific transcriptional programs . Our analysis suggests that such epigenetic functions mainly apply to large bivalent CpG islands that also carry PRC1 . It remains to be seen whether small PRC1-negative bivalent domains have distinct regulatory functions or are simply byproducts of the mechanisms that have evolved for establishment of the former . Further studies are needed to determine the precise DNA elements and protein interactions that mediate PcG recruitment . As discussed above , the proposed central role for CG-rich sequences implies the involvement of CXXC domains or other proteins that recognize CG dinucleotides . However , several factors complicate the interpretation of our genomic findings . In particular , CpG islands are at least partly a consequence of reduced CpG deamination rates in regions that lack DNA methylation in the germ line [27] . PcG-occupied regions are largely un-methylated at the DNA level , at least in ES cells [57] , and this could favor retention of CG-rich sequences . Thus , it remains possible that evolutionary dynamics and/or the generally high CpG content of target regions are masking other key sequence features . Finally , it should be emphasized that our findings on the relationships among PRC2 and PRC1 and the sequences that underlie their genomic localizations pertain specifically to ES cells . PcG complexes show remarkable tissue-specificities in terms of their expression levels , stoichiometry and localization [2] , [3] , [11] , [12] . Further study is needed to understand how the genomic localizations and regulatory functions of PcG complexes vary with differentiation , lineage specification , environment , and disease .
Mouse v6 . 5 ( genotype 129SvJae×C57BL6 , male , passages 10–15 ) ES cells were cultured on fibroblast feeders in DMEM ( Sigma ) with 15% fetal bovine serum ( Hyclone ) , GlutaMax ( Invitrogen ) , MEM non-essential amino acids ( Invitrogen ) , pen/strep ( Invitrogen ) , ESGRO ( Chemicon ) and 2-mercaptoethanol ( Sigma ) , incubating at 37°C , 5% CO2 [16] . Prior to harvest , these cells were passaged 2–3 times on feeder-free gelatinized tissue culture plates . A transgenic ES cell line expressing a fusion between Ring1B and biotin ligase recognition peptide from the endogenous Ring1B locus and the BirA biotin ligase from the Rosa26 locus ( H . K . , unpublished ) was cultured as described above . Human H9 ( female , passage 45 ) ES cells were cultured as described [58] and at http://www . WiCell . org . Briefly , the human ES cells were cultivated on irradiated MEFs ( strain DR4 ) in Knockout DMEM ( Invitrogen ) containing 10% Knockout Serum Replacement ( Invitrogen ) , 10% Plasmanate ( Bayer Healthcare ) , GlutaMax ( 2 mM ) , pen/strep , MEM non-essential amino acids ( 0 . 1 mM ) , 10 ng/ml β-FGF ( Invitrogen ) and 2-mercaptoethanol . Cells were incubated at 37°C , 5% CO2 . MEF-free ES cells were used for analysis . MEF-free culture was prepared in the following manner: First , MEFs were depleted at the time of trypsin passaging through brief transfer ( thirty minutes ) of hES cells onto gelatin-coated plates . MEF-subtracted ES cells were then propagated on plates coated with Matrigel ( Invitrogen ) . ES cells grown on Matrigel were supported with the aforementioned human ES cell medium that had first been conditioned on MEFs for 24 hours . Fresh β-FGF was added to the conditioned medium immediately prior to use . Doxycyclin-inducible Flag-Bmi1 transgenic ES cell line was generated by PCR amplifying a 1× flag tagged Bmi1 ORF ( Addgene ) with primers that incorporate a 3× flag tag as well as EcoRI and XbaI restriction enzyme sites ( 5′-GGAATTCCACCATGGACTACAAAGACCATGACGGTGATTATAAAGATCATGATATCGACTACAAGGACG-3′ , 5′- GCTCTAGAGCACCAGATGAAGTTGCTGATGACCCATTTAGTGATGATTTT-3′ ) . This was cloned into the pLox vector ( pPGK-loxP-neoEGFP ) and incorporated into Ainv15 mouse ES cells using a cre recombinase expression vector as previously described [59] . Flag-Bmi1 ES cells were cultured similarly to wild-type mES cells as described above . Prior to harvest , Flag-Bmi1 expression was induced by incubating with 1 µg/ml of Doxycycline for two days on gelatinized culture plates . ChIP experiments for H3K4me3 , H3K27me3 and H3K36me3 , Ring1B and Flag-Bmi1 were carried out as described [15] , [16] . ES cells were crosslinked in 1% formaldehyde , lysed and sonicated with either a Branson 250 Sonifier ( mouse ES cells ) or a Diagenode bioruptor ( human ES cells ) to obtain chromatin fragments in a size range between 200 and 700 bp . Solubilized chromatin ( whole cell lysate or ‘WCE’ ) was diluted in ChIP dilution buffer ( 1∶10 ) and incubated with antibody overnight at 4°C . Protein A sepharose beads ( Sigma ) were used to capture the antibody-chromatin complex and washed with low salt , LiCl , as well as TE ( pH 8 . 0 ) wash buffers . Enriched chromatin fragments were eluted at 65°C for 10 min , subjected to crosslink reversal at 65°C for 5 hrs , and treated with Proteinase K ( 1 mg/ml ) , before being extracted by phenol-chloroform-isoamyl alcohol , and ethanol precipitated . ChIP DNA was then quantified by Quant-iT Picogreen dsDNA Assay kit ( Invitrogen ) . ChIP experiments for Ezh2 and Suz12 were carried out on nuclear preps . Crosslinked ES cells were incubated in swelling buffer ( 0 . 1 M Tris pH 7 . 6 , 10 mM KOAc , 15 mM MgOAc , 1% NP40 ) , on ice for twenty minutes , passed through a 16G needle 20 times and centrifuged to collect nuclei [60] . Isolated nuclei were then lysed , sonicated and immunoprecipitated as described above . BioChIP assays were carried out using transgenic Ring1B-Biotin ligase recognition peptide ES cells ( above ) . Nuclei were isolated , lysed and sonicated as described above . Dynabeads M-280 Streptavidin ( Invitrogen 112 . 05D ) were used to capture biotinylated Ring1B-DNA complex . Beads were washed with a 2% SDS buffer and a high salt buffer ( 50 mM HEPES , pH 7 . 5 , 1 mM EDTA , 500 mM NaCl , 1% Triton X-100 , 0 . 1% Deoxycholate ) , in addition to the regular washes . Elution and cross-link reversal were done simultaneously by incubating Dynabeads in 300 mM NaCl at 65°C overnight [46] . DNA was isolated as described above . Antibodies used in this study include anti-H3K4me3 ( Abcam ab8580 ) , anti-H3K27me3 ( Upstate 07-449 ) , anti-H3K36me3 ( Abcam ab9050 ) , anti-Ezh2 ( Active Motif 39103 ) , anti-Suz12 ( Abcam ab12073 ) , anti-Ring1B [61] and anti-Flag ( M2 ) ( Sigma F1804 ) . Details on antibody specificity are provided in Text S1 . Library preparation and ultra high-throughput sequencing were carried out as described [16] . Briefly , one to ten nanograms ( ng ) of ChIP DNA were end-repaired and 5′phosphorylated using END-It DNA End-Repair Kit ( Epicentre ) . We then followed steps four through seven of Illumina standard sample prep protocol ( v1 . 8 ) using Genomic DNA Sample Prep Kit ( Illumina ) with minor modifications . A single Adenine was added to 3′ ends by Klenow ( 3′→5′ exo− ) , and double-stranded Illumina Adapters were ligated to the ends of the ChIP fragments . Adapter-ligated ChIP DNA fragments between 275 bp to 700 bp were gel-purified and subjected to 18 cycles of PCR . Prepared libraries were quantified using PicoGreen and sequenced on the Illumina Genome Analyzer per standard operating procedures . Sequence reads ( 36 bases ) from each ChIP experiment were compiled , post-processed and aligned to the appropriate reference genome using a general purpose computational pipeline as described previously [16] . Aligned reads are used to estimate the number of end-sequenced ChIP fragments that overlap any given genomic position ( at 25-bp resolution ) . For each position , we counted the number of reads that are oriented towards it and closer than the average length of a library fragment ( ∼300 bp ) . The result is a high-resolution density map that can be viewed through the UCSC Genome Browser [62] and is used for downstream analyses . Prior comparisons to microarray analysis and quantitative real-time PCR have shown that ChIP-Seq density maps accurately reflect enrichment [16] . ChIP-Seq data can be accessed at http://www . broad . mit . edu/seq_platform/chip/ . We used a Hidden Markov Model ( HMM ) to demarcate chromosomal segments likely to be enriched for a given chromatin modification or PcG protein [16] . In order to model ChIP-Seq read density variations along the genome , we define four observed states: masked , low density , medium density , and high density . This discretization of the data into the four states was based on the signal intensity in known modified regions versus known unmodified regions as determined in prior ChIP-Seq , microarray and ChIP-PCR analyses [15] , [16] , and adjusted for each sample . The model was then used to discriminate enriched and unenriched intervals genome wide . In order to more properly classify enriched regions containing several short interspersed peaks and facilitate subsequent analyses intervals within 2 kb were merged . We defined 17760 mouse and 18522 human promoters for 17442 and 17383 genes , respectively , as the sequences between −0 . 5 kb and +2 . 0 kb of the annotated transcription start site , using the mouse mm8 and human hg18 genome builds . Transcripts were defined for these genes as the range from transcription start to end [62] . To identify regions enriched for histone marks or chromatin-associated proteins , we generated a null-hypothesis background model by dividing the alignable parts of each chromosome into 200 bp bins and randomly redistributing the reads aligned on this chromosome . Based on a histogram of the cumulative distribution of reads per bin , a cutoff threshold was determined . Stability of the calculated background cutoff threshold was confirmed through 1000 independent simulations for each ChIP-Seq track and showed remarkable invariance . For promoters , a 200 bp sliding window was moved across the 2 . 5 kb promoter region and the ratio of median read density over background was calculated . The maximum enrichment achieved in any window at this promoter site was then used for further analysis . Maximum enrichment cutoff thresholds were determined empirically for all tracks , and promoters were then classified based on the maximum enrichment for the various histone marks and PcG proteins . The same procedure was applied to a pan-H3 ( modification-insensitive ) ChIP-Seq dataset as control where virtually no significant enrichment over background was found . Ring1B-positive bivalent promoters were defined based on normalized ChIP-Seq signal and comprise 40% of all bivalent promoters . A set of Ring1B-negative bivalent promoters was also defined based on absence of ChIP-Seq enrichment , and includes another 40% of all bivalent promoters . The remaining bivalent promoters ( 20% ) with indeterminate Ring1B ChIP-Seq signals were excluded from this analysis . For conservation analyses of human and mouse promoter states , we used NCBI HomoloGene ( build 58 ) gene clusters to assign orthologous human promoters and transcripts to the 17442 mouse promoters and transcripts , yielding a set of 13200 orthologous promoters and 13625 orthologous transcripts for which human and mouse chromatin state could be compared ( ftp://ftp . ncbi . nih . gov/pub/HomoloGene/ ) . Genes with multiple start sites were excluded from this analysis . Promoters were associated with CpG states as described previously [16] . For comparison of Ezh2 and Ring1B occupancy at target genes , a reduced Ezh2 read set was generated by randomly selecting the same number of reads that were available for Ring1B from the full Ezh2 read pool ( ∼3 . 5 million ) . Read mapping to the mouse genome and analysis of promoter state were performed as described above . PCR primer pairs were designed to amplify designated genomic regions using Primer3 ( http://fokker . wi . mit . edu/primer3/input . htm ) . Real-time PCR assays were carried out on ABI 7000 or 7500 detection systems . We used Quantitect SYBR green PCR mix ( Qiagen ) with 0 . 1 ng ChIP or 0 . 1 ng un-enriched input DNA ( WCE ) as template . Log2 enrichment was calculated from geometric means obtained from three independent ChIP experiments , each evaluated by duplicate PCR assays . Background was subtracted by normalizing over negative genomic control . Gene expression data for Ring1A/B-dKO ( Ring1A−/−;Ring1Bfl/fl;Rosa26::CreERT2 ) ES cells ( 2 day post-tamoxifen treatment and no-treatment control , H . Koseki unpublished data ) and Eed KO ES cells ( Eed −/− and control Eed+/+ ES ) [13] , acquired with Affymetrix Mouse Genome 430 2 . 0 Arrays , were normalized using the Genepattern expression data analysis package ( http://www . broad . mit . edu/cancer/software/genepattern ) . CEL files were processed with RMA , quantile normalization and background correction [63] . For a given comparison ( Ring1A/B-dKO vs control; or Eed −/− vs +/+ ) , we only considered probes in which at least one of the experiments had a “P” significance call . Fold changes were calculated for each passing probe . Genes with multiple corresponding probes were assigned the geometric average fold change value . Gene expression data for mouse v6 . 5 mES and NPCs were obtained from previously published Affymetrix mRNA profiles [16] . Gene ontology ( GO ) functional annotation for the Ring1B positive and negative sets was done using DAVID analysis tool ( http://david . abcc . ncifcrf . gov/home . jsp ) . P-values were adjusted for multiple hypothesis testing using Bonferroni correction . The HMM described above was used to define enriched intervals for each modification or chromatin protein from the mouse ES cell ChIP-Seq data . We determined the extent to which Ezh2 intervals ( and those for other epitopes ) overlap with CG-rich sequences . CpG island coordinates were obtained from the UCSC Genome Browser [62] . We identified all Ezh2 intervals that overlap these CpG island coordinates within 500 bp . Next , the EMBOSS analysis package [64] was used to determine the portion of remaining Ezh2 intervals overlapping a ‘mini’ CpG island defined as a 100 bp window with at least 50% GC content and an O∶E ratio >0 . 6 ( instead of the standard CpG island window of 200 bp ) . We next classified CpG islands according to their chromatin state ( e . g . , Ezh2-positive v . Ezh2-negative , H3K4me3 v . bivalent ) . This was done by computing the median ChIP-Seq read density across each defined CpG island , and setting thresholds using a null background model of randomized reads . For these analyses we excluded CpG islands that fall within unalignable regions , typically due to low complexity sequence , and thus could not be evaluated by ChIP-Seq ( <7% of all CpG islands ) . To maximize discriminatory power , we excluded intermediate CpG islands with sub-threshold Ezh2 signal . We computed median values and distributions for length , CG density and observed-to-expected ratio for the different CpG island sets , and also evaluated nucleotide content by calculating the frequencies of all 16 dinucleotide combinations . Conservation scores were determined for each CpG island by aligning the regions between mouse and rat , and performing a dinucleotides level comparison of the conservation between the two species . Both CpG and non-CpG dinucleotides were conserved at slightly higher levels in the Ezh2-bound CpG islands ( Figure S7 ) . We next screened the CpG island sets for TF motif occurrences . 668 position weight matrices ( PWMs ) were obtained from the Jaspar ( Release 3 . 0 [34] ) and TRANSFAC ( Release 9 . 4; [35] ) databases , excluding any non-vertebrate factors . We prepared sets of Ezh2-positive and Ezh2-negative sequences by extracting each CpG island along with flanking sequence equal to 50% of its length . The MAST algorithm [36] was then used to search for significant PWM matches ( p<5e-5 ) in the Ezh2-positive and negative sets . Occurrences were length-normalized and used to calculate ratios that reflect the enrichment in the Ezh2-positive set relative to the Ezh2-negative set , or vice versa . We identified significantly over-represented motifs using Fisher's exact test with Bonferroni-adjusted p-values . These candidate motifs were then scrambled , re-scored , and excluded if any enrichment was observed in the scramble . We used a clustering algorithm to collapse similar motifs identified as enriched in one of the sets to a single consensus sequence [65] . This was necessary due to high motif redundancy in the databases . After clustering , all intra-cluster motif occurrences overlapping by more than 50% were counted as a single instance . Expression values for corresponding DNA binding proteins were determined from previously published Affymetrix mRNA profiles for v6 . 5 ES cells [16] . A simple count-based model was used to determine the extent to which motif occurrences are predictive of Ezh2 status . The motif content which allowed for maximum discrimination in mouse is as follows: a CpG island was predicted to be Ezh2-positive if it either ( i ) contained >8 ‘Ezh2-positive’ motifs or ( ii ) contained >4 ‘Ezh2-positive’ motifs and <2 ‘Ezh2-negative’ motifs . Ezh2 status in human was predicted using the motifs identified in mouse but with the following metric: a CpG island was predicted to be Ezh2-positive if it contained >15 ‘Ezh2-positive’ motifs and <2 ‘Ezh2-negative’ motifs . In order to quantify Ring1B presence in CpG islands , we considered the distribution of ChIP-Seq reads in control regions . We specifically used all alignable , H3K4me3-only CpG islands as our null hypothesis background model . The distribution of Ring1B ChIP-Seq read densities across these islands was calculated and a threshold was set to minimize the false positive detection rate . We then calculated Ring1B ChIP-Seq read density in sliding 200 bp windows in all Ezh2-positive CpG islands , with a CpG island assigned the maximum enrichment in any of its 200 bp windows . For maximum discriminatory power , we excluded 20% of CpG islands with sub-threshold Ring1B signal . Ring1B status was predicted using the length of CpG-richness in PRC2-positive CpG islands . Islands were predicted to be Ring1B-positive if they were either >1200 bp or within 2 kb of another CpG island . | Polycomb-group ( PcG ) proteins play essential roles in the epigenetic regulation of gene expression during development . PcG proteins are repressors that catalyze lysine 27 tri-methylation on histone H3 . They are antagonized by trithorax-group proteins that catalyze lysine 4 tri-methylation . Recent studies of ES cells revealed a novel chromatin pattern consisting of overlapping lysine 27 and lysine 4 tri-methylation . Genomic regions with these opposing modifications were termed “bivalent domains” and proposed to silence developmental regulators while keeping them “poised” for alternate fates . However , our understanding of PcG regulation and bivalent domains remains limited . For instance , bivalent domains affect over 2 , 000 promoters with diverse functions , which suggests that they may function in diverse cellular processes . Moreover , the mechanisms that underlie the targeting of PcG complexes to specific genomic regions remain completely unknown . To gain insight into these issues , we used ultra high-throughput sequencing to map PcG complexes and related modifications genomewide in human and mouse ES cells . The data identify two classes of bivalent domains with distinct regulatory properties . They also reveal striking relationships between genome sequence and chromatin state that suggest a prominent role for the DNA sequence in dictating the genomewide localization of PcG complexes and , consequently , bivalent domains in ES cells . | [
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"biology/developmental",
"molecular"... | 2008 | Genomewide Analysis of PRC1 and PRC2 Occupancy Identifies Two Classes of Bivalent Domains |
Correlations between the activities of neighboring neurons are observed ubiquitously across systems and species and are dynamically regulated by several factors such as the stimulus' spatiotemporal extent as well as by the brain's internal state . Using the electrosensory system of gymnotiform weakly electric fish , we recorded the activities of pyramidal cell pairs within the electrosensory lateral line lobe ( ELL ) under spatially localized and diffuse stimulation . We found that both signal and noise correlations were markedly reduced ( >40% ) under the latter stimulation . Through a network model incorporating key anatomical features of the ELL , we reveal how activation of diffuse parallel fiber feedback from granule cells by spatially diffuse stimulation can explain both the reduction in signal as well as the reduction in noise correlations seen experimentally through independent mechanisms . First , we show that burst-timing dependent plasticity , which leads to a negative image of the stimulus and thereby reduces single neuron responses , decreases signal but not noise correlations . Second , we show trial-to-trial variability in the responses of single granule cells to sensory input reduces noise but not signal correlations . Thus , our model predicts that the same feedback pathway can simultaneously reduce both signal and noise correlations through independent mechanisms . To test this prediction experimentally , we pharmacologically inactivated parallel fiber feedback onto ELL pyramidal cells . In agreement with modeling predictions , we found that inactivation increased both signal and noise correlations but that there was no significant relationship between magnitude of the increase in signal correlations and the magnitude of the increase in noise correlations . The mechanisms reported in this study are expected to be generally applicable to the cerebellum as well as other cerebellum-like structures . We further discuss the implications of such decorrelation on the neural coding strategies used by the electrosensory and by other systems to process natural stimuli .
Understanding how the brain processes sensory information in order to lead to perception and behavior remains a central problem in neuroscience . Mounting evidence suggests that studying correlations between neurons is required to understand the neural code [1]–[4] . Such correlations have been observed across systems and species and can have profound impact on neural population coding by , e . g . , either decreasing or increasing information transmission depending on their sign [2] , [3] , [5]–[7] . Experimental results have further shown that correlations between neurons are not static but are instead dynamically regulated by the spatiotemporal structure of sensory input [8]–[10] as well as higher order cognitive processes such as attention [11] , [12] . In particular , attentional processes can reduce correlations between neural responses [11] , which is thought to reduce redundancy and thus maximize information transmission as originally proposed by Barlow [13] , [14] . Theoretical studies have proposed cellular and circuit mechanisms that can modulate correlated activity [15]–[18] but it is at best unclear how applicable these are in general [7] . Wave-type gymnotiform weakly electric fish offer an attractive model system for studying modulation of correlated activity because of well-characterized anatomy and natural sensory stimuli [19]–[21] . These fish actively generate an electric field around their body through the electric organ discharge ( EOD ) . They can sense perturbations of this field caused by objects with conductivity different than that of the surrounding water ( e . g . prey , conspecifics ) through an array of electroreceptors on their skin surface that synapse onto pyramidal cells within the electrosensory lateral line lobe ( ELL ) . Anatomical and physiological studies have shown large heterogeneities within the pyramidal cell population: on one hand , superficial pyramidal cells ( SPs ) have large apical dendritic trees and receive large amounts of plastic indirect feedback via parallel fibers [22] , [23] while , on the other hand , deep pyramidal cells ( DPs ) instead have small apical dendrites and are thought to receive little or no indirect feedback [22] , which is supported by results showing that pharmacological inactivation of indirect feedback input has a strong effect on SPs and little or no effect on DPs [24] , [25] . While all pyramidal cells project to the midbrain Torus semicircularis and higher brain areas , only DPs project to the Eminentia Granularis posterior ( EGp ) and give rise to the feedback input to SPs [22] . Multiunit recording from ELL pyramidal cells have revealed that the baseline activities of neighboring pyramidal cells are significantly correlated [26] , [27] . Further , such correlated activity is highly modulated based on the spatial extent of the stimulus . Indeed , stimuli mimicking prey items whose spatial extent was constrained to a small portion of the sensory epithelium ( i . e . local ) induced stronger correlations than stimuli mimicking conspecifics whose spatial extent was commensurate with the sensory epithelium ( i . e . global ) [26] . Interestingly , this effect was observed for both signal ( i . e . correlations due to the fact that the neurons receive a common stimulus ) and noise ( i . e . correlations between the trial-to-trial variabilities of neurons ) correlations . Pyramidal cells receive large amounts of feedback including a diffuse pathway consisting of parallel fibers from granule cells within the EGp [28] , thereby making the ELL a cerebellar-like structure [29] . One of the functions of this feedback pathway , which is activated by global but not local stimulation [22] , [24] , [25] , [30] , is to attenuate the responses of single SPs but not DPs to low temporal frequency ( i . e . <15 Hz ) global stimulation relative to local stimulation by providing a negative image that “cancels out” the stimulus: thereby decorrelating the stimulus and the single neuron response [22] , [24] , [25] , [30]–[34] . Recent studies have shown that a burst-time dependent plasticity observed experimentally is necessary to obtain this negative image at the single neuron level and to observe such decorrelation [32] , [33] , [35] . At the population level , experimental and theoretical work suggests that this feedback pathway can contribute to reducing signal correlations amongst ELL pyramidal cells but did not include the burst-time dependent plasticity [26] , [27] . Thus , how indirect feedback onto ELL pyramidal cells in the form of a negative stimulus image produced by burst-time dependent plasticity actually reduces signal correlations between the activities of ELL pyramidal cells has not been investigated to date . Moreover , the mechanisms that underlie the experimentally observed changes in noise correlations remain unknown . In order to make progress towards understanding the mechanisms that reduce both signal and noise correlations in ELL pyramidal cells under global stimulation , we built a network model of the ELL based on recently published results that accurately reproduces the effects of feedback on single pyramidal cell activity [32] , [33] and that incorporates spiking activity from individual granule cells . We systematically varied model parameters in order to understand the regimes in which activation of granule cell input can reduce both signal and noise correlations . We found that the formation of a negative image by burst-timing dependent plasticity led to a reduction in signal correlations but not noise correlations . On the other hand , including trial-to-trial variability in the spiking responses of granule cells reduced noise but not signal correlations . Thus , our model made two important predictions: 1 ) that activation of the same feedback input onto ELL pyramidal cells can simultaneously reduce both signal and noise correlations and; 2 ) that the reduction in signal and noise correlations are mediated by independent mechanisms . We next validated these predictions experimentally by pharmacologically inactivating parallel fiber input onto ELL pyramidal cells . Consistent with prediction 1 ) , we observed an increase in both signal and noise correlations after inactivation and; consistent with prediction 2 ) , the increase in signal correlations was not related to the increase in noise correlations . Our combined experimental and modeling results thus provide a generic mechanisms by which parallel fiber feedback originating from cerebellar granule cells can simultaneously reduce both signal and noise correlations that are likely to be generally applicable across cerebellum and cerebellar-like structures in the brain .
Our model is described in Fig . 1 . The electroreceptor afferent population activity is assumed to faithfully follow the timecourse of the stimulus as observed experimentally [19] , [36] ( note that electroreceptor afferent spiking activities do not display significant noise correlations [37] ) and synapse onto SPs . Since experimental studies have shown that noise correlations were proportional to the amount of receptive field overlap between pyramidal cells [26] , which is largely due to feedforward input from electroreceptor afferents [23] , we assumed that noise correlations between our model SPs were due to the fact that they receive shared input from electroreceptor afferents and were modeled using shared noise with √c being the fraction of common feedforward noise input ( see Methods ) . DPs are also assumed to faithfully follow the timecourse of the stimulus as seen experimentally [24] , [25] and relay this information to granule cells within the EGp . As seen experimentally , we assume that granule cells phase lock in response to sinusoidal global stimulation and are otherwise silent in the absence of stimulation as well as during local stimulation [22] , [24] , [25] , [38]–[40] . Further , as assumed in previous modeling studies [32] , [33] , only a few granule cells are active at a given phase of the sinusoidal stimulus . However , unlike these , we implemented spiking mechanisms for each granule cell , considered trial-to-trial variability in their responses to the stimulus , and moreover considered the effects of granule cell input onto ELL pyramidal cells on correlated activity at the populations level ( see Methods ) . We recorded from pyramidal cell pairs ( see Methods ) in response to sinusoidal stimuli with frequency 4 Hz that were delivered either locally ( Fig . 2A ) via a small dipole positioned lateral to the animal or globally ( Fig . 2B ) via two electrodes positioned far from the animal on each side . These stimuli were used because of their relative simplicity , behavioral relevance , and because previous studies have shown that globally but not locally given 4 Hz sinusoidal stimuli will activate feedback from the EGp onto ELL pyramidal cells [22] . It is important to realize that the temporal aspect of the stimulus is the same in both cases and that it is only the spatial extent that increases as we go from local to global stimulation . Correlations between the recorded spike trains were quantified using the cross-correlogram ( CCG ) that gives the number of coincident spikes per unit time relative to chance levels as a function of lag . The CCGs obtained under local and global 4 Hz sinusoidal stimulation were mostly symmetric with respect to the 0 lag , indicating that correlated activity most likely arises because of common input ( Fig . 2C ) . However , we found marked differences in their structure that were contingent on the stimulus' spatial extent . Indeed , despite the fact that global stimulation impinges on most if not all of the sensory epithelium and might thus be expected to increase correlated activity , the CCG obtained under local stimulation was actually larger than that obtained under global stimulation ( Fig . 2C , compare red and blue lines ) . To separate the contributions of signal and noise correlations to the CCG , we used the shuffle predictor [41] and computed the noise CCG . The noise CCG obtained under local stimulation was also significantly larger than that obtained under global stimulation ( Fig . 2D , compare red and blue lines ) . Previous studies have shown that activation of granule cell input onto ELL pyramidal cells under global stimulation can reduce single neuron responses to stimulus by forming a negative image of the stimulus [22] , [24] , [34] , which can attenuate signal correlations at the network level [27] . The formation of this negative image is mediated by anti-Hebbian burst-time dependent plasticity at parallel fiber-ELL synapses [32] , [33] , [35] . In order to gain better understanding as to how activation of parallel fiber input onto ELL pyramidal cells could mediate the experimentally observed changes in correlated activity , we used the model described in Fig . 1 that includes the anti-Hebbian burst-time dependent plasticity ( see Methods ) in order to mediate the formation of a negative image . We found that , for suitable parameters , our model was able to reproduce the experimentally observed changes in both the raw ( Fig . 2E ) and the noise CCG ( Fig . 2F ) . The magnitude of correlation was assessed using the cross-correlation coefficient ( see Methods ) . Overall , the cross-correlation coefficient as well as the noise cross-correlation coefficient were reduced by >40% when we transition from local to global stimulation in the data ( Fig . 2G ) . We found that our model was able to successfully reproduce these results ( Fig . 2H ) . Why does our model successfully reproduce the experimental data ? We first investigated the effects of burst-timing dependent plasticity . Consistent with previous results [32] , the synaptic weights settled to their equilibrium values over time to form a negative image of the stimulus over time ( Figs . 3A , 3B ) : weights of granule cells that fire at the stimulus' local maximum were depressed the most while those of granule cells that fire at the stimulus' local minimum were depressed at least ( Fig . 3A , compare red traces ) . As expected , the progressive formation of the negative image progressively reduced the variations in the single model SP cell's firing rate due to the stimulus ( Fig . 3C ) , which is consistent with previous results [32] and experimental data [22] . At the population level , our results show that the formation of the negative image reduces the overall correlations between model SP cells over time ( Fig . 3D ) . However , as we did not observe any changes in noise correlations ( Fig . 3D ) , we conclude that it is signal correlations only that are attenuated . Therefore , our model shows that the mechanisms that lead to the formation of a negative image are sufficient to explain the overall reduction in signal correlations observed when parallel fiber feedback input onto ELL pyramidal cells is activated . However , as no change in noise correlation was observed , we conclude that these are regulated by other mechanisms . In order to gain understanding as to the mechanisms by which activation of granule cell input onto ELL pyramidal cells reduces noise correlations in our model , we plotted the spiking activities of four example model granule cells each firing at a different phase of the sinusoidal input in the deterministic regime ( i . e . no intrinsic noise ) and in the stochastic regime ( i . e . intrinsic noise ) . In the former regime , there is no trial-to-trial variability in the spiking activity of a given granule cell ( Fig . 4A ) while this is not the case in the latter regime ( Fig . 4B ) ( we note that previous studies did not consider spiking activity of granule cells or trial-to-trial variability [32] , [33] ) . We found that increasing the noise intensity ρ in the granule cells led to a decrease in the noise correlation coefficient under global stimulation relative to that obtained under local stimulation ( Fig . 4C ) . Importantly , the noise correlation coefficients obtained under local and global stimulation were equal in the absence of noise ( i . e . ρ = 0 ) . In contrast , increasing the noise intensity ρ did not affect the signal correlation coefficient obtained under global stimulation relative to that obtained under local stimulation ( Fig . 4D ) . Thus , trial-to-trial variability in the granule cell spiking activity is not necessary in order to observe a reduction in the amount of signal correlations , which is consistent with previous studies [27] and is discussed further below . However , our results reveal that trial-to-trial variability in the spiking activities of granule cells is necessary in order to observe reduced noise correlations under global stimulation . Thus , our model shows that the mechanism that mediates the reduction in noise correlations ( i . e . trial-to-trial variability in granule cell spiking activity ) is independent of the one that mediates the reduction in signal correlations ( i . e . the negative image that is formed because of anti-Hebbian burst-timing dependent plasticity ) . We next systematically varied model parameters in order to test whether the changes in noise correlation were robust . We first varied the correlation coefficient between the noise sources to each SP cell c as well as the correlation coefficient e between the noise sources to each granule cell and the DP cell population . Thus , intuitively , c represents the correlation coefficient between the variabilities in peripheral receptor afferent activities projecting to each SP cell , which of course increases with increasing fraction of shared receptor afferent input . On the other hand , e represents the correlation coefficient between the noise received by the granule cells and that received by the DP cells . Thus , e will be high if most of the variability displayed by granule cells is inherited from the input that they receive from DP cells and low if most of the variability is due to intrinsic mechanisms ( i . e . random openings of ion channels ) . We note that we assumed that the sources of noise to the DP and SP cells within a given ELL column were the same , which is consistent with anatomical findings showing almost complete overlap from sources of feedforward input [23] ( see Methods ) . Under simulated local stimulation ( i . e . no feedback ) , the noise correlation coefficient is solely determined by the amount of shared noise c received by both SPs ( Fig . 5A ) . Thus , we have Rnoise , local = 1 when c = 1 and Rnoise , local = 0 when c = 0 . Since we assume that the granule cell population is silent during local stimulation , the fraction of shared noise with the SPs e has no effect on Rnoise , local . Under simulated global stimulation , for which the granule cells are active , we observed a reduction in noise correlation as both c and e tend towards zero ( Fig . 5B ) . Plotting the difference between the noise correlation coefficients obtained under local and global stimulation revealed that the greatest reduction in noise correlations was seen for c near unity and e close to zero ( Fig . 5C ) . Thus , our model predicts that , for a given intensity , trial-to-trial variability in the granule cell spiking activity is most effective at reducing noise correlations amongst SPs when the trial-to-trial variabilities of SP cells are strongly positively correlated in the first place and when trial-to-trial variability in granule cell firing activity is weakly correlated with trial-to-trial variability in the DP cell population . Intuitively , this makes sense as the variability of the granule cell population is then largely independent of the variability in the SP cells , which will reduces noise correlations between the spiking activities of SP cells . We next systematically varied both the fraction of shared noise received by the SPs c as well as the stimulation frequency f . For simulated local stimulation ( i . e . no feedback ) , the noise correlation coefficient Rnoise , local had a similar dependence on c independently of stimulation frequency f ( Fig . 6A ) . However , under simulated global stimulation ( i . e . with feedback ) , the noise correlation coefficient Rnoise , global decreased as a function of stimulation frequency f for a given value of c except for c = 1 and c = 0 ( Fig . 6B ) . As such , plotting the difference between Rnoise , local and Rnoise , global reveals that the reduction in noise correlation tends to be greatest for low stimulation frequencies ( i . e . <16 Hz ) as well as when SPs receive large amounts of correlated input ( Fig . 6C ) . Thus , our model predicts that the reduction in noise correlation will be greatest for low stimulation frequencies and for pyramidal cell pairs with high fraction of shared input ( i . e . large amounts of receptive field overlap ) . Intuitively , this makes sense as the tendency for pyramidal cells to display burst firing is greatest for low frequency stimulation [32] , [42] and because burst firing in ELL pyramidal cells tends to increase correlations between their spiking activities [26] . Our model made two important predictions: 1 ) that the activation of the same feedback pathway simultaneously reduces both signal and noise correlations and; 2 ) that it does so via independent mechanisms . We tested these predictions experimentally by pharmacologically inactivating parallel fiber input onto ELL pyramidal cells ( Fig . 7A , see Methods ) . Consistent with modeling prediction 1 ) , pharmacological inactivation under global stimulation led to a significant increase in both signal and noise correlations ( p<0 . 05 , signrank tests ) ( Figs . 7B , C , D ) . We note in passing that previous studies have shown that pharmacological inactivation under local stimulation did not have such noticeable effects [22] , [24] , [25] . Moreover , to test prediction 2 ) , we plotted the increase in signal correlation as a function of the increase in noise correlations ( Fig . 7D ) . If our modeling prediction were correct , then we would expect to see no significant relationship between both quantities . Consistent with our modeling prediction , no significant correlation was observed ( R = 0 . 32 , p = 0 . 18 , N = 14 ) .
In this study , we presented new experimental results showing that correlated activity in ELL pyramidal cells is reduced under global 4 Hz sinusoidal stimulation relative to local 4 Hz sinusoidal stimulation . We observed significant reduction in both signal and noise correlations as was observed previously using broadband ( 0–120 Hz ) noise stimulation [26] , [27] . In order to explain the observed reduction in noise correlations , we extended a previously published model that accurately describes how feedback can attenuate the responses of single pyramidal cells to global stimulation [32] , [33] to include: 1 ) correlated activity of multiple pyramidal cells and; 2 ) trial-to-trial variability in the spiking activities of individual granule cells . Simulation of this model revealed that it could qualitatively explain the changes in both the signal and noise correlations observed experimentally . The formation of a negative image of the stimulus via anti-Hebbian burst timing dependent plasticity was necessary to induce a reduction in signal but not noise correlations . In contrast , the magnitude of trial-to-trial variability in granule cell spiking activity strongly influenced the magnitude by which noise but not signal correlations are reduced under global stimulation . By systematically varying model parameters , we found that this reduction was strongest in our model when the shared noise amongst pyramidal cells was highest , when the noise sources between the pyramidal and the granule cell populations were least correlated , and for low sinusoidal stimulation frequencies . Our model made the important predictions that activation of the same feedback pathway can simultaneously reduce both signal and noise correlations via independent mechanisms . We verified these predictions by pharmacologically inactivating parallel fiber feedback input onto ELL pyramidal cells . Consistent with modeling predictions , pharmacological inactivation led to increases in both signal and noise correlations that were not significantly related to one another . The effects of noise correlations on neural coding have been the subject of much study and it is generally agreed that noise correlations will limit information transmission by introducing redundancy in the neural code [2] , [3] , [43]–[46] ( but see [47] ) . In particular , even small amounts of noise correlations can have significant effects on information transmission by large neural populations [45] . From this point of view , if the brain is trying to maximize information transmission , then redundancy must be minimized by reducing noise correlations [14] . While the reduced noise correlations observed under some behavioral states [11] , [48] does lend some support to this hypothesis , the fact that neural correlations are dynamically regulated across systems and species instead suggest that the brain uses different coding strategies based on both its internal state as well as the spatiotemporal characteristics of the stimulus [7] , [13] . In wave-type gymnotiform weakly electric fish , local and global stimuli arise in different behavioral contexts: local stimuli are mostly caused by prey [49] while global stimuli are caused in part by conspecifics [50] , [51] . The fact that single pyramidal neurons display reduced responses to low frequency global stimuli [22] , [24] , [25] , [31] , [52] is thought to better enable them to detect signals caused by prey items [27] . At the population-level , we propose that activation of parallel fiber feedback input onto ELL pyramidal cells by low frequency global stimuli contributes to reducing noise correlations in the superficial pyramidal cell population . This would enable downstream neurons within the midbrain Torus semicircularis to better detect transient increases in correlated activity that would be caused either by prey stimuli as well as communication stimuli . Indeed , previous studies have shown that single ELL pyramidal cells will increase the precision of their spike timing in response to chirp stimuli which is expected to give rise to increase in correlated activity at the population level [53] , [54] . While additional support for this hypothesis comes from evidence showing that neurons within the torus semicircularis can respond selectively to communication stimuli [55] , [56] ( see [20] for review ) , further studies investigating how ELL pyramidal cell populations respond to communication stimuli are needed in order to test these predictions . Previous studies have shown that there are large heterogeneities in the pyramidal cell population: while SPs receive the largest amount of feedback , DPs instead receive little or no feedback [23] , [57] . Previous studies point to different functional roles for these different classes as DPs actually give rise to the feedback input to SPs [22] . All evidence shows that the activities of single DPs is not dependent on the stimulus' spatial extent and that these cells tend to be broadly tuned [24] , [25] , [58] . At the population-level , correlations between DPs are also largely independent of the stimulus' spatial extent [27] . All these results support the hypothesis that deep pyramidal cells give a faithful representation of the incoming sensory input that is: 1 ) sent to higher brain centers and 2 ) used to reduce single SP cell responses as well as decorrelated SP cell activity at the population level to low frequency global stimuli . Our results showing that activation of granule cell spiking activity by spatially diffuse stimuli can reduce noise correlations are likely to be applicable to other systems . This is because the ELL shares many anatomical features with the mammalian cerebellum as well as other cerebellar-like structures such as the dorsal cochlear nucleus for which a layer of principal cells receive parallel fiber input from a set of granule cells [59] . In fact , our proposed mechanisms by which activation of granule cell input onto ELL pyramidal cells actually reduces correlated activity are likely to be applicable to other cerebellum-like structures as well as the cerebellum . In fact , they might help explain seemingly paradoxical experimental results showing that correlations between the simple activities of pairs of cerebellar Purkinje cells is low even when they receive common parallel fiber input [60] . We note that our modeling assumption that granule cells fire at all phases of the input is reasonable given available anatomical data [61] . The trial-to-trial variability in granule cell firing responses to the stimulus is also consistent with experimental data from mormyrid weakly electric fish [62] . While the source of this trial-to-trial variability is still a matter of debate , the fact that granule cells are compact and thus receive only a small number of inputs makes it likely that this variability is the result of both the random openings of ion channels ( intrinsic ) as well as inherited from input from the DP population . Our modeling results show that the relative amount of noise shared with pyramidal cells does not qualitatively influence the overall reduction of noise correlations over a wide range . Recent results from a recent study in mormyrid weakly electric fish have shown that , while granule cells are activated with a wide distribution of delays , the distribution was not uniform [62] , which is unlike the assumption made here . However , we do not expect this to be an issue as long as there is at least one granule cell that is firing at any given phase of the sinusoidal input whose trial-to-trial variability will effectively increase the amount of uncorrelated noise received by the pyramidal cell population and thereby reduce noise correlations . Finally , we note that there exists important similarities between the electrosensory and visual systems . Indeed , like primary visual cortical ( i . e . V1 ) neurons [63] , ELL pyramidal neurons respond to stimulation within a particular region of sensory space ( i . e . the classical receptive field ) [31] . Importantly , responses to classical receptive field stimulation are modulated by stimulation outside but within the non-classical receptive field in similar ways in both systems . Specifically , non-classical receptive field stimulation decorrelates the single neural response from low frequency as well as enhances information transmission about higher frequency classical receptive field stimulation [52] , [64] , [65] . Modeling studies in the visual system have proposed that such decorrelation is a form of predictive coding that removes the redundant aspects of natural visual stimuli [66]–[68] . We note that this is also the case for ELL pyramidal cells [22] , [27] . Previous studies have shown that , for ELL pyramidal cells , at least part of the non-classical receptive field effects are being mediated by indirect feedback [24] . In the visual system , both anatomical [69] and modeling [66] studies support the hypothesis that the non-classical receptive field of V1 neurons also originates , at least in part , from feedback input . It is thus likely that the effects described here also apply to the neurons within the primary visual cortex , where activation of feedback input via non-classical receptive field stimulation also decorrelates neural responses to classical receptive field stimulation at the population level [64] . We have shown a viable mechanism by which sensory neuron populations can have correlations within their trial-to-trial variabilities markedly reduced under a particular behavioral context . This mechanism is expected to be generally applicable to cerebellum and other cerebellar-like structures . Further experimental studies recording from cerebellar granule cells in gymnotiform weakly electric fish are needed to verify our modeling predictions .
McGill University's animal care committee approved all procedures . The weakly electric fish Apteronotus leptorhynchus was used exclusively in these studies . Fish were purchased from tropical fish suppliers and were acclimated to laboratory conditions and housed in groups of 8–10 . Water conductivity was between 400 and 800 µS , the pH was maintained between 6 . 8 and 7 . 2 , and the temperature was kept between 27 and 29°C [70] , [71] . Surgical procedures were explained in detail previously [26] , [72]–[74] . Briefly , to immobilize the fish , we injected 0 . 1–0 . 5 mg of tubocurarine ( Sigma ) intramuscularly . We then transferred the fish to a recording tank and respirated it via a mouth tube at a flow rate of 10 mL/min . We glued a metal post rostral to the exposed area of the skull after topical application of lidocaine ( 2% ) to stabilize the head during recording . We then drilled a small hole of ∼2 mm2 over the cerebellum and the ELL area , caudal to the border between hindbrain and midbrain in order to access the pyramidal neurons [56] , . Extracellular recordings from pyramidal cells within the centro-lateral and lateral segments were obtained using metal filled micropipettes [77] . Recordings from N = 18 pyramidal cell pairs were achieved as described previously [26] , [27]: two separate electrodes were advanced independently in order to ensure that a well-isolated single unit was present on each one . Recordings were sampled at 10 kHz and were digitized using a Power1401 with Spike2 software ( Cambridge Electronic Design , Cambridge , UK ) . Previous studies have shown a strong negative correlation between the baseline firing rate ( i . e . the firing rate in the presence of the animal's unmodulated EOD ) and dendritic morphology [22] , [57] , such that deep pyramidal cells tend to have the highest ( >30 Hz ) firing rates while superficial pyramidal cells tend to have the lowest ( <15 Hz ) firing rates [24] , [25] , [31] , [58] , [72] , [78] . Previous techniques were used to pharmacologically inactivate indirect feedback onto N = 9 pairs of ELL pyramidal cells [22] , [24]–[27] , [30] , [74] , [79] . Briefly , a double-barrel pipette was advanced into the ELL molecular layer . One barrel contained a glutamate solution ( 1 mM ) while the other contained a solution of CNQX ( 1 mM ) , which is a non-NMDA glutamate receptor antagonist . All drugs were obtained from Sigma and were dissolved in saline . Both barrels were connected to a picospritzer ( Hannifin ) . Pressure ejection of glutamate was used to determine whether the double-barrel pipette was in the vicinity of the cell pair recorded from: close vicinity typically resulted in short latency excitation of both cells [26] . We then ejected CNQX as done previously [24]–[26] . As the electric organ discharge of A . leptorhynchus is neurogenic , it is not affected by immobilization with curare-like drugs . All stimuli consisted of amplitude modulations ( AMs ) of the animal's own EOD and were produced by applying a train of sinusoidal waveforms to the fish . Each sinusoid was triggered at the zero crossing of each EOD cycle and had a period slightly less than that of the EOD waveform; hence the train remains synchronized to the animal's discharge and , depending on its polarity , either adds to or subtracts from the animal's own discharge . A modulation waveform was then multiplied with the train of sinusoidal waveforms ( MT3 multiplier; Tucker Davis Technologies ) and the resulting signal was first isolated from ground ( A395 linear stimulus isolator; World Precision Instruments ) before being delivered using either global or local stimulation geometry . For global stimulation , signals were delivered through pairs of chloridized silver wire electrodes positioned 15 cm away from the fish in either side of the recording tank . In contrast , for local stimulation , we used a small local dipole electrode that was located 1–3 mm from the skin . The intensities of local and global stimuli were similar to those used previously [56] , [74] , [75] and were adjusted such as to give rise to similar changes in EOD amplitude as measured by a small dipole close to the animal's skin [22] , [31] . Stimuli consisted of 4 Hz sinusoidal AMs of the animal's own EOD . All analysis was performed using custom-built routines in Matlab ( The Mathworks , Natick , MA ) . Action potential times were defined as the times at which the signal crossed a suitably chosen threshold value . From the spike time sequence we created a binary sequence X ( t ) with binwidth dt = 0 . 5 ms and set the content of each bin to equal the number of spikes the time of which fell within that bin . The auto-correlogram ( ACG ) A ( τ ) was computed using: ( 1 ) where N is the total number of action potentials and m is the mean number of action potentials per unit time ( i . e . the mean firing rate ) . The cross-correlogram ( CCG ) between spike trains X1 ( t ) and X2 ( t ) , C ( τ ) , was computed using: ( 2 ) where N1 is the total number of action potentials for spike train X1 ( t ) and m2 is the number of action potentials per unit time for spike train X2 ( t ) . We note that the sum is performed over the spikes of cell 1 and that the labeling of cells within the pair is completely arbitrary . The particular cell used for averaging does not matter for our data as the CCGs were symmetric with respect to lag 0 ( see Fig . 2 ) . The cross-correlation coefficient was computed for each cell pair as [44]: ( 3 ) where denotes the average over lag τ . We distinguished the contributions of signal and noise correlations to the CCG using the shuffle predictor [41] , [80] . Let M be the total number of cycles of the sinusoidal stimulus and let Xi , j ( t ) be the response of neuron i to cycle j at time t , the shuffle predictor is then a measure of signal correlations and is given by: ( 4 ) where N1 , k is the total number of action potentials for spike train X1 , k ( t ) and m2 , j is the number of action potentials per unit time for spike train X2 , j ( t ) . The noise CCG , Cnoise ( τ ) , is then given by: ( 5 ) Our model is an extension of a model considered by previous studies [32] , [33] . We consider two superficial ELL pyramidal cells ( i . e . SP cells ) from transmembrane voltages are solutions to the following system of equations: ( 6 ) where , for cell i , Vi is the transmembrane voltage , Ci is the membrane capacitance , gleak , i is the leak conductance , Ii is a constant bias current , σi is the noise standard deviation , ξSP , i is low-pass filtered ( fourth order Butterworth with cutoff frequency 500 Hz ) Gaussian white noise with zero mean and variance unity , κ is the stimulus amplitude , f is the stimulation frequency , DAPi ( t ) is the depolarizing afterpotential current ( described below ) , gGABA , i is a constant inhibitory conductance with reversal potential EGABA , and gAMPA , i , s ( t ) is the time varying excitatory conductance of parallel fiber s with reversal potential EAMPA . The function F ( x ) half-wave rectifies the input ( i . e . F ( x ) = x for x>0 and F ( x ) = 0 otherwise ) . The current DAPi ( t ) is given by [32] , [33] , [81]: ( 7 ) where , for neuron i , rs , i is the absolute somatic refractory period , rd , i ( t ) is the absolute dendritic refractory period , tn . i is the last spike time , tn-1 , i is the next to last spike time , is the time immediately after tn . i , and s ( t , a ) is an alpha function given by: ( 8 ) The dendritic refractory period obeys the following system of equations: ( 9 ) Each SP cell is modeled using integrate-and-fire formalism [82] . Thus , for cell i , when the voltage Vi ( t ) reaches a threshold value Vthreshold , i , an action potential is said to have occurred and Vi ( t ) is then immediately reset to the resting potential Vrest , i and is maintained there for the duration of the absolute somatic refractory period rs , i . There are 100 AMPA synapses on each SP cell each emanating from one granule cell via a parallel fiber . Anatomical findings suggest that a single parallel fiber does indeed make synaptic contact with multiple pyramidal cells , but that the synaptic boutons are located far away from one another in the ELL [28] . It is thus likely that neighboring pyramidal cells receive synaptic input from largely disjoint sets of granule cells ( Maler , Pers . Comm . ) . Thus , we assumed that each SP cell receives input from different sets of granule cells . The time varying post-synaptic conductance of AMPA synapse s , gAMPA , i , s ( t ) , obeys the following equation: ( 10 ) where gmax is the maximum conductance , wi , s ( t ) is the synaptic weight , tk , s is the kth spike of presynaptic granule cell s , Ns is the total number of spikes fired by granule cell s , and Θ ( x ) is the Heaviside function ( Θ ( x ) = 1 if x>0 and Θ ( x ) = 0 otherwise ) . Each granule cell is modeled using an integrate-and-fire formalism similar to that used for the SP cell with the same threshold , reset , and absolute refractory period ( note that we assume that separate sets of granule cells project to each pyramidal cell ) . For granule cell i , the transmembrane voltage obeys the following equation: ( 11 ) where ρi is the noise standard deviation , ξPF , i is low-pass filtered ( fourth order Butterworth with 500 Hz cutoff frequency ) , and di , the delay for granule cell i , is given by: ( 12 ) We note that DPs are assumed to faithfully relay the sinusoidal stimulus to the granule cells , which is consistent with available experimental data [21] , [31] , [37] , [58] . Each synaptic weight wi , s follows anti-Hebbian plasticity as described previously [32] , [33] . Specifically , a previously described burst-dependent plasticity [35] learning rule reduces the value of a specific synaptic weight when pre and post-synaptic bursts of activity are coincident within a given time window . The learning rule is dependent on the length of the SP burst , we considered 2-spike bursts ( 2 spikes within 15 ms ) as well as 4-spike bursts ( 4 spikes within 45 ms ) separately . Note that a given spike can only be part of one burst , and the 4-spike burst takes priority . As such , spike trains were analyzed for bursts every time the SP cell produces a new spike . The new spike and the three preceding spikes are analyzed and ( a ) if none of them are part of bursts already and ( b ) the first and last spike are within 45 ms apart of each other , then the group of spikes is considered a 4-spike burst . If they do not constitute a 4-spike burst , then the fourth most recent spike and the fifth most recent spike are analyzed and ( a ) if neither spike is part of a burst already and ( b ) the spikes are within 15 ms of each other , then they are considered a 2-spike burst . In this way , no spike that may become part of a 4-spike burst is mistakenly placed in a 2-spike burst , and every spike that cannot be part of a 4-spike burst is checked to see if it can be placed in a 2-spike burst . When a burst in SP cell i is recorded , all the synaptic weights wi , s are updated according to: ( 13 ) where g = 2 , 4 depends on whether the SP cell burst consists of 2 or 4 spikes , tburst , pre is the onset time of the pre-synaptic burst , tburst , post is the onset time of the SP cell burst , ηg is a constant gain term , and LWg is the length of the time windows for g-spike SP cell burst . Thus , parameters η2 and LW2 are used for 2-spike bursts while parameters η4 and LW4 are used for 4-spike bursts . The pre-synaptic onset burst times , tburst , pre , were taken to be the times at which the sinusoidal input to each granule cell reaches a local maximum . A non-associative potentiation rule is also included in order to ensure that not all synaptic weights reach zero due to the aforementioned depression rule . Thus , all the synaptic weights evolve according to: ( 14 ) where τw≫1 . Previous studies have shown that neighboring pyramidal cells within the centrolateral and lateral segments tend to display significant correlations between their baseline ( i . e . in the absence of stimulation ) activities [26] , which correlates well with anatomical findings showing significant shared input from peripheral receptor afferents [23] . We mimicked this shared input by decomposing the sources of noise onto both the SP cells as well as the granule cells . Specifically , we have: ( 15 ) where the noise ξshared ( t ) and ξunshared , i ( t ) have the same statistics as ξSP , i ( t ) except that the former is common between both SP cells while the ξunshared , i ( t ) are independent and identically distributed . Thus , the correlation coefficient between the noise sources to both SP cells is c . Similarly , the noise to each granule cell , ξPF , i ( t ) , consists of a component that is inherited from the deep pyramidal cells from which it receives input , ξDP ( t ) , and a component that is independent to each granule cell ξi ( t ) : ( 16 ) where ξi ( t ) are independent and identically distributed and e is the fraction of shared noise with the pyramidal cell . Since anatomical studies have shown that there is almost complete overlap between the feedforward inputs to DPs and SPs within the same column [23] , we took ξDP ( t ) = ξSP , i ( t ) . Thus , using eq . ( 15 ) , we have: ( 17 ) Where ξshared ( t ) is common to all granule cells , ξunshared , j ( t ) is common to all granule cells projecting to pyramidal cell j , and ξi ( t ) is independent across granule cells . Thus , granule cells will tend to display more correlations in their trial-to-trial variabilities when both c and e are close to 1 . We assumed a homogeneous network for SPs and granule cells and , unless otherwise specified , parameter values used were: Vthreshold = −65 mV , Vrest = −68 . 8 mV , rs = 0 . 7 ms , I = 0 . 313µA/cm2 , σ = 0 . 412 µA/cm2 , ρ = 0 . 412 µA/cm2 , κ = 0 . 21 µA/cm2 , τw = 4900 s , Eleak = −68 . 8 mV , EAMPA = 0 mV , EGABA = −68 . 8 mV , gleak = 0 . 14 mS/cm2 , gGABA = 0 . 14 mS/cm2 , gmax = 0 . 024 mS/cm2 , C = 1 µF/cm2 , τAMPA = 5 . 26 ms , A = 0 . 6 , B = 2 , D = 0 . 7 ms , E = 24 . 5 ms , γ = 0 . 2 , α = 10 . 9 µA/cm2 , τ = 7 ms , LW2 = 10 ms , LW4 = 100 ms , η2 = 0 . 0018 , η4 = 0 . 0036 , c = 0 . 25 , e = 1 . The model was simulated numerically using an Euler-Maruyama algorithm with dt = 0 . 05 ms . As previous studies have shown that local stimuli did not elicit significant feedback input onto pyramidal cells [22] , [24]–[26] , [30] , we mimicked this stimulation in our model by setting both gmax and gGABA to zero . For global stimulation , we initially set all synaptic weights to 1 and allowed them to settle to equilibrium over a simulation time of 1000 s ( i . e . “training” ) . These weights were then kept fixed at these values for simulations that were run over 100 trials of 100 sec each that are presented in the results except for Fig . 3 . We note that similar results were obtained when the weights were allowed to evolve . For the results presented in Figs . 3A , B , the weights were allowed to vary according to the plasticity rule ( eq . 13 ) . For Figs . 3C , 3D , the weights were kept fixed at their values for the corresponding time during training in order to avoid non-stationarities and thus be able to compute the shown quantities . | Correlated activity is observed ubiquitously in the CNS but how activation of specific neural circuits affects correlated activity under behaviorally relevant contexts is poorly understood . Here , through a combination of electrophysiology , pharmacology , and mathematical modeling , we show that activation of the same parallel fiber feedback pathway leads to simultaneous reductions in both signal and noise correlations via independent mechanisms . Specifically , we show that feedback in the form of a negative image of the stimulus is necessary in order to attenuate signal but not noise correlations . Moreover , we show that trial-to-trial variability in the spiking responses of neurons providing this feedback is necessary to attenuate noise but not signal correlations . Our model thus predicts that activation of the same feedback pathway can simultaneously reduce both signal and noise correlations through independent mechanisms . In agreement with modeling prediction , pharmacological inactivation led to a strong increase in both signal and noise correlations but the magnitude of the change in signal correlation was not related to the magnitude of the change in noise correlations . Our proposed mechanism for simultaneous control of both signal and noise correlations is generic and is thus likely to be applicable to the cerebellum and to other cerebellar-like structures . | [
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] | 2015 | Activation of Parallel Fiber Feedback by Spatially Diffuse Stimuli Reduces Signal and Noise Correlations via Independent Mechanisms in a Cerebellum-Like Structure |
Bartonella spp . are facultative intracellular vector-borne bacteria associated with several emerging diseases in humans and animals all over the world . The potential for involvement of ticks in transmission of Bartonella spp . has been heartily debated for many years . However , most of the data supporting bartonellae transmission by ticks come from molecular and serological epidemiological surveys in humans and animals providing only indirect evidences without a direct proof of tick vector competence for transmission of bartonellae . We used a murine model to assess the vector competence of Ixodes ricinus for Bartonella birtlesii . Larval and nymphal I . ricinus were fed on a B . birtlesii-infected mouse . The nymphs successfully transmitted B . birtlesii to naïve mice as bacteria were recovered from both the mouse blood and liver at seven and 16 days after tick bites . The female adults successfully emitted the bacteria into uninfected blood after three or more days of tick attachment , when fed via membrane feeding system . Histochemical staining showed the presence of bacteria in salivary glands and muscle tissues of partially engorged adult ticks , which had molted from the infected nymphs . These results confirm the vector competence of I . ricinus for B . birtlesii and represent the first in vivo demonstration of a Bartonella sp . transmission by ticks . Consequently , bartonelloses should be now included in the differential diagnosis for patients exposed to tick bites .
Bartonella spp . are facultative intracellular gram-negative bacteria , which commonly infect mammals , particularly rodents . Some of these are associated with emerging or re-emerging diseases in humans and animals [1] . To date , 13 Bartonella species or subspecies have been associated with a large spectrum of clinical syndromes in humans including Carrion's disease , trench fever , cat scratch disease , bacillary angiomatosis , Parinaud's oculoglandular syndrome , endocarditis , peliosis hepatis , myocarditis , neuroretinitis , fever , fatigue and neurological symptoms [2] . Although all bartonellae are presumed to be transmitted by arthropods , primary vectors have been identified with certainty for only five Bartonella spp . : the louse Pediculus humanus humanus transmits B . quintana [3] , the cat flea Ctenocephalides felis is responsible for the transmission of B . henselae [4] , the sand fly Lutzomyia verrucarum is the vector of B . bacilliformis [5] , and the flea Ctenophthalmus nobilis is implicated in the transmission of B . grahamii and B . taylorii to bank voles [6] . The potential for involvement of ticks in transmission of Bartonella spp . has been heartily debated for many years ( see reviews by [7]–[9] ) . However , most of the data supporting bartonellae transmission by ticks come from molecular and serological epidemiological surveys in humans and animals providing only indirect evidences without a direct proof of tick vector competence for bartonellae . The only direct evidence of transmission of a Bartonella sp . by ticks to a susceptible animal was reported in 1926 by Noguchi who described experimental transmission of B . bacilliformis by Dermacentor andersoni [10] . In that study , adult D . andersoni ticks , which had been fed for several days upon infected monkeys , were allowed to reattach to naïve animals . These recipient naïve monkeys became infected , likely because of mechanical transfer of the pathogen on blood-contaminated mouth parts . Neither the tick's vector competence nor bacterial transtadial transmission throughout the tick life's cycle were assessed . A recent study using an artificial feeding system provided first experimental data supporting vector competence of ticks for bartonellae [11] . Immature I . ricinus ticks were able to acquire B . henselae while feeding on artificially infected blood , maintain the pathogen through the molt , and secreted it into uninfected blood during the subsequent artificial feeding . Cats inoculated with dissected salivary gland of these ticks developed typical B . henselae infection , proving the viability of transstadially passaged bacteria . However , ticks were fed via an artificial feeding system on blood supplemented with bacteria just prior the feeding that does not reflect natural infection of reservoir animals . Therefore , experimental transmission studies using infected ticks and live susceptible animals are required to unequivocally demonstrate the vector competence . B . birtlesii sp . nov . was originally isolated from wild rodents ( Apodemus spp . ) [12] and later shown to be infectious for laboratory mice [13] , [14] . Considering the high natural frequency of infestation in wild rodents with I . ricinus , we assessed vector competence of this tick species for B . birtlesii by demonstrating its ability to acquire the pathogen from an infected host and transmit it to naïve susceptible animals during the subsequent feeding .
This study was carried out in strict accordance with the good animal care practise of the recommendations of the European guidelines . The protocol was approved by the Committee on the Ethics of Animal Experiments of the national Veterinary School of Alfort ( Permit Number: 2008-11 ) . All efforts were made to minimize suffering of animals . All experiments were performed with Ixodes ricinus colony reared in our laboratory at 21°C and 95% relative humidity , under a 12 h light/dark cycle . For ticks colony maintenance , nymph and adult ticks were fed on uninfected rabbits ( HYPHARM , Roussay , France ) , while larvae were fed on sheep blood ( bioMérieux , Lyon , France ) using the artificial membrane feeding technique previously described [15] . At each developmental stage , ticks were starved for at least three months between molting and the next feeding . Bartonella birtlesii ( IBS325T strain [12] ) was grown on 5% defibrinated sheep blood Columbia agar plates ( CBA ) incubated at 35°C with 5% CO2 . After 5 days , bacteria were harvested and suspended in sterile phosphate-buffered saline ( PBS ) immediately before being used for mouse infection . Specific immune serum was generated by subcutaneous injection immunization of a Balb/C mouse ( Charles River Laboratories , L'Arbresle , France ) with 108 CFU of B . birtlesii after a freeze-thaw step , and with a boost two weeks later . Blood was collected 26 days after the boost from the retro-orbital sinus and the serum was stored at −20°C . A 4-weeks old OF1 female mouse ( Charles River Laboratories ) was experimentally infected by intravenous injection in the tail vein with B . birtlesii ( 5×108 CFU in 100 µl of PBS ) . Blood samples were collected from the retro orbital sinus at seven , thirteen and nineteen days post infection , and the presence of Bartonella DNA was confirmed by semi-nested PCR as previously described [11] . For tick infestation , the B . birtlesii-infected mouse was briefly anaesthetized with 3% Isoflorane and a plastic cap opened at the top was glued on its shaved back with wax as described [16] . On days 13 and 14 postinoculation , hungry larvae ( approximately 150 ) and nymphs ( 25 ) were placed in the cap , which was sealed with sticking plaster . Ticks were allowed to feed on the mouse for five days . At that time , the cap was opened , and the engorged ticks were collected and stored under standard conditions described above for molting into the next stage . Nymphs fed as larvae upon the B . birtlesii-infected mouse were placed on naïve uninfected mice at approximately 3 months after the molt in order to evaluate bacterial transmission from ticks to mice . Three 4-weeks old OF1 naïve female mice were each infested with 8 nymphs ( 24 ticks in total ) as described above . Ticks were allowed to feed until repletion . Blood samples were collected from each mouse on the day of infestation before tick attachment ( day 0 ) and at seven and 16 days after tick attachment . Mouse blood ( 25 ul ) was incubated in 500 ml of Schneider Drosophila medium for 6 days at 35°C , 5% CO2 as previously described [17] . As B . birtlesii does not grow on blood agar after liquid medium culture ( unpublished data ) , the presence of bacteria was confirmed by 2 methods: semi-nested PCR of Bartonella spp . 16S DNA as previously described [11] and immunofluorecence assay on 100 µl of the cell suspension . Briefly , cytospin is used to spin cell suspension onto the slide , which were fixed with 4% paraformaldehyde and washed in PBS . Slides were covered with mouse anti- B . birtlesii serum diluted at 1∶150 in PBS and incubated for 45 min . After washing , slides were incubated for 20 min with an anti-mouse secondary antibody ( Alexa Fluor® 488 goat anti–mouse IgG , Invitrogen ) diluted per manufacturer's specifications . Samples were then mounted in VECTASHIELD® Fluorescent Mounting Media ( Vector Laboratories , Peterborough , UK ) and examined under microscope . At Day 16 , the mice were euthanized and the livers were removed . Half of the liver was stored at −80°C , the other part was homogenized in 500 µL of F12 medium ( Invitrogen , Cergy Pontoise , France ) . 250 µL of the homogenate were spread on CBA plates incubated at 35°C with 5% CO2 . The plates were checked daily for bacterial growth , and the identity of appearing bacterial colonies as B . birtlesii was confirmed by nested-PCR amplification of Bartonella spp 16S RNA encoding gene followed by sequencing of the 337-bp amplified fragment as previously described [11] . Female I . ricinus derived from nymphs that fed upon the B . birtlesii-infected mouse were fed four months later by membrane feeding technique as previously described [11] , [15] . Thirteen females from the infected cohort were placed on a membrane feeder together with 13 males from our uninfected colony ( for mating ) and fed on sheep blood ( bioMérieux ) changed every 24 h . After tick attachment , the presence of B . birtlesii DNA in the used blood was detected by semi-nested PCR as previously described [11] . Once Bartonella spp DNA had been detected in blood , four females were removed and used for immunohistological assay . Two females from an uninfected cohort feeding simultaneously on a separate feeder were used as control . The partially engorged female ticks were fixed in their entirety , 15 min in Carnoy's solution ( 3∶1 , absolute ethyl alcool∶glacial acetic acid ) before cutting the legs , and then left over night in the same fixative . Ticks were washed twice in 70% ethanol for 15 minutes , once in 95% ethanol for 1 hour and 4 times in 100% ethanol for 1 hour . Finally ticks were washed 3 times in butanol for 24 h before embedding in paraffin . For immunohistochemistry analysis , 4-µm thick sections were cut , dewaxed and pretreated for 6 min . with protéinase K ( Sigma ) at 37°C and in 3% hydrogen peroxide ( Gifrer , Decines , France ) for 10 min . at room temperature . Sections were then blocked for 20 min with 20% normal goat serum ( Dako , Glostrup , Denmark ) . Mouse antiserum against B . birtlesii , diluted at 1∶150 was used as primary antibodies and incubated on slides in 2% BSA ( Sigma ) for 1 h at 37°C . The corresponding pre-immune serum was used as negative control . Anti-mouse ( Dako ) biotinylated secondary antibodies were then incubated on slides in 2% BSA for 30 min and antigen-Antibody binding was revealed with streptavidin-PAL ( Dako ) and Fast-Red Substrate for immunoperoxidase ( Dako ) , according to the manufacturer's instructions . The slides were counterstained with Gill's hematoxylin ( Surgipath , Peterborough , UK ) and examined under microscope with magnification ×400 .
PCR amplification of Bartonella spp . DNA in blood samples collected from the mouse infected with B . birtlesii showed that the mouse was bacteremic at days seven , 13 and 19 postinoculation . Therefore , ticks were placed on this mouse at days 13 and 14 . After repletion , a total of 120 engorged larvae and 25 engorged nymphs were allowed to molt to nymphal and adult stage , respectively . In order to assess the ability of I . ricinus nymphs acquisition-fed as larvae upon an infected mouse to transmit B . birtlesii to a susceptible host , 24 of these nymphs were allowed to feed on three uninfected mice −8 per mouse . Of these , a total of 11 ticks fed to repletion – three , two and six from each of the mice . PCR detected the presence of Bartonella spp . DNA in Schneider Drosophila medium inoculated with blood samples from each of the three mice on days seven and 16 , but not on day zero ( Figure 1A ) . All amplified fragments were 100% identical to the B . birtlesii corresponding fragment of the 16S rRNA gene ( accession number AF204274 ) . B . birtlesii was also detected in the same samples by immunofluorescence ( Figure 1B ) . This confirms the presence and viability of B . birtlesii bacteria in the blood of mice fed upon by B . birtlesii-infected ticks . In addition , B . birtlesii colonies ( also confirmed by PCR amplification and sequencing ) were isolated from livers of the three recipient mice , demonstrating persistence of live bacteria for at least 16 days after mice had been bitten by infected nymphs . Identical results were obtained for all three recipient mice in both assays . Thirteen female I . ricinus fed at the preceding nymphal life stage upon a B . birtlesii–infected mouse were re-fed with uninfected sheep blood on a membrane feeder . Blood samples were withdrawn from the feeder every 24 h during the 8–day feeding period to detect the presence of B . birtlesii DNA . B . birtlesii DNA was detected in samples drawn on days three through eight of tick attachment ( Figure 2 ) , indicating that adult ticks were successfully emitting the bacteria into the previously uninfected blood during feeding . Four partially engorged females from the infected cohort and two partially engorged uninfected females were detached from the respective membrane feeders at 72 h post-attachment and used for histological examination . B . birtlesii bacilli were identified as dense particles of approximately 1 µm both in the cytoplasm of salivary gland cells and at the periphery of striated muscle section of all four ticks from the infected cohort , while no bacteria could be detected on uninfected ticks ( Figure 3 ) . No bacteria were detected in the midgut of the ticks ( data not shown ) .
The question of whether any of the Bartonella spp . may possibly be transmitted by ticks has been debated for several years . Indeed , although it is believed that most Bartonella spp . are transmitted by an arthropod vector , these pathogens are always associated with erythrocytes and endothelium in their vertebrate hosts , and the ability of these bacteria to survive for many weeks and months between successive tick feedings in the absence of such cells is uncertain . Numerous data have been published to date regarding identification of Bartonella DNA in both engorged ticks collected from their natural hosts and questing ticks collected from the environment ( for detailed reviews see [7] , [9] ) . As various Bartonella spp are common in wild and domestic animals , acquisition of these erythrocyte associated microorganisms by feeding ticks with a blood meal can be expected , and thus detection of bacterial DNA in engorged or partially engorged ticks does not add to the debate . However , positive PCR results in questing ticks do indicate that the bacterium ( or at least its DNA ) can survive in the tick through the molt from one life stage to another . In addition , a number of studies have reported co-infections in both humans and animals with Bartonella spp . and known tick-borne pathogens such as Borrelia spp . , Anaplasma spp . or Babesia spp . , suggesting that these might be co-transmitted by the same vectors [12]–[22] . Bartonella spp have also been detected by either PCR , serology , or culture in humans and animals after tick bites without any known contact with other arthropods [19] , [23] , [24] , [25] . Recently , Angelakis et al . reported detection of B . henselae infection in three patients , who developed scalp eschar and neck lymphadenopathy following tick bites [26] . A Dermacentor sp . tick removed from one of these patients contained DNA of B . henselae , although it is unclear whether the person acquired an infection from the tick , or the tick from the person . Our previous study demonstrated an innate ability of live B . henselae to be ingested by I . ricinus ticks with the blood-meal , maintained transstadially , and discharged again during the subsequent feeding [11] . In that study , however , ticks were acquisition-fed continuously on membrane feeders on blood containing 106 CFU/ml . This concentration is the one that could be encountered in an infected cats , however , the experimental model remains an experimental model and does not reproduce ideally the natural conditions of pathogen transmission using ticks and animals and therefore , the vector competence of ticks could not be definitively established . The present study used live hosts as both the source and the recipients of bacterial infection in order to confirm vector competence of I . ricinus for a Bartonella sp . Because of biosafety concerns associated with tick feeding upon cats infected with B . henselae , we decided to use a mouse model of B . birtlesii infection that has been studied in our laboratories for several years . The B . birtlesii strain used in this study was a low passage isolates from a field mouse Apodemus sp . [12] . Using this model , we showed that I . ricinus larvae and nymphs placed on an infected animal at the peak of bacteremia were able to acquire B . birtlesii from the host . Nymphs , infected at the larval stage , were able to inject B . birtlesii into mice , which in turn became bacteremic . Judging by the results of blood-PCR , the recipient mice developed bacteremia within seven days after placement of Bartonella-infected ticks and remained bacteremic at least until day 16 . This timetable is comparable with those observed when mice were needle-inoculated with the same pathogen [13] , [14] , [27] , [28] . Notably , we have re-isolated B . birtlesii from the liver of tick-infected mice , which confirms colonization of that organ by the pathogen observed earlier using needle-inoculation ( unpublished data - MVT; [29] ) . When we placed a cohort of infected adult ticks on a membrane feeder , Bartonella DNA was detected in all samples of the used blood removed later than 72 hours , but not in those tested at 24 and 48 hours . Similar results were obtained in our previous study [11] . Interpretation of these results requires several important considerations . Our previous experience shows that ticks placed on a membrane feeder may take up to 48 hours or even longer to find an attachment place , lacerate the skin-membrane , produce the cement cone , and to begin feeding . Once ticks are feeding on a membrane feeder , a few microliters of tick saliva are mixed with five ml of blood contained in the feeder resulting in a colossal dilution effect , that can reduce the concentration of the saliva-introduced bacteria in the sampled blood below the detectable threshold . Therefore , a delay in detection of Bartonella in the blood used for tick feeding may be due to ( a ) a necessary reactivation period , ( b ) a 48-hour delay in initiation of actual tick feeding , or ( c ) a gradual increase of the number of attached feeding ticks and consequently of the volume of infected tick saliva injected into the feeder . In addition , there is the possibility of proliferation of the saliva-introduced agent in the blood contained within the membrane feeder . However , because the blood in the feeder was completely replaced every day , if some bacteria were inoculated within the first 48 hours after placement of ticks on a membrane , they would have the same chance for growth and detection as those inoculated and detected each day after 72 h hours . The molting success of larvae fed upon a Bartonella-infected mouse was low , and molted nymphs were not tested due to their paucity . Therefore , the prevalence of infection in molted ticks and the efficiency of transstadial transmission could not be accessed directly . Nevertheless , each of the three mice exposed to nymphs from the infected cohort became infected with Bartonella , even those on which only two and three ticks successfully fed to repletion . This suggests that the prevalence of infection in this cohort of nymphs was 40% or higher . On the other hand , all four of the partially engorged female ticks examined at 72 hours after placement on a membrane feeder contained bacteria in the muscle and salivary gland tissues , but not in the midgut . These results imply a passage of B . birtlesii , acquired with the blood meal , through the epithelial cells of the gut during or after the acquisition-feeding followed by dispersal of bacteria throughout the body of the tick including the muscle cells . It also indicates that each of the females was infected during the nymphal feeding and retained the infection through both the molt and the following three-month long period of starvation . Therefore , it appears that the efficiency of both the acquisition of B . birtlesii by I . ricinus larvae and nymphs from an infected host , and of the transstadial transmission is high . Absence of B . birtlesii in the midgut of the tick is in contrast with the known distribution of other bartonellae in Anoplura and Siphonaptera vectors . For example , B . quintana inhabits the louse intestinal lumen and is excreted in louse feces throughout the lifespan of an infected human body louse [30]; and B . henselae remains in the gut of the cat flea – C . felis for up to 9 days [31] . The lack of B . birtlesii in the midgut of feeding ticks and its presence in the salivary glands confirms that its transmission to the host occurs with saliva and not through contaminated feces . It remains to be studied whether initiation of the next feeding is necessary for bacterial invasion of salivary glands and the subsequent transmission into a susceptible host . Together , results of this study demonstrate that both larval and nymphal I . ricinus are capable of acquiring B . birtlesii from an infected host , transmitting it through the molt to the next life stage , maintaining the infection for several months of starvation , and ejecting it with saliva during the subsequent feeding . Using a murine model , we show for the first time the ability of the erythrocyte-associated bacterium to survive and disseminate in a tick vector , where it escapes from the midgut into the hemocoel and infects salivary and muscle tissues . This work represents the first in vivo demonstration of a Bartonella sp . transmission by ticks . It does not claim that ticks are principal vectors of Bartonella spp , but it does corroborate a prospect that ticks play a role in the natural cycles of some of the bartonellae including those pathogenic for humans . Consequently , bartonelloses should be included in the differential diagnosis for patients exposed to tick bites . | Bartonella spp . are bacteria that infect the red blood cells and that are associated with several diseases in humans and animals all over the world . They are transmitted by arthropod vectors including fleas , lice and sand-flies , but new potential vectors are suspected and in particular ticks . Diseases transmitted by ticks , currently in emergence , have diverse etiology ( viral , bacterial , parasitic ) and are responsible for high morbidity and mortality rates around the world . The potential for involvement of ticks in transmission of Bartonella spp . has been heartily debated for many years because of the numerous but indirect proofs of its existence . In this study , the authors used a murine model to assess the ability of the tick Ixodes ricinus to transmit Bartonella bacteria to mice . Results of the study confirm the vector competence of I . ricinus and represent the first in vivo demonstration of a Bartonella sp . transmission by ticks . Consequently , bartonelloses should be now included in the differential diagnosis for patients exposed to tick bites . | [
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"Results",
"Discussion"
] | [
"veterinary",
"science",
"biology"
] | 2011 | Vector Competence of the Tick Ixodes ricinus for Transmission of Bartonella birtlesii |
Staphylococcus aureus , a Gram-positive bacterium causes a number of devastating human diseases , such as infective endocarditis , osteomyelitis , septic arthritis and sepsis . S . aureus SraP , a surface-exposed serine-rich repeat glycoprotein ( SRRP ) , is required for the pathogenesis of human infective endocarditis via its ligand-binding region ( BR ) adhering to human platelets . It remains unclear how SraP interacts with human host . Here we report the 2 . 05 Å crystal structure of the BR of SraP , revealing an extended rod-like architecture of four discrete modules . The N-terminal legume lectin-like module specifically binds to N-acetylneuraminic acid . The second module adopts a β-grasp fold similar to Ig-binding proteins , whereas the last two tandem repetitive modules resemble eukaryotic cadherins but differ in calcium coordination pattern . Under the conditions tested , small-angle X-ray scattering and molecular dynamic simulation indicated that the three C-terminal modules function as a relatively rigid stem to extend the N-terminal lectin module outwards . Structure-guided mutagenesis analyses , in addition to a recently identified trisaccharide ligand of SraP , enabled us to elucidate that SraP binding to sialylated receptors promotes S . aureus adhesion to and invasion into host epithelial cells . Our findings have thus provided novel structural and functional insights into the SraP-mediated host-pathogen interaction of S . aureus .
The serine-rich repeat glycoproteins ( SRRPs ) are a family of adhesins encoded by Gram-positive bacteria that mediate attachment to a variety of host cells or bacteria themselves [1] . SRRPs typically consist of a signal peptide at the N-terminus , a short SRR ( SRR1 , ∼50–170 residues ) , a ligand-binding region ( BR , ∼250–500 residues ) followed by a much longer SRR ( SRR2 , ∼400–4000 residues ) , and a C-terminal LPXTG motif anchoring to the cell wall [1] . The BRs of SRRPs from different pathogenic bacteria have varying primary sequences and bind to diverse targets from carbohydrates to proteins [1] . In addition to having highly variable sequences , the BRs from different bacteria are composed of distinct modules . The diversity of BR modules and combinations contributes to the multiple functions of SRRPs . The only four known BR structures to date have identified five distinct modules [2]–[4] . The BR of Streptococcus parasanguinis Fap1 contains two modules: an N-terminal helical module and a C-terminal CnaA module [2] , whereas Streptococcus gordonii GspB has a BR of three modules: CnaA , Siglec and a unique module of unknown function [3] . In addition , the recently reported BR structures of the two SRRP paralogs ( Srr1 and Srr2 ) from Streptococcus agalactiae defined two immunoglobulin-fold modules , which specifically bind to the host fibrinogen [4] . However , the module composition and corresponding molecular functions of most BRs remain unknown , which largely impedes the understanding of the pathogenesis mechanism of SRRPs . S . aureus is a human pathogen that causes a wide range of debilitating and life-threatening infections [5] . S . aureus encodes a 2 , 271-residue SRRP termed serine-rich adhesin for binding to platelets ( SraP ) , that is involved in the pathogenesis of infective endocarditis [6] . Moreover , the BR ( residues Phe245–Asn751 ) of SraP , termed SraPBR , mediates intraspecies interaction and promotes bacterial aggregation [7] . We determine the 2 . 05 Å crystal structure of SraPBR , revealing a rod-like tandem organization of four discrete modules: a legume lectin-like module , a module with a β-grasp fold , and two tandem cadherin-like modules that create the rigid stem of SraPBR . Further structural and biochemical analyses reveal that the legume lectin-like module specifically binds to N-acetylneuraminic acid ( Neu5Ac ) , which may mediate adhesion to host sialylated receptors . These findings increase our knowledge of the diverse BR modules of SRRPs , and provide structural insights into a novel surface protein that mediates interaction of S . aureus with host epithelial cells .
Each asymmetric unit of the final model at 2 . 05 Å resolution contains a single SraPBR molecule of residues Thr251–Asn751 . The N-terminal residues Phe245–Thr250 are not visible due to their poor electron density . SraPBR folds into a slightly bent , rod-like structure of 160 Å in length that has four discrete modules: a head-like N-terminal module followed a stem of three all-β modules ( Fig . 1A ) . All modules have a dominant β-strand secondary structure . During the model building and refinement process , three peaks of electron density at the 24 σ level were observed in the |Fo|−|Fc| Fourier difference map , indicating the presence of three metal ions . Atomic absorption spectroscopy assigned these metal ions to Ca2+; we thus termed them Ca-1 , Ca-2 and Ca-3 accordingly . The structure also contains a sucrose and a 2- ( N-morpholino ) -ethanesulfonic acid ( MES ) molecule , which were introduced from the cryoprotectant and crystallization buffer , respectively . The N-terminal module adopts a jelly-roll fold with a β-sandwich ( β1–β17 ) core architecture of two antiparallel β-sheets packed against each other ( Fig . 1B ) . Beyond the core structure , two α-helices ( α1 and α2 ) pack on either side of the lateral of the β-sandwich and partially seal the hydrophobic lateral openings between the two β-sheets . A molecule of sucrose binds to the protruding loops at the distal end of the N-terminal module ( Fig . 1B ) . A structural similarity search using the DALI server [8] revealed that this module is most similar to legume lectins , despite sharing a sequence identity of ≤20% . The top hits include lectins from legume plants such as Pisum sativum ( PDB 2BQP ) [9] and Robinia pseudoacacia ( PDB: 1FNY ) [10] , with a Z-score of 22–23 and root mean square deviation ( RMSD ) of 2 . 3–2 . 5 Å over ∼200 Cα atoms . Thus , we termed the N-terminal module L-lectin . The second module possesses a ubiquitin-like β-grasp fold ( β-GF ) in the immunoglobulin ( Ig ) -binding protein superfamily [11] . The module contains a twisted four-stranded mixed β-sheet ( β19-β18-β23-β21 ) packed against a two-stranded antiparallel β-sheet ( β20–β22 ) and an α-helix α3 ( Fig . 1B ) . The C-terminal tandem repeat of two modules share a sequence identity of 56% and a similar overall structure with an RMSD of 0 . 81 Å over 81 Cα atoms . The two modules are linked in a head-to-tail fashion , indicating duplication of the coding region during evolution . Each module consists of a β-sandwich of three β-sheets ( Fig . 1B & 1C ) . Structural analysis revealed that the two modules resemble eukaryotic cadherins of known-structure ( PDB 3UBF , Z-score 8 . 6 , RMSD 2 . 3 Å over 85 Cα atoms and PDB 4APX , Z-score 9 . 5 , RMSD 2 . 3 Å over 87 Cα atoms ) [12] , [13] . Thus the tandem cadherin-like ( CDHL ) modules were termed CDHL-1 and CDHL-2 , respectively . The L-lectin module is structurally similar to legume lectins ( Fig . 2A ) , a large family of carbohydrate-binding proteins with diverse activities [14] . Legume lectins commonly coordinate a Ca2+ in addition to a transition metal ion , usually Mn2+ [15] . In contrast , the L-lectin module has only a Ca2+-binding site . At the apex of the L-lectin module , the Ca2+ ( named Ca-1 ) was embedded in a 7-coordinate geometry ( Fig . 2B ) . The seven coordinates are from the side-chain oxygen atoms of Asp365 ( bidentate coordination from Oδ1 and Oδ2 ) , Asp382 ( Oδ2 ) , Asn369 ( Oδ1 ) , the main-chain oxygen atom of Tyr367 and two water molecules ( Wat1 and Wat2 ) . The two water molecules were further stabilized by the main-chain oxygen atoms of Asp330 , Ala349 , Oδ1 of Asp382 and another water molecule ( Wat3 ) ( Fig . 2B ) . A molecule of sucrose is fixed by a cluster of loops protruding from the apex of the L-lectin module . The sucrose molecule has a “bent-back” conformation with the glucose and fructose moieties perpendicular to each other ( Fig . 2B ) . The glucose moiety is inserted in the pocket , and adopts a conformation nearly parallel to the two β-sheet layers . The sugar ring makes hydrophobic interactions with Tyr367 and the morpholine ring of MES , whereas the hydroxyl groups are stabilized by hydrogen bonds with Ala478-Nα , Asp330-Oδ1 , and three water molecules ( Wat3–5 ) , which are further fixed by residues Asp330 , Asn347 and Ala349 ( Fig . 2B ) . In contrast , the solvent-exposed fructose moiety is bent through interactions with MES and two water molecules ( Wat6 and Wat7 ) ( Fig . 2B ) . The sucrose binding residues , especially the stacking residue Tyr367 , are structurally conserved in the legume lectins ( Fig . 2C ) . To identify the favored saccharide of SraP , we first detected the binding affinity of the L-lectin module towards eight common monosaccharides using the surface plasmon resonance assays . Among these monosaccharides , only Neu5Ac bound to the L-lectin module ( Fig . 2D ) . In consequence , we determined that the L-lectin module has an equilibrium dissociation constant ( Kd ) of 0 . 54 mM towards Neu5Ac ( Fig . 2E ) , comparable to previously reported values of legume lectins [16] . Afterwards , we attempted to obtain the Neu5Ac-complexed structure without success . Therefore , we docked Neu5Ac to the structure of the L-lectin module with the sucrose-binding pocket as the search grid . Neu5Ac was docked at a position overlapping the glucose moiety of sucrose , with a shift of ∼1 . 5 Å towards Ca-1 . In the model , Neu5Ac is stacked against Tyr367 via hydrophobic interactions , and makes direct polar interactions with the side chains of Ser293 , Asn347 , Tyr367 and Asn369 and the Nα atoms of Gly477 and Ala478 ( Fig . 2F ) . The β-GF module adopts a ubiquitin-like β-grasp fold in the Ig-binding superfamily [11] . DALI search [8] suggested the module resembles the B1 domain of mucus-binding protein type 2 repeat Mub-R5 from Lactobacillus reuteri [17] ( PDB code 3I57 , Z-score 9 . 1 , RMSD 1 . 6 Å , over 68 Cα atoms ) , and protein L ( PpL ) from Peptostreptococcus magnus [18] ( PDB code 1HEZ , Z-score 4 . 5 , RMSD 2 . 5 , over 55 Cα atoms ) . The B1 domain belongs to a family of Ig-binding proteins [19] that have a core structure of an α-helix packed against a four-stranded β-sheet [17] , [20] . The major differences are from the helix α3 and the two lateral β-strands ( β19 and β21 ) . In addition , the β-GF module of SraPBR contains two extra β-strands , β20 and β22 , which are substituted by loops or α-helix extensions in the B1 domain ( Fig . 3 ) . Mub-R5 interacts in vitro with a large repertoire of mammalian Ig proteins including secretory IgA , whereas PpL binds to the VL domain of Ig κ chain [19] , [21] . Complex structures indicated that formation of a β-zipper is necessary for the binding of PpL to the VL domain of Ig κ chain [18] and IgG or IgM [22] . However , the corresponding β-strands β19 and β21 are much shorter in the β-GF module , which might not be capable of forming a β-zipper . The tandem CDHL modules resemble eukaryotic cadherins in a superfamily of calcium-dependent adhesions ( Fig . 4A & 4B ) . Unlike the eukaryotic cadherins , each of which coordinates three Ca2+ ions ( PDB: 1L3W ) [23] , CDHL-1 and CDHL-2 binds to Ca-2 and Ca-3 with a 7-coordinate geometry , respectively . Ca-2 is fixed by Asp573 and Asp601 ( bidentate coordination from Oδ1 and Oδ2 ) , Asn602 ( Oδ1 ) , Asp645 ( Oδ2 ) , and the main-chain oxygen atom of Lys575 ( Fig . 4C ) , whereas Ca-3 is coordinated to Asp661 and Asp690 ( bidentate coordination from Oδ1 and Oδ2 ) , Asn691 ( Oδ1 ) , Asp734 ( Oδ2 ) , and the main-chain oxygen atom of Thr663 ( Fig . 4D ) . The coordinate bonds between Ca2+ and corresponding residues have a length from 2 . 2 to 2 . 6 Å . The small-angle scattering of X-rays ( SAXS ) is usually applied to address the flexibility and conformational states of biological macromolecules in solution [24] . To explore the role of Ca2+ , we used SAXS to compare the overall structure of SraPBR in the presence or absence of Ca2+ . A difference in wide-angle scattering curves of SAXS indicated that SraPBR adopts different conformations with or without Ca2+ . Compared to the Ca2+-free form , the envelope of Ca2+-bound SraPBR correlates much better with the SraPBR crystal structure ( Fig . 5A ) . The larger discrepancies of the Ca2+-free SraPBR are mainly resulted from the tandem CDHL modules , indicating that binding of Ca2+ makes the tandem CDHL modules more rigid , and thereby facilitates the extended conformation of SraPBR in solution . We also performed molecular dynamics simulations of SraPBR in the presence or absence of Ca2+ . Ca2+-bound SraPBR remains extended and shows slight conformational changes over the simulation time , whereas removal of Ca2+ caused the curling of SraPBR ( Fig . 5B ) . Geometric analysis of the Ca2+-coordinating residues revealed larger fluctuations at the two Ca2+-binding junctions of Ca2+-free SraPBR , suggesting the importance of Ca2+ for the structural integrity of SraPBR . Moreover , the increased RMSD values indicated the two junctions in Ca2+-free SraPBR undergo dramatic conformational changes . Given the CDHL-2 modules superimposed , the other three modules adopt a more curved conformation and are projected to the opposite side upon the loss of Ca2+ ( Fig . 5B ) . The rod-like , four-module structure of SraPBR has three junctions with a buried interface of 700 , 400 and 220 Å2 , respectively ( Fig . 1B ) . The interface between L-lectin and β-GF is maintained by several hydrogen bonds including Asn257-Glu493 , Arg303-Asn519 , Arg303-Ser494 , Leu341-Asn519 , and Asn466-Asn562 ( Fig . 5C ) . The second interface is formed by residues from β-GF and CDHL-1 via hydrogen bonds of Lys536-Asn602 , Gly537-Asn602 and Asp503-Thr604 ( Fig . 5D ) . At the third junction , Glu587 and Val588 of CDHL-1 form two hydrogen bonds with Asn691 of CDHL-2 ( Fig . 5E ) . The second and third interfaces are relatively small; however , they are more stable due to the contribution from coordinate bonds of Ca-2 to Asn602 and Ca-3 to Asn691 . To further investigate the plasticity of these junctions , we determined three crystal structures for each of two consecutive modules: L-lectin&β-GF ( Phe245–Lys575 ) , β-GF&CDHL-1 ( Ser494–Thr663 ) , and CDHL-1&2 ( Ala576–Asn751 ) . We superimposed each of these three structures over the corresponding two modules of the full-length SraPBR structure , always with the C-terminal modules aligned . The results revealed slight twisting and/or translation with intermodule angle changes of 9 . 4° , 11 . 7° and 14 . 6° for the three junctions , respectively ( Fig . 5C∼E ) . In detail , residues Val496-Gln498 in L-lectin&β-GF , Phe571&Thr572 in β-GF&CDHL-1 , and Asp661-Thr663 in CDHL-1&2 undergo slight conformational changes . Except for a short disordered segment at the N-terminus of CDHL-1&2 ( Fig . 5E ) due to the deletion of two Ca-2 coordinate residues Asp573 and Lys575 , we did not find secondary-structure change in any module pairs . Moreover , merging these three structures via sequential alignment of the same module resulted in a total twist of 5 . 4° along the axis of SraPBR structure ( Fig . S1 ) , suggesting that the slight interdomain twists of the three junctions are randomly occurred and could be canceled out . Together , the results indicated that in the presence of calcium , SraPBR adopts a relatively rigid rod-like structure under all conditions tested . Bacterial attachment and colonization at the surface of host cells have been thought to be mediated by specific binding of BRs to glycoconjugates [1] . It has recently been reported that SraP binds to the salivary agglutinin gp340 via the Neu5Ac moiety of the trisaccharide Neu5Acα ( 2–3 ) Galβ ( 1–4 ) GlcNAc [25] . In fact , gp340 and homologs are also expressed in lung epithelial cells [26] . To determine if the L-lectin module mediates SraPBR adhesion , we incubated a monolayer of human lung epithelial A549 cells with green fluorescent protein ( GFP ) -fused SraPBR and individual modules . The results indicated that full-length SraPBR and the L-lectin module , but not the CDHL-1&2 modules or the β-GF module , specifically adhered to the A549 cells ( Fig . 6 ) . Tyr367 is a key residue to make hydrophobic interactions with sucrose and the docked Neu5Ac , we thus constructed the Y367G mutant proteins to test the adhesion to A549 cells . As shown in the CD spectra , the mutation of Y367G did not introduce significant structural changes to SraPBR or the L-lectin module ( Fig . S2 ) . In contrast , a Y367G mutation in both the full-length SraPBR and the L-lectin module almost completely abolished the adhesion capacity . Moreover , the addition of 5 mM Neu5Ac completely inhibited the adhesion of SraPBR to the A549 cells ( Fig . 6 ) . Quantification of the fluorescent signal of three representative frames for each image further confirmed that only the full-length SraPBR and the L-lectin module are capable of specific binding to A549 cells ( Fig . S3 ) . These results demonstrate that the adhesion of SraPBR to A549 cells is mediated by the specific recognition of the L-lectin module towards Neu5Ac . Furthermore we performed comparative assays of bacterial adhesion and invasion to A549 cells using S . aureus strain NCTC 8325 and an isogenic ΔsraP mutant . Deletion of sraP resulted in an approximately 40% decrease in adhesion , as compared to the wild-type ( Fig . 7A ) . As a result the level of invasion was decreased by ∼50% ( Fig . 7B ) . These results indicate that SraP contributes to S . aureus adhesion to and invasion into host cells . Together with the recently reported SraP ligand , the trisaccharide Neu5Acα ( 2–3 ) Galβ ( 1–4 ) GlcNAc [25] , our results strongly suggested that the specific binding of SraPBR to the ligand promotes the S . aureus adhesion to host cells . We initially tried to determine the complex structure of the trisaccharide with SraPBR or the L-lectin module without success . As an alternative , we docked the trisaccharide to the structure of the L-lectin module ( Fig . 7C ) . In the docking model , the Neu5Ac moiety of the trisaccharide adopts an almost same position to that of Neu5Ac ( Fig . 2F ) . In addition , the moieties of Gal and GlcNAc are stabilized via hydrogen bonds by residues Asn374 and Ser371 , respectively ( Fig . 7C ) . To determine whether the interactions between the L-lectin module and the trisaccharide contribute to the SraPBR-mediated adhesion , we used neuraminidase , β-galactosidase or N-acetylglucosaminidase to differentially remove Neu5Ac , Gal , and GlcNAc from the surface of A549 cells ( Fig . 7D ) . Treating A549 cells with neuraminidase alone resulted in an approximately 36% decrease in the adhesion of S . aureus NCTC 8325 . In contrast , the digestion with either β-galactosidase or N-acetylglucosaminidase did not significantly affect the adhesion . Further addition of β-galactosidase and N-acetylglucosaminidase to the neuraminidase-treated A549 cells did not significantly lower the adhesion level ( Fig . 7D , the 6th and 7th columns ) . These results indicated that Neu5Ac is the moiety at the non-reducing end of the trisaccharide , and a major receptor to the SraP-mediated bacterial adhesion of A549 cells . Consistently , the wild-type S . aureus and ΔsraP mutant showed comparable levels of adhesion to the neuraminidase-treated epithelial cells ( Fig 7D , the 4th and 8th columns ) . The adhesion level of the wild-type S . aureus to the neuraminidase-treated A549 cells ( Fig . 7D , the 4th column ) is similar to that of the ΔsraP mutant to the untreated A549 cells ( Fig . 7A ) . We thus concluded that SraP is the major S . aureus adhesin that recognizes the sialylated host receptors .
Structural analyses combined with epithelial adhesion experiments demonstrate that SraP plays an important role in mediating bacterial adhesion to host cells by recognizing the L-lectin module . Legume lectins are a large family of proteins primarily found in the seeds of legume plants that have a similar fold but distinct carbohydrate-binding specificities [14] . They typically adopt a quaternary structure of dimers or tetramers that enhances sugar binding specificity or affinity [27] . The architecture of the legume lectin fold has also been found in the animal calcium-dependent lectin ERGIC-53/MR60 , a mannose-binding protein involved in the export of soluble glycoproteins from the endoplasmic reticulum [28] . We report here the first legume lectin fold-containing protein in bacteria . This prokaryotic monomeric L-lectin module mediates adhesion to host cells by recognizing Neu5Ac , usually the non-reducing terminal residue of glycoconjugates of extracellular receptors . Bioinformatics analysis suggested that the L-lectin module might also exist in other proteins in Staphylococci and Streptococci ( Fig . S4A ) . Moreover , the residues involved in Neu5Ac binding are relatively conserved among these putative L-lectins ( Fig . S4B ) . Therefore , we hypothesize that this adhesion mechanism mediated by the L-lectin module operates in other staphylococcal and streptococcal species . To scan the host receptors , the L-lectin module should be projected outwards from the bacterial surface . In addition to a long region of SRR2 that crosses the bacterial cell wall , SraP has a relatively rigid stem of three modules: a β-GF and two CDHL modules . The classic cadherins in vertebrates bridge the intermembrane space between neighboring cells by forming trans-adhesive homodimers through membrane-distal extracellular domains [29] . The extracellular domain of most cadherins often contains three conserved Ca2+-coordination sites at the interdomain junction [30] . In contrast , each CDHL module in SraPBR binds to only a single Ca2+ that does not superimpose on any of the three Ca2+ ions in eukaryotic cadherins . Nevertheless , multiple-sequence alignments suggested strong conservation among Gram-positive bacteria of SraPBR Ca2+-binding residues ( Fig . 8A ) . Unlike the monomeric form of SraPBR , the recombinant CDHL1&2 exists as a dimer in solution as confirmed by size-exclusion chromatography and chemical cross-linking assays ( Fig . S5 ) . The structure revealed that the homodimer of CDHL1&2 buries an interface area of ∼600 Å2/per subunit , as calculated by PISA ( http://www . ebi . ac . uk/msd-srv/prot_int/cgi-bin/piserver ) server [31] . The interface is mainly stabilized by polar interactions , in contrast to the hydrophobic dimeric interfaces of eukaryotic cadherins [29] . CDHL-2 from one subunit packs against the junction between CDHL-1 and CDHL-2 from the symmetric subunit , and vice versa ( Fig . 8B ) . This dimerization pattern may explain the result that SraP mediates intraspecies interaction and promotes aggregation of S . aureus ISP479C [7] . However , we did not observe significant decrease of aggregation or biofilm formation upon the deletion of sraP in S . aureus NCTC 8325 . The different results might be due to the variation of S . aureus strains . Sequence homology search indicated that some surface proteins in Gram-positive bacteria contain two or more CDHL modules , suggesting that those proteins with multiple CDHL modules have a high potential to mediate intraspecies aggregation . SRRPs have been identified in a variety of Gram-positive bacteria and function as virulence factors in a wide spectrum of infections [1] . The diversity of these infections ( e . g . endocarditis , meningitis and pneumonia ) correlates with the variability of BRs . Furthermore , the few BR ligands that have been identified to date range from carbohydrates ( such as sialyl-T antigen ) [32] to proteins ( such as keratins ) [33] , [34] . In addition to the five distinct modules defined in the four previously reported BR structures [2]–[4] , our SraPBR structure identified three types of unique modules . Notably , although the β-GF module resembles the Ig-binding proteins , such as B1 domain of Mub-R5 and PpL [19] , [21] , the binding of the β-GF module towards human IgG , IgA or IgM could not be detected , in agreement with our structural analysis . We thus propose that the β-GF module , in addition to the two CDHL modules , functions as a relatively rigid stem to project the L-lectin module outwards . The two structure-known SRRPs , Fap1 and GspB , undergo significant inter-module angle changes , which are regulated by pH and ligand binding , respectively [2] , [3] . In contrast , SraPBR appears to adopt a relatively rigid , rod-like conformation when colonizing a host , for the concentration of free Ca2+ in the extracellular space or blood is stringently maintained at 1 . 1–1 . 3 mM [35] . The rigid architecture of SraPBR enables the globular L-lectin module to extend outwards from the bacterial surface for scanning host receptors . This strategy to expose the functional modules has been observed for other bacterial adhesins such as the fibrillar antigen I/II from S . mutans [36] , and the rod-like surface protein SasG from S . aureus [37] .
The genomic DNA from Staphylococcus aureus NCTC 8325 was prepared for gene cloning . The DNA sequences ( GeneBank , the accession number of YP_501439 . 1 ) encoding SraPBR ( Phe245–Asn751 ) and other SraP truncates were cloned into pET28a with an N-terminal His6-tag or pET28a with a C-terminal GFP-tag , respectively . The constructs were overexpressed in E . coli strain BL21 ( Novagen ) using LB culture medium ( 10 g NaCl , 10 g Bacto-Tryptone , and 5 g yeast extract per liter ) . The cells were grown at 37°C to an OD600nm of 0 . 6 . Expression of the recombinant protein was induced with 0 . 2 mM isopropyl β-D-1-thiogalactopyranoside ( IPTG ) at 16°C for another 20 hr before harvesting . Bacteria were collected by centrifugation at 8 , 000×g for 10 min and resuspended in 30 ml lysis buffer ( 20 mM Tris-Cl , pH 8 . 8 , 100 mM NaCl ) . After sonication for 2 . 5 min followed by centrifugation at 12 , 000×g for 25 min , the supernatant containing the His-tagged protein was collected and loaded onto a Ni-NTA column ( GE Healthcare ) equilibrated with the binding buffer ( 20 mM Tris-Cl , pH 8 . 0 , 100 mM NaCl ) . The target protein was eluted with 300 mM imidazole , and loaded onto a Superdex 200 column or Superdex 75 column ( GE Healthcare; 20 mM Tris-Cl , pH 8 . 0 , 100 mM NaCl ) . The purity of protein was assessed by electrophoresis and the protein sample was stored at −80°C . The selenium-Met ( SeMet ) labeled L-lectin&β-GF protein was expressed in E . coli strain B834 ( DE3 ) ( Novagen ) . Transformed cells were grown at 37°C in SeMet medium ( M9 medium with 25 µg/ml SeMet and the other essential amino acids at 50 µg/ml ) containing 30 µg/ml kanamycin until the OD600nm reached 0 . 6 , and were then induced with 0 . 2 mM IPTG at 16°C for 20 hr . SeMet substituted protein was purified with the same procedure as the native protein . The oligomerization state of CDHL-1&2 was analyzed by Superdex 75 column . The protein markers are bovine serum albumin , ovalbumin , chymotrypsinogen A , myoblobulin and ribinuclease A , which have a molecular weight of 67 , 44 , 25 , 17 and 13 . 7 kDa , respectively ( GE Healthcare ) . All crystals were grown using the hanging drop vapor diffusion method , with a drop of 1 µl protein solution mixed with 1 µl of reservoir solution equilibrated against 500 µl of the reservoir solution . The proteins for crystallization were concentrated by ultrafiltration ( Millipore Amicon ) to 30 , 38 , 20 and 20 mg/ml for the full-length SraPBR , L-lectin&β-GF , β-GF&CDHL-1 and CDHL-1&2 , respectively . The SeMet substituted L-lectin&β-GF protein for crystallization was concentrated to 38 mg/ml . Crystals of SraPBR were grown at 28°C , whereas others were grown at 16°C . Crystals were obtained from 0 . 8 M ( NH4 ) 2SO4 and 0 . 1 M MES , pH 6 . 0 for SraPBR; 2 . 5 M sodium formate , 0 . 1 M sodium acetate pH 4 . 6 for the native L-lectin&β-GF; 10% PEG 6000 , 0 . 1 M MES , pH 6 . 0 , and 1 . 0 M lithium chloride for the SeMet substituted L-lectin&β-GF; 2 . 0 M ( NH4 ) 3PO4 , 0 . 1 M Tris-Cl , pH 8 . 5 for β-GF&CDHL-1 and 1 . 8 M ( NH4 ) 2SO4 , 0 . 1 M HEPES , pH 7 . 5 for CDHL-1&2 . The crystals of SraPBR , β-GF&CDHL-1 and CDHL1&2 were transferred to the cryoprotectant with the reservoir solution supplemented with 50% sucrose . The cryoprotectant for the crystals of L-lectin&β-GF consists of the reservoir solution supplemented with 30% glycerol . All crystals in the cryoprotectant were flash-cooled with liquid nitrogen prior to X-ray diffraction . Data for a single crystal were collected at 100 K in a liquid nitrogen stream using beamline 17U with a Q315r CCD ( ADSC , MARresearch , Germany ) at the Shanghai Synchrotron Radiation Facility ( SSRF ) . All diffraction data were integrated and scaled with the program HKL2000 [38] . The crystal structure of L-lectin&β-GF was determined using single-wavelength anomalous dispersion ( SAD ) phasing [39] method from a single crystal of SeMet-substituted protein to a maximum resolution of 2 . 10 Å . The AutoSol program implemented in PHENIX [40] was used to locate the selenium atoms and calculate the phase , which was further improved with the program Buccaneer [41] . Automatic model building was carried out using Autobuild in PHENIX . The initial model was refined in REFMAC5 [42] and Phenix . refine and rebuilt interactively using the program COOT [43] . The model was used as the search model against 2 . 05 Å SraPBR data by molecular replacement using Molrep program as part of CCP4i [44] program suite . Electron density maps showed clear features of secondary structural elements for automatically building the C-terminal tandem cadherin-like modules using IPCAS [45] . The structures of β-GF&CDHL-1 and CDHL-1&2 were determined by molecular replacement using the corresponding modules in the full-length SraPBR structure as the search model . The initial models were refined by simulated annealing using Phenix . refine to reduce the phase bias . Then the models were refined interactively using COOT and REFMAC5 until the R-factor and R-free values converged . All final models were evaluated with the programs MOLPROBITY [46] and PROCHECK [47] . Crystallographic parameters were listed in Table 1 . The |Fo|-|Fc| omit map of the sucrose molecule contoured at 3 σ was calculated by FFT implemented in CCP4i . All structure figures were prepared with PyMOL ( http://www . pymol . org/ ) . The purified SraPBR , CDHL-1&2 , BRY367G and L-lectinY367G in 20 mM Tris-Cl , pH 8 . 0 , 100 mM NaCl were concentrated to 30 , 38 , 30 and 21 mg/ml , respectively , and applied to the analyses . Briefly , 500 µl of protein sample was subjected to digestion by the aqueous method using the HNO3 and HClO4 ( 4∶1 , v/v ) method . Afterwards , the digested samples were diluted with deionized water and analyzed by atomic absorption spectroscopy ( Atomscan Advantage , Thermo Ash Jarell Corporation , USA ) . The binding affinities of the L-lectin module towards varying monosaccharides were determined by SPR . SPR experiments were performed at 25°C using a Biacore 3000 instrument using HBS ( 10 mM HEPES , pH 7 . 5 , 150 mM NaCl ) containing 0 . 005% ( v/v ) Tween 20 and a flow rate of 5 µl/min . The L-lectin module was covalently immobilized on the carboxymethyldextran surface of the CM5 chip . The chip was activated with EDC ( N-ethyl-N-[3-dimethylaminopropyl] carbodi-imide ) /NHS ( N-hydroxysuccinimide ) solution , and the L-lectin module in 10 mM acetate buffer ( pH 5 . 5 ) was injected into the flow channel . At the end , the sensor surface was blocked with 1 M ethanolamine . The blank channel was treated in the same way without protein injected . Each monosaccharide in the running buffer was incubated for 1 min in the flow-cells using the kinject mode . Both injection and dissociation steps last for 5 min . The sensor surface was regenerated with 50 mM NaOH . All analyses were performed with the BIAeval software . The equilibrium responses were plotted versus monosaccharide concentrations and fitted to a 1∶1 Langmuir binding model using the Origin 8 . 0 software ( OriginLab Corp . ) . The docking of Neu5Ac or the trisaccharide Neu5Acα ( 2–3 ) Galβ ( 1–4 ) GlcNAc to the L-lectin module of SraPBR was performed with AutoDock Vina software ( version 1 . 0 ) [48] , which uses a unique algorithm that implements a machine learning approach to its scoring function . This docking allowed a population of possible conformations and orientations for the ligand at the binding site to be obtained . Using AutoDock Tools ( ADT ) 1 . 5 . 4 [49] , polar hydrogen atoms were added to the L-lectin structure , and its non-polar hydrogen atoms were merged . The protein and ligands were converted from a PDB format to a PDBQT format . All single-bonds within Neu5Ac were set to allow rotation . A grid box covering the entire sucrose-binding site was used to place Neu5Ac freely . The results were sorted by binding affinity and visually analyzed using PyMOL . Chemical cross-linking of purified CDHL-1&2 was performed using formaldehyde and bis ( sulfosuccinimidyl ) suberate ( BS3 ) , which is a homobifunctional sulfo-N-hydroxysuccinimide ester analog with a spacer arm length of 1 . 14 nm ( Pierce ) . Briefly , for formaldehyde cross-linking assays of CDHL-1&2 , 20 µl of the recombinant protein ( 2 mg/ml ) was mixed with 20 µl PBS containing 2% formaldehyde , and the samples were incubated at 25°C for 30 min and 1 hr , respectively . For BS3 cross-linking assay of CDHL-1&2 , 100 µl recombinant protein ( 2 mg/ml ) was incubated with 5 mM BS3 at 25°C for 30 min and 1 hr , respectively . The reaction was quenched by the addition of 20 mM Tris-HCl , pH 8 . 0 . Then the samples were separated by 10% SDS-PAGE and were stained by Coomassie Brilliant Blue G250 . Molecular mass markers for SDS-PAGE were purchased from Thermo Scientific ( Wilmington , DE ) : beta-galactosidase , bovine serum albumin , ovalbumin , lactate dehydrogenase , REase Bsp98I , beta-lactoglobulin , lysozyme , which have a molecular weight of 116 , 66 . 2 , 45 , 35 , 25 , 18 . 4 and 14 . 4 kDa , respectively . Staphylococcus aureus NCTC 8325 and its derivative strains were grown in LB medium , and when necessary , erythomycin ( 2 . 5 mg/ml ) and chlorampenicol ( 15 mg/ml ) were added . To generate the insertion-deletion mutagenesis , approximately 500 bp of upstream and downstream fragments of the BR region of sraP gene was amplified by polymerase chain reaction ( PCR ) , digested with PstI/SalI and BamHI/XbaI , respectively , and then ligated to either end of a double-digested ( BamHI/SalI ) erythromycin-resistance gene ( ErmR ) , which was amplified from plasmid pEC1 . The three fragments were ligated with the erythromycin-resistance gene in the middle , and then cloned in the temperature-sensitive shuttle vector pBT2 . The resulting plasmid was transformed by electroporation into S . aureus strain RN4220 for propagation , and then transformed into S . aureus NCTC 8325 for allelic exchange . Mutants were screened and further checked by PCR and sequencing . Adhesion and invasion experiments were performed as described previously [50] . A549 human respiratory epithelial cells were grown in Dulbecco's modified Eagle's medium ( DMEM , Gibco ) supplemented with 10% fetal bovine serum ( FBS ) , 5 mM glutamine , penicillin ( 5 µg/ml ) and streptomycin ( 100 µg/ml ) . Approximately 5×105 cells were seeded into 24-well tissue culture plates , and allowed to grow in 5% CO2 at 37°C . Before use , the monolayers were washed twice with PBS . For adhesion assays , S . aureus grown to OD600nm of 0 . 5 in LB medium were resuspended in cell culture medium without serum . Bacteria were diluted to a concentration of 2×107 CFU/ml and were used to infect the confluent cell monolayers at 37°C for 1 hr . After incubation , the infected monolayers were washed five times with PBS to remove non-adhered bacteria , and treated with 200 µl trypsin ( 2 . 5 mg/ml ) at 37°C for 3 min to release the adhered bacteria . A549 cells were lysed with 0 . 05% Triton X-100 . The number of adhered bacteria was determined by plating serial dilutions of the recovered bacterial suspensions onto LB agar . For the deglycosylation of A549 cells , The cells were incubated with DMEM media containing 0 . 008 units ml−1 purified Clostridium perfringens neuraminidase ( Sigma ) , 40 nM purified β-galactosidase ( Spr0565 ) from S . pneumoniae or 0 . 0025 units ml−1 of S . pneumoniae β-N-acetylglucosaminidase ( Sigma ) at 37°C for 4 hr in 5% CO2 [34] . For invasion assays , the bacteria in each well , after incubated in 0 . 5 ml DMEM for 1 hr , were incubated in 1 ml fresh DMEM containing 14 µg/ml of gentamicin ( Sigma ) for another 1 hr . Cell monolayers were washed three times with sterile PBS and lysed with 0 . 05% Triton X-100 . The internalized bacteria were counted by plating serial dilutions of the recovered bacterial suspensions onto LB agar . Experiments were performed in triplicate . Data corresponding to adhesion and invasion were compared using the Mann-Whitney tests . Statistical differences were determined with the t-test . Immunofluorescence assays were performed as described previously [51] . Briefly , GFP and GFP-fused proteins at 10 µM were suspended in the media in the absence of serum and antibiotics , and incubated on ice for 2 hr with the monolayer of A549 cells grown on the 12-mm glass coverslips . For inhibition assays , Neu5Ac at 5 and 10 mM was pre-incubated with GFP-fused SraPBR protein , respectively . After incubation , cells were sequentially washed three times with cold PBS , fixed with 4% paraformaldehyde for 10 min , permeabilized with 0 . 1% Triton X-100 for 2 min , and incubated with GFP-tag mouse antibody and a fluorescein isothiocyanate ( FITC ) -conjugated goat anti-mouse IgG antibody . The nuclei were stained with 4′ , 6-diamidino-2-phenylindole ( DAPI ) reagent . Slides were examined with a Zeiss LSM710 confocal scanning fluorescence microscope ( Carl Zeiss , Jena , Germany ) with a Plan-Apocromat 20×/0 . 8NA objective . Confocal parameters set for immunofluorescence detection were taken as standard settings . The excitation wavelength is 405 nm and the emission wavelength is 410–492 nm . The confocal images were collected and processed with the software ZEN 2009 . Experiments were performed in triplicate , with three or more replicate wells tested for each experimental condition . Molecular dynamics simulations were performed for SraPBR in the Ca2+-bound and the Ca2+-free states , respectively . For each simulation , the system was placed in a TIP3P [52] water box with a distance of at least 12 Å to the box boundaries . Ions were added to neutralize the system and to result in a concentration of 0 . 15 M NaCl . The solvated protein was subjected to energy minimization employed the steepest descent algorithm and conjugate gradient , respectively . Simulations were performed with a parallel implementation of the GROMACS ( version 4 . 5 . 5 ) package [53] using the AMBER03 force field [54] . MD productions were run for 20 ns using a time step of 2 fs and the NPT ensemble [55] . Covalent bonds were constrained using the LINCS algorithm [56] , while the cutoff distances for the Coulomb and van der Waals interactions were set to 0 . 9 and 1 . 4 nm , respectively . The long-range electrostatic interactions were treated by the PME algorithm [57] with a tolerance of 10−5 and an interpolation order of 4 . Structure visualization was performed with VMD [58] . SAXS was used to investigate the overall conformations of SraPBR in the presence or absence of calcium . The full-length SraPBR at 1 . 0 and 5 . 0 mg/ml was analyzed , either in 10 mM calcium chloride or in 50 mM EDTA . SAXS data were collected on the 12ID beamline of Advanced Photon Sources ( APS ) at the Argonne National Laboratory using the Pilatus 2M detector ( DECTRIS , Switzerland ) . The scattering patterns were measured with a 1–2 second exposure time for each collected frame , and twenty frames were taken for each sample to optimize the signal-to-noise ratio . To reduce the radiation damage , a flow cell made of a cylindrical quartz capillary with a diameter of 1 . 5 mm and a wall of 10 µm was used during the data collection process . No concentration effect was observed . All SAXS curves were measured at the room temperature over the range of momentum transfer 0 . 006<s<0 . 82 Å−1 ( where s = 4π sin ( θ ) /λ , 2θ is the scattering angle , and the X-ray wavelength λ is 1 . 033 Å ) . The data were processed using the PRIMUS [59] program package and standard procedures . The forward scattering ( I ( 0 ) ) and the radius of gyration ( Rg ) were evaluated using the Guinier approximation assuming that at very small angles ( s<1 . 3/Rg ) the intensity is represented as I ( s ) = I ( 0 ) exp ( − ( sRg ) 2/3 ) . The program GNOM [60] was used to calculate Dmax and the interatomic distance distribution function p ( r ) . The particle shape of each measured sample was reconstructed ab initio using the programs DAMMIN [61] and GASBOR [62] . The scattering patterns of the atomic crystal structures for SraP were calculated using the program CRYSOL . For the ab initio analyses and modeling , multiple runs were performed to verify the stability of the solution . | Staphylococcus aureus is an important pathogen that causes a range of human diseases , such as infective endocarditis , osteomyelitis , septic arthritis and sepsis . The increasing resistance of S . aureus to most of the current antibiotics emphasizes the need to develop new approaches to control staphylococcal infections . As a surface-exposed serine-rich repeat glycoprotein ( SRRP ) , S . aureus SraP is involved in the pathogenesis of infective endocarditis via its ligand-binding region ( BR ) adhering to human platelets . However , little is known about how SraP interacts with its host receptor ( s ) . Through structural and functional analyses of the BR domain , we have discovered a specific binding of SraP to N-acetylneuraminic acid ( Neu5Ac ) , in agreement with a recent report of the trisaccharide ligand of SraP . Further mutagenesis analysis showed that SraP binding to Neu5Ac and the trisaccharide promotes S . aureus adhesion to and invasion into host epithelial cells . These findings increase our knowledge of surface protein mediated interaction of S . aureus with host epithelial cells . | [
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"sc... | 2014 | Structural Insights into SraP-Mediated Staphylococcus aureus Adhesion to Host Cells |
Multiple lines of evidence suggest that Bordetella species have a significant life stage outside of the mammalian respiratory tract that has yet to be defined . The Bordetella virulence gene ( BvgAS ) two-component system , a paradigm for a global virulence regulon , controls the expression of many “virulence factors” expressed in the Bvg positive ( Bvg+ ) phase that are necessary for successful respiratory tract infection . A similarly large set of highly conserved genes are expressed under Bvg negative ( Bvg- ) phase growth conditions; however , these appear to be primarily expressed outside of the host and are thus hypothesized to be important in an undefined extrahost reservoir . Here , we show that Bvg- phase genes are involved in the ability of Bordetella bronchiseptica to grow and disseminate via the complex life cycle of the amoeba Dictyostelium discoideum . Unlike bacteria that serve as an amoeba food source , B . bronchiseptica evades amoeba predation , survives within the amoeba for extended periods of time , incorporates itself into the amoeba sori , and disseminates along with the amoeba . Remarkably , B . bronchiseptica continues to be transferred with the amoeba for months , through multiple life cycles of amoebae grown on the lawns of other bacteria , thus demonstrating a stable relationship that allows B . bronchiseptica to expand and disperse geographically via the D . discoideum life cycle . Furthermore , B . bronchiseptica within the sori can efficiently infect mice , indicating that amoebae may represent an environmental vector within which pathogenic bordetellae expand and disseminate to encounter new mammalian hosts . These data identify amoebae as potential environmental reservoirs as well as amplifying and disseminating vectors for B . bronchiseptica and reveal an important role for the Bvg- phase in these interactions .
Bordetella species are gram-negative bacteria that infect the respiratory tracts of mammals . The highly genetically conserved classical Bordetella species comprise B . pertussis and B . parapertussis , the etiological agents of whooping cough in humans [1] , as well as B . bronchiseptica , which infects a variety of mammals and immunocompromised humans [1–3] . The major virulence genes in the classical bordetellae are regulated under the Bordetella virulence gene ( BvgAS ) two-component system , which senses environmental cues and controls transcription of over 100 virulence-associated factors [4 , 5] . The “Bvg positive ( Bvg+ ) phase” refers to the activated state of the BvgAS system [5 , 6] in which the expression of genes that have been shown to be necessary for mammalian respiratory tract infection and survival are induced [6–9] . In contrast , at lower temperatures , in the “Bvg negative ( Bvg- ) phase , ” the expression of virulence factors is repressed , and a similarly large set of genes , including those that enable flagella-mediated motility and growth in dilute nutrients , are specifically expressed [6 , 8 , 10] . Mutants that are locked in the Bvg- phase are rapidly cleared from inoculated animals , revealing the critical role of Bvg+ “virulence factors” during infection [11] . In contrast , bacteria locked in the Bvg+ phase efficiently infect hosts , indicating that the Bvg- phase is not required for successful interactions with the host . Explanations for the conservation of the large set of Bvg- genes include speculated roles for the Bvg- phase in survival in some unknown extrahost environment , so far supported by anecdotal evidence [12–15] . We have recently described a search of the National Center for Biotechnology Information ( NCBI ) nucleotide database that revealed evidence of Bordetella species in a large number of soil and water samples [15] . Phylogenetic analyses suggested that Bordetella species from these environments are the ancestral source from which modern respiratory pathogens emerged . To be successful in these environments , bordetellae are expected to be well adapted to interact with other bacteria and environmental predators . Thus , we hypothesize that Bordetella species have evolved mechanisms to successfully interact with predators and that these are associated with the Bvg- phase . Amoebae are common environmental protists that feed on bacteria and have been isolated from soil , air , water , and nasal mucosa of both healthy and sick human volunteers [16–18] . When food ( e . g . , bacteria ) is plentiful , amoebae survive and proliferate as single-celled amoebae . However , once the food source has been depleted from an area , some species of amoebae cooperate to spread to new , more fertile hunting grounds . In the case of D . discoideum , this cooperation involves a cyclic adenosine monophosphate ( cAMP ) signal that triggers aggregation of amoebae to ultimately form a multicellular fruiting body comprising a stalk and a sorus containing amoeba spores [19] . Sori can be disseminated in various ways , such as by wind shifting leaf litter or by the shuffling of passing animals , allowing the spores a chance to be deposited onto new food sources where they can germinate and once again feed as single-celled organisms . While many species of bacteria serve as a food source for amoebae , some bacteria , including several human pathogens such as Legionellae pneumophila and Francisella tularensis [20] , have evolved means of surviving amoeba predation by persisting in single amoeba cells and blocking their host’s ability to differentiate into mature fruiting bodies [21–23] . Since amoebae and immune cells share similarities in their mechanisms used to phagocytize and kill bacteria [24] , the ability of these pathogens to survive intracellularly during in vivo infection may be linked to an evolved mechanism for avoiding amoeba predation . Moreover , the relatively frequent isolation of amoebae from healthy human nasal mucosa [25] indicates that persistent nasal colonizers such as Bordetella spp . frequently encounter amoebae in vivo , raising the possibility that complex interactions between these organisms may have evolved over time . We have previously shown that B . bronchiseptica can occupy an intracellular niche within macrophages during infection [3] , an ability shared with other organisms that survive amoeba predation . Here , we show that B . bronchiseptica not only survives amoebic predation but also successfully infects and persists within amoeba cells . Unlike other bacteria that block fruiting body development [21–23] , we show that B . bronchiseptica permits the complete D . discoideum life cycle and even localizes to the sori of the amoeba fruiting bodies for further propagation . Importantly , B . bronchiseptica is sequentially carried along with amoebic spores to new locations through many passages on other bacterial “food , ” providing sustainable expansion/dissemination during a viable life cycle outside of a mammalian host . We show that the Bvg- phase is advantageous for B . bronchiseptica survival in the amoeba sori and therefore identify a role for the Bvg- phase in this potential ex vivo life cycle . When associated with the amoeba sori , B . bronchiseptica can be transferred by flies or ants and can efficiently infect mice , suggesting that amoebae can act as amplifying and transmission vectors for B . bronchiseptica in addition to being environmental reservoirs . Together , these data suggest a role for the Bvg- phase in a life cycle that does not require a mammalian host , which may explain the complexity and high conservation of genes specifically expressed in the Bvg- phase .
We and others have previously documented the ability of B . bronchiseptica to survive within phagocytic mammalian cells in vitro [3 , 26–31] and in vivo [2 , 3 , 32 , 33] during infection . Amoebae , which are similar to macrophages both morphologically and structurally [24] , serve as environmental hosts for some intracellular human pathogens [34–37] . As the likely ancestors of pathogenic Bordetella species are environmental species found in soil and water where amoebae are prevalent [15] , we hypothesized that B . bronchiseptica may survive within phagocytic amoeba cells . In order to determine whether B . bronchiseptica can survive intracellularly in amoeba cells , we performed a gentamicin protection assay and enumerated intracellular bacterial numbers at 1 and 24 h post gentamicin ( p . g . ) ( Fig 1 and S1 Fig ) . While Klebsiella pneumoniae ( previously referred to as K . aerogenes ) failed to be recovered intracellularly at 1 h post infection , high numbers of B . bronchiseptica ( 3 . 1 x 107 colony-forming units [CFU] or approximately 10% of the inoculum ) were recovered intracellularly from amoeba cells ( p < 0 . 00005 ) ( Fig 1 ) . In fact , substantial numbers of B . bronchiseptica persisted for at least 24 h after gentamicin treatment ( S1 Fig ) , suggesting that B . bronchiseptica has the ability to invade and survive in amoeba cells . To visualize the association , D . discoideum were exposed to mCherry-expressing B . bronchiseptica . Following treatment with gentamicin that killed extracellular bacteria , D . discoideum were stained with endolysosomal ( p80 ) and lysosomal ( vatA and lamp-1 ) markers ( Fig 2A ) . The association of these intracellular markers with the presence of fluorescent B . bronchiseptica further supports the location of the bacteria within D . discoideum ( Fig 2A ) . Visual examination of images of D . discoideum exposed to RB50 expressing mCherry taken at various time points revealed that ~90% of amoebae contained at least one B . bronchiseptica bacterium at 1 h , 2 h , and 4 h post gentamicin treatment ( S2 Fig ) . Furthermore , we have imaged 65-nm sections of an amoeba population at 1 h post gentamicin treatment using electron microscopy . This imaging showed the majority of amoebae harboring intracellular bacteria with numbers ranging from 1 to 8 intracellular bacteria ( average of 2 . 3 per amoeba ) in these sections ( Fig 2B & S3 Fig ) . While the intracellular bacterial numbers from thin microscopy sections do not accurately represent the total number of bacteria per amoeba cell , it is clear from the images that B . bronchiseptica are intracellular and that amoebae are able to contain multiple bacteria . These fluorescent confocal and electron microscopy images , in conjunction with the intracellular recovery data , support the ability of B . bronchiseptica to survive within a large proportion of the amoeba population for an extended period of time . We hypothesized that the mechanism that allowed for the intracellular survival of B . bronchiseptica in D . discoideum would enable it to survive in other amoeba species . Therefore , we tested the ability of B . bronchiseptica to survive in Acanthamoeba castellanii , a free-living amoeba known to cause keratitis and encephalitis in humans . A gentamicin protection assay was performed similar to the one described above , and the number of bacteria that survived intracellularly was enumerated . B . bronchiseptica was recovered at 4 h post gentamicin treatment ( S4 Fig ) , demonstrating its ability to survive within multiple species of amoebae for extended periods of time . Thus , in addition to the common soil organism D . discoideum , other amoebae may provide an environmental niche for these important mammalian respiratory pathogens . Future studies should assess whether the association of B . bronchiseptica with amoebae that naturally infect mammals could contribute to dissemination and transmission . Several bacterial species that resist amoeba predation do so by surviving in single-celled amoebae [20] . Interestingly , these amoeba-resistant bacteria disrupt feeding , motility , and/or other behavior , effectively inhibiting the differentiation of D . discoideum into fruiting bodies [21 , 38 , 39] . The small number of bacterial species that do not prevent fruiting body formation actually aid the amoeba by serving as food , increasing amoeba spore counts over time , or producing metabolites that negatively affect competitor amoeba species [40–42] . We therefore investigated whether B . bronchiseptica prevents D . discoideum fruiting body formation . When added to lawns of B . bronchiseptica , D . discoideum spores were able to form mature fruiting bodies in the area where amoeba spores were deposited , indicating that B . bronchiseptica does not kill the amoeba or inhibit D . discoideum aggregation and fruiting body formation ( S5A Fig ) . Additionally , similar numbers of amoeba spores were recovered from sori of D . discoideum grown on lawns of B . bronchiseptica and K . pneumoniae over time , suggesting that B . bronchiseptica does not negatively affect amoeba spore formation or persistence ( S5B Fig ) . B . bronchiseptica was able to survive intracellularly within the amoebae ( Figs 1 and 2 ) yet permitted the full amoeba life cycle ( S5 Fig ) ; therefore , we hypothesized that B . bronchiseptica survives throughout the amoeba life cycle and may be carried to the fruiting body sorus . To determine whether bacteria are carried to the sorus and remain viable thereafter , amoeba sori grown on B . bronchiseptica or K . pneumoniae lawns were harvested at various time points , and the bacteria were enumerated ( Fig 3 ) . By day 9 post inoculation , the amoebae had formed fruiting bodies , which contained large numbers of B . bronchiseptica ( approximately 5 x 103 CFU/sorus ) . The number of B . bronchiseptica recovered from sori increased ~400% by day 16 ( ~2 x 104 CFU/sorus ) and doubled again by day 23 ( ~5 x 104 CFU/sorus ) , indicating that B . bronchiseptica are able to survive and multiply in amoeba sori over time . In comparison , K . pneumoniae was not recovered from sori throughout the time course ( Fig 3 ) . Imaging the sori grown on B . bronchiseptica RB50 harboring pLC003 , a mCherry-containing plasmid , revealed fluorescence within the fruiting body ( Fig 4 and S6 Fig ) . These results show that , in contrast to bacterial species that merely serve as food for amoebae , B . bronchiseptica are able to evade D . discoideum predation and travel with the amoebae to the fruiting body . Furthermore , these data indicate that B . bronchiseptica are able to survive , persist , and replicate in the fully formed amoeba fruiting bodies . In order to determine if B . bronchiseptica are located intracellularly within the spores of the amoeba sori , we treated the sori with gentamicin ( S1 Table ) . In contrast to our data above demonstrating B . bronchiseptica survival within a single-cell amoeba ( Fig 1 ) , B . bronchiseptica in the sori were not protected against gentamicin killing . Furthermore , confocal microscopy of sori grown on B . bronchiseptica RB50 pLC003 with calcofluor-stained spores showed that while B . bronchiseptica localizes to the amoeba sorus , it is outside of the spores ( Fig 5 and S7 Fig ) . Together , these results suggest that B . bronchiseptica travels to the sorus but escapes D . discoideum cells , persisting and growing in their periphery . The ability to associate with and replicate in amoeba sori suggests that B . bronchiseptica can take advantage of the amoeba strategy for geographic dissemination [19] . In order to determine whether B . bronchiseptica can be transported along with D . discoideum , we conducted a sorus passaging assay . Sori formed from amoebae grown on a B . bronchiseptica lawn for 16 d were collected , quantified , and diluted , and a fraction was then delivered to a fresh plate of K . pneumoniae . After the growth cycle was completed on that plate of K . pneumoniae , sori were collected and transferred to a fresh plate of K . pneumoniae . This process was repeated through seven passages ( Fig 6 ) . At each passage , B . bronchiseptica was recovered at high numbers from amoeba sori; from the fourth passage through the seventh , the bacterial load recovered per plate averaged ~7 x 105 CFU ( Fig 6 ) . Interestingly , while B . bronchiseptica was not observed intracellularly in amoeba spores , it maintained its association with the sori through multiple passages , despite the overwhelming abundance of an alternate food source , K . pneumoniae . The high number of bacteria recovered at each passage highlights the ability of B . bronchiseptica to utilize D . discoideum as a vector for expanding its numbers . At each passage , the sori containing B . bronchiseptica were diluted 10-fold when transferred to a new lawn of K . pneumoniae . Yet , B . bronchiseptica proliferated such that high CFUs of B . bronchiseptica were recovered at each passage . Thus , by the seventh passage , B . bronchiseptica had expanded approximately 10 , 000 , 000-fold within sori . These data indicate that B . bronchiseptica are able to use D . discoideum as an expansion vector and can disseminate and grow along with the amoeba through consecutive life cycles . The virulence genes up-regulated in the Bvg+ phase have been shown to be necessary for the infection of a variety of mammalian hosts [7 , 9 , 13] , while the genes associated with the Bvg- phase have been hypothesized to be important for environmental survival outside of the mammalian host [5 , 9] . In order to determine whether the Bvg two-component system regulates genes involved in interactions with amoebae , we grew D . discoideum on lawns of wild-type B . bronchiseptica ( RB50 ) or RB50 derivatives locked either in the Bvg- ( RB54 ) or Bvg+ ( RB53 ) phase . When sori from these plates were collected 10 d later , ~70% fewer Bvg+ phase-locked mutants were recovered than either wild-type or Bvg- mutants ( Fig 7 ) . Since wild-type B . bronchiseptica is expected to be in the Bvg- phase at the amoeba growth temperature ( 21°C ) [4] , these data suggest that the genes expressed in the Bvg- phase mutant are important for B . bronchiseptica survival in amoeba sori , while the Bvg+ phase is less conducive to B . bronchiseptica transport to or survival in sori . Notably , in three independent experiments , the small number of RB53 that were recovered included a substantial proportion of spontaneous Bvg- mutants ( S2 Table ) , supporting a strong selective advantage for the Bvg- phase during interactions with amoebae . These data suggest that genes expressed in the Bvg- phase mediate successful interactions with amoebae that are ubiquitous in the environment . The apparent advantage of the Bvg- phase bacteria during interactions with amoebae suggests that the expression pattern of B . bronchiseptica genes within the sori will be similar to the Bvg- phase . Moreover , D . discoideum survive and grow at 21°C , indicating that B . bronchiseptica within the sori ( B . bronchisepticasori ) may be in the Bvg- phase . Therefore , we compared the expression of B . bronchiseptica genes in the sori to Bvg- and Bvg+ phase-lock mutants grown under standard liquid culture conditions . The genes chosen for comparison are up-regulated under either Bvg- ( cheZ , flhD ) or Bvg+ ( cyaA , fimC , fhaB ) conditions [4] . Consistent with our hypothesis , B . bronchisepticasori expressed the chemotaxis protein gene cheZ similarly to the Bvg- mutant and significantly ( p < 0 . 001 ) higher than the Bvg+ mutant ( Fig 8A ) . In contrast , B . bronchisepticasori expression of flhD , a regulator of flagellum assembly , was significantly different from either Bvg- ( p = 0 . 035 ) or Bvg+ ( p = 0 . 002 ) mutants , potentially reflecting altered flagella-based motility within sori ( Fig 8A ) . The expression of the adenylate cyclase gene cyaA in B . bronchisepticasori was similar to the Bvg- mutant ( Fig 8B ) , supporting our hypothesis that B . bronchiseptica in the sori resembles the Bvg- phase . However , B . bronchisepticasori had significantly higher expression of both the fimbriae biogenesis gene fimC ( p = 0 . 002 ) and adhesion gene fhaB ( p < 0 . 001 ) compared to the Bvg- mutant ( Fig 8B ) . Altogether , we compared the expression of five genes , two of which ( cheZ and cyaA ) were supportive of our hypothesis that B . bronchisepticasori is in the Bvg- phase , while one gene ( fimC ) suggests a Bvg+ phenotype . Notably , the genes ( fimC , flhD , and fhaB ) that disagree with our initial hypothesis are involved in motility and adherence , which could affect bacteria persisting in the sori in a variety of ways . The sticky sorus adheres to passing objects or animals to mediate the physical dispersal of D . discoideum spores . We therefore hypothesized that localization to the sori may similarly allow B . bronchiseptica to spread geographically . In order to demonstrate whether B . bronchiseptica in sori can be transmitted to a new location via an intermediary , we used flies to mechanically disperse fruiting body contents . To rigorously test the possibility , we coated SM/5 agar in a 50-mL conical tube with a lawn of K . pneumoniae ( estimated >100 , 000 , 000 CFU ) and introduced the contents of a sorus containing D . discoideum spores and a relatively small number ( estimated <100 , 000 ) of B . bronchiseptica . Therefore , the only B . bronchiseptica present on the agar were those delivered in association with the amoeba sori . After fruiting bodies spread across the plate , flies were added for 1 min and then transferred to new plates either containing a lawn of K . pneumoniae ( to assess amoeba transmission ) or Bordet-Gengou ( BG ) agar with streptomycin ( to assess B . bronchiseptica transmission ) ( Fig 9A ) . Transmission of amoeba spores to a new location via flies was demonstrated by the formation of plaques and fruiting bodies on amoeba-specific plates; plaques formed along the path walked by the fly , evidently growing where spores were deposited at each fly footstep ( Fig 9B ) . Similarly , B . bronchiseptica colonies formed on BG plates corresponding to the fly’s path , demonstrating that B . bronchiseptica within the sori can travel with amoeba spores to seed colonies in new locations ( Fig 9C ) . To confirm the apparent association between the mechanical distribution and subsequent bacterial growth , we also tested the ability of ants to act as dissemination vectors ( Fig 10 ) . The ants’ progression across the BG plates was filmed , and their movements were analyzed by tracking software ( Fig 10 ) . When the positional data of the ant’s thorax were overlaid with the BG plate , it became evident that the growth of B . bronchiseptica correlates with the ant’s tracks across the plate ( Fig 10 ) . Thus , B . bronchiseptica can be transmitted with amoeba spores to new locations by environmental mechanical vectors including insects . The ability of B . bronchiseptica to successfully disseminate along with amoebae is a compelling explanation for the high conservation of the genes expressed in the Bvg- phase . However , the value of maintaining two distinct life cycles associated with two different hosts , rather than specializing to one , should be dependent on the ability to switch between these distinct ecological niches . To assess the ability of B . bronchiseptica to move from the amoeba to the mammalian host , mice were challenged either with B . bronchiseptica grown in culture or with sori containing B . bronchiseptica at matched inocula of 5 x 105 CFU in a volume of 50 μL . Bacteria from amoeba sori and bacteria grown in culture similarly colonized the lungs and tracheas of mice by day 3 post inoculation ( Fig 11 ) . In order to rigorously test how efficiently B . bronchiseptica passaged on amoebae can colonize mice , we administered a very low dose of bacteria to the mouse ( 25 CFU in 5 μL ) . Even this very small number of bacteria , a tiny fraction of those present in individual sori , was sufficient to efficiently colonize mice ( S8 Fig ) . Survival in amoeba therefore does not inhibit B . bronchiseptica transmission to mammalian hosts . Furthermore , B . bronchiseptica from amoeba sori that had been serially passaged on lawns of K . pneumoniae four consecutive times still retained the ability to efficiently colonize the mammalian respiratory tract even when administered at a low-volume and a low-dose inoculum of 25 CFU in 5 μL ( S8 Fig ) . Together , these data suggest that while B . bronchiseptica association with amoebae involves the ability to modulate to the Bvg- phase , it does not inhibit the ability to modulate back to the Bvg+ phase in order to colonize a mammalian host .
Herein , we describe for the first time the ability of the respiratory pathogen B . bronchiseptica to thrive and disseminate outside the mammalian host while also identifying a novel role for the enigmatic Bvg- phase . B . bronchiseptica is shown to survive both within amoeba cells ( Figs 1 and 2 ) and in association with amoeba sori ( Figs 3 , 4 and 5 ) . While the ability of B . bronchiseptica to survive within the amoeba cells appears to be transitory , it is sufficient for the bacteria to evade amoebic predation and localize to the amoeba sori . Moreover , B . bronchiseptica forms a persistent relationship with the amoebae such that the two can be disseminated to new locations together via environmental vectors , such as insects ( Figs 9 and 10 ) . Once in the amoeba sori , B . bronchiseptica expand in number and disseminate along with D . discoideum as the latter feeds on other bacterial species , and B . bronchiseptica are repeatedly incorporated into the new fruiting bodies formed once the other bacterial food is depleted ( Fig 6 ) . Even after repeated passaging , B . bronchiseptica retains the ability to shift to the Bvg+ phase and efficiently infect a mammalian host ( Fig 11 and S8 Fig ) . Thus , amoebae can act as environmental reservoirs and amplification vectors as well as modes of dissemination/transmission for B . bronchiseptica ( Fig 12 ) . For a century , D . discoideum has served as a model organism for studies of cell migration , cell signaling , cytokinesis , cellular development , altruism , and phagocytosis [43–48] . Relatively recently , several important human pathogens were shown to survive amoeba predation , and amoebae are now studied as potential environmental reservoirs for these pathogens [21 , 49 , 50] . Interestingly , many of these amoeba-resistant bacteria have been shown to interfere with the amoeba life cycle—for example , preventing D . discoideum differentiation into mature fruiting bodies [21]—and such pathogens have only been shown to survive in single-celled amoebae . Meanwhile , other amoeba-resistant bacteria such as Burkholderia spp . have been shown to decrease spore production even when grown on an abundant food source [41] . Here , we have shown that B . bronchiseptica are able to utilize the amoeba life cycle such that they can be recovered from amoeba sori and disseminate with the amoeba to new geographical locations through multiple passages ( Figs 3 , 4 , 8 and 9 ) while having no obvious detrimental effect on the amoeba ( S5 Fig ) . This work reveals novel B . bronchiseptica-amoebic interactions that involve not only the utilization of the amoeba mechanism for dissemination but successful and successive growth and propagation along with the amoebae through multiple life cycles . The ability of D . discoideum to form symbiotic relationships with bacteria has previously been described [42 , 51] , as many amoebae ( nonfarmers ) consume all of their bacteria prey , while others ( farmer amoebae ) can carry a bacterial food source ( i . e . , K . pneumoniae ) to novel locations . Recent work has shown that Burkholderia spp . have two distinct clades that are able to associate with both farmer and nonfarmer amoebae [51 , 52] . The farmer-associated Burkholderia clade promotes growth of farmer amoebae and inhibits nonfarmer amoebae while allowing farmers to carry both food and nonfood bacteria [51 , 53] . In contrast , the clade that colonizes nonfarmer amoebae imbues the amoeba with farmer characteristics , such as carriage of bacteria and association through multiple generations of growth [52] and decreased spore production in food-rich areas by excreting small molecules . Our data indicate that B . bronchiseptica has an apparent symbiotic relationship with D . discoideum that permits growth and expansion of both amoebae and bacteria . This relationship is with a nonfarmer amoeba strain as shown by the inability of K . pneumoniae to evade D . discoideum predation in the single-cell stage ( Fig 1 ) or localize to the sori ( Fig 3 ) . Therefore , the Burkholderia spp . clade able to interact with nonfarmers would be most relevant to contrast with our work . Similar to those Burkholderia spp . , B . bronchiseptica is able localize to the amoeba sori ( Fig 3 ) and continue to associate with the amoeba through multiple life cycles even when grown on another viable and more plentiful food source ( Fig 6 ) . In contrast to observations with Burkholderia spp . , however , we did not observe a reduction in spore production from the fruiting bodies of amoebae grown on B . bronchiseptica relative to those grown on K . pneumoniae ( S5 Fig ) . Although Burkholderia spp . and Bordetella spp . are relatively closely related and are likely to share some aspects of their ability to successfully interact with amoebae , these dissimilarities suggest B . bronchiseptica may have novel mechanisms of interaction with amoebae . This work also demonstrates advantages for the bacterial traveler along with the D . discoideum dissemination mechanism , as well as the contribution of this interaction to the natural history of the bordetellae . Importantly , the ability to successfully transmit in these two independent settings , potentially regulated in part by the BvgAS two-component system , is uniquely demonstrated here for Bordetella spp . but may be observed in other soil-adapted organisms . The enigmatic Bvg- phase of the Bvg regulon has previously been hypothesized to be important for temporary , short-term ex vivo survival either during transmission from host to host or in a hypothetical and undefined environmental reservoir [12–14] . Here , we show that the Bvg- phase contributes to a previously uncharacterized life cycle outside of the mammalian host . Based on these results , we propose a novel perspective of the Bvg+ and Bvg- phases that allows for independent but interconnected life cycles in distinct niches ( Fig 12 ) . The virulence genes associated with the Bvg+ phase allow for colonization of the mammalian host and the complex processes involved in dissemination between mammalian hosts ( the Bvg+ life cycle ) . In contrast , the Bvg- phase not only enables survival in an extrahost environment but also contributes to a complete life cycle that includes propagation and dissemination in association with amoebae in the environment . The stable association through multiple modes of amoeba dissemination , as well as through several generations of the amoeba life cycle , demonstrates a well-adapted association . This novel life cycle provides the first potential explanation for the many genes that are highly conserved and specifically expressed in the Bvg- phase . During their apparently independent adaptation to a closed life cycle within humans , B . pertussis and B . parapertussis have lost hundreds of genes [54–56] . It may be that the role for many of these lost genes involves environmental survival and interactions with predators primarily encountered outside the mammalian host . An example consistent with this view is the Pseudomonas fluorescens strain that became an amoebic food source due to a genetic mutation in an inedible ancestor [40] . Future work will determine whether the ability to interact with amoebae is similarly affected by loss of genes in these and other Bordetella species . In addition to surviving intracellularly in multiple species of amoebae ( Fig 1 and S3 Fig ) , B . bronchiseptica recovered from amoeba sori was able to efficiently reinfect mice ( Fig 11 ) . Therefore , the widely prevalent D . discoideum , or some other amoeba , could serve as a transmission vector for B . bronchiseptica . Large-scale studies testing the prevalence of amoebae have shown that 9% of healthy human volunteers have amoebae present in the nasal mucosa , indicating that amoebae can colonize humans and appear to comprise part of the microbiota in healthy mammals [57] . Also , the high levels of amoebae recovered from water systems and the transmission of amoeba-associated bacteria via air handling systems [57–59] indicate that transmission of B . bronchiseptica to healthy and immunocompromised individuals via amoebae is possible . These findings have important implications for management strategies to control the spread of Bordetella species , which may require taking amoebae into account as an environmental reservoir and transmission vector .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the Institutional Animal Care and Use Committee at the Pennsylvania State University at University Park , Pennsylvania ( #46284 Bordetella-Host Interactions ) , or at the University of Georgia at Athens , Georgia ( Bordetella-Host Interactions A2016 02-010-Y2-A3 ) . Mice used in these experiments were humanely killed by using carbon dioxide inhalation . B . bronchiseptica strains RB50 ( wild-type ) , RB53 ( Bvg+ phase-locked ) , and RB54 ( Bvg- phase-locked ) and K . pneumoniae ( previously known as K . aerogenes ) have been previously described [9 , 60] . B . bronchiseptica was grown and maintained on BG agar ( Difco ) supplemented with 10% defibrinated sheep’s blood ( Hema Resources ) and 20 μg/ml streptomycin ( Sigma ) . K . pneumoniae was grown and maintained on Luria Bertani ( LB ) Media agar ( Difco ) . For inoculation with culture-grown bacteria , B . bronchiseptica was grown overnight at 37°C to mid-log phase in Stainer Scholte ( SS ) liquid broth [61] , and K . pneumoniae was grown at 37°C to mid-log phase in liquid LB Media Broth . The plasmid pLC002 was constructed by cloning a gentamicin resistance gene into pBBR1- mcs2 [62] . The gentamicin cassette was amplified from pBBR1-mcs5 [62] using the primers 5′-AAAAAGCTTATGTTACGCAGCAGCAACG-3′ and 5′-ATAGAATTCTTAGGTGGCGGTACTTGG-3′ . PCR products were purified using the Zymo DNA Clean and Concentrator kit ( Irvine , California , US ) prior to a double digestion with HindIII and EcoRI ( cut sites in the primers are indicated by italics ) . The amplicon was then ligated into pBBR1-mcs2 , which was similarly digested with HindIII and EcoRI . Sequence analysis was used to confirm the plasmid pLC002 . Inserting a mCherry gene into pLC002 created the plasmid pLC003 . The following primers were used to amplify mCherry from pSCV26 [63]: 5′-AAGGGATCCATGGTGAGCAAGGGCGAG-3′ and 5′-AGCACTAGTTTACTTGTACAGCTCGTCC-3′ . BamHI and SpeI sites , shown in italics , were designed within the primers to facilitate cloning of the mCherry gene into pLC002 . PCR products were purified using the Zymo DNA Clean and Concentrator kit ( Irvine , California , United States ) prior to double digestion with BamHI and SpeI , as necessary . pLC002 was similarly digested with BamHI and SpeI . The digested mCherry amplicon was ligated into this plasmid and confirmed with sequence analysis to generate pLC003 . The plasmid pLC018 contains the mCherry gene cloned into pBBR1-mcs4 [62] . The following primers were used to amplify mCherry from pSCV26: 5′-AAGGGATCCATGGTGAGCAAGGGCGAG-3′ and 5′-ACCGAATTCTTACTTGTACAGCTCGTCC-3′ . BamHI and EcoRI sites , shown in italics , were designed within the primers to facilitate cloning of the mCherry gene into pBBR1-mcs4 . PCR products were purified using the Zymo DNA Clean and Concentrator kit ( Irvine , California ) prior to double digestion with BamHI and EcoRI . The PCR products were ligated into pBBR1-mcs4 , which was also digested with BamHI and EcoRI . Sanger sequencing confirmed the proper ligation of the digested mCherry PCR product into pBBR1-mcs4 to generate pLC018 . Fluorescent images of B . bronchiseptica in association with D . discoideum were taken using a Nikon A1 Confocal Laser Microscope . | Bordetella species are infectious bacterial respiratory pathogens of a range of animals , including humans . Bordetellae grow in two phenotypically distinct “phases , ” each specifically expressing a large set of genes . The Bvg+ phase is primarily associated with respiratory tract infection ( RTI ) and has been well studied . The similarly large set of genes specifically expressed in the Bvg- phase is poorly understood but has been proposed to be involved in some undefined environmental niche . Recently , we reported the presence of Bordetella species in many soil and water sources , indicating extensive exposure to predators . Herein , we show that the Bvg- phase mediates B . bronchiseptica interactions with the common soil predator D . discoideum . Surprisingly , the bacterium not only can evade predation but can propagate and disseminate via the complex developmental process of D . discoideum . After multiple passages and over a million-fold expansion in association with D . discoideum , B . bronchiseptica retained the ability to efficiently colonize mice . The conservation of the genes involved in these two distinct phases raises the possibility of potential environmental sources for the frequently unexplained outbreaks of diseases caused by this and other Bordetella species . | [
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"bi... | 2017 | Bordetella bronchiseptica exploits the complex life cycle of Dictyostelium discoideum as an amplifying transmission vector |
Cardioviruses , including encephalomyocarditis virus ( EMCV ) and the human Saffold virus , are small non-enveloped viruses belonging to the Picornaviridae , a large family of positive-sense RNA [ ( + ) RNA] viruses . All ( + ) RNA viruses remodel intracellular membranes into unique structures for viral genome replication . Accumulating evidence suggests that picornaviruses from different genera use different strategies to generate viral replication organelles ( ROs ) . For instance , enteroviruses ( e . g . poliovirus , coxsackievirus , rhinovirus ) rely on the Golgi-localized phosphatidylinositol 4-kinase III beta ( PI4KB ) , while cardioviruses replicate independently of the kinase . By which mechanisms cardioviruses develop their ROs is currently unknown . Here we show that cardioviruses manipulate another PI4K , namely the ER-localized phosphatidylinositol 4-kinase III alpha ( PI4KA ) , to generate PI4P-enriched ROs . By siRNA-mediated knockdown and pharmacological inhibition , we demonstrate that PI4KA is an essential host factor for EMCV genome replication . We reveal that the EMCV nonstructural protein 3A interacts with and is responsible for PI4KA recruitment to viral ROs . The ensuing phosphatidylinositol 4-phosphate ( PI4P ) proved important for the recruitment of oxysterol-binding protein ( OSBP ) , which delivers cholesterol to EMCV ROs in a PI4P-dependent manner . PI4P lipids and cholesterol are shown to be required for the global organization of the ROs and for viral genome replication . Consistently , inhibition of OSBP expression or function efficiently blocked EMCV RNA replication . In conclusion , we describe for the first time a cellular pathway involved in the biogenesis of cardiovirus ROs . Remarkably , the same pathway was reported to promote formation of the replication sites of hepatitis C virus , a member of the Flaviviridae family , but not other picornaviruses or flaviviruses . Thus , our results highlight the convergent recruitment by distantly related ( + ) RNA viruses of a host lipid-modifying pathway underlying formation of viral replication sites .
Picornaviridae is a large family of positive-sense RNA viruses [ ( + ) RNA viruses] comprising many clinically relevant human and animal pathogens . Members of the genus Enterovirus include important human viruses like poliovirus ( PV ) , the causative agents of poliomyelitis , coxsackieviruses ( CV ) , causing meningitis and myocarditis , and rhinoviruses ( RV ) , responsible for the common cold and exacerbations of asthma and chronic obstructive pulmonary disease . Perhaps the best-known non-human picornavirus is foot-and-mouth-disease virus ( FMDV , genus Aphtovirus ) , which can cause devastating outbreaks in cattle leading to severe economic loss . Closely related to the Apthovirus genus is the genus Cardiovirus , composed of three species: Theilovirus ( TV ) , encephalomyocarditis virus ( EMCV ) and the more recently discovered Boone cardiovirus . The species Theilovirus includes , among others , Theiler’s murine encephalomyocarditis virus ( TMEV ) and Saffold virus ( SAFV ) , a human cardiovirus . While TMEV is known to cause enteric infections and sometimes more severe encephalitis or chronic infection of the central nervous system [1] , as yet , SAFV has not been firmly associated with a clinical disease [2] . EMCV can infect a wide range of animals , of which rodents are considered the natural reservoir . Of all domesticated animals , pigs are most prone to EMCV infection , which can lead to fatal myocarditis [3] , reproductive failure in sows or sudden death of piglets [4–6] . Like other ( + ) RNA viruses—such as hepatitis C virus ( HCV ) , dengue virus ( DENV ) , chikungunya virus ( ChikV ) and coronavirus ( CoV ) —picornaviruses replicate their genomic RNA on specialized , virus-modified intracellular membranes . These remodeled membranes termed replication organelles ( ROs ) arise from the concerted actions of both viral nonstructural proteins and co-opted host factors . Enteroviruses , for instance , hijack members of the secretory pathway for replication and formation of ROs [7 , 8] . Among the viral nonstructural proteins , 2B , 2C , 3A as well as their precursors 2BC and 3AB contain hydrophobic domains which confer them membrane-modifying properties [9–11] . Considerable interest has been given to the study of the small viral protein 3A , which is the key viral player involved in membrane rearrangements . 3A interacts with and recruits secretory pathway components GBF1 ( Golgi-specific brefeldin A-resistance guanine nucleotide exchange factor 1 ) and PI4KB ( phosphatidylinositol-4 kinase type III isoform β ) to ROs [12–16] . Despite intensive investigation , the role of GBF1 in enterovirus replication is not yet elucidated ( reviewed in [8] ) . Recruitment of PI4KB to ROs leads to a significant local increase of membranes in its enzymatic product PI4P [15] . This PI4P-rich environment serves to further recruit other essential viral and host factors to replication sites , such as the viral polymerase 3Dpol , which is able to specifically bind PI4P in vitro . Recently , we and others revealed that PI4P plays a central role in enterovirus replication by recruiting the oxysterol-binding protein ( OSBP ) to ROs [17–19] . In uninfected cells , OSBP bridges the ER and Golgi membranes by binding to the ER integral membrane protein VAP-A and to PI4P and Arf1-GTP at the trans-Golgi [20] . Through its sterol-binding domain , OSBP shuttles cholesterol from ER to the Golgi and PI4P from the Golgi to the ER , thereby generating a lipid counterflow at ER-Golgi membrane contact sites ( MCSs ) . In enterovirus infection , OSBP exchanges PI4P for cholesterol most likely at ER-RO MCSs [18] . The unique lipid and protein composition of enterovirus ROs determines their particular 3D architecture , which consists of a complex tubulo-vesicular network , as shown in cells infected with PV and coxsackievirus B3 ( CVB3 ) [21 , 22] . The lipid transfer function of OSBP at membrane contact sites is not only vital for enteroviruses , but also for HCV [23] . HCV genome replication occurs in association with an ER-derived network of specialized membrane vesicles called the membranous web ( MW ) . Like enterovirus ROs , the HCV MW is enriched in PI4P lipids and cholesterol [23–25] . In the case of HCV , PI4P are generated through recruitment and activation of the ER-localized enzyme PI4KA ( phosphatidylinositol-4-phopshate kinase type III isoform α ) by the viral protein NS5A [24 , 26] . Thus far , information regarding virus-host interactions that govern the formation of cardiovirus ROs remains scarce . In a report by Zhang et al , it was suggested that autophagy supports EMCV replication [27] . The study showed that EMCV infection triggered an accumulation of autophagosome-like vesicles in the cytoplasm and that EMCV 3A colocalized with the autophagy marker LC3 . However , inhibition of autophagy exerted only minor effects on virus replication [27] , which argues against a strong implication of the autophagy pathway in cardiovirus genome replication and/or formation of ROs . Evidence for a role of autophagy in virus replication also exists for enteroviruses and flaviviruses , but rather related to non-lytic virus release or modulation of host innate immune responses than viral genome replication [28–31] . Based on observations that cardioviruses do not require GBF1 or PI4KB for replication [32–34] , it is generally believed that cardiovirus replication strategies are distinct from those of enteroviruses . Here , we set out to elucidate whether cardiovirus replication depends on another PI4K isoform . By siRNA-mediated knockdown , we identified PI4KA as a key player in the replication of EMCV . EMCV 3A interacts with and recruits PI4KA to ROs , which increases local PI4P synthesis , eventually leading to downstream recruitment of OSBP . We show that the cholesterol-PI4P shuttling activity of OSBP is important for the global distribution of the ROs and for virus genome replication . Our data reveal that , by exploiting the same cellular pathway , the cardiovirus replication strategy profoundly resembles that of the distantly related HCV and is dissimilar to those of other characterized picornaviruses and flaviviruses in this critical aspect . Thus , the similarity between EMCV and HCV is a striking case of functional convergence in virus-host interactions , indicating that diverse RNA viruses might have a limited choice of pathways in the remodeling of host membrane network for virus replication .
Unlike enteroviruses , cardioviruses do not require PI4KB for replication [34] . To investigate whether other PI4Ks might be involved in cardiovirus replication , we depleted each of the four distinct cellular PI4Ks by siRNA-mediated gene knockdown using a set of siRNA sequences ( Ambion ) which we previously tested for efficiency and toxicity [35] , and monitored the subsequent effects on replication of EMCV . We observed inhibitory effects on EMCV replication when silencing PI4KA , but not upon silencing of the other PI4Ks ( Fig 1A ) . To confirm the importance of PI4KA for EMCV replication , we performed another series of knockdown experiments using another set of siRNA sequences ( Qiagen ) . Depletion of PI4KA , but not PI4KB , significantly reduced EMCV infection , measured by end-point titration of progeny virus production ( Fig 1B ) . We next wondered which step in the virus life cycle is dependent on PI4KA . To omit the step of virus attachment and cell entry , EMCV RNA was in vitro transcribed and subsequently transfected in cells depleted of PI4KA by siRNAs . Virus replication was strongly inhibited upon PI4KA silencing , as measured by end-point titration of progeny virions ( Fig 1C ) . This indicated that PI4KA is involved in a post-entry step in the virus life cycle . To elucidate whether EMCV requires PI4KA for viral genome amplification , we infected cells with a Renilla luciferase-encoding EMCV ( RLuc-EMCV ) and quantified the luciferase activity as a measure of viral RNA replication . EMCV RNA replication was severely impaired in cells lacking PI4KA , but not PI4KB ( Fig 1D ) . We excluded that inhibition of EMCV replication by PI4KA silencing was due to cytotoxic effects by a cell viability assay ( Fig 1E ) and verified the knockdown efficiency by western blot analysis ( Fig 1F ) . Altogether , these results showed that PI4KA plays a key role in EMCV genome RNA replication . Next , we investigated whether EMCV required the enzymatic activity of PI4KA using AL-9 , a PI4K inhibitor that also blocks PI4KB , but at 5-fold higher concentration [36] . Cells were infected with EMCV or RLuc-EMCV at MOI 0 . 1 and treated with increasing concentrations of AL-9 for 8 h . Coxsackievirus B3 ( CVB3 ) , as well as all other enteroviruses , has been previously shown to hijack the Golgi-localized PI4KB for replication [15 , 34] and was included as a control . As measured by end-point titration and analysis of the luciferase activity ( Fig 1G and 1H ) , EMCV replication was efficiently inhibited by AL-9 in a dose-dependent manner with complete inhibition detected at 10 μM , while CVB3 replication was hampered only at 50 μM ( Fig 1G ) , which is in line with the 5-fold preference of AL-9 for PI4KA over PI4KB . Dipyridamole , a well-established inhibitor of EMCV RNA replication , was included here as positive control . Importantly , AL-9 inhibited EMCV replication also when infection was performed at high MOI ( S1A Fig , MOI 10 ) . To corroborate that PI4KA activity is required for the step of viral genome replication , we performed a time-of-addition experiment in which AL-9 was added to the cells at different time points after infection with RLuc-EMCV . Similar to dipyridamole , AL-9 strongly inhibited replication when added up to 3 h after infection ( S1B Fig ) , indicating that not entry but rather a step during genome replication was blocked by AL-9 . Next , we tested whether other members of the cardiovirus genus also depended on PI4KA for replication . Similar to EMCV , replication of the human cardiovirus Saffold virus 3 ( SAFV3 ) ( species Theilovirus ) was also sensitive to AL-9 treatment ( Fig 1I ) . The cell viability assay demonstrated that AL-9 treatment only exerted slight cytotoxic effects at the highest concentration tested ( Fig 1J ) . These results indicated that different cardiovirus species required the enzymatic activity of PI4KA for genome replication . Soon after infection , the cytoplasm of EMCV-infected cells accumulates an impressive amount of vesicular membranous structures [37 , 38] . As yet , there is little information available regarding which viral proteins and host factors are associated with these new virus-induced organelles [27 , 39] . We set out to investigate whether PI4KA was present at EMCV ROs . Despite repeated efforts , we were unable to detect the endogenous kinase by immunofluorescence staining in any of the cell lines tested . As an alternative , we chose to analyze possible changes in the subcellular distribution of ectopically expressed PI4KA upon EMCV infection . In mock-infected cells ( Fig 2A , upper panel ) , GFP-PI4KA was distributed diffusely throughout the entire cytoplasm , as previously reported by others [40 , 41] . In infected cells visualized by dsRNA staining , we instead observed a clear difference in the localization of the kinase , which was redistributed to discrete cytoplasmic punctae in a perinuclear region ( Fig 2A , lower panel ) . We next aimed to elucidate whether these PI4KA punctae coincided with the viral ROs . The small picornaviral protein 3A and its precursor 3AB are membrane-associated and play key roles in viral RNA replication and recruitment of essential host factors [15 , 42–45] . Hence , we considered 3AB as a suitable marker for EMCV ROs and compared the staining of PI4KA to that of 3AB in infected cells . We observed a striking overlap of GFP-PI4KA with 3AB-positive structures ( Fig 2B , lower panels ) and could confirm this phenotype when analyzing the localization of ectopically expressed PI4KA bearing an HA-tag ( S2 Fig ) . By contrast , the signal for GFP-PI4KB , which was mainly localized at the Golgi in non-infected cells ( Fig 2C , top panel ) , failed to overlap with 3AB ( Fig 2C , lower panels ) . Interestingly , although in close proximity to dsRNA signals ( Fig 2A , lower panel ) , PI4KA did not clearly overlap with dsRNA ( Fig 2A , insets ) . Taken together , these data demonstrated that PI4KA is selectively recruited to EMCV ROs . Interestingly , we noticed a loss of the typical Golgi localization of PI4KB in EMCV-infected cells ( Fig 2C , lower panel ) , suggesting that Golgi integrity might be affected upon EMCV infection . Prompted by this and our finding that EMCV utilizes the ER-localized PI4KA for replication , we set out to elucidate whether other ER or Golgi components are present at EMCV ROs . In order to be able to use more antibody combinations in immunofluorescence , we constructed a recombinant EMCV bearing an HA-tag in the nonstructural protein 2C . The tag was introduced after the second amino acid , leaving the 2B-2C cleavage site intact ( S3A Fig ) , and did not impair virus replication ( S3B Fig ) . First , we checked whether 2C-HA and 3AB are present on the same membranes by immunofluorescence microscopy . Indeed , 2C and 3AB signals greatly overlapped ( S3C Fig ) , supporting the idea that these proteins occupy the same membranes of the ROs . Using this tagged EMCV , we noticed that the Golgi structure was indeed altered in infected cells , from 4 h p . i . onwards , as revealed by the dispersed pattern of both cis- and trans-Golgi markers GM130 ( Fig 3A ) and TGN46 , respectively ( Fig 3B ) . However , neither TGN46 nor GM130 were present at 2C-HA-positive structures , suggesting that EMCV ROs are not Golgi-derived . ERGIC53 , a marker of the ER-Golgi intermediate compartment also appeared scattered throughout the cytoplasm in infected cells , but without overlapping 2C-HA ( Fig 3C ) . We next compared the localization of 3AB with Sec13 ( COPII-coatomer complex component ) , an ER exit site ( ERES ) marker , and the ER marker calreticulin . While in non-infected cells , Sec13 displayed mainly a typical perinuclear localization , in EMCV-infected cells it appeared dispersed , but without significantly colocalizing with 3AB ( Fig 3D , Mander’s colocalization coefficient M2 = 0 . 14 ± 0 . 01 , fraction of Sec13 overlapping 3AB ) . We observed a greater degree of overlap between 3AB and calreticulin ( Fig 3E , M2 = 0 . 4 ± 0 . 02 , fraction of calreticulin overlapping 3AB ) . Images acquired with higher magnification revealed that most of 3AB was in close contact with ER tubules ( Fig 3F ) . Taken together , these data suggested that EMCV possibly replicates on ER-derived membranes . Based on the extensive overlap between PI4KA and 3AB and the drastic change in PI4KA pattern in infected cells , we hypothesized that PI4KA might be recruited to replication sites by interacting ( directly or indirectly ) with one or more of the viral nonstructural proteins . To investigate this , we used the stable cell line Huh7-Lunet/T7 that allows ectopic protein expression under the control of a T7 promoter and has been previously optimized and validated as a reliable and reproducible cellular system to study PI4KA-protein interactions by radioactive Co-IP assays [40 , 46] . Myc-tagged EMCV nonstructural proteins 2A , 2B , 2C , 3A , 3AB , 3C and 3D were individually co-expressed together with HA-PI4KA in Huh7-Lunet/T7 cells , radioactively labeled , and affinity purified from cell lysates using anti-myc specific antibodies . Autoradiography analysis showed that HA-PI4KA was specifically co-purified by 3A and 3AB , but not by the other viral proteins ( Fig 4A ) . To confirm this interaction by co-immunoprecipitation ( co-IP ) coupled with western blot analysis , myc-tagged EMCV 3A was co-expressed with HA-PI4KA and subjected to affinity purification using either monoclonal or polyclonal anti-myc antibodies . As shown in Fig 4B , HA-PI4KA only interacted with EMCV 3A , but not with CVB3 3A , which interacts with PI4KB [15 , 16 , 45] and was included here as a negative control . These data implied that EMCV nonstructural protein 3A is responsible for PI4KA recruitment to ROs . Interestingly , a diffuse band just below 17 KDa appears to co-purify with EMCV 3C when HA-PI4KA is co-expressed ( Fig 4A , indicated by * ) . We reasoned this could be indicative of a temporal regulation of the PI4KA activity during infection via 3C-dependent degradation . To explore this possibility , we performed western blot analysis of endogenous PI4KA during the time course of infection , but did not detect any bands indicative of degradation , neither in Huh7-Lunet/T7 or HeLa R19 cells ( S4 Fig ) . To test if 3A alone can recruit PI4KA , we examined by immunofluorescence the subcellular localization of HA-PI4KA when co-expressed with 3A , 3AB or 2B , which we considered as a negative control . When expressed alone , HA-PI4KA localized throughout the cell in a diffuse pattern ( Fig 4C , top panel ) , as previously described [40] . EMCV 3A- and 3AB-myc were both localized throughout the cytoplasm and at discrete punctate structures , of which a subset was also positive for PI4KA ( Fig 4C ) . 2B-myc was also distributed in punctae throughout the cytoplasm , but failed to recruit PI4KA ( Fig 4C ) . Collectively , these results indicated that EMCV 3A is the viral protein responsible for engaging PI4KA in replication . While PI4KB produces PI4P at Golgi membranes , PI4KA is responsible for the synthesis of the PI4P pool at the plasma membrane , where it dynamically localizes [41 , 47–49] . Our finding that PI4KA activity was critical for EMCV RNA replication prompted us to investigate whether PI4P metabolism is altered during virus replication . Given that EMCV replicates on intracellular membranes , we monitored potential changes in the subcellular distribution of both plasma membrane ( PM ) and intracellular ( IC ) pools of PI4P in Huh7Lunet/T7 cells following EMCV infection . The two pools of PI4P can be selectively visualized using two different immunocytochemistry protocols previously established by Hammond et al [50] . While the plasma membrane pool of PI4P appeared unaffected in EMCV-infected cells ( Fig 5A ) , the intracellular PI4P distribution changed from a perinuclear , Golgi-like pattern in mock-infected cells to dispersed throughout the cytoplasm in EMCV-infected cells ( Fig 5A ) . We observed similar PI4P phenotypes in HeLa cells ( S5 Fig ) , indicating that the observed effects were not cell line-specific . Notably , quantitative analysis of the fluorescent PI4P signals revealed a marked increase in the level of intracellular PI4P in infected cells ( Fig 5B ) . To rule out a possible involvement of PI4KB in establishing the elevated PI4P levels observed in EMCV infected cells , we treated cells with the PI4KB inhibitor BF738735 ( Compound 1 ) [34] . For simultaneous detection of PI4P and viral ROs by immunofluorescence , we infected cells with EMCV-2C-HA . Short treatment with BF738735 severely depleted the Golgi PI4P pool in non-infected cells ( Fig 5C ) , thus reflecting an effective inhibition of PI4KB activity . However , the PI4P phenotype remained unaltered in infected cells ( Fig 5C ) , demonstrating that the EMCV-induced accumulation of intracellular PI4P was not mediated by PI4KB . Most PI4P localized in the vicinity of 2C-HA , with at least a small subset of PI4P overlapping with 2C-HA ( Fig 5C ) . These data together with the finding that EMCV requires PI4KA activity suggested that PI4KA-derived PI4P lipids play a central role in EMCV genome replication . Various cellular proteins carrying a PI4P-binding domain called the pleckstrin-homology domain ( PH ) , such as the ceramide-transfer protein ( CERT ) , four-phosphate-adaptor protein 1 ( FAPP1 ) , or the oxysterol-binding protein ( OSBP ) can sense and specifically bind PI4P lipids [47 , 51–53] . Recently , we and others showed that enteroviruses generate PI4P-enriched membranes to recruit OSBP , which in turn exchanges PI4P for cholesterol at ROs [17 , 18 , 54] . Moreover , we showed that EMCV is sensitive to itraconazole , which we identified to be an OSBP inhibitor [17] , and that cholesterol shuttling is important for EMCV replication [54] . We therefore reasoned that in EMCV-infected cells one purpose of PI4P lipids might be to recruit OSBP to replication membranes to support viral RNA replication . To test if OSBP is required for EMCV replication , we efficiently reduced OSBP expression in HeLa cells by siRNA gene silencing ( Fig 6A ) and evaluated the subsequent effects on EMCV replication by end-point titration analysis . Replication of EMCV was significantly reduced in cells in which OSBP was depleted compared to control-treated cells ( Fig 6A ) , indicating that OSBP is required for efficient replication . We further used OSW-1 , an OSBP ligand that interferes with normal OSBP functioning [55] , to pharmacologically inhibit OSBP and analyze whether its lipid transfer function is linked to EMCV infection . Using luciferase-encoding EMCV , we observed a complete inhibition of genome RNA replication after 7 h of treatment with OSW-1 at nanomolar concentrations , with no cytotoxicity present ( Fig 6B ) . A similar inhibition by OSW-1 was observed when infection was performed at high MOI ( S6 Fig , MOI 10 ) . Furthermore , by performing OSW-1 time-of-addition experiments , we excluded the possibility that OSBP was involved in early steps in the virus life cycle ( Fig 6C ) . Similar results were obtained when using 25-hydroxycholesterol ( 25-HC , Fig 6C ) , another established OSBP ligand [20 , 56] . Next , we wondered whether endogenous OSBP was present at EMCV ROs and if so , whether this localization was dependent on the PI4P pool generated by PI4KA . To this end , cells were infected with EMCV for 5 . 5 h and then treated with DMSO or AL-9 for 30 min to acutely deplete PI4P , prior to immunofluorescence analysis . While in non-infected cells OSBP localized throughout the cytoplasm and at the Golgi , OSBP was mainly found at ROs in infected cells , where it largely colocalized with 3AB ( Fig 6D , Pearson’s correlation coefficient = 0 . 71 ) . Since other Golgi proteins were not present at the ROs ( Fig 3A and 3B ) , these results suggested that OSBP is specifically recruited by EMCV . Following inhibition of PI4KA by short treatment with AL-9 , we observed a strong and significant reduction of OSBP and 3AB colocalization ( Fig 6D , Pearson’s coefficient = 0 . 58 ) . Importantly , the subcellular localization of OSBP in non-infected cells was not affected by AL-9 treatment ( Fig 6D ) , demonstrating that the presence of OSBP at EMCV replication structures is conditioned by PI4KA-produced PI4P . Given the colocalization of OSBP with 3AB , we sought to verify whether EMCV 3A was responsible for OSBP recruitment . To this end , myc-tagged EMCV 3A- , 3AB- or 2B-myc were ectopically expressed in Huh7-Lunet/T7 cells and recruitment of endogenous OSBP was analyzed by immunofluorescence analysis . In cells expressing 3A and 3AB , OSBP was redistributed in punctate structures throughout the cytoplasm , with some of these punctae colocalizing with 3A/3AB ( Fig 6E ) . By contrast , OSBP remained localized at the Golgi and did not localize at 2C-positive punctae ( Fig 6E ) . These data indicated that during EMCV infection , OSBP is recruited to ROs via 3A . To test whether OSBP is involved in transferring cholesterol to ROs in a PI4P-dependent manner , cells were infected with EMCV for 4 h , treated with AL-9 or OSW-1 for 2 h to block PI4KA activity or OSBP function respectively , and subjected to immunofluorescence analysis . In non-infected cells , cholesterol mainly localizes at endosomes in the perinuclear area and at the plasma membrane , as visualized by filipin staining [54] . In infected cells treated with DMSO , we detected cholesterol primarily colocalizing with 3AB-positive structures ( Pearson’s coefficient = 0 . 62 ) , while in drug-treated cells this colocalization was markedly reduced ( Fig 7 , Pearson’s coefficient = 0 . 32 for AL-9 and 0 . 39 for OSW-1 ) . This result confirmed that EMCV ROs acquire cholesterol through the actions of both PI4KA and OSBP .
Using a pharmacological inhibitor of PI4KA , we prove that EMCV and SAFV , which belong to distinct cardiovirus species , both require the lipid kinase activity for replication . In agreement with this result , we observed elevated PI4P levels at intracellular membranes in infected cells , suggesting an important role of PI4P lipids in cardiovirus replication . In non-infected cells , OSBP plays a critical role in lipid homeostasis by exchanging cholesterol for PI4P at the interface of ER and Golgi membranes , to which it localizes under normal conditions [20] . In this process , PI4P lipids also serve as a membrane anchor for OSBP . We hypothesized that in cardiovirus infection , PI4P may serve to recruit OSBP and cholesterol to viral replication sites . Indeed , OSBP was present at EMCV ROs , where it colocalized with the viral protein 3AB . This colocalization was markedly reduced upon AL-9 treatment , demonstrating that OSBP is recruited through PI4KA-produced PI4P . OSBP is an essential cardiovirus host factor , since both genetic depletion by siRNA treatment and pharmacological inhibition by OSW-1 and 25-HC blocked viral genome replication . Cholesterol was redistributed to EMCV ROs upon infection , and treatment with AL-9 or OSW-1 resulted in a significantly reduced colocalization of cholesterol with 3AB , arguing that accumulation of cholesterol at ROs is mediated by both PI4KA and OSBP . These data are in agreement with our recent findings that cholesterol shuttling is important for cardiovirus genome replication [54] and that cardioviruses are sensitive to itraconazole , which we recently discovered to be an . inhibitor of OSBP [65] . Our results indicate that PI4P and cholesterol are vital for the global organization of EMCV ROs . However , as these lipids fulfill multiple functions in various cellular processes [53 , 69–71] , other roles in virus replication should be envisaged . A potential task of PI4P in virus replication may be linked to the PI ( 4 , 5 ) P2 synthesis pathway , since PI4P is the major precursor of PI ( 4 , 5 ) P2 lipids , which were recently attributed an important role in HCV replication [72] . Cholesterol homeostasis was recently shown to play an important role in efficient PV polyprotein processing [73] . Whether cholesterol also ensures a proper microenvironment that supports cardiovirus polyprotein processing remains to be determined . Interestingly and in apparent parallel with the distantly related enteroviruses , exploitation of the PI4K-OSBP pathway by HCV correlates with the induction of membranes of positive curvature [58] . By contrast , the flavivirus DENV , although closely related to HCV , does not require PI4K or OSBP [23] and generates membranes of negative curvature [74] . Hence , the interplay between PI4P and cholesterol may dictate the positive curvature of the membranes at which diverse RNA viruses replicate their genomes . Through co-IP assays , we identified PI4KA as a novel interaction partner of EMCV proteins 3A and its precursor 3AB . EMCV 3A is a small protein ( 88 amino acids ) of unknown structure , containing a predicted hydrophobic domain in the C-terminus half . Expression of 3A alone was sufficient for PI4KA recruitment in intact cells , arguing that in infection , PI4KA is recruited to ROs by this viral protein . Enteroviruses and kobuviruses recruit PI4KB to ROs also via their 3A protein [15 , 16 , 45 , 57] , raising the possibility that diverse picornaviruses might use an evolutionary conserved and 3A-mediated mechanism to generate PI4P-enriched membranes . However , the 3A proteins of entero- , kobu- and cardioviruses do not share any apparent sequence similarity , their name simply reflecting the occupancy of the same locus ( 3A ) in the respective viral genomes . With the exception of their catalytic domain , also the PI4KA and PI4KB isoforms do not share any sequence similarity [75] . Furthermore , unlike 3A of most enteroviruses , cardiovirus 3A does not interact with GBF1 nor blocks protein transport in the secretory pathway when expressed alone [76] , highlighting the functional diversification associated with these small viral proteins . Several lines of evidence suggest that the picornavirus EMCV and the distantly related flavivirus HCV have evolved to exploit common host components in assisting virus RNA replication . First , HCV genome replication occurs at the “membranous web” ( MW ) , a network of single and DMVs that , like the EMCV RO , also mainly originates from the ER [58] . Second , both EMCV and HCV express a viral protein dedicated to recruitment of PI4KA , in order to induce a PI4P-rich environment at the replication sites [24 , 46] . Third , in HCV infection , PI4P lipids were also shown to be important for the recruitment of OSBP , which mediates cholesterol transfer to the MW [23] . Fourth , inhibition of either PI4KA or OSBP induced clear alterations in the global distribution of EMCV ROs , which appeared more “clustered” upon treatment with AL-9 or OSW-1 . A similar clustering effect was also observed for replication structures of the HCV MW upon PI4KA or OSBP inhibition [23 , 24] , whereas no obvious disruption of the enterovirus ROs was observed upon PI4KB or OSBP inhibition [18 , 19 , 65] . Together , these observations indicate that EMCV and HCV replication structures share critical host components , and possibly also a similar architecture , although the latter still remains to be determined . Based on at least two lines of emerging evidence in the context of phylogeny of flavi- and picornaviruses , EMCV and HCV have likely converged on rather than retained their functional similarities upon divergence from the common ancestor ( Fig 8 ) . First , the observed commonalities between EMCV and HCV are not shared by other characterized viruses in their respective families , indicating that they are not a manifestation of the properties conserved among the two families . For instance , picornaviruses from different genera exhibit different host factor requirements . Members of the Cardiovirus genus hijack the ER-localized PI4KA ( this study ) , whereas members of the Enterovirus and Kobuvirus genera depend on the Golgi-localized PI4KB [15 , 34 , 57] . In contrast , equine rhinitis A virus ( ERAV , member of Aphthovirus genus , which is prototyped by FMDV ) and hepatitis A virus ( Hepatovirus genus ) seem to replicate independent of both PI4KB and PI4KA ( S7 Fig and [34 , 77] ) . Likewise , the flaviviruses DENV and WNV , representing a sister genus to that of HCV , do not rely on either PI4KA or PI4KB [23 , 78] . While DENV was also shown not to require OSBP [23] , for WNV this is not known yet . Importantly , cardioviruses targeting PI4KA occupy a lineage that is farther from the root compared to those of entero- and kobuviruses targeting PI4KB ( Fig 8 ) . This phylogenetic pattern is indicative of the relatively recent emergence of the EMCV-specific target properties . Second , EMCV and HCV employ apparently unrelated proteins to mediate the interaction with PI4KA , namely 3A and NS5A ( although HCV NS5B may contribute as well [24 , 46] ) . Both proteins are membrane-bound , albeit through a hydrophobic domain located at either N-terminus ( NS5A ) or in the C-terminus-half ( 3A ) , and each includes another region which is among the least conserved in the nonstructural proteins of the respective families [79 , 80] . Our study contributes to the hypothesis that viruses may be confronted with powerful constraints that limit the diversity of host pathways recruited for efficient replication . Thus , a common pathway is used by different RNA viruses that either only moderately diverged ( e . g . different species of same genus ) or converged on a host target while diverging profoundly ( different families–e . g . EMCV and HCV ) . To date , only a small number of ( + ) RNA viruses have been studied in terms of host lipid requirements . Identification of the lipid pathways used by other viruses will hopefully provide a deeper insight on the constraints that viruses are confronted with during the endeavor to replicate their genome .
Buffalo green monkey ( BGM ) kidney cells , baby hamster kidney 21 ( BHK-21 ) and HeLa R19 cells were grown at 37°C and 5% CO2 in Dulbecco’s modified Eagle’s medium ( DMEM , Lonza ) supplemented with 10% fetal bovine serum ( FBS ) . Huh7Lunet/T7 cells ( provided by R . Bartenschlager , Department of Molecular Virology , University of Heidelberg , Heidelberg , Germany ) [82] were grown in DMEM ( Lonza ) supplemented with 10% FBS and 10 μg/ml Blasticidin ( PAA ) . BGM cells were purchased from ECACC and BHK-21 cells were purchased from ATCC . HeLa R19 cells were obtained from G . Belov ( University of Maryland and Virginia-Maryland Regional College of Veterinary Medicine , US ) [83] . AL-9 and OSW-1 were kind gifts from J . Neyts ( Rega Institute for Medical Research , University of Leuven , Leuven , Belgium ) and M . D . Shair ( Department of Chemistry and Chemical Biology , Harvard University , Cambridge , USA ) respectively . 25-HC was purchased from Santa Cruz . BF738735 [84] was provided by Galapagos NV . Filipin III and dipyridamole were from Sigma . Constructs pTM-HA-PI4KA [24] , pEGFP-PI4KA ( provided by G . Hammond , NICHD , National Institutes of Health , Bethesda , USA ) [41 , 85] and p3A ( CVB3 ) -myc [86] were described previously . pGFP-PI4KB was a kind gift from N . Altan-Bonnet ( Laboratory of Host-Pathogen Dynamics , National Institutes of Health , Bethesda , USA ) . To generate C-terminal myc-tagged EMCV proteins , genes encoding EMCV nonstructural proteins 2A , 2B , 2C , 3A , 3AB , 3C and 3D were amplified by PCR using the plasmid pM16 . 1 [87] and primers introducing restriction sites BamHI and HindIII . pM16 . 1 contains the full-length infectious cDNA sequence of EMCV , strain mengovirus . The PCR products were then cloned into the p3A ( CVB3 ) -myc backbone from which the CVB3-3A gene was removed using the same restriction enzymes . To allow ectopic expression of PI4KA under a CMV promoter , the gene encoding HA-PI4KA was amplified by PCR using pTM-HA-PI4KA as template and introduced in the pEGFP-N3 backbone using restriction enzymes SalI and NotI . EMCV-2C-HA infectious clone was generated by introducing the HA coding sequence ( YPYDVPDYA ) in-frame after codon 2 in 2C of pM16 . 1 using mutagenesis primers and the Q5 Site-Directed Mutagenesis Kit ( New England Biolabs ) . EMCV , EMCV-2C-HA and RLuc-EMCV , which contains the Renilla luciferase gene upstream of the capsid coding region [54] , were obtained by transfecting BHK-21 cells with RNA transcripts derived from full length infectious clones pM16 . 1 , pM16 . 1-2C-HA and pRLuc-QG-M16 . 1 , respectively , linearized with BamHI . GFP-EMCV , which contains the EGFP gene upstream the capsid region , was generated similar as RLucEMCV [54] . CVB3 ( strain Nancy ) was obtained by transfecting BGM cells with RNA transcripts of the full length infectious clone p53CB3/T7 [86] linearized with SalI . Saffold virus ( type 3 ) was described previously [2] . ERAV ( NM11/67 ) was kindly provided by David Rowlands and Toby Tuthill ( University of Leeds , United Kingdom ) . Virus infections were performed by incubating subconfluent cell monolayers for 30 min at 37°C with virus , after which the virus-containing medium was removed and fresh ( compound-containing ) medium was added to the cells ( t = 0 ) . In the time-of-addition experiments , medium without compound was added at t = 0 and replaced by medium with compound at the indicated time points . At the given time points post infection , cells were either fixed for immunolabeling , freeze-thawed to determine virus titers or , in the case of RLuc-EMCV , lysed to determine replication by measuring the intracellular Renilla luciferase activity using the Renilla Luciferase Assay System ( Promega ) . Virus titers were determined by endpoint titration according to the method of Reed and Muench and expressed as 50% tissue culture infective doses ( TCID50 ) . HeLa R19 or Huh7Lunet/T7 cells were grown to subconfluency on coverslips in 24-well plates . Where indicated , cells were transfected with 400 ng of plasmids using Lipofectamine2000 according to the manufacturer’s protocol and/or infected with EMCV at the specified multiplicity of infection ( MOI ) , followed by compound treatment where specified . At the indicated time points , cells were fixed with 4% paraformaldehyde ( PFA ) for 20 min at room temperature ( RT ) . Permeabilization was done with PBS-0 . 5% Triton X-100 for 15 min or PBS/0 . 2% saponin/5% BSA for 5 min , in the case of filipin staining . Cells were incubated sequentially with primary and secondary antibodies diluted in PBS containing 2% normal goat serum ( NGS ) . The following primary antibodies were used for detection: mouse monoclonal anti-GM130 ( BD Biosciences ) , rabbit polyclonal anti-TGN46 ( Novus Biologicals ) , mouse monoclonal anti-ERGIC53 ( Enzo Life Sciences ) , rabbit polyclonal anti-Sec13 ( kindly provided by B . L Tang , Department of Biochemistry , The National University of Singapore , Singapore ) , rabbit polyclonal anti-calreticulin ( Sigma ) , rabbit polyclonal anti-HA ( Santa Cruz ) , mouse monoclonal anti-HA ( Abcam ) , mouse monoclonal anti-C-Myc ( Sigma ) , rabbit polyclonal anti-myc ( Thermo Scientific ) , mouse anti-PI4P IgM ( Echelon Biosciences ) , mouse monoclonal anti-dsRNA ( J2 , English & Scientific Consulting ) , mouse monoclonal anti-EMCV 3AB ( kind gift from A . G . Aminev ) [88] and rabbit polyclonal anti-OSBP ( kindly provided by M . A . De Matteis , Telethon Institute of Genetics and Medicine , Naples , Italy ) [65] . Alexa Fluor 488- , 594-conjugated IgG and Alexa Fluor 488- or 594-conjugated IgM ( Invitrogen , Molecular Probes ) were used as secondary antibodies . Cholesterol was stained with 25 μg/ml filipin III ( Sigma ) for 1 h at room temperature , included during the incubation with the secondary antibody . Nuclei were counterstained with DAPI . Staining of plasma membrane or intracellular PI4P was performed as described elsewhere [50] . Briefly , for PM staining , cells were fixed at RT in 4% PFA and 0 . 2% glutaraldehyde . All subsequent steps were performed on ice . Cells were blocked and permeabilized for 45 min in buffer A ( 20mM Pipes , pH 6 . 8 , 137 mM NaCl , 2 . 7 mM KCl ) containing 5% NGS , 50 mM NH4Cl and 0 . 5% saponin . Slides were incubated with primary and secondary antibodies in buffer A containing 5% NGS and 0 . 1% saponin for 1 h . Finally , slides were post-fixed in 2% PFA in PBS for 10 min . The intracellular PI4P staining was performed at RT as follows: cells were fixed with 2% PFA , then permeabilized for 5 min in 20 μM digitonin in buffer A , blocked for 45 min in buffer A with 5% NGS and 50 mM NH4Cl and then incubated sequentially with primary and secondary antibodies in buffer A with 5% NGS , before post fixation in 2% PFA . All coverslips were mounted with FluorSave ( Calbiochem ) . Images were acquired with a Leica SPE-II DMI-4000 confocal laser scanning microscope or a Nikon Ti Eclipse microscope equipped with an Andor DU-897 EMCCD-camera . PI4P quantification was performed for at least 40 cells for each condition , using the ImageJ software as described elsewhere [46] . To determine colocalization of Sec13 or calreticulin with 3AB , images were first deconvoluted using NIS advanced Research 4 . 3 software ( Nikon ) ( 10 iterations ) and further processed using Image J as follows . Individual infected cells were outlined and a mask was created , and all signal outside the mask was cropped to exclude it from the calculations . Manders’ colocalization coefficient was calculated for at least 10 cells for each condition using the JACoP plugin [89] with a manually set threshold . Colocalization of OSBP with 3A in infected cells was analyzed using ImageJ by determining Pearson’s coefficient for at least 15 cells per condition using the Coloc 2 plugin with default settings . To quantify colocalization of filipin with 3AB , images were first deconvoluted using NIS software ( 20 iterations ) , then ImageJ was used to select infected cells and the Pearson’s coefficient of colocalization for at least 15 cells per condition was calculated using the Coloc 2 plugin with default settings . HeLa R19 cells were reverse-transfected with 2 pmoles of siRNA per well of a 96-well plate ( 2000 cells/well ) using Lipofectamine 2000 ( Invitrogen ) according to the manufacturer’s indications . Scrambled siRNA ( AllStars Neg . Control , Qiagen ) was used as a control . SiRNA against hPI4KA ( cat . no . S102777390 ) and hPI4KB ( target sequence: 5’-UGUUGGGGCUUCCCUGCCCTT-3’ ) were from Qiagen . siRNA against hOSBP ( two siRNAs mixed at 1:1 ratio , target sequences: 5’- CGCUAAUGGAAGAAGUUUA[dT][dT]-3’ and 5’-CCUUUGAGCUGGACCGAUU[dT][dT]-3’ ) ) was from Sigma . 48 h p . t . , cells were either infected with virus , transfected with in vitro transcribed RNA derived from the full length infectious clone pM16 . 1 or harvested to evaluate the knockdown efficiency by western blot analysis . Cell viability was determined in parallel with virus infection as follows . One day after seeding cells in a 96-well plate , the compounds were added to the cells and incubated for 8 h . Alternatively , cells were transfected with siRNAs and incubated for 48 h . Subsequently , the medium was replaced with CellTiter 96 AQueous One Solution Reagent ( Promega ) and optical densities were measured at 490 nm . The obtained raw values were converted to percentage of untreated samples or samples transfected with scrambled siRNAs , following correction for background absorbance . Metabolic labeling of myc-tagged EMCV proteins and HA-PI4KA was performed as described elsewhere [46] . Briefly , Huh7-Lunet/T7 cells seeded in 6-well plates were co-transfected with 2 μg of plasmid encoding EMCV nonstructural proteins and 2 μg of either pTM HA-PI4KIIIa or an empty pTM vector ( mock ) using Lipofectamine2000 ( Invitrogen ) according to the manufacturer’s instructions . 7 h later , cells were starved in methionine/cysteine-free medium for 1 h . Radiolabeling of cells was done by overnight incubation in methionine/cysteine-free medium , supplemented with 10 mM glutamine , 10 mM Hepes , and 100 μCi/ml of Express Protein labeling mix ( Perkin Elmer , Boston ) . Cells were then harvested and lysed in lysis buffer ( 50 mM Tris-Cl [pH 7 . 5] , 150 mM NaCl , 1% Nonidet P-40 and protease inhibitors ) for 1 h on ice , followed by centrifugation at 14 , 000 g for 10 min at 4°C . Supernatants were further subjected to immunoprecipitation by a 3 h incubation at 4°C with anti-c-myc rabbit polyclonal antibody ( Santa Cruz ) . Immunocomplexes were then captured with protein G-sepharose beads ( Sigma ) by an additional 3 h incubation at 4°C . Beads were washed three times in lysis buffer , followed by elution of immunocomplexes by boiling in sample buffer , separation by polyacrylamide-SDS gel electrophoresis and detection by autoradiography . For co-IP followed by western blot , cells were seeded in 55 cm2 dishes and transfected with 3 . 5 μg of each plasmid using polyethylenimine ( PEI ) ( Polysciences ) . Immunoprecipitation was carried out as described above , but using protein A-sepharose beads ( GE Healthcare ) and mouse monoclonal anti-C-Myc ( Sigma ) or rabbit polyclonal anti-myc ( Thermo Scientific ) antibodies . Samples separated by SDS-PAGE were transferred to nitrocellulose membranes ( Bio-Rad ) . Membranes were incubated with the following primary antibodies: rabbit polyclonal anti-PI4KA ( Cell Signaling ) , rabbit polyclonal anti-PI4KB ( Upstate ) , rabbit polyclonal anti-OSBP ( ProteinTech ) , rabbit polyclonal anti-EMCV capsid ( kind gift from Ann Palmenberg ) and mouse monoclonal anti-β-actin ( Sigma ) . Secondary antibodies included IRDye 680-conjugated goat anti-mouse or IRDye 800-conjugated goat anti-rabbit ( LI-COR ) . Images of blots were acquired with an Odyssey Fc Imaging System ( LI-COR ) . Where indicated , unpaired one-tailed Student’s t-test or two-tailed Mann–Whitney test were applied as statistical analyses using the GraphPad Prism software . | All positive-sense RNA viruses [ ( + ) RNA viruses] replicate their viral genomes in tight association with reorganized membranous structures . Viruses generate these unique structures , often termed “replication organelles” ( ROs ) , by efficiently manipulating the host lipid metabolism . While the molecular mechanisms underlying RO formation by enteroviruses ( e . g . poliovirus ) of the family Picornaviridae have been extensively investigated , little is known about other members belonging to this large family . This study provides the first detailed insight into the RO biogenesis of encephalomyocarditis virus ( EMCV ) , a picornavirus from the genus Cardiovirus . We reveal that EMCV hijacks the lipid kinase phosphatidylinositol-4 kinase IIIα ( PI4KA ) to generate viral ROs enriched in phosphatidylinositol 4-phosphate ( PI4P ) . In EMCV-infected cells , PI4P lipids play an essential role in virus replication by recruiting another cellular protein , oxysterol-binding protein ( OSBP ) , to the ROs . OSBP further impacts the lipid composition of the RO membranes , by mediating the exchange of PI4P with cholesterol . This membrane-modification mechanism of EMCV is remarkably similar to that of the distantly related flavivirus hepatitis C virus ( HCV ) , while distinct from that of the closely related enteroviruses , which recruit OSBP via another PI4K , namely PI4K IIIβ ( PI4KB ) . Thus , EMCV and HCV represent a striking case of functional convergence in ( + ) RNA virus evolution . | [
"Abstract",
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"Methods"
] | [] | 2015 | Modulation of the Host Lipid Landscape to Promote RNA Virus Replication: The Picornavirus Encephalomyocarditis Virus Converges on the Pathway Used by Hepatitis C Virus |
Prion infections cause inexorable , progressive neurological dysfunction and neurodegeneration . Expression of the cellular prion protein PrPC is required for toxicity , suggesting the existence of deleterious PrPC-dependent signaling cascades . Because group-I metabotropic glutamate receptors ( mGluR1 and mGluR5 ) can form complexes with the cellular prion protein ( PrPC ) , we investigated the impact of mGluR1 and mGluR5 inhibition on prion toxicity ex vivo and in vivo . We found that pharmacological inhibition of mGluR1 and mGluR5 antagonized dose-dependently the neurotoxicity triggered by prion infection and by prion-mimetic anti-PrPC antibodies in organotypic brain slices . Prion-mimetic antibodies increased mGluR5 clustering around dendritic spines , mimicking the toxicity of Aβ oligomers . Oral treatment with the mGluR5 inhibitor , MPEP , delayed the onset of motor deficits and moderately prolonged survival of prion-infected mice . Although group-I mGluR inhibition was not curative , these results suggest that it may alleviate the neurological dysfunctions induced by prion diseases .
The decisive event in the pathogenesis of prion diseases is the conversion of the normal cellular prion protein ( PrPC ) into an aggregated conformational variant called PrPSc [1] . Expression of PrPC at the cell surface is not only required for the self-propagation of prions , but also for mediating the toxicity induced by PrPSc [2] , a process that results in endoplasmic reticulum ( ER ) stress and ultimately in impaired protein translation [3] . But how can PrPC , an extracellular GPI-linked protein , initiate intracellular central nervous system ( CNS ) toxicity ? Most likely this process requires mediation by transmembrane constituents . Indeed PrPC has been shown to interact with transmembrane signal-transducing proteins [4] and disturbing these interactions might lead to the neurotoxicity seen in prion diseases [5] . Among the proteins interacting with PrPC are glutamate receptors [6] . N-methyl-D-aspartate receptors ( NMDAR ) are crucial regulators of glutamatergic transmission , and loss of both synapses and neurons has been attributed to inappropriate NMDAR activation [7 , 8] . Metabotropic glutamate receptors ( mGluRs ) may also play a role in prion diseases . Changes in mGluR1 , leading to reduced expression levels of phospholipases , were observed in the cerebral cortex of Creutzfeldt-Jakob disease ( CJD ) patients [9] . Also , impairment of the mGluR1/1-phosphatidylinositol 4 , 5-bisphosphate phosphodiesterase 1 ( PLC1 ) /protein kinase C ( PKC ) signaling pathway has been observed in a murine model of BSE . Abnormal mGluR1 signaling correlated with PrPSc deposition , histological changes , and clinical scores [10] . A role for group-I mGluRs is emerging in a multitude of CNS disorders including Fragile X syndrome , ischemia , multiple sclerosis , amyotrophic lateral sclerosis , Huntington’s , and Parkinson’s disease [11–18] . In Alzheimer’s disease ( AD ) , PrPC and mGluR5 may directly contribute to disease manifestation and toxicity of amyloid-β ( Aβ ) aggregates . Aβ oligomers can bind to PrPC at the cell surface [19] and form complexes that contain mGluR5 [20] . In a mouse model of Aβ deposition , cognitive decline and synaptic alterations were rescued by mGluR5 inhibition [21] . Furthermore , PrPC-mGluR5 coupling is involved in Aβ-mediated inhibition of LTP and Aβ-facilitated LTD in vivo [22] , and genetic ablation of mGluR5 reverses disease-related memory deficits in a murine model of AD ( APPswe/PS1ΔE9 ) [23] . In another study , exposure of cortical APPswe/PS1ΔE9 neuronal cultures to Aβ oligomers upregulated mGluR1 and PrPC α-cleavage , whereas activation of group-I mGluRs increased PrPC shedding from the membrane [24] . In primary hippocampal neurons , membrane-bound Aβ oligomers induce toxicity by promoting clustering of mGluR5 in synapses , resulting in elevated intracellular calcium and synaptic failure [25] . All these studies suggest an involvement of group-I mGluRs in the pathogenesis of AD . On the other hand , others have reported that neither PrPC ablation nor overexpression had any effect on neurotoxicity in AD models [26–29] . As a possible explanation for these discrepancies , it has been suggested that only a limited oligomeric fraction of Aβ [30] interacts with mGluR5 [31] . Here we focused on the role of group-I mGluR-PrPC interaction in prion disease . We found that toxic prion-mimetic compounds increased mGluR5 clustering and accumulation at dendritic heads , close to the synaptic source of glutamate . Moreover , pharmacological inhibition of mGluR1 and mGluR5 , as well as genetic ablation of the Grm5 gene encoding mGluR5 , protected organotypic slice cultures against the toxicity of prions and of prion-mimetic compounds . Finally , pharmacological inhibition of mGluR5 improved the neurological status and , to some extent , the survival of prion-infected mice .
Cerebellar and hippocampal organotypic cultured slices ( COCS and HOCS , respectively ) [32 , 33] prepared from PrPC overexpressing tga20 mice [34] can be infected with the Rocky Mountain Laboratory ( RML ) strain of prions and undergo neurodegeneration after ca . 5 weeks [32] . The time course and extent of neurodegeneration can be measured by morphometric assessment of the area of the cerebellar granule cell layer ( CGL ) immunoreactive to antibodies against the neuronal NeuN antigen . We inoculated COCS and HOCS with brain homogenate from CD1 mice that had been infected with RML prions ( passage #6 , henceforth called RML6 ) . For control , slices were inoculated with non-infectious brain homogenate ( NBH ) derived from healthy CD1 mice . Starting at 21 days post infection , slices were treated with a range of concentrations of either N-cyclohexyl-6-N-methylthiazolo[3 , 2-a]benzimidazole-2-carboxamide ( YM202074 ) [35] , 2-methyl-6- ( phenylethynyl ) -pyridine ( MPEP ) [36] or Mavoglurant ( AFQ056 ) [37] which specifically inhibit mGluR1 and mGluR5 , respectively . MPEP , AFQ056 and YM202074 prevented CGL loss in COCS at concentrations as low as 10 nM ( Fig 1A and 1B ) and 36 nM ( Fig 1C , 1D , 1G and 1H ) , respectively . The protective effect of YM202074 and MPEP was further confirmed in wild-type slices ( S1A and S1B Fig ) . Extremely high MPEP concentrations ( 3–10 μM ) were not intrinsically toxic ( S1C Fig ) as previously reported [36] , but failed to protect against prion toxicity in tga20 mice ( S1C and S1D Fig ) . Also in HOCS , prepared from 4–6 days old tga20 mice , MPEP significantly suppressed neuronal loss after prion infection at concentrations as low as 36 nM ( Fig 1E and 1F ) . The beneficial effects of mGluR5 inhibition ex vivo encouraged us to assess whether MPEP can potentially rescue prion pathogenesis in vivo . C57BL/6J male mice were inoculated intracerebrally with 3 or 5 log LD50 units of RML6 prions as described [38] and chronically treated with MPEP . Control mice were inoculated with NBH . In order to record the neurological deficits associated with prion disease , we utilized the rotarod behavioral test which measures a combination of motor performance , coordination and balance [39] . Rotarod performance was similar in RML6- and NBH-inoculated mice until 18 weeks following prion inoculation . Starting from 19 weeks post inoculation , mice receiving control food showed a progressive decline in rotarod performance . The performance of MPEP-treated mice declined , but less rapidly . This improvement was lasting and detectable until the very late stages of the disease ( 22–23 weeks post inoculation; Fig 2A and 2B ) , suggesting that the progression of the disease was delayed by MPEP . At very late time points , the general health status of all mice deteriorated to an extent that made it impossible to accurately measure their rotarod performance and eventually required euthanasia . Nevertheless , MPEP-treated mice showed a modest , though significant , prolongation of survival ( Fig 2C and 2D ) . The median survival for untreated vs MPEP-treated RML6-inoculated C57BL/6J mice was , respectively , 183 vs 190 days post inoculation ( dpi ) after injection with 3 log LD50 units of prions and 188 vs 195 dpi after inoculation with 5 log LD50 units ( P = 0 . 0008 and 0 . 0231 respectively; log-rank test ) . Control mice injected with NBH and treated with MPEP exhibited stable rotarod performance during the entire test period , up to 23 weeks post-injection ( S2A Fig ) . No significant changes in average food and water consumption were observed between control and treatment groups during the experiment ( S2B Fig ) . To determine the exposure of the brain to MPEP , mice treated with control and MPEP food were sacrificed at two time points , corresponding to the active and the inactive phase of the mice across the circadian circle . The average brain-to-blood ratio for the MPEP concentration was around 1 , indicating good brain penetration of MPEP ( S2C Fig , S1 Table ) . Antibody-derived molecules targeting the globular domain ( GD ) of PrPC ( termed GDLs ) are acutely neurotoxic [40 , 41] and activate similar cascades as bona fide prion infection [42] . Single chain POM1 miniantibodies ( scPOM1 ) , fusion proteins containing only the variable regions of the heavy ( VH ) and light chains ( VL ) of the antibody connected with a short linker peptide , were previously shown to be sufficient to induce toxicity in COCS [41] . To investigate if pharmacological inhibition of mGluR1 and mGluR5 rescues GDL toxicity , we exposed tga20 COCS to the GDL agent scPOM1 , followed by YM202074 , MPEP and AFQ056 treatments . Treatment with scPOM1 led to almost complete CGL loss within 8 days of treatment . No CGL loss occurred in control treatment where scPOM1 was blocked by pre-incubation with a molar excess of recombinant PrP ( recPrP ) . Treatment with MPEP significantly reduced CGL loss in scPOM1-treated slices . As with prion infections , MPEP treatment ( at concentrations as low as 10 nM ) was sufficient to rescue the loss of CGL , whereas high concentrations ( ≥1μM ) did not show protective activity ( Fig 3A and 3B ) . Even lower MPEP concentrations ( 3nM ) were sufficient to rescue scPOM1-induced toxicity in COCS ( S3E and S3F Fig ) . AFQ056 and YM202074 treatment ( at concentrations as low as 36nM ) also significantly reduced the toxicity of scPOM1 ( Fig 3C , 3D , 3G and 3H ) in COCS . The protective effect of mGluR1 and mGluR5 inhibitors ( YM202074 and MPEP respectively ) was further confirmed in wild-type slices . No additional effect was observed upon double MPEP/YM202074 inhibition ( S3A and S3B Fig ) . Similarly to COCS , HOCS treated with scPOM1 exhibited conspicuous toxicity after 8 days of treatment . Neuronal loss was monitored by morphometric analysis of NeuN immunofluorescence , and was readily visible in GDL-treated samples , whereas the survival of hippocampal neurons exposed to scPOM1 ( Fig 3E and 3F ) was greatly increased by treatment with MPEP . In contrast , no protection was observed upon treatment with the selective group III agonist L-2-amino-4-phosphonobutyrate ( L-AP4 ) [43] and the potent group II/III antagonist ( RS ) -α-Cyclopropyl-4-phosphonophenylglycine ( CPPG ) [44] of metabotropic glutamate receptors ( S3C and S3D Fig ) . Hence toxicity of both infectious prions and prion-mimetic GDLs was prevented by pharmacological inhibition of mGluR1 or mGluR5 . Cerebellar organotypic slice cultures from Grm5-/- , Grm5+/- and Grm5+/+ littermates were treated with the anti-GD single-chain miniantibody scPOM1 [45] , which acts as a prion-mimetic compound . Exposure to scPOM1 led to the loss of cerebellar granular layer ( CGL ) neurons in Grm5+/+ slices , but neither in Grm5-/- nor in Grm5+/- slices ( Fig 4A and 4B ) . We then inoculated cerebellar and hippocampal organotypic slice cultures from Grm5-/- , Grm5+/- and Grm5+/+ littermates with RML6 prions or control NBH homogenate . In COCS , both Grm5-/-and Grm5+/- slices are protected against RML6 toxicity ( Fig 4C and 4D ) . In HOCS , genetic ablation of mGluR5 was protective against prion-induced toxicity ( Fig 4E and 4F ) . To assess the role of mGluR5 in prion infections in vivo , we infected Grm5-/- , Grm5+/- and Grm5+/+ littermates with RML6 prions ( 5 log LD50 ) . In line with a recently published study [46] , no significant difference in survival was observed between Grm5-/- , Grm5+/- and Grm5+/+ mice ( S4A Fig ) . The latter finding was unexpected and prompted us to investigate the possibility of compensatory mechanisms . Both group-I metabotropic glutamate receptors , mGluR1 and mGluR5 , can associate with PrPC and induce similar intracellular pathways [47] suggesting functional redundancy between these two receptors . In order to detect a possible epistasis between mGluR1 and mGluR5 , we assessed mGluR1 and mGluR5 protein levels in cerebellum , cortex and hippocampus of Grm5-/- , Grm5+/- and Grm5+/+ mice ( S4C and S4D Fig ) . At 10 days of age , mGluR5 expression was similar in cerebellum , hippocampus and cortex as described [48] , whereas mGluR1 was highest in the cerebellum ( S4C Fig ) . Interestingly , we observed an increased expression of mGluR1 in all the three tested regions of Grm5-/- brains . We further assessed mGluR1 and mGluR5 levels at later time points ( 45–180 days ) . Expression of mGluR5 decreased in all brain regions with increasing age , whereas expression of mGluR1 remained stable . However , we detected increased mGluR1 expression in Grm5-/- brains . In the cortex , we observed increased expression of mGluR1 in samples from 45-day old Grm5-/- mice compared to Grm5+/+ littermates ( S4D Fig , middle right panel ) . In the hippocampus , we observed increased expression of mGluR1 in samples from 90-day old Grm5-/- mice ( S4D Fig , bottom right panel ) and in samples from both Grm5+/- and Grm5-/- 180-day old mice ( S4D Fig , lower right panel , lanes 7 , 8 & 9 and quantification ) . In the cerebellum , we observed increased expression of mGluR1 in samples from 90-day old Grm5-/- mice compared to wild-type control littermates ( S4D Fig , upper right panel ) . We then tested whether treatment with MPEP also enhances the expression of mGluR1 . mGluR1 expression levels were assessed in whole-brain lysates from 1-year old control wild-type mice , NBH-inoculated wild-type mice , and NBH-inoculated wild-type mice that received MPEP food . However , no differences were observed in the mGluR1 expression levels between the samples ( S4B Fig ) , suggesting that compensatory Grm1 upregulation is developmentally controlled . PrPC interacts with mGluR1 and mGluR5 [21 , 47] . We confirmed these results by immunoprecipitating brain homogenates from wild-type ( C57BL/6J ) or Prnp knockout mice ( Prnpo/o ) using antibody POM1 against PrPC , followed by Western blotting with antibodies to mGluR1 and mGluR5 . The group-I mGluRs , which migrate as SDS-resistant oligomers at 250kDa [49] , were found to co-precipitate with PrPC ( Fig 5A ) . When we blocked the antigen-recognition domain of POM1 with recombinant PrP , mGluR1 and mGluR5 no longer co-precipitated with PrPC ( Fig 5A ) . Western blots of brain lysates ( total extracts; TEs ) did not reveal any changes in the concentration of mGluR1 and mGluR5 protein between wild-type tga20 and Prnpo/o homogenates ( Figs 5A and S5A ) . In contrast , mGluR6 and mGluR2/3 did not co-precipitate , confirming the specificity of the interaction ( S5B Fig ) . The residues 91–153 of PrPC participate to the interaction with mGluR5 [20] . To confirm these findings and to identify the domain of PrPC mediating its interaction with mGluR5 , we studied a panel of transgenic mice expressing variants of PrPC bearing deletions in the flexible tail ( FT ) regions , designated ΔC , ΔCC , ΔF , ΔOR , and ΔHC [50–54] ( S5E Fig ) . In each line of mice , we immunoprecipitated PrPC from brain using POM1 antibody ( specific information and binding sites on PrPC are provided in S5F Fig and Table 1 ) and measured the co-precipitation of mGluR5 . Most FT-mutated PrPC variants showed an impaired capacity to co-precipitate mGluR5 , with deletions of residues 51–90 and 32–134 showing the most striking reduction ( S5C Fig ) . Conversely , when we performed immunoprecipitations of mGluR5 followed by Western blotting for PrPC , we found that deletions spanning residues 111–134 affected the interaction most profoundly ( Fig 5B ) . We also analyzed the capacity of PrPC mutants to immunoprecipitate mGluR1 . While all examined FT mutations decreased the interaction of PrPC with mGluR1 , deletions affecting residues 51–90 showed the most significant reduction ( S5D Fig ) . Immunoprecipitation of mGluR1 revealed that PrPC deletions spanning residues 51–90 and 111–134 had the strongest effect on its interaction with mGluR1 ( Fig 5C ) . Finally , we observed that deletion of mGluR5 had no effect on co-precipitation of PrPC with mGluR1 ( Fig 5C ) , indicating that mGluR1 and mGluR5 interact with PrPC independently of each other . These results suggest that the interaction domain between PrPC and mGluR5 resides at the N-terminal region of PrPC and is larger than previously inferred , with residues 32–114 participating to the in vivo interaction . The interaction domain between PrPC and mGluR1 also resides at the N-terminal region of PrPC and spans residues 51–90 and 111–134 . PrPSc deposition is accompanied by neurodegeneration , vacuole formation and activation of microglia and astrocytes [55] . MPEP treatment did not affect the accumulation of PrPSc in prion-infected mice and slices ( S6A–S6C Fig ) , yet it reduced vacuole formation . Although the numbers of vacuoles in control and MPEP treated groups were similar , vacuoles were smaller in cerebella of MPEP-treated mice ( Fig 6A and 6B ) . Astrogliosis , assessed by immunohistochemistry for glial fibrillary acidic protein ( GFAP ) , was prominent in terminally sick prion-infected mice but not in NBH-inoculated mice . MPEP treatment reduced the astrogliosis in the hippocampus of prion-infected mice ( Fig 6C and 6D ) , but not in the cerebellar granule cell layer ( S6D Fig ) , as expected from the decreased expression of mGluR5 in the cerebellum of older mice . These findings corroborate the interpretation that MPEP reduces prion toxicity even if it does not affect prion load . Clusters of mGluR5 accumulate around excitatory synapses , but are also found at extra-synaptic sites ( S7A Fig ) . Increased size of synaptic mGluR5s clusters is associated with toxic calcium influx [21 , 25 , 56] . Therefore , we asked whether the prion-mimetic POM1 antibody altered the clustering of mGluR5s . POM2 and POM3 antibodies were also used in parallel ( for details about POM antibodies and their epitopes , see Table 1 ) . Specific information and binding sites on PrPC for all antibodies are provided in ( S5F Fig , Table 1 ) and materials and methods . Exposure of live neurons to POM1 , significantly increased the size of mGluR5s clusters compared to POM2 or POM3 exposure ( Fig 7A and 7B ) , however no change was observed with the NMDA and AMPA receptor clusters ( S7B–S7E Fig ) , suggesting formation of abnormal , potentially deleterious mGluR5 signaling platforms [57] . Next , we examined the fluorescence of dendritic spines of neurons expressing an mGluR5-pHluorin fusion protein . Spines in mGluR5-pHluorin transfected neurons indeed co-localize with post-synaptic marker Homer , which is also a scaffolding protein for mGluR5 ( S7F Fig ) . We observed increased accumulation of mGluR5s in dendritic spines following exposure to POM1 , but not to POM2 or POM3 ( Fig 7C and 7D ) . Both mGluR5 and PrPC are enriched in postsynaptic densities [21] . In order to assess if the changes in mGluR5s level in spines correlated with PrPC level in spines , we performed photo-activated localization microscopy ( PALM ) on neurons expressing a PrPC tagged with dendra2 fusion protein [58] ( Fig 7E ) . PALM images were obtained from single-molecule detection with a pointing accuracy of 20 nm [58] . The PrPC-Dendra fluorescence patterns showed both clustered and diffused staining ( Fig 7E , control ) ; we observed an increased enrichment within dendritic spines following POM1 but not POM1+2 exposure ( Fig 7E and 7F ) . Furthermore , exposure to Fab1-POM2 , which was previously found to protect against POM1 toxicity [41] , induced a small but significant reduction in PrPC enrichment within dendritic spines . Therefore , Fab1-POM1 and Fab1-POM2 may exert opposite effects on the topology and size of mGluR5 clusters , with POM1 inducing abnormal accumulation and translocation to dendritic spines .
Prion toxicity is ultimately mediated by unfolded-protein responses [3 , 59] , yet it is unclear how these are triggered by PrPSc which is primarily extracellular . The group-I metabotropic glutamate receptors mGluR5 and mGluR1 , G protein-coupled receptors that interact with PrPC [19 , 21 , 25] , may represent one such link . We found that mGluR5 and mGluR1 inhibitors prevented neurodegeneration in prion-infected organotypic slice cultures and protected against prion-mimetic globular-domain ligands [41] . Inhibition of group-I mGluRs may reduce glutamatergic signaling and calcium overload in prion-infected cells [60] , similarly to models of Alzheimer’s disease [21 , 25] . PrPC associates with group-I mGluRs [47] and modulates the signaling activity of mGluR5 [20] . If prion toxicity depends on the direct interaction of PrPC to group-I mGluRs , it may modify the subcellular distribution of mGluR5 . Indeed , prion-mimetic antibodies selectively increased clustering of mGluR5 ( but not of AMPA and NMDA receptors ) in dendritic spine heads , potentially sensitizing them to synaptic glutamate . Prion-mimetic antibodies also increased the level of PrPC in spines , reinforcing the notion that mGluR5 and PrPC are part of the same complex whose accumulation at excitatory synapses instigates neurotoxicity in prion diseases . The impact of POM1 on mGluR5 enrichment within dendritic spines is modest , possibly because only a small fraction of mGluR5 is associated with PrPC . Increased cell surface clustering may also slow down endocytosis , thereby increasing the amount of functional mGluR5s [21 , 23 , 61] . Thus , mGluR5 clustering at synapses may amplify responses to glutamate , thereby exaggerating Ca2+ influx and leading to spine loss , a primary event in prion diseases [62] . The POM2 antibody [45] against the Flexible Tail ( FT ) of PrPC is neuroprotective in vivo and in vitro . Since both POM2 and mGluR5 bind to the N-terminus of PrPC , binding of mGluR5 to PrPC may facilitate its activation whereas POM2 may compete for PrPC binding ( Fig 8 ) . Although mGluR5 inhibition delayed neurological deterioration , survival was only modestly ( though significantly ) improved . These findings support the concept that mGluR5 inhibition alleviates the symptoms of the disease whereas prion replication progresses unabated . Eventually , the prion load may exert neurotoxicity through mGluR5-independent mechanisms including mGluR1 activation . Not all neurons express mGluR5 [63 , 64]; neurons essential for survival may be mGluR5-negative and possibly mGluR1-positive . Upregulation of mGluR5 can go along with glial activation [56 , 65 , 66] . We observed reduced GFAP immunoreactivity in hippocampi of MPEP-treated animals ( Fig 6C ) . Conversely , MPEP was unable to suppress glial activation in adult cerebella ( S6D Fig ) where mGluR5 expression is low , suggesting that dampened neuroinflammation was beneficial . Genetic ablation of Grm5 was protective against the toxicity of prion-mimetic antibodies and prion infections in organotypic slices . This effect was haploinsufficient , as hemizygous Grm5+/- slices were also protected . Surprisingly , a previous report [46] and this study show that Grm5 ablation does not ameliorate the clinical manifestation of scrapie in vivo . This discrepancy is most likely due to the conspicuous mGluR1 upregulation in Grm5-/- and Grm5+/- mice . Co-immunoprecipitations from transgenic mice expressing PrPC with amino-proximal deletions [50–54] showed that both mGluR1 and mGluR5 independently interact with the N-proximal flexible tail of PrPC . However , the boundaries of the interacting domain differ , with PrPC residues 32–134 ( with residues 51–90 ( ΔOR ) and 111–134 ( ΔHC ) acting as important interaction sub-regions ) mediating the interaction with mGluR5 . The interaction domain appears to extend over the previously reported borders [31] . The interaction domain between PrPC and mGluR1 also resides at the N-terminal region of PrPC and spans residues 51–90 ( ΔOR region ) and 111–134 ( ΔHC region ) . Although both Grm5 genetic deletion and mGluR5 pharmacological inhibition ( MPEP ) did not prevent prion disease , MPEP significantly improved locomotor abilities until the later stage of disease , decreased the size of spongiform vacuoles , and reduced the extent of hippocampal astrogliosis . These observations are aligned with reports of abnormal expression of group-I mGluRs and mGluR1 signaling in Creutzfeldt-Jakob disease and bovine spongiform encephalopathy [10 , 67] . Additional mGluRs may also play a role , and a genome-wide association study identified an mGluR8 variant as a marker for sCJD risk outside the PRNP locus [68] . The above data suggest that group-I mGluRs inhibition may attenuate dysfunctions associated with prion diseases , for which there are no disease-modifying therapies . It is unsurprising that mGluR5 antagonists have only a moderate effect on survival , since this therapeutic modality is likely to affect downstream consequences of prion toxicity rather than quenching prion propagation . Because of their orthogonal mode of action , these antagonists may represent ideal compounds for combination therapy with compounds inhibiting prion replication . Because they are well-tolerated and have high bioavailability and blood-brain-barrier penetration [15 , 69 , 70] , mGluR5 antagonists may be useful for enhancing the quality of life of prion patients—a legitimate and important aim even if the overall life expectancy may not be dramatically improved .
The purpose of this study was to evaluate the therapeutic potential of group I metabotropic glutamate receptor ( mGluR1 , mGluR5 ) inhibition in ex vivo and in vivo models of prion disease . We selected highly specific and well-studied pharmacological inhibitors of mGluR1 and mGluR5 , YM202074 and MPEP and AGQ056 respectively , with known specificity and efficiency . To ensure availability of the inhibitors to the brain of prion-infected mice thorough pharmacokinetic and pharmacodynamic analyses were performed . We further extended our study to transgenic mice , knock out for the glutamate receptors being studied . For slice experiments , treatments were randomly assigned to individual wells . For mouse experiments , treatments were randomly assigned to age- and sex-matched mice; experimenters were blinded to experimental group while performing the animal experiments . For experiments with transgenic mice , similar number of heterozygotes and wild-type littermates were included as controls . Mice were sacrificed at the terminal stage of the disease . For analysis , random numbers were assigned to each subject or experimental group . All animal procedures were approved by the local Ethical Committee ( Animal Experimentation Committee of the Canton of Zurich , permit 200/2007; 41/2012; 90/2013 ) in accordance with the Swiss federal , Ethical Principles and Guidelines for Experimenting on animals ( 3rd edition , 2005 ) . All efforts were made to minimize the suffering and the number of animals used . C57BL/6J wild-type mice were purchased from Jackson laboratories . Male mice were selected because they do not have estrous cycles that can complicate pharmacology . Prnpo/o and Prnpo/o;tga20+/+ ( tga20 ) , were on a mixed 129Sv/BL6 background [71 , 72] . Transgenic mice expressing mutated PrPC were utilized for immunoprecipitation experiments . The production and relevance to disease phenotype of the Tg mice expressing N-terminal deletion mutants of PrPC ( termed ΔC , ΔCC , ΔF , ΔOR , and ΔHC ) have been previously reported [50–54] . Grm5+/- embryos [73 , 74] were acquired from Dr . Gasparini and were revitalized at the transgenics facility of the University Hospital of Zurich . Grm5 null mice were derived from breeding of these mice . 2-Methyl-6- ( phenylethynyl ) -pyridine ( MPEP ) [36] chronic treatment was initiated at the time of prion inoculation . A dose of 30 mg of MPEP/kg of body weight was selected [75] . The drug was incorporated into chow to achieve voluntary consumption and constant drug administration . Control , untreated groups received the same type of food lacking the drug . For this study , mice between 2 and 4 months of age at the time of prion inoculation/beginning of MPEP treatment were utilized . To determine PK values in mice fed with food pellets containing MPEP ( 250mg/kg; Provimi Kliba SA , Rinaustrasse 380 , CH-4303 Kaiseraugst ) , 10 C57BL/6J mice were fed MPEP-food pellets for 15 days and sacrificed to measure the blood/brain ratio of MPEP . Based on an average intake of 3 gram food pellets per day and a body weight of approximately 25 g , a dose of 30mg/kg/day was established . The MPEP concentration was determined by liquid chromatography separation followed by mass spectrometry ( LC-MS ) . Control mice ( a total of 8 C57BL/6J mice ) received normal food . Mice were sacrificed at two different time points , corresponding to the active and the inactive phase of the mice across the circadian circle and exposures of MPEP in blood and brain were measured . Organotypic cerebellar cultured slices , 350 μm thick , were prepared from 9–12 day-old pups according to a previously published protocol [32] . Organotypic hippocampal cultured slices , 350 μm thick , were prepared from 4–6 day-old pups according to a previously published protocol [33] . Cultures were kept in a standard cell incubator ( 37°C , 5% CO2 , 95% humidity ) and the culture medium was changed three times per week . Inoculations were performed with either infectious brain lysate ( RML6 ) or non-infectious brain homogenate ( NBH ) . Slices were inoculated ( as free-floating sections for 1 h at 4°C ) with 100μg brain homogenate per 10 slices . After washing in GBSSK , they were cultured on a 6-well Millicell-CM Biopore PTFE membrane insert ( Millipore ) according to previously published protocol [60] . Drug-treated tga20 slices were maintained until 45 dpi , fixed and analyzed by NeuN morphometry ( analySIS vc5 . 0 software ) . Neurotoxicity was defined as significant NeuN+ neuronal layer loss over NBH treatment . Slices prepared from GRM5-/- , GRM5+/- and GRM5+/+ littermates were maintained until 60 dpi , fixed and analyzed by NeuN morphometry ( analySIS vc5 . 0 software ) . Neurotoxicity was defined as significant NeuN+ neuronal layer loss over NBH treatment . For globular domain ligand ( GDL ) treatment , toxicity in slices was induced by exposure to ligands , toxic anti- PrPC antibodies targeting the globular domain , such as single chain scPOM1 mini-antibody , after a 14-day recovery period; allowing the initial gliosis induced by tissue preparation to subside , according to previously published protocol [41] . tga20 COCS were exposed to scPOM1 ( 200 nM , 8 dpe ) , or to control treatment ( 200 nM scPOM1/210nM recPrP , 8 dpe ) , immunostained for the neuronal marker NeuN and counterstained with DAPI . Slices were imaged and analysed as previously described . Antibody treatment was randomly assigned to individual wells . Treatment with the specific inhibitors 2-Methyl-6- ( phenylethynyl ) -pyridine ( MPEP ) [36] , AFQ056 ( Mavoglurant ) [37] or N-cyclohexyl-6-N-methylthiazolo[3 , 2-a]benzimidazole-2-carboxamide ( YM202074 ) [35] was initiated at the time of GDL addition ( 14dpe ) for the GDL toxicity model ( treated slices were maintained until 28 dpe for POM1 treatment and until 22dpe for scPOM1 treatment ) [41] and at 21 days post-inoculation ( dpi ) for prion-infected slices , when PrPSc accumulation was already discernible [32] . Drug treatments were re-added at every media change [36] . Post-treatment slices were fixed in 4% paraformaldehyde ( PFA ) , immunostained for the neuronal marker NeuN and counterstained with DAPI . Slices were imaged at 4x magnification on a fluorescence microscope ( BX-61 , Olympus ) analyzed by NeuN morphometry ( analySIS vc5 . 0 software ) . Neuroprotection was defined as significant neuronal layer rescue over toxic-antibody treated , non-drug treated slices . Inoculum of the RML6 strain of mouse-adapted scrapie prion was prepared from pooled 10% w/v brain homogenates of RML6 terminally sick CD1 mice . C57BL/6J mice were inoculated with serial dilutions ( 10−3 and 10−5 ) of the RML6 inoculum . C57BL/6J mice were injected intracerebrally ( i . c . ) with 30μl of brain homogenate prepared in a solution of PBS/5% BSA , containing 3log LD50 units or 5log LD50 units of the RML6 strain . Control mice received 30μl of NBH derived from healthy CD1 mice . Scrapie was diagnosed according to clinical criteria ( ataxia , kyphosis , priapism , and hind leg paresis ) . Mice were sacrificed on the day of onset of terminal clinical signs of scrapie . The operator was blinded to drug treatment . The rotarod test was used to assess motor coordination and endurance at defined timepoints after prion inoculations . A rotarod machine ( Ugo Basile ) with five cylinders ( 3cm diameter ) separated by dividers ( 25cm diameter ) in five lanes , each 57mm wide , was utilized . Before the training sessions , the mice were habituated to stay on the rotating rod ( 4 rpm lowest speed ) for 3 sessions lasting 1–2 minutes each and separated by 10 minute intervals . The test phase started 30 minutes after the last habituation session and consisted of 3 trials separated by 15 minute inter-trial intervals . For each test session the mouse was placed on a rotating rod , which accelerated from 5 to 40 rpm . Each test session lasted a maximum of 5min . Latency to fall was assessed when the mouse was no longer capable of riding on the accelerating rod and slipped from the drum . Test sessions were always performed at the same time of the day , mice were tested in a randomized manner and the operator was blind to drug treatment . Adult Prnpo/o , tga20+/+ ( tga20 ) , and C57BL/6J mice were euthanized and their brains were dissected . Brain samples were snap frozen in liquid nitrogen . Samples were subsequently homogenized in ice cold Lysis Buffer ( 1% Igepal ( NP-40 ) in 1x PBS , pH 7 . 4 ) supplemented with protease ( EDTA-free ) and phosphatase inhibitor cocktail mix ( Roche ) . Protein concentration was determined using the bicinchoninic acid assay ( Pierce ) . Following immunoprecipitation of PrPC with a specific anti-PrP monoclonal antibody ( POM1 or POM2 ) and addition of Dynabeads M-280 Sheep anti-mouse ( #311201D , Thermo Fischer Scientific ) , samples were prepared in loading buffer ( NuPAGE , Invitrogen ) and incubated at 37°C for 30 min . For the immunoprecipitation data shown in S4B and S4C Fig , the samples were incubated at 95°C for 5 min; this resulted in disruption of dimers of mGluR5 . However this did not have any effect on the immunoprecipitated fractions . The samples were migrated on 4–12% NuPage gels and transfered onto the PVDF membrane . For reverse immunoprecipitation experiments , the subsequent experimental set-up was used . Following immunoprecipitation of mGluR1 or mGluR5 with a specific anti-mGluR1/5 polyclonal antibody ( Cell Signalling Technology #12551 or #55920 respectively ) and addition of Dynabeads Protein G ( #10003D , Thermo Fisher Scientific ) , samples were prepared in loading buffer ( NuPAGE , Invitrogen ) and incubated at 37°C for 10–30 min [76] . The samples were migrated on 4–12% NuPage gels and transferred onto the PVDF membrane . All compounds were purchased from Sigma-Aldrich unless otherwise stated . Monoclonal anti PrP antibody POM1 ( 1:5000 ) was generated as described previously [45] . Anti-mGluRs antibodies against representative receptors of each group , targeting the N-terminal domain were utilized: anti-mGluR5 #ab53090 ( Abcam ) or AB5675 ( Millipore ) , anti-mGluR1 [EPR13540] ( ab183712 ) ( Abcam ) , anti-mGluR2+3 #ab6438 ( Abcam ) and anti-mGluR6 #AGC-026 ( Alomone labs ) . Secondary antibodies were horseradish peroxidase ( HRP ) - conjugated rabbit anti–mouse IgG1 ( 1:10 , 000 , Zymed ) and goat anti–rabbit IgG1 ( 1:10 , 000 , Zymed ) . Blots were developed using SuperSignal West Pico chemiluminescent substrate ( Pierce ) and visualized using the VersaDoc system ( model 3000 , Bio-Rad ) . Rocky Mountain Laboratory strain ( RML; passage #6 ) prions ( RML6 ) were amplified in CD1 mice by intracerebral inoculation into the lateral forebrain of 30 μl of 1% ( wt/vol ) brain homogenate . The mGluR5 antagonists MPEP and AFQ056 were kindly provided by Novartis . The mGluR1 antagonist YM202074 was purchased from Tocris Bioscience ( Ellisville , USA ) . Immunohistochemistry of fixed organotypic slices and subsequent NeuN morphometric analysis was performed according to previously published protocols [41 , 60] . Stainings were performed on sections from brain tissues fixed in formalin and treated with concentrated formic acid to inactivate prions . Partially protease-resistant prion protein deposits , astrogliosis and microglia deposition were visualized by staining brain sections with the SAF84 antibody ( 1:200 , SPI bio ) , GFAP ( 1:1000 , Millipore ) and IBA1 ( 1:2500 , WAKO ) respectively on a NexES immunohistochemistry robot ( Ventana instruments ) using an IVIEW DAB Detection Kit ( Ventana ) , after preceding incubation with protease 1 ( Ventana ) . Images of DAB stained sections were acquired using the NanoZoomer scanner ( Hamamatsu Photonics ) and NanoZoomer digital pathology software ( NDPview; Hamamatsu Photonics ) . Quantifications of IBA1 , GFAP staining and vacuoles in mouse sections were performed on acquired images; regions of interest were drawn on a Digital Image Hub ( Leica Biosystems ) and analyzed as previously described [77] . Hippocampal neurons were prepared from embryonic day 18 ( E18 ) C57/BL6 mice ( Janvier Labs , France ) . Freshly dissociated ( trypsin ) cells were plated ( 80 , 000 cells per 18 mm coverslip per ml ) in neuronal attachment media consisting of 10% horse serum , 1 mM sodium pyruvate , and 2 mM glutamine in MEM for 3h . The attachment medium was replaced and cells were maintained in serum-free neurobasal medium supplemented with B27 ( 1X ) and glutamine ( 2 mM ) . 300 μl of fresh medium was added once a week . mGluR5-pHluorin construct [78]was generated and kindly provided by Lili Wang and Christian Specht . Dendra2 was inserted between residues Q222 and A223 of mouse prion protein . GluN2A-GFP was kindly provided by Andrea Yao and Pierre Paoletti . Transfection was performed on DIV 17–18 neurons using Lipofectamine as described recently [58] . Transfection medium ( TM ) was composed of 1 mM sodium pyruvate and 2 mM glutamine in nerobasal medium ( Invitrogen ) . 0 . 5 μg of plasmid and 2 μl of lipofectamine- 2000 reagent were used for each coverslip . All in vitro experiments were performed on mature neurons ( DIV 21–24 ) Immunocytochemistry of mGluR5 ( rabbit polyclonal , Millipore , AB5675 , 1:200 dilution ) or GluR2-AMPA receptor ( rabbit polyclonal , Synaptic System , 182103 , 1:400 dilution ) was performed following methanol fixation / permeabilization ( 10 min at -20°C; methanol pre-stored at -20° ) . Image thresholding using wavelet decomposition to identify fluorescent clusters ( mGluR5 and GluR2-AMPA immunoreactivity or GluN2-GFP fluorescence ) has been described in previous studies [25 , 58] . Size of clusters denotes the total fluorescence intensity of the given cluster . Images were acquired using Leica Inverted Spinning Disk microscope ( DM5000B , Coolsnap HQ2 camera , Cobolt lasers ) using 100X objective ( field of view = 1392 x 1040 pixels ) and a pixel size of 60 . 5nm . For estimation of mGluR5 fluorescence within dendritic spines , ratio of fluorescence within a circular region of fixed size ( 6 pixel ) on spine head to the shaft below was measured using ImageJ program . PALM was performed on live neurons expressing PrPc-Dendra2 and the microscope setup and lasers used have been recently described in detail [58] . Unconverted Dendra2 has excitation and emission maxima at 490 and 507 nm ( green range ) while converted Dendra2 protein has excitation and emission maxima at 553 and 573 nm ( red range ) . First , all signal in red channel was photo-bleached to allow detection of single molecule events arising due to the switching of Dendra2 from green to red channel . Single molecule events of Dendra2 were imaged using laser 561 nm ( 0 . 5kW , used at 300-400mW ) while activating with 405 nm laser ( 100 mW power , used at 2–5 mW ) . PrPc-Dendra2 was imaged for 5000–6000 frames . Single molecule detections using in-house software has been used and described in previous publications [58] . Density of detections ( number/area ) of single-molecule on spine head was divided by density of detections over a dendritic shaft to obtain spine enrichment of PrPC-Dendra2 . Dendrites were not filled with any additional post-synaptic marker . Mature neurons ( DIV 21–24 ) were transfected with mGluR5-SuperEcliptic pHluorin . The pHluorin-tag allows the visualization of only cell-surface mGluR5s and the neuronal membrane , which is then visually recognizable . We have recently used this plasmid to compute the diffusion dynamics of mGluR5s within dendritic spines [78] In this study , we quantified the spines enrichment of all recognizable spines; considering that visually recognizable spines in mGluR5-pHluorin transfected neurons indeed colocalize with post-synaptic marker , Homer ( which is also the scaffold of mGluR5 ) . Detailed image analysis information is provided in the figure legends . For NeuN morphometric analysis ( Figs 1 , 3 , 4 , S1 and S3 ) , NeuN values are normalized to the median NeuN value of the NBH or Ctrl samples respectively . Two-way ANOVA , followed by Bonferroni correction or Log-rank ( Mantel-Cox ) test was performed in Fig 2 , to measure statistical differences between groups . One-way ANOVA followed by Dunnet’s post-hoc test was performed to measure statistical differences between groups . Two-way ANOVA , followed by Bonferroni correction was performed for Fig 4G . For Western Blot quantification in S4 Fig , mGluR1/actin ratios were normalized to the mean Grm5+/+ sample mGluR1/actin ratio in each timepoint ( 45days , 90days , 180days ) . One-way ANOVA followed by Tukey’s post-hoc test was performed to measure the statistical differences between the groups . For IP quantification in Figs 5 and S5 , densitometric quantitation of PrP signal or mGluR1/5 respectively from the immunoprecipitation was normalized over the ration of PrP/Actin or mGluR1/Actin or mGluR5/Actin signal in TEs respectively . One-way ANOVA followed by Tukey’s post-hoc test was performed to measure the statistical differences between the groups . For immunohistochemistry analysis in Figs 6 and S6 , number of GFAP+ cells or vacuoles was quantified in different brain regions . GFAP expression , quantified as the percentage of the “brown” surface occupied by the GFAP staining over the total measured area . Vacuolation , quantified as the percentage of “white” surface occupied over the total measured area . Two-way ANOVA , followed by Bonferroni correction was performed to measure statistical differences between groups . Non-parametric Mann-Whitney test was performed in Fig 7 to measure the statistical differences between the distributions . GraphPad Prism ( GraphPad Software ) was chosen for the statistical analysis . | Prion diseases are a result of ordered accumulation of the misfolded conformer of cellular prion protein ( PrPC ) , a GPI anchored protein expressed on the cell surface . Similar pathogenetic principles operate in several other neurodegenerative diseases . Currently no disease-modifying therapies exist and the situation is compounded by a dearth of validated therapeutic targets . In our present study , we have discovered that genetic ablation , or pharmacological inhibition , of group-I ( i . e . activating ) metabotropic glutamate receptors is beneficial against prion neurotoxicity in vitro and in vivo . Mice treated with these inhibitors exhibited impressive suppression of neurological signs and a delayed onset of the symptoms . These results further suggest that activation of these metabotropic glutamate receptors is a downstream event of prion replication and targeting these receptors could be a therapeutic option to alleviate the neurological symptoms , thereby ameliorating the quality of life in patients having prion infection . | [
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"neurotr... | 2017 | Inhibition of group-I metabotropic glutamate receptors protects against prion toxicity |
The World Health Organization ( WHO ) released the Global Leprosy Strategy 2016–2020 towards a leprosy-free world . The author described the progress made towards the elimination of leprosy and suggested recommendations for the acceleration towards a Leprosy-free country according to WHO laid out criterion . Case record review of Leprosy patients managed between the years 1992 to 2015 were registered and analyzed . Data were collected from annual reports of the Ministry of Health including demographics , classification of leprosy new cases , relapse , childhood , grades of disability ( GD ) and multidrug therapy ( MDT ) completion rates . Leprosy prevalence rate declined from 1 . 64 to 0 . 09 per 10 , 000 population during the period 1992 and 2015 ( p<0 . 0001 ) . Between 2005 and 2015 , 77 patients were diagnosed with Leprosy as per definition and 75/77 ( 98% ) had smear or biopsy positive . Of these , 53 ( 69% ) cases were among foreign-born ( non-national ) ( p<0 . 003 ) and 19 ( 25% ) were among women . Most of the leprosy cases were notified in Muscat governorate 29 ( 38% ) and among patients between 25–44 years of age 41 ( 53% ) , followed by ≥45 years 29 ( 38% ) and 6 ( 8% ) were children age ≤ 14 years . Multi-bacillary ( MB ) cases reported 60 versus 17 for Pauci-bacillary ( PB ) ( p< 0 . 01 ) , while MB was highest among both nationals ( 83% ) and foreign-born ( 75% ) . MDT completion rate was 100% and no relapse cases were notified among nationals . The rate of new patients diagnosed with leprosy related disability was 2 . 3 per million population , and grade 2 disability ( G2D ) rate among nationals was 0 . 9 per million population . No disability was recorded among women or children less than 14 years within the nationals group from 2013 . Almost all the foreign-born patients didn’t complete their treatment in Oman as they left the country shortly after diagnosis of leprosy due to a very short term contract , discretionary employment practices by the employers and prefer to go home to complete their treatment . Oman has met the elimination goals and made great strides towards becoming a leprosy-free country . However , challenges such as improving surveillance system efficiency and sensitivity for detecting timely leprosy cases , as well as foreign-born workers are still a major concerns .
National leprosy program ( NLP ) in Oman was launched in 1981 along with the tuberculosis ( TB ) program in collaboration with central leprosy center unit at Al Nahdha Hospital ( national tertiary care for the dermatology ) , aiming at reducing the morbidity and disability . Oman also has established an independent joint TB and leprosy technical expert committee to review the status of leprosy elimination , later the committee functions were integrated into the national communicable diseases committee , aiming on provides advice on corrective actions for achieving and maintaining successful leprosy-free country . Furthermore , the program has been integrated into the Primary Health Care ( PHC ) , secondary and tertiary care services provided by the Ministry of Health ( MOH ) and non-MOH institutions . All leprosy-related activities , including surveillance and laboratory testing of suspected leprosy cases , are financially supported by the MOH and provided free of charge in government health institutions . The program strategy is being implemented at several levels , the first at primary health care ( PHC ) level i . e . in dermatology clinics where cases are notified . Thereafter , the suspected case is referred to the nearest dermatology clinics for finalization and confirmation of diagnosis post confirmation follow-up is done and the assigned dermatologist usually administers MDT . The national guidelines were developed in 1998 , spelled out the policy and the package of care to be delivered through leprosy strategy . The strategy included case notification , pre-and post-test counseling , client flow , skin biopsy sample flow and roles and responsibilities of the health team at primary health care ( PHC ) , dermatology clinics , follow-up and flow of patient at different care centers . Furthermore , contact tracing was also spelled out , has evolved and is widely implemented [6] . In this report , the author describes the progress made towards a Leprosy-free nation and the challenges ahead to achieve and maintain this target in Oman .
In March 1991 , the National Communicable Disease Surveillance System was formally launched and the Department of Communicable Disease Control , functions as the apex body . The national surveillance system ensures the collection and use of appropriate and timely data for dealing with the target priority diseases . Governmental public health specialist are assigned at each governorate to oversee the different surveillance activities [6 , 8] . Leprosy reporting is integrated into the National Communicable Disease Surveillance System , under group B . The surveillance monitors leprosy incidence , prevalence along with other notifiable vaccine-preventable diseases . Both active and passive leprosy surveillance were adopted nationally , maintained and intensified in Oman . The disease leprosy is classified based on WHO case definition [9] as pauci-bacillary ( PB ) or multi-bacillary ( MB ) on the basis of clinical examination of the patient and slit skin smear . A leprosy case is diagnosed when more than five skin lesions or at least two enlarged peripheral nerves or positive of acid-fast bacilli slit skin smears is named multi-bacillary ( MB ) case , whereas more than five skin lesions and no more than one enlarged peripheral nerve and no presence of acid-fast bacilli in positive slit skin smears is named pauci-bacillary ( PB ) case ( Fig 2 ) . The skin biopsy is reviewed by leprologist . The suspected patient is usually screened based on symptoms and sign as per the algorithm [Fig 3]; once confirmed as suspected referred to the nearest dermatology clinics close to the patient’s residential area . The MOH leprosy reporting policy requires all health institutions , including non-MOH ( private ) institutions , to notify suspect and confirmed leprosy cases as early as possible and within 7 days of suspicion to the nearest assigned governorate Communicable Diseases Surveillance Control Unit ( CDSU ) . Leprosy reporting policy also requires to report zero monthly reporting of leprosy from all reporting institutions . For every suspected case a clinical evaluation , skin smears and/or biopsy are performed at a dermatology clinic . Samples are tested using slit skin smear for Mycobateria leprae at the dermatology specialized clinic based on WHO recommendations [9] and national guidelines . The program adopted a policy of giving MDT regimens as recommended by WHO [9] for PB and for MB cases . Leprosy cases ( smear positive and negative ) are managed and followed-up at secondary and tertiary care dermatology clinics where medicines are offered free of charge including foreign–born residents . The diagnosis of leprosy cases is based on clinical signs and/or skin smear and biopsy and all cases are evaluated , assessed for disability and treated by leprologist . The patients are also assessed during subsequent visits for relapse , and long-term leprosy disability . All patients are offered pre-test counseling covering the health implications of leprosy , and post-test counseling services after test results are ready . All close household contacts of an indexed leprosy case are promptly screened and followed-up annually up to 5 years . The governorate Communicable Disease Surveillance Unit is collecting and compiling monthly summaries from dermatology clinics of both public and non-public ( private ) providers . These include number of total positive leprosy cases , residence , sex , age , nationality , residential and close contacts details . Monitoring also includes adherence , treatment failure , severe reactions , complications and possible toxicities of MDT , and record of leprosy-related disabilities . Such data are disaggregated from subunits to the central level on a monthly base . The data entry is carried out at the dermatology clinic level where there is adequate human resources to ensure timely and effective leprosy data entry . The governorate surveillance unit is ultimately forwarding the aggregate data to central department of surveillance on a monthly base . Furthermore , the leprosy-positive cases are usually followed and monitored at the dermatology clinics at secondary or tertiary care , and contact tracing and screening of all close contacts , especially household contacts , is also conducted by the governmental epidemiologist or sanitary inspector at the PHC . Training of staff at PHC and dermatology clinics level are being conducted regularly to ensure a standard knowledge , suspect and diagnosis of leprosy case as well as reminding then about the requirement for timely reporting of the leprosy cases especially at non-MOH institutions . The MOH governorate Communicable Diseases Units regularly forwards communications out to all PHC physicians reminding them about the requirement for timely and zero reporting of leprosy cases . In addition , regular technical supervision and knowledge are updated . The leprosy program at central level oversees the progress of the program , compiling and aggregating the national monthly reports of the leprosy cases , contact screening and defaulter retrieval reports and produces an annual report . Regular series of articles , interviews and media programs are delivered to the community , specifically to the patients , their families and contacts as well as among foreign-born community , for motivating people to seek for medical advice and treatment as well as minimizing the discrimination .
The MOH set-up good records to keep track of leprosy patients in order to provide appropriate post treatment care to prevent and manage residual post-treatment disabilities . These supports are provided free of charge by the MOH institutions and other related ministries .
By 2015 , Oman has achieved elimination as a public health problem goals ( Target< 1/10 , 000 per population ) by implementing the WHO global elimination main strategies [2] including achieving and sustaining good national surveillance system , integration into other disease communicable control program which facilitated supervision and monitoring of leprosy program as well as maintaining inclusive high universal coverage . As a result of implementing these strategies , leprosy incidence rate decreased by 99% since 1992 . Oman has thus successfully eliminated Leprosy by WHO standards . The next goal is to achieve a zero-leprosy state . The drop of leprosy cases from 1998 to 2000 may be attributed to immigration policy , where the majority of the foreign-born were from India . Oman also made impressive progress towards becoming a leprosy-free country to meet the key targets [2]; Firstly , no leprosy cases among age group ≤14 years since 2013 and zero disabilities reported among newly diagnosed children ( Target zero G2D ) [9] . Similar findings were reported from 39-member states zero new G2D among children cases [9] . Secondly , the incidence of leprosy declined to < 1 case per million population by 2015 and thirdly , the implementation of free and universal coverage of MDT as well as providing management of complications and prevention of disabilities with focus on children , women and all population indiscriminately . Similar findings reported by WHO , out of the total member states , 62 informed on foreign-born patients , out of those , 44-member states reported zero cases while 18 reported 743 foreign-born cases being treated in their respective national leprosy program [4] . Oman is among few countries reporting leprosy among foreign-born patients as well as providing leprosy diagnosis and treatment free of charge without discrimination . Furthermore , Oman has a high population of foreign-born ( non-Omani ) residence workers and their families ( 44% ) , out of which 85% are male and 92% are above age of 20 years and 70% of the foreign-born are coming from India , Bangladesh and Pakistan [7] . Low proportion of female in new cases among foreign-born ( 19% ) reported is reflective of high population of male work force ( 85% ) [7] . The foreign-born population frequently travels in and out from high leprosy endemic countries . The possible reasons for foreign-born patients leaving the country shortly after diagnosis of leprosy were the very short term contract ( 6 to 8 months ) , discretionary employment practices by the employers and the preference of patients to go home to complete their leprosy treatment . In order to facilitate reducing the incidence as well as to minimize delay in identification of leprosy cases among foreign-born population , currently the Executive Board of the Health Ministers' Council for Cooperation Council States [10] adopted screening policy on leprosy during pre and post arrival for foreign-born population . In the future , the program should also undertake and reinforce screening procedures upon arrival to the country , and in private sectors institutions where most of the foreign-born populations seek medical advice in order to detect leprosy early and to provide MDT at the earliest instance , which remains the fundamental principles of leprosy control . Oman has achieved leprosy elimination since 1996 , but is still reporting low number of leprosy cases among national 24 ( 31% ) , the program therefore needs to review each case in-depth to find out the possible source of infection especially the possibility of close contact with foreign-born house help who might having undiagnosed leprosy among other possibilities of contamination . The study findings showed no detection of leprosy cases among children ≤14 years since 2014 , reflecting negligible or low transmission of the disease in the community which are reflected by overall low leprosy prevalence ( < 1/10 , 000 per population ) , and cases notified between 2005 to 2015 especially among nationals ( 31% ) . The study showed MDR completion and cure rate reached almost 100% and no relapse among national cases indicating treatment adherence and completion by the patients and good access to health services across the country . The high zero proportion of patients with G0D and G1D among newly diagnosed leprosy patients indicates and reflects the efficiency of early detection of leprosy and high awareness of leprosy by the health staff . While the high proportion of patients with G2D among newly diagnosed leprosy patients ( 100% ) reflects the delay in detection , diagnosis or leprosy patients receiving inappropriate heath care services . However , this finding was higher than the average G2D rate of leprosy in China ( 25 . 4% ) [11] . The risk of acquiring leprosy for close contact living in same households with MB and PB patients is almost 5–10 and 2–3 times higher respectively , compared to people not living in such households . Therefore , unrecognized leprosy cases and subclinical infections among household contacts contribute a significant proportion of overall new leprosy cases [12] . Our study showed high proportion of MB cases for both nationals ( 83% ) and foreign-born ( 75% ) reflecting delay in detection of leprosy in the community . Therefore , the national leprosy control program is to focus on promoting early detection monitoring thorough measurement of disability in new cases and conducting prompt investigation looking at reasons for delay in detection , identifying the source of infection , and reviewing the mandatory contact tracing programs . These steps would undoubtedly reduce the disease burden . Investigating for stigma as an important cause of delayed diagnosis facilitating transmission of infection with communities is also important . Additionally , national efforts are also needed to involve dermatologists in private sector to sustain high-quality leprosy services especially providing treatment . Although many member states declared leprosy elimination at the national level , leprosy as a public health problem has remained at sub-national levels [13–15] , and many other leprosy concerns were unrecognized and unresolved [16 , 17] . In our study we found that Muscat and north Batinah Governorates are reporting highest leprosy cases , these two urban governorates are the most populated governorates and comprise of 49% of the total population with highest population density 346 and 87 /square kilometer in Muscat and North Batinah Governorates respectively , therefore , implementation of targeted active case search in high-risk governorates is required . The program is to introduce surveillance for MDT antimicrobial resistance as well . The progress made towards a leprosy-free Oman has been facilitated by several factors including strong political commitment demonstrated by a leprosy program that is more than 30 years old , good universal coverage of MDT which may be due to free management of leprosy cases with all residents and well-trained governorate dermatologist across the country . Additional factors include counselling services provided to the leprosy patients . Oman is committed to the global leprosy-free goal and have achieved leprosy-free targets among those mainly zero G2D among paediatric leprosy and reduction of G2D to close to 1/1 , 000 , 000 population by 2015 . However , Oman also needs to embark on rigorous methods to evaluate the program at grass root to identify the potential areas for improvement towards zero incidences of leprosy , and update the national guidelines to meet the new global WHO strategies as well as establish leprosy validation and verification task force aimed at oversight and coordination efforts among different stakeholders . The current study has some limitations including missing MDT completion rate , calculation of G2D to <1 case per million population wasn’t calculated and information collected on country of origin , duration between entering the country and initial diagnosis and proportion of household contacts screened among leprosy cases weren’t available among foreign-born and nationals population . In conclusion , Oman has met the elimination as a public health problem goal and made great strides towards becoming a leprosy-free country however , challenges remain with regards to sustaining these achievements and moving towards becoming a leprosy free-country . The National Leprosy Control Program should focus efforts in the near future to reduce the burden of leprosy to zero by maintaining and improving surveillance system efficiently and increase sensitivity for timely detection of leprosy cases . The program is to also establish an independent verification committee to reinforce screening procedures among foreign-born population regularly review the status of leprosy-free patients and provide advice on corrective actions . These steps will prove critical to achieving and maintaining successful zero leprosy case . | Leprosy is a chronic infectious disease caused by Mycobacterium Leprae that involves many body organs but mainly skin , peripheral nerves and mucous membranes and occasionally other organ systems that affects equally all races , ages and both sexes . While most individuals exposed to an infectious case of leprosy become infected , only less than 5 percent of those infected develop the disease and subsequent disability . In Oman by World Health Organization Standard’s , Leprosy has nearly been eliminated as a public health problem over the past two decades . We are however , striving to meet the new three pillars set by WHO to achieve a completely leprosy free country . The three main strategies employed include: strengthening government ownership , coordination and partnership to stop leprosy and its complications; and to stop discrimination and promote inclusion [1–3] . We hope that through fervent efforts aim towards Oman having zero leprosy cases by WHO standards in the very near futures . | [
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"diseases",... | 2017 | Progress towards a leprosy-free country: The experience of Oman |
We explore the possible role of elastic mismatch between epidermis and mesophyll as a driving force for the development of leaf venation . The current prevalent ‘canalization’ hypothesis for the formation of veins claims that the transport of the hormone auxin out of the leaves triggers cell differentiation to form veins . Although there is evidence that auxin plays a fundamental role in vein formation , the simple canalization mechanism may not be enough to explain some features observed in the vascular system of leaves , in particular , the abundance of vein loops . We present a model based on the existence of mechanical instabilities that leads very naturally to hierarchical patterns with a large number of closed loops . When applied to the structure of high-order veins , the numerical results show the same qualitative features as actual venation patterns and , furthermore , have the same statistical properties . We argue that the agreement between actual and simulated patterns provides strong evidence for the role of mechanical effects on venation development .
For many years leaf venation motifs have marveled people , whether scientists or not . Venation patterns are different from one leaf to another , even in the same plant , but share some common features that are preserved throughout all angiosperm leaves [1] . A remarkable characteristic of these patterns is the vein hierarchy , characterized by their radii , that originates in the successive formation of veins during leaf growth [2] , [3] . A second , very robust , feature of the venation pattern is the abundance of closed loops: the leaf surface is divided into small polygonal sectors by the venation array; only the fine veins of the highest orders do not connect at both ends and are often open ended ( see Figure 1 ) . It has been argued that the vein architecture might ensure optimal water distribution [4] , [5] . However , the straightforward optimization of steady state irrigation within the leaf must lead to tree-like open topologies [4] , [5] with strictly no loops [6] . The high redundancy of paths from the leaf base to any point in the leaf surface might nevertheless be very advantageous with regard to local damages ( Magnasco MO , personal communication ) . Also , it has been suggested that venation may play a mechanical stabilization role for the leaf , but the optimization of the mechanical stabilization leads to very unnatural venation geometries [5] . From a developmental perspective , the leaf venation is puzzling , too . Since the pioneering works of Sachs [7]–[9] , it is known that the growth hormone auxin has an enormous effect on the venation pattern [10]–[12] . It is believed that auxin is synthesized in the growing leaf ( either homogeneously or at localized sites ) and that there is a net auxin flow towards the leaf base from where it is transported towards the plant roots . Furthermore , it has been found that mutations that affect the auxin transport lead to strongly modified venation patterns [13] , [14] . These findings have led to models of venation formation based on a positive canalization feedback [7]–[9] , [12]: on the one hand , the auxin flow is canalized into veins and vein precursors ( procambium ) . On the other hand , high auxin concentrations ( or , in a different variant , high transport values ) trigger the differentiation into procambium . In its simple form , this model cannot lead to any loop but gives rise to tree-like structures [15]–[18] , and this is a serious drawback of the model . Several studies have tried to correct this unrealistic part of the model with varying success [19]–[23] . For instance , Rolland-Lagan and Prusinkiewicz [20] have proposed the possibility that localized auxin sources on the leaf move around when veins develop . They show that closed loops can be formed in this way . This model seems to require a rather complex and coordinate displacement of auxin sources as veins are formed . On the other hand , Dimitrov and Zucker [21] have considered a homogeneous production of auxin on the surface of the leaf , and suggested that closed loops are formed when new vein segments propagate from existing ones , and meet at the point of highest auxin concentration . From a basic perspective , it seems that this model requires a very precise coordinated progression of the new vein segments , as otherwise the first segment reaching the highest auxin concentration point would inhibit further growth and open structures would be obtained . Along the same lines , Runions and collaborators have devised geometric algorithms that give rise to aesthetically very appealing venation patterns [22] . Closed loops are obtained in this case ( as in [21] ) by the tips of three vein segments meeting in points of high auxin concentration . Nevertheless , it is an open question whether the auxin sources postulated in [21] , [22] for the formation of high-order veins actually exist , since Scarpella et al . [12] failed to observe them in their experiments . An alternative model has been recently introduced by Feugier and Iwasaa [17] , [23] . In this model , loops are formed when a vein tip curves towards and meets an older vein at some intermediate point . It is suggested that this behavior is induced by the existence of ‘flux bifurcators’ in some of the cells with high auxin concentration . Note that this mechanism is incompatible with the one proposed in [21] , [22] , as here the loops close at intermediate points of older veins . Whether the hypothesis of Feugier and Iwasaa can generate realistic venation patterns is an open question . In general , we find that the modifications to the canalization hypothesis necessary to explain the existence of closed loops are not generic and rather unnatural , and the mechanism on which they are based require a lot of fine tuning . Couder et al . [24] have pointed out that the difficulties encountered in creating realistic , loop forming models on the basis of auxin transport are intrinsically related to the scalar nature of the concentration fields . In contrast , the growth in a tensorial field gives rise to hierarchical networks in a very robust manner . They suggested that this tensorial field could be the mechanical stress field in the growing leaf . ( In a certain sense , the PIN protein polarization field in [23] can also be considered as a kind of tensorial field . ) In their work they put forward the hypothesis that mesophyll cells that are submitted to compressive stress exceeding a threshold value start a differentiation process that eventually transform them into procambium . This process would be similar to the one observed in experiments on botanical tissues in which oriented cell divisions are forced by externally applied compressive stresses [25] , [26] . Evidence supporting this hypothesis is two-fold . On the one hand , micrographs taken in the early steps of leaf venation development show that in the first stages of differentiation , cells forming the procambium can be distinguished from the remaining cells by a mechanical distortion , consisting in a shrinkage of the cells perpendicular to the vein direction ( see , for example , the images of Figure 2 of [2] ) . This suggests that stresses play a role in this distortion . On the other hand , it has been shown that typical large-scale morphologies of leaf venation patterns can be reproduced as crack patterns in an appropriately prepared layer of a slurry that dries in contact with a substrate [24] . This visual similarity between crack and venation patterns led us to investigate in more detail the fundamental ingredients in crack pattern appearance . Crack patterns on the surface of mud or other materials require the existence of two quasi-two dimensional layers of material , the substrate and the covering , the latter contracting with respect to the former upon desiccation . ( A pioneering work by Skjeltorp and Meakin [27] analyzes experimental and computer models of crack growth in a two-dimensional system consisting on two layers growing at different rates . ) A rather similar situation may indeed occur in a growing leaf . In fact , a growing leaf consists of two epidermal layers separated by a softer tissue called mesophyll . This mechanical unit has to keep its integrity through the growing process . In the first stages of cellular growing and division , the three layers keep their status of uni-cellular layers . However , the growing rate of the epidermis and the mesophyll are not equal but the mesophyll tends to grow more rapidly than the epidermis [28] . This generates compressive stresses in the mesophyll that can force cells to grow and divide along particular directions , favored by the local stress field . In fact it is in this stage where evidence of collapsed cells of the mesophyll has been obtained [2] . We interpret the existence of elongated cells as evidence of a larger mechanical stress along the directions perpendicular to the largest axis of the deformed cells . Note that the similarity between crack patterns and our mechanical model for leaf venation has an important difference: crack patterns are obtained under contraction of the active layer relative to the substrate , whereas venation patterns should appear when there is an expanding active layer ( mesophyll ) relative to a rigid frame ( the epidermis ) . The suggestion of Couder et al . on the importance of elastic factors in vein formation [24] has not been further studied from a modeling point of view . In this paper , we present a numerical model based on this hypothesis . We will show that this approach , assuming the existence of a mechanical collapse instability of the mesophyll cells , generally leads to patterns that are not only qualitatively similar to actual venation patterns , but also show comparable statistical properties .
In actual leaves , there is an obvious dependency between the morphology of veins and its rank in the venation structure . In other words , initial vein generations are strongly dependent on the form of the leaf and most probably , on genetic factors . It is this large-scale pattern that is repetitive within the same species and allows a broad leaf classification according to their venation patterns . It is also in these initial vein generations where the role of auxin is relatively well established . High order vein generations are much more isotropic , and much more universal in its statistical properties . It is to this stage that we intend to apply our model in its present form to compare statistical properties . A comprehensive mathematical description of our model is given in the last section , but here we summarize the main hypotheses to ease the reading of this part . We assume that during growth , the inner cell layer ( the mesophyll ) is elastically attached to the epidermis . The epidermis is assumed to grow at a lower rate than mesophyll , and is otherwise supposed to be inert , i . e . , it undergoes no deformations during growth . Due to the different growth rates of mesophyll and epidermis , compressive stresses develop in the mesophyll . Our main assumption is that the elastic properties of the mesophyll are such that this compressive stress can give rise to a shape change of the mesophyll cells . Such cells will acquire an elongated shape perpendicular to the main applied stress . These assumptions are basically equivalent to the description of collapsing surface layers presented in [29] . As in this work , the elastic properties of the mesophyll are included in the definition of a local free energy that has two minima: an isotropic ‘intact’ minimum , and a ‘collapsed’ one that corresponds to the deformed cell . ( From a biological point of view , what we are describing as the ‘collapse’ of a cell from a rather spherical shape to an elongated shape could occur as preferential growth along the easiest direction , i . e . , perpendicularly to the compressive stress field . ) We use an algorithm in which the elasticity of the cells is assumed to be linear , the non-linear behavior is introduced by a scalar field Φ ( x , y ) . The value of Φ ( x , y ) carries the full information of the complete tensorial stress field and the state of the system at the ( x , y ) position . As will be clear in the last section , the field Φ has two preferred values , defining two elastic states with different density and shear modulus . They represent the intact and collapsed states of the cells in our model . Sectors of the system that are in the intact or collapsed states are recognized by their different values of Φ ( see typical profiles of Φ in the last section ) . We will typically refer to collapsed sectors as ‘veins’ , although it must be kept in mind that the definitive differentiation of a vein will require a further process that we are not modeling here . At each step of the simulation the system evolves towards the configuration that minimizes the total free energy . At the same time , a parameter η ( see the precise definition in the last section of this paper ) is used to control the global growing of the leaf: increasing the parameter η simulates the increasing of the overall leaf size . For technical simplicity we maintain the size of our simulation mesh ( typically 1024×1024 nodes with periodic boundary conditions ) , and the increase in η means that we are effectively ‘zooming out’ with the leaf growth . This means that new veins will be seen as thinner ones , while older veins keep their thickness during the simulation . In order to have a reasonable description of the hierarchical process of sequential vein formation , a sort of ‘irreversibility’ condition is implemented . It guarantees that once a new vein is created , it is forced to remain in the collapsed state during the leaf growth . In actual leaves , a similar mechanism explains why older veins are thicker: once a cell becomes a vein cell , the process of cellular division generates new cells that will also be vein cells . The implementation of the irreversibility condition in the model is explained in detail in the last section . To avoid an extremely uniform initial condition , we typically seed the simulation with a few large-scale veins that provide the initial veins of our numerical leaf . This first division is not significant in the statistical analysis we perform on the final patterns . We show results in which we prepare the system with tree-like thick initial veins , or divide the sample into two pieces . When new veins are formed ( upon increasing of η ) , they typically propagate rapidly through the system , reaching in most ( but not all ) cases an older vein , where they stop . This propagation , once triggered , occurs essentially at constant η , i . e . , it is not driven by the growing itself . A few snapshots during the numerical evolution are shown in Figures 2 and 3 , where we plot the points of the numerical mesh for which Φ have positive values ( associated to the collapsed state ) . The hierarchical nature of the process can be clearly observed in these figures , as new veins are progressively thinner than old ones . We stress that the observed hierarchical patterns are a direct consequence of the irreversibility condition . In this way the history of the growth process remains encoded in the statistics of vein widths . Moreover , notice that hierarchical patterns can also be obtained in a very simple and well-controlled model such as that described in Text S1 . Before going to the quantitative characterization of the patterns obtained , two important features are worth noting . One is that in many cases several thin free-ended veins are observed . This also occurs in actual leaves and we propose an explanation in the next section . Another feature is that some minor veins are completely disconnected from other veins . They typically appear at the center of intact regions ( where the stress is maximum ) , and seem unrealistic , since vein patterns in leaves are almost always connected . Although they might be due to an artifact in our simulations ( in fact , the thickness of these disconnected veins is already comparable to our numerical discreteness ) , recall that our patterns are actually showing the places where the tension is high enough to generate collapsed cells that will eventually , but not necessarily , differentiate into veins . If the later differentiation process requires the canalization of a flux through the network of collapsed cells , differentiation of the disconnected segments into disconnected veins will not occur . In order to test whether our simulation results are comparable with actual leaf patterns , we computed the vein width , length and angles from our simulation results , and compare them with data from actual leaves . The same numerical image processing technique was used for the two data sets; see a detailed explanation in [30] . The image processing converts the venation patterns in sets of segments , nodes and free endings , each segment having a given length and width . In Figure 4 we show the average length of the vein segments as a function of its width , w . Data from actual leaves of Figure 4A show that at first glance the typical length of segments is independent of the segment width , except for very thin segments , since there is a minimum thickness below which there are essentially no segments . This result is obtained also in the toy model presented in Text S1 . An interesting deviation of this trend is found however when averaging many different data sets , where we see that thicker segments tend to be slightly longer than thinner ones ( see the inset of Figure 4A ) . Going back to snapshots of actual leaves ( Figure 1 ) , it is clear that this result originates in the fact that thin segments have some difficulty in reaching thick segments and many open ends of thin segments are typically found near thick ones . Notably , this feature is reproduced in our numerical model ( see Figures 2 and 3 ) , and the increase of length as a function of segment width is in fact observed in the statistical plot of Figure 4B . The reason for the difficulty of thin segments to reach thicker ones in our model ( and probably also in actual leaves ) is the following . A given vein segment relaxes mechanical stresses in some neighborhood of it . The size of this relaxed zone increases with the vein width . When a thin vein is approaching a thick one , it enters a region where elastic stresses have diminished , and in many cases this relaxation is sufficient to stop the advance of the thin vein before it actually hits the thicker one . In case of approaching veins of approximately the same thickness this tendency is lower , and it does not seem to be strong enough to stop the vein advance before contact . Moving to the description of the results of Figure 5 for the number of vein segments with a given width , N ( w ) , first of all we note the overall similarity of real and numerical curves . Also , a shoulder in N ( w ) is observed both in the numerical as in the real data for the region of thick segments . In our numerical leaves we relate this behavior with the way in which we seed the simulation . In our runs , the first generation of veins appears quite rapidly and generates a number of thick segments . We observed that such distribution of thick veins is quite constant during the evolution of the system , whereas the region of the curve fitted by a power decay appears in later stages of the growing . The evolution of N ( w ) can be observed in Figure 6 , where we plot the histograms of widths for the four snapshots of Figure 2 . For intermediate values of thickness , the results of our model are compatible with a power decay of N ( w ) , with an exponent close to 2 ( see Figure 5B ) . This result is also obtained with the minimal model described in Text S1 , showing that our model generates a hierarchical pattern along the lines we have already discussed . From the data of actual leaves of Figure 5A we see that N ( w ) can be fitted by a power law decay , and this is a nice indication that a hierarchical mechanism is at work in actual leaves . However , in this case the decay exponent of N ( w ) is larger than 2 , rather close to 3 . Although it is probably too ambitious to try to give an explanation of this discrepancy , we want to present the following argument . One of the implicit assumptions in our scaling method is that all distances measured over the leaf surface grow at the same rate during leaf growth . This is reasonable as long as the cellular layers involved are one-cell thick . However , once some cells have been committed to become a vein , they must give rise to a cylindrical object . The hypothesis of two-dimensionality does not work for veins . If , on biological grounds , we assume that the rate of cellular division is constant , and take it independent of the kind of cell , we arrive to the conclusion that vein width increases as square root of time , instead of linearly . If this fact is taken into account in a counting as we did in the model described in Text S1 , the result is that N ( w ) gets an additional factor w−1 , justifying a more rapid decay for N ( w ) in actual leaves than in our model , which assumes all distances measured in the leaf surface grow at the same rate . Finally , we analyze the behavior of the angles between vein segments at the points where three vein segments meet . As pointed out in [30] , the values of the three angles of a node are directly related to the local hierarchy of the meeting vein sizes . The authors found that the relation between angles and radii ( or widths ) is a general property of all the leaves they studied . We analyze our patterns to see whether it is possible to find in the numerical leaves the kind of organizational law obtained in actual venation patterns . For each node , we measure the three angles obtained and relate them with the radii of the vein segments . Thus , αLS is the angle between the thickest and the thinnest segments , αLI is the angle between thick and intermediate segments , and αIS is the angle between intermediate and thin segments . We calculated the averages of the three angles and plot them as a function of the ratio between the radius of the thinnest ( RS ) and thickest ( RL ) segments . The configuration of radii is well defined with the parameter RS/RL because the segment of intermediate radius has usually a value close to RL . In Figure 7 we compare the numerical and the real data by adding our numerical results to the ones of Figure 14 of [30] . A very good agreement is obtained . The behavior observed can be understood by analyzing the two limiting cases . For RS/RL close to one , all radii are almost equal and the three angles are near to 120 degrees . This describes a situation in which a vein has bifurcated into two . Since the three segments are then created almost simultaneously , the three radii are similar . On the other hand , RS/RL near to zero correspond to the case in which a thin vein reaches a thick one . In this case , the angle αLI between thick and intermediate segments tends to be 180 degrees , meaning that the thick vein is almost unperturbed by the thin one . A continuous and rather linear variation is observed between these two extreme situations . Although the overall coincidence of measured angles in our simulations and in actual leaves is encouraging , a full understanding of the origin of a general relation between angles and radii is not achieved yet . In our model , the free energy of a vein can be conceived as a interface energy between the two sectors into which the vein divides the leaf . In the case that all veins are of the same width , the minimization of this interface energy would give rise to a foam-like pattern with 120 degrees angles . However , irreversibility gives rise to the formation of veins of different thickness and free energy minimization produces angles whose values are correlated with the veins' age . The ‘force model’ proposed in [30] shows that if a force is assigned to each vein segment , pointing along the segment direction and with an intensity proportional to the vein radius , the angles between segments correspond to the situation in which the three forces emerging from each node are in equilibrium . The applicability of the force model to our numerical results could be justified by the following argument . Assuming that three segments of given radii have to meet , our modeling prescribes that the structure they form must have the minimum accessible free energy . If we assume that a rough measure of the free energy is given by the area covered by the veins , a line tension can be associated with each vein , which is proportional to its radius , and from here the prediction of the force model follows immediately . In any case , this is a point that deserves further study .
Our main assumption is that vein formation is triggered by the elastic collapse of cells of the mesophyll , growing at a larger rate than the ( assumed rigid ) epidermis to which they are attached . An appropriate approach would be to describe the mesophyll as an elastic layer with a highly non-linear behavior modeling an irreversible local collapse . The natural way to theoretically describe the behavior of an elastic layer is by constructing a free energy in terms of the elastic displacement field , u . Two main contributions to the free energy should be considered: the elastic interaction between the inner cells and the epidermis , and the energy of the deformed cells that can have two possible internal configurations associated to the intact and collapsed states ( see the schematic representation of Figure 8 ) . When this problem is studied in two dimensions the fundamental variable u is a two-dimensional vector field . To avoid some technical difficulties that otherwise could appear , instead of studying a non-linear elasticity model directly in terms of u , we choose an algorithm in which the elasticity of the cells is assumed to be linear , the non-linear behavior is introduced through an additional field Φ , which is coupled with the elasticity field through a term of the form Φ∇u . The coupling generates the non-linear behavior of the mesophyll in an effective way . This kind of models was successfully used to study phase separation processes in alloys [32]–[35] . They are described by continuum ( differential ) equations , and thus the cellular structure of the biological tissues is not considered in detail . A free energy in terms of the elastic displacement field u in the plane of the leaf and the additional phase field Φ , is introduced in the form: ( 1 ) Here , f0 is a Ginzburg-Landau local free energy for Φ that has two different minima , representing the intact and collapsed states:A regularization term proportional to |∇Φ|2 is included to obtain smooth profiles of the fields by penalizing rapid spatial variations of Φ . It is introduced to make the behavior of the system almost isotropic and independent of the underlying numerical lattice . This term is also useful because allows the simulation of a continuous growth through the rescaling of the parameters , as will be explained later . The parameter α is a measure of the coupling between the fields Φ and u . The term fel is the usual elastic free energy density in the reference state in which Φ = 0 , expressed in terms of the bulk and shear moduli , K and μ , and the displacement field u:We consider the bulk modulus K as constant . However , in order to obtain collapsed regions that can be tentatively associated to growing veins it has to be assumed that the elastic properties of collapsed cells correspond to a lower volume and lower shear modulus than the intact cells ( see the morphologies observed in [29] ) . Thus , the shear modulus μ will depend on whether the medium is in the collapsed or intact state: ( 2 ) As we said , due to the f0 term , the field Φ has two preferred values Φ± = ± ( r0/s0 ) 1/2 . When these values are introduced in Equations 1 and 2 they define two different elastic states with different density and shear modulus , representing the intact and collapsed states of the cells in our model . The fact that the variable Φ is continuous , however , guarantees the possibility of a smooth transition between these states . The only difference between these expressions and those in the works [32] , [33] , [35] is the presence , in our model , of a term proportional to γ giving a perfectly harmonic , elastic interaction to a rigid layer that represents the epidermis . Although there are actually two epidermis layers , we suppose their roles are equivalent and thus a single substrate layer is considered in the model . As the growing rate of mesophyll is assumed to be larger than the growing rate of the epidermis , compressive stresses into the mesophyll appear to produce the collapse of some parts of it . This situation corresponds formally to an elastic layer expanding with respect to a rigid substrate , a situation that has been recently studied by one of us [29] . A formal transformation in the model should be made before implementation in the computer . If in the free energy of Equation 1 we were able to integrate out the field Φ , we should end up with a non-linear elastic model written completely in terms of the displacement field u . However , the approach we follow is the inverse . Through a well documented procedure [34] , [35] , the elastic field u is integrated out of the model to first order in μ1 , and an effective model in terms of Φ is obtained . The new model is non-linear and non-local in Φ , describing in an effective way the non-linear elastic behavior of the system . The free energy takes the form:where Xij = ∂i∂j− ( δij/2 ) ∇2 , gE = μ1 α2/L02 , gL = γ/L0 , L0 = K+μ0 in 2D , Aij = 〈∇j ui〉 , andAt this point , all the information is encoded in the field Φ . In particular , different values of Φ in different spatial positions will tell whether that portion of the system is in the intact state , or in the collapsed state . The temporal evolution is governed by an equation compatible with a non-conserved order parameter , i . e . , dΦ/dt = −δF/δΦ . In this way the system tries to adapt dynamically to the external conditions in order to minimize the value of F . The main external condition that drives the evolution of the system is the fact that the leaf is growing . The natural way to model the growth ( which mimics most closely the real situation ) is to assume that , although the parameters of the model do not change upon growing , the linear dimension of the system L ( t ) increases in time . We suppose the growth is sufficiently slow that at each moment the system is in mechanical equilibrium . The initial condition for the minimization at time t+Δt should be the result of the minimization at time t , but stretched by a factor L ( t+Δt ) /L ( t ) . This approach is quite difficult to implement in the simulation , because of the problems that appear in changing the size of the system under temporal evolution . Technically more simple , but fully equivalent to the previous procedure , is to keep the size over which we integrate the equations of the model , but change its parameters in such a way that the same numerical mesh simulates progressively a larger system . This is like saying that we ‘zoom out’ with the system growth . The scaling parameter that will do such rescaling is called η , and the growing process is implemented in terms of changes in the parameters as follows . If in Equation 1 we formally change from r to ηr , the only parameters that are rescaled ( in addition of an unimportant global rescaling of the free energy ) are C and γ , which become C/η2 and γη2 . This means that changing C and γ in this fashion is precisely the way in which the growing process can be simulated . We start the runs with a value of η = 1 , and increase it progressively during the simulation . Note the scaling effect in the simulations: Decreasing C will produce a sharper interface between intact and collapsed region , which is a reasonable effect as we zoom out with the system growth . In addition , the increase of the substrate interaction will produce the effective increase of compressive stresses in the active layer , and this will trigger the appearance of new collapsed sectors in order to relieve the accumulated elastic energy . Our modeling is compatible with the hypothesis that when a new vein has been nucleated in an actual leaf , it will continue to grow at the same pace than the rest of the leaf . In particular its thickness should increase with time . In our modeling , due to our zooming out procedure this means that veins must preserve its width during the evolution and newer veins are progressively thinner than older ones . In order to achieve this , we have to avoid that the older ( thicker ) veins become thinner as the spatial scale in the system is changed . As we said , this implies a kind of irreversibility condition that guarantees that when a new vein was created , it is committed to grow at a fixed rate . The implementation of the irreversibility condition in the model is as follows . We include the condition that Φ ( x , y ) in the time step t+dt can not be in the relaxed phase if its value in the previous time step corresponds to the collapsed phase . This is done by defining a threshold value Φ0 , namely , if at a certain stage of the simulation some point has a value Φ ( x , y ) >Φ0 , then this point is forced to remain with a value of Φ at least as large as Φ0 . Our numerical results indicate that the final patterns are reasonably independent on the value of the threshold we use to define each phase . Irreversibility is what stabilizes the existence of thick veins , as can be observed in Figure 9 , where we show a typical profile of Φ for a fixed value of x at two different stages of the growth . In this plot , values of Φ close to 2 represent the section of a vein , whereas negative values of Φ are intact sectors . These results where obtained by using a value Φ0 = 2 . Note in the bottom panel how the interface sharpness is greater ( because of the increase in the effective C ) and how the new nucleated veins are significantly thinner . It is worth emphasizing the effect that the term that was used to generate irreversibility has on the simulations . In the absence of this term , the same parameters which lead to the snapshots of Figures 2 and 3 , produce now patterns like that in Figure 10 . A lateral wandering and thinning of veins during evolution is clearly observed . As a consequence , the hierarchical structure is completely lost . Note that in actual leaves a mechanism generating a similar kind of irreversibility can be claimed to be present . In fact , once the germ of a vein has been nucleated , all daughter cells are committed to become part of the vein . This is why older veins are thicker and it is an additional ingredient on top of mechanical energy minimization . We also include in our model a stochastic noise of small amplitude that helps to nucleate new veins . The evolution equation becomes dΦ/dt = −δF/δΦ+fT , where fT is a stochastic force with the properties 〈fiT〉 = 0 and 〈fiT ( t ) fjT ( t' ) 〉 = 2 kB T δ ( t–t' ) δij . The existence of random noisy effects on the growing of an actual leaf cannot be denied , and then our inclusion of a stochastic term in the evolution equation could be ultimately justified . However , we emphasize that we do not intend to model any precise physical process with this . We only want to include in a simple form the fact that there is some randomness in the nucleation events , which eventually make individual leaves of the same species to differ from one another . In order to be sure that the stochastic term does not introduce systematic spurious effects , we have explored the effect of the noise by applying it in three different ways: 1 ) a ‘static version’ in which the noisy term is included only in the initial condition , 2 ) a dynamic noise as described in the previous paragraph , and 3 ) an intermediate version , in which a fixed noisy landscape affect the leaf during its evolution . We found that the main characteristics of our patterns as well as its statistical properties are the same in the three cases . Then we present results only for the noisy dynamics , which in addition we consider to be the most realistic one , as fluctuations at the cellular level produced by discrete cellular division events can be considered as some sort of noise during the growing process . | Leaf venation patterns of most angiosperm plants are hierarchical structures that develop during leaf growth . A remarkable characteristic of these structures is the abundance of closed loops: the venation array divides the leaf surface into disconnected polygonal sectors . The initial vein generations are repetitive within the same species , while high-order vein generations are much more diverse but still show preserved statistical properties . The accepted view of vein formation is the auxin canalization hypothesis: a high flow of the hormone auxin triggers cell differentiation to form veins . Although the role of auxin in vein formation is well established , some issues are difficult to explain within this model , in particular , the abundance of loops of high-order veins . In this work , we explore the previously proposed idea that elastic stresses may play an important role in the development of venation patterns . This appealing hypothesis naturally explains the existence of hierarchical structures with abundant closed loops . To test whether it can sustain a quantitative comparison with actual venation patterns , we have developed and implemented a numerical model and statistically compare actual and simulated patterns . The overall similarity we found indicates that elastic stresses should be included in a complete description of leaf venation development . | [
"Abstract",
"Introduction",
"Results",
"Discussion"
] | [
"plant",
"biology/plant",
"growth",
"and",
"development",
"physics/interdisciplinary",
"physics",
"computational",
"biology"
] | 2008 | The Role of Elastic Stresses on Leaf Venation Morphogenesis |
Rapid reprogramming of the macrophage activation phenotype is considered important in the defense against consecutive infection with diverse infectious agents . However , in the setting of persistent , chronic infection the functional importance of macrophage-intrinsic adaptation to changing environments vs . recruitment of new macrophages remains unclear . Here we show that resident peritoneal macrophages expanded by infection with the nematode Heligmosomoides polygyrus bakeri altered their activation phenotype in response to infection with Salmonella enterica ser . Typhimurium in vitro and in vivo . The nematode-expanded resident F4/80high macrophages efficiently upregulated bacterial induced effector molecules ( e . g . MHC-II , NOS2 ) similarly to newly recruited monocyte-derived macrophages . Nonetheless , recruitment of blood monocyte-derived macrophages to Salmonella infection occurred with equal magnitude in co-infected animals and caused displacement of the nematode-expanded , tissue resident-derived macrophages from the peritoneal cavity . Global gene expression analysis revealed that although nematode-expanded resident F4/80high macrophages made an anti-bacterial response , this was muted as compared to newly recruited F4/80low macrophages . However , the F4/80high macrophages adopted unique functional characteristics that included enhanced neutrophil-stimulating chemokine production . Thus , our data provide important evidence that plastic adaptation of MΦ activation does occur in vivo , but that cellular plasticity is outweighed by functional capabilities specific to the tissue origin of the cell .
Macrophages ( MΦ ) are central to many immune and homeostatic processes and adopt a variety of activation phenotypes . During bacterial infections ‘classically activated’ MΦ produce anti-microbial effector molecules , exhibit enhanced antigen presentation capacity and produce proinflammatory cytokines . In contrast , during helminth infection MΦ are activated by IL-4Rα-signaling and called M ( IL-4 ) , or ‘alternatively activated’ [1 , 2] . M ( IL-4 ) express low levels of co-stimulatory molecules , produce molecules associated with wound healing , and are considered anti-inflammatory [1] . Of note , the MΦ activation phenotype is not fixed and the prevailing view is that MΦ can adopt a variety of activation states in response to their environment [3–6] . Although MΦ plasticity is well established , most of the data supporting this concept are based on in vitro findings or ex vivo derived cells . Moreover , full activation , especially in infectious settings in vivo , rarely occurs in 100% of the MΦ present [7–9] . Thus , the observed plasticity might be due to hitherto quiescent subsets of MΦ responding rather than true plasticity of previously activated cells . Furthermore , recent data have highlighted distinct mechanisms for MΦ accumulation in different infection settings [10] . During bacterial infection bone marrow-derived blood monocytes infiltrate from the vasculature and differentiate into anti-bacterial effector MΦ or dendritic cells ( DC ) [11 , 12] while the tissue resident MΦ population is usually lost from the site of infection in a process called the MΦ disappearance reaction [11 , 13 , 14] . In contrast the type 2 immune response associated with helminth infection can result in proliferative expansion of tissue resident MΦ with minimal recruitment of monocyte-derived cells [8 , 15] . Of note tissue resident peritoneal MΦ can originate from either prenatal sources or bone marrow derived precursors depending on the age of the animal [16 , 17] but the use of the term ‘origin’ in the context of this study refers to the tissue of origin ( blood vs . peritoneal cavity ) within the time frame of the infection . Critically , the functional significance of resident cell expansion vs monocyte recruitment is not yet clear . The finding that some helminth infections also lead to recruitment of blood monocytes [9] and that monocyte-recruited MΦ show marked disparity in the transcriptional response to recombinant interleukin 4 ( IL-4 ) as compared to tissue resident MΦ [18] , suggests important functional differences . Thus , the distinct tissue origin of MΦ during infection combined with a current lack of in vivo evidence for cell-intrinsic changes in activation state raised two questions . 1 ) Does MΦ activation plasticity occur in vivo at the level of the individual cell and 2 ) is plasticity and/or MΦ origin relevant to infection outcome ? To address these questions , we turned to co-infection models , which provide physiologically relevant insight into MΦ polarization and plasticity [19] . We utilized murine co-infection with Heligmosomoides polygyrus bakeri ( H . polygyrus ) and attenuated Salmonella enterica subsp . enterica serovar Typhimurium ( S . enterica ser . Typhimurium; SL3261 , ΔaroA ) . H . polygyrus is a natural gastrointestinal nematode parasite of mice inducing a strong type 2 immune response; infection leads to pronounced proliferative expansion and M ( IL-4 ) activation of tissue resident MΦ in the peritoneal cavity [10 , 15 , 20] . Infection with Salmonella species induces a potent pro-inflammatory response required for bacterial clearance [21] . In co-infection experiments , mice were infected with H . polygyrus and at peak expansion of resident peritoneal MΦ SL3261 was inoculated i . p . Cell-intrinsic plasticity of the M ( IL-4 ) MΦ was observed in response to bacterial infection but plasticity did not appear relevant for protection within the first 5 days of SL3261 infection . Indeed , inflammatory recruitment of monocyte-derived MΦ and neutrophils was unhampered in co-infected mice . Moreover , the inflammatory influx was accompanied by the marked disappearance of the helminth-expanded resident MΦ . Microarray and functional analyses demonstrated that nematode-expanded MΦ do adapt their activation phenotype in co-infection settings , but the tissue origin places limitations on the functional response of these cells .
In order to examine the capacity of nematode-elicited MΦ ( NeMΦ ) to change their M ( IL-4 ) phenotype , peritoneal exudate cells from mice infected 14 days previously with H . polygyrus were stimulated in vitro with lipopolysaccharide ( LPS ) , recombinant IL-4 ( rIL-4 ) or without additional stimulus . As a reference for the magnitude of the response we used non-polarised thioglycollate-elicited peritoneal MΦ , which respond well to polarising stimuli [22] . LPS stimulation induced upregulation of NOS2 expression in NeMΦ ( Fig 1A ) , which varied in magnitude relative to control thioglycollate-elicited MΦ between experiments ( S1 Fig ) . Importantly , induction of NOS2 expression in NeMΦ was consistently observed , indicating that NeMΦ retain their capacity to respond to bacterial stimuli . NeMΦ also remained responsive to rIL-4 as demonstrated by enhanced Relm-α expression ( Fig 1A ) . Of note , re-polarization of classically activated MΦ was less evident; MΦ isolated from animals harbouring a bacterial infection ( S . enterica ser . Typhimurium , SL3261 ) showed strong upregulation of NOS2 expression following stimulation with LPS , notably in excess of the response observed in thioglycollate elicited MΦ ( Fig 1B ) . In contrast only very limited non-significant upregulation of Relm-α was observed in response to rIL-4 ( Fig 1B ) . Thus , bacterial stimuli seemed to provide a more restrictive activation signal , placing limitations on the plasticity of the response and favouring anti-bacterial outcomes of activation . To assess whether the upregulation of NOS2 in NeMΦ was restricted to a subpopulation that had not previously responded to IL-4Rα signaling , we analysed co-expression of NOS2 and Relm-α ( Fig 1C ) . Irrespective of whether the isolated MΦ expressed Relm-α or not both populations showed equal upregulation of NOS2 in response to LPS ( Fig 1D ) indicating that classical activation was not restricted to previously non-responding cells . To directly confirm that M ( IL-4 ) can switch their phenotype in vivo , resident MΦ that had been expanded and activated by in vivo delivery of IL-4 complex , were transferred into the peritoneal cavity of SL3261 infected animals . The transferred cells showed equivalent induction of NOS2 expression as host MΦ ( S1 Fig ) . Thus , activation plasticity of M ( IL-4 ) also occurred in vivo despite potential competition with host MΦ for activating stimuli As described previously H . polygyrus infection leads to the proliferative expansion of peritoneal , tissue resident MΦ [15] whereas S . enterica ser . Typhimurium induces influx of blood monocyte-derived MΦ [23] . Thus , to address whether the presence of large numbers of nematode expanded , tissue resident MΦ , in an M ( IL-4 ) activation state , influenced bacterial induced recruitment of cells ( gating strategy depicted in ( S2 Fig ) we established a consecutive co-infection model . Mice were orally infected with H . polygyrus followed 9 days later by i . p . injection of SL3261 ( S3A Fig ) . In this co-infection model peak expansion and accumulation of H . polygyrus driven M ( IL-4 ) occurs before bacterial challenge ( S3B Fig ) . Effects on MΦ were analysed 5 days later , a timepoint when T cell-independent , partially NOS2-dependent control of bacterial growth can be observed [24] . All infections significantly increased the total number of cells in the peritoneal cavity as compared to naïve animals ( Fig 2A ) . As expected there was preferential influx of eosinophils in nematode infected animals as compared to a more neutrophilic inflammation in SL3261 infected mice . In co-infected animals neutrophil influx was not impeded by prior H . polygyrus infection indicating normal recruitment of these cells from the circulation . In contrast the number of eosinophils was reduced by the presence of bacteria confirming crossregulation between the anti-helminth and anti-bacterial immune response in our model . The number of MΦ in the peritoneal cavity increased both in bacterial and helminth infection , but co-infection had no additive effect ( Fig 2A ) . Consistent with the differences in tissue origin , MΦ accumulating in singly-infected nematode or bacterial infections differed phenotypically [8] , expressing high or low surface levels of F4/80 respectively ( Fig 2B ) . Consecutive co-infection led to the simultaneous appearance of both MΦ populations ( F4/80high and F4/80low ) ( Fig 2C ) . Furthermore , similar to neutrophils , F4/80low MΦ were expanded to an equivalent level between single SL3261 and co-infected animals ( Fig 2C ) . At the same time a significant reduction in the number of F4/80high MΦ was observed in co-infected relative to single H . polygyrus infected animals . Moreover , the degree of F4/80low cell recruitment depended on the inoculating dose of SL3261 and correlated with the loss of F4/80high MΦ ( Fig 2D ) . The loss of F4/80high MΦ and concomitant recruitment of F4/80low MΦ made us question whether the plastic response of NeMΦ observed in vitro ( Fig 1 ) did occur in vivo . Utilising the consecutive co-infection model described above we found no statistical difference in the induction of intracellular NOS2 expression by peritoneal MΦ in single SL3261 or co-infected animals ( Fig 3A ) . Furthermore , induction of NOS2 was not restricted to newly recruited F4/80low MΦ , as F4/80high MΦ showed at least equal if not enhanced capacity to induce NOS2 expression ( Fig 3B & 3C ) . Moreover , SL3261 co-infection led to loss of the M ( IL-4 ) activation phenotype in NeMΦ , as measured by intracellular Relm-α expression , but induction of NOS2 was independent of previous M ( IL-4 ) activation as indicated by equivalent expression in Relm-α positive and negative cells ( Fig 3D & 3E ) . Thus , nematode expanded F4/80high , resident-derived MΦ in co-infected animals showed clear and efficient induction of anti-bacterial effector mechanisms , which was further evidenced by enhanced expression of MHC-II ( Fig 3F ) . In line with the upregulation of anti-bacterial effector molecules by F4/80high MΦ and unaltered recruitment of F4/80low MΦ , no significant difference was found in the number of bacteria present in the spleen of co-infected animals as compared to single SL3261 infected animals ( Fig 3G ) . Thus , the more than 7-fold greater number of MΦ present in the peritoneal cavity of H . polygyrus infected mice at the time of bacterial inoculation ( S3B Fig ) did not provide any protection , despite their apparent activation plasticity . We considered that the inability to provide protection may be due to injection of relatively large quantities of bacteria . We therefore titrated the dose of SL3261 but no effect of helminth infection on splenic bacterial burden was detected , even when only 50 bacteria were injected into the peritoneal cavity ( Fig 3H ) . Similarly , when mice were co-infected with high doses of SL3261 ( ~3x10^6 CFU ) , no difference in resistance was observed ( S4 Fig ) . F4/80low , blood monocyte derived MΦ exposed to tissue specific factors can give rise to tissue resident F4/80high MΦ [16 , 17] . Thus , whilst the loss of F4/80high MΦ may be due to their disappearance or death , a conversion of F4/80high nematode-expanded MΦ to F4/80low MΦ could have occurred under co-infection settings . To discriminate between these possibilities we utilized partially protected chimeras in which the peritoneal cavity is shielded from lethal irradiation [8 , 25] . This protects the tissue resident peritoneal population and prevents their replacement by bone marrow-derived cells but results in a chimeric blood monocyte population ( S5 Fig ) . By comparing the proportion of donor cells within a given MΦ population with the proportion in blood monocytes it can be determined whether the population is derived from the bone marrow ( ratio equal to monocytes ) or from tissue resident MΦ ( low donor ratio ) . Peritoneal MΦ from single H . polygyrus or SL3261 infected animals showed low or high proportions of donor cells , respectively , confirming their tissue resident MΦ or bone marrow derived origins ( Fig 4A ) . Importantly the two MΦ populations present in co-infected mice displayed identical chimerism to their respective counterparts in single infected mice . The F4/80high MΦ included a very low percentage of donor bone marrow cells while F4/80low MΦ displayed similar chimerism to blood monocytes ( Fig 4A & 4B ) . The data demonstrate that no conversion between these two MΦ populations occurred and recruitment of anti-bacterial F4/80low MΦ was unperturbed by the presence of large numbers of helminth-expanded F4/80high MΦ . We further confirmed the blood monocyte origin of F4/80low cells using CCR-2 deficient mice , which fail to recruit MΦ to inflammatory sites in part due to defective egress of monocytes from the bone marrow [26] . Recruitment of F4/80low MΦ in response to SL3261 was ablated in Ccr2-/- animals both in single and co-infection whereas expansion of resident F4/80high MΦ was unaltered during H . polygyrus infection ( Fig 4C & 4D ) . Of note the disappearance of F4/80high cells observed in wildtype mice was much less pronounced in co-infected Ccr2-/- animals indicating a link between recruitment of F4/80low MΦ and the disappearance reaction . Enhanced cell death is likely part of the explanation for the loss of F4/80high MΦ as indicated by increased Annexin V staining specifically in this population following SL3261 single or co-infection in wild-type mice ( S6 Fig ) . Thus , F4/80high nematode-expanded MΦ did not convert to an F4/80low phenotype but were displaced from the peritoneal cavity during bacterial co-infection by new , blood monocyte derived MΦ . Notably , the magnitude of blood cell recruitment was the same in single bacterial or co-infected animals . The recruitment of monocyte derived MΦ in conjunction with the displacement of F4/80high MΦ following bacterial inoculation of H . polygyrus infected animals strongly suggested that these two cell populations have distinct capacities and functions during co-infection . To elucidate these differences we subjected F4/80high and F4/80low MΦ populations from co-infected animals to microarray gene expression analysis . Both populations were isolated from the same animal by fluorescence activated cell sorting ( Fig 5A ) . F4/80high MΦ from naïve or H . polygyrus single infected as well as F4/80low MΦ from SL3261 single infected animals were used as controls . The quality and reproducibility of the data was confirmed by hierarchical clustering analysis of global expression profiles , which grouped according to biological conditions ( S7 Fig ) . Principal component analysis showed F4/80high MΦ in naïve and H . polygyrus infected animals clustering together , while F4/80low MΦ in single SL3261 or co-infected animals clustered together , reaffirming the different tissue origins of these cell populations . Notably , F4/80high MΦ in co-infected animals clustered separately from either of these populations revealing a unique response profile in response to bacterial infection ( Fig 5B ) . To specifically address whether F4/80high MΦ could effectively contribute to resistance against SL3261 infection we compared expression of known resistance-associated genes [27] across all experimental groups ( Fig 5C ) . F4/80low MΦ from both single SL3261 and co-infected animals showed similar , largely enhanced expression of these genes . F4/80high MΦ in co-infected animals also showed enhanced expression of most resistance genes , including Nos2 , as compared to MΦ from naïve and H . polygyrus infected animals , confirming plastic adaptation of the activation phenotype . However , the overall response in F4/80high MΦ was muted and most resistance-associated genes had less differential expression than in F4/80low MΦ . Specifically Slc11a1 ( i . e . Nramp ) , which was significantly upregulated in F4/80low MΦ by bacterial infection , was virtually unchanged in tissue resident derived MΦ . Co-infection also induced a divergence with regard to the production of IL-1 in which Il1b expression was highly increased in F4/80low MΦ while Il1a expression was dramatically increased in F4/80high MΦ . To further highlight functional differences , we performed pathway analyses on differentially expressed ( DE ) genes ( q-value ( FDR ) <0 . 01 , log2FC ±0 . 5 ) in a pairwise comparison of F4/80high and F4/80low MΦ from co-infected animals utilising Ingenuity Pathway Analysis ( IPA ) . To avoid bias caused by different cellular origins , we compared the list of DE genes between F4/80low and F4/80high MΦ in our dataset with DE genes in F4/80low and F4/80high MΦ in naïve animals obtained from publicly available datasets ( Immunological Genome Project , http://www . immgen . org [28] ) and restricted the analysis to genes unique to the co-infection setting ( S8 Fig & S1 Table ) . This analysis revealed enrichment of DE genes in several pathways associated with anti-bacterial or pro-inflammatory responses ( Fig 5D ) . The pattern of gene expression between F4/80low and F4/80high MΦ within these pathways provided further evidence that the anti-bacterial response was less potent in F4/80high MΦ during co-infection . Of note , pairwise comparison of F4/80high MΦ from co-infected vs F4/80high MΦ from single H . polygyrus infected animals revealed anti-microbial pathways as differentially up-regulated ( Fig 5E ) . Once again , this confirmed that F4/80high MΦ during co-infection adopt anti-microbial characteristics . Interestingly , other affected pathways in F4/80high MΦ from co-infected vs . single H . polygyrus infected animals included several pathways associated with induction of apoptosis ( S9 Fig ) . Alongside the pronounced Annexin V staining ( S6 Fig ) these data suggest enhanced cell death as a major contributor to the MΦ disappearance reaction . Overall the gene expression analysis revealed that F4/80high , parasite expanded MΦ altered their activation phenotype to adapt to the bacterial infection . However , the anti-bacterial response was limited in comparison to monocyte-derived cells . Although F4/80high MΦ showed an overall limited anti-bacterial response , certain genes did not follow this pattern ( Fig 5C ) . Specifically Il1a showed divergent , unexpectedly enhanced expression ( Fig 5C ) in F4/80high MΦ from co-infected animals . This made us question whether these cells adopted functionalities other than anti-bacterial effector mechanisms . In this context Schiwon et al . recently highlighted a two-step model of inflammation with tissue resident Ly6C- and newly recruited Ly6C+ MΦ adopting different , non-redundant roles in the recruitment and activation of neutrophils during uropathogenic E . coli infection [29] . In line with these findings F4/80high MΦ from co-infected animals in our model expressed enhanced levels of key neutrophil chemotactic factors ( e . g . Cxcl1 , Cxcl2 , Pf4 ) [30] or enzymes involved in the generation of neutrophil chemotactic factors ( e . g . Alox5 , Ptgs1 ) ( Fig 6A & 6B ) . Thus , F4/80high , helminth expanded tissue resident derived MΦ seemed to retain their tissue sentinel capacity and may contribute to resistance to bacterial infection through recruitment of neutrophils and other inflammatory cells . The distinct capacity of F4/80high resident derived MΦ to promote neutrophil recruitment was supported by data from Ccr2-/- mice . Recruitment of neutrophils was only marginally reduced in SL3261 infected or co-infected Ccr2-/- mice despite failing to recruit F4/80low MΦ ( Figs 6C & 4C ) .
Inflammatory MΦ are commonly recruited through influx and differentiation of blood monocytes [12 , 31] whereas some helminth infections , and Th2 cytokines , lead to the proliferative expansion of tissue resident MΦ [10] . The functional importance of this divergence in recruitment is not completely clear , yet helminth infections can last years and avoidance of chronic inflammation might be the evolutionary driver of this phenomenon [10 , 32] . Furthermore MΦ are well known to flexibly adapt their activation phenotype to changes in their environment in vitro [33 , 34] . Nonetheless , limited data exist on the true plasticity of individual cells in vivo , and on the relative importance of plasticity vs . cellular recruitment . Here we show that H . polygyrus—expanded peritoneal MΦ can effectively upregulate anti-bacterial defense mechanisms ( i . e . NOS2 , MHC-II ) in response to bacterial stimulation in vitro and in vivo . Importantly , plasticity was not restricted to newly recruited or previously non-activated cells . However , the magnitude of the response was limited in the resident population and MΦ instead adopted specific characteristics dependent on their tissue origins . Thus , plasticity of MΦ activation as defined by a change in activation phenotype did exist in vivo , albeit with certain restrictions on the degree of repolarization . Limitations on MΦ activation make evolutionary sense to allow fine tuning of the ensuing immune response depending on the persistence and virulence of the invading pathogen [29] . Altered , often reduced responses of MΦ to a later challenge following primary stimulation have been described before [35] . IL-4 and IFNγ have been shown to induce distinct non-overlapping enhancers of gene transcription which are retained even when stimulation ceases [36] . Hence stimulation of MΦ can generate an epigenetic memory , which influences and dictates future responses [37 , 38] . Indeed , helminth infections are in general assumed to impart a detrimental effect on resistance to bacterial infections in part by altering MΦ activation [19 , 39–42] . This is also evident in our findings . F4/80low MΦ from co-infected animals showed reduced induction of anti-bacterial effector genes as compared to F4/80low MΦ from single SL3261 infected animals . However , the limiting effect of previous cytokine exposure was overlaid by the much more profound effect of the immediate origin of the MΦ ( tissue resident vs . blood monocyte derived ) . F4/80high and F4/80low MΦ in co-infected animals responded in unique ways to bacterial infection indicating differing functional roles . Notably , tissue resident colon MΦ , although originally blood monocyte derived , have been shown to maintain a similar restricted and preferentially anti-inflammatory phenotype in the face of inflammation [43] as do the peritoneal MΦ discussed here . Furthermore exposure of MΦ to tissue environmental factors has been described to affect and shape MΦ responses to infection [16] . Therefore , independent of embryonic or bone marrow derived origins , tissue MΦ responses seem largely determined by previous exposure to tissue factors and adoption of a resident phenotype . Thus , plasticity of MΦ activation as defined by the adoption of a full anti-bacterial phenotype by helminth expanded MΦ did not happen in vivo . Rather tissue resident derived MΦ , whether expanded by helminth infection or not , responded in a unique , non-redundant fashion to the bacterial infection likely necessary for an optimal induction of the immune response . Whether these unique properties of tissue resident MΦ are of functional relevance to the expansion of these cells in some helminth infections [10] but not others [9] remains unclear . Previous studies suggest that early innate recognition of bacteria has the potential to overcome the normally detrimental impact of helminth infection on resistance to bacterial infection [44 , 45] . Furthermore helminth expanded resident MΦ have been shown to exert a protective effect in models of sepsis [46 , 47] . Thus , expansion of these cells might serve the dual purpose of rapid initiation of anti-bacterial effector mechanisms while avoiding excessive , potentially self-harming immune activation dependent on the virulence and persistence of the invading pathogen . In this context it is interesting to note that in our co-infection experiments the disappearance reaction of tissue resident MΦ seemed linked to the recruitment of monocytes and monocyte derived MΦ . Although the exact mechanism remains unclear , recruited MΦ appear to displace the resident population in a time and dose dependent manner likely through the induction of apoptosis . In the light of their activation plasticity and non-redundant role in triggering an anti-bacterial immune response their displacement raises the question of why tissue resident MΦ are removed in a persisting bacterial infection ? In line with the muted pro-inflammatory response found here the MΦ disappearance reaction might reflect differences of tissue resident and recruited MΦ in the capacity to deal with intracellular pathogens [48 , 49] or their interaction with the adaptive immune system [50] . Thus , although relevant in early phases of the immune response , persistence of tissue resident MΦ may render the host more susceptible to the infection or dampen the ensuing T cell response . Taken together our data indicate that plastic adaptation of MΦ responses to consecutive co-infections does occur in vivo but is outweighed by cellular recruitment due to functional restrictions imposed by the tissue origin of the MΦ .
All animal experiments were performed in accordance with the UK Animals ( Scientific Procedures ) Act of 1986 under a Project License ( 60/4104 ) granted by the UK Home Office and approved by the University of Edinburgh Ethical Review Committee . Euthanasia was performed by giving an overdose of anaesthetic ( Ketamine/Medetomidine; 1/1; v/v ) . C57BL/6 mice were bred and maintained in specific pathogen–free facilities at the University of Edinburgh . Experimental mice were age and sex matched . Heligmosomoides polygyrus bakeri life cycle was maintained in house and infective third-stage larvae ( L3 ) were obtained as described elsewhere [51] . Mice were infected with 200 H . polygyrus L3 by oral gavage . Fecal egg burden was determined on day 13 of the infection using a McMaster counting chamber ( Hawksley ) . The attenuated , aroA deficient Salmonella enterica serovar Typhimurium strain SL3261 [52] was cultured as stationary overnight culture from frozen stock in Luria-Bertani broth . Unless indicated otherwise animals were injected i . p . with ~3-5x10^4 CFU diluted in PBS . Infectious doses and splenic bacterial burdens were enumerated by plating inocula or tissue homogenates in 10-fold serial dilutions in PBS on LB-Agar plates . IL-4–anti–IL-4 mAb complex ( IL-4c ) was prepared as described previously [53] , and mice were injected i . p . with 5 μg of recombinant IL-4 ( 13 . 5 kD; PeproTech ) complexed to 25 μg 11B11 ( Bio X Cell ) or 100 μL PBS vehicle control on days 0 and 2 , and peritoneal exudate cells were harvested on day 4 . For in vitro experiments mice were injected with 400 μL 4% Brewer modified thioglycollate medium ( BD Biosciences ) three days prior to necropsy . Mice were sacrificed by exsanguination via the brachial artery under terminal anesthesia . After sacrifice , peritoneal cavity exudate cells ( PEC ) were obtained by washing the cavity with 9 mL lavage media comprised of RPMI 1640 containing 1% normal mouse serum ( AbD serotec ) , 100 U/mL penicillin and 100 μg/mL streptomycin . Erythrocytes were removed by incubating with red blood cell lysis buffer ( Sigma Aldrich ) . Cellular content was assessed by cell counting using a Cellometer Auto T4 Cell Counter ( Nexcelom Bioscience ) in combination with multicolor flow cytometry . Equal numbers of cells or 20 μL of blood was stained for each sample . Blood samples were mixed and washed with Hank’s buffered saline solution containing 2 mM EDTA ( Life Technologies ) . Cells were stained with LIVE/DEAD cell viability assay ( Life Technologies ) . All samples were then blocked with 5 μg/mL anti- CD16/32 ( 2 . 4G2; produced in-house ) and heat-inactivated normal mouse serum ( 1:10 ) in FACS buffer ( 0 . 5% BSA and 2 mM EDTA in Dulbecco’s PBS ) before surface staining on ice with antibodies to F4/80 ( BM8 ) , Siglec-F ( E50-2440 ) , Ly-6C ( HK1 . 4 ) , Ly-6G ( 1A8 ) , Gr-1 ( RB6-8C5 ) , B220 ( RA3-6B2 ) , TCRβ ( H57-597 ) , CD11b ( M1/70 ) , CD11c ( N418 ) , I-A/I-E ( M5/114 . 15 . 2 ) , CD19 ( eBio1D3 or 6D5 ) , CD4 ( GK1 . 5 ) , CD8α ( 53–6 . 7 ) , CD115 ( AFS98 ) , CD45 . 1 ( A20 ) , or CD45 . 2 ( 104; eBioscience or BD ) . Erythrocytes in blood samples were lysed using FACS Lyse solution ( BD Biosciences ) . All antibodies were purchased from Biolegend UK unless stated otherwise . Detection of intracellular Relm-α and NOS2 was performed directly ex vivo . Cells were stained for surface markers then fixed with 2% paraformaldehyde ( Sigma Aldrich ) and permeabilized using Permeabilization Buffer ( eBioscience ) . Cells were then stained with purified polyclonal rabbit anti-Relm-α ( PeproTech ) or directly labeled Abs to NOS2 ( CXNFT; eBioscience ) , followed by Zenon anti–rabbit reagent ( Life Technologies ) . Expression of Relm-α and NOS2 was determined relative to appropriate polyclonal or monoclonal isotype control . Samples were acquired on a BD LSR II or BD FACSCanto II using BD FACSDiva software ( BD Bioscience ) and post-acquisition analysis performed using FlowJo v9 software ( Tree Star Inc . ) . Bone marrow chimeric mice were constructed by exposing anaesthetized C57BL/6 Cd45 . 1 mice to a single dose of 11 . 5 cGy γ radiation while shielding the abdomen , thorax , head and fore limbs with a 2-inch lead screen followed by i . v . injection of 5 x 10^6 donor bone marrow cells from congeneic Cd45 . 2 mice . Chimeric mice were left for 8 weeks before further experimental manipulation . For in vitro conversion experiments H . polygyrus- , SL3261- or thioglycollate-elicited PEC were counted as described above and seeded to 96-well plates at 2x10^5 cells per well in RPMI 1640 containing 5% foetal bovine serum , 2 mM L-glutamine , 100 U/mL penicillin and 100 μg/mL streptomycin and stimulated with murine recombinant IL-4 ( rIL-4 , 20ng/mL , Peprotech ) , lipopolysaccharide ( LPS , 100ng/mL; Escherichia coli 0111:B4; Sigma-Aldrich ) or medium alone for 24h and analysed for MΦ activation markers by flow cytometry . MΦ were purified using FACS-sorting on a FACSAria cell sorter ( BD Biosciences ) according to their expression of surface molecules ( F4/80+ , SiglecF- , CD11b+ , CD11c- , B220- , CD3-; all antibodies purchased from BioLegend or eBioscience ) reaching purities of above 96% . Isolated MΦ were frozen at -70C and total RNA isolated using RNeasy mini columns ( Qiagen ) . Sample preparation , quality control , running the microarray and initial bioinformatics analysis were carried out by the Bioinformatics and Genomic Technologies Core Facilities at the University of Manchester . In brief 10 ng of total RNA were converted to cDNA using the GeneChip WT Pico Kit ( Affymetrix ) and hybridized to Affymetrix GeneChip Mouse Gene 1 . 0 ST Array according to the manufacturer’s instructions . Mouse Transcriptome Assay 1 . 0 data were processed and analysed using Partek Genomics Solution ( version 6 . 6 , Copyright 2009 , Partek Inc . , St . Charles , MO , USA ) with the following options: probesets were quantile normalised and RMA background correction applied . Probesets were summarised to genes by calculating the means ( log 2 ) . Validation and gene enrichment strategies consisted of the following steps . Step 1 , to establish relationships and compare variability between replicate arrays and experimental conditions , principal components analysis ( PCA ) was used . PCA was chosen for its ability to reduce the effective dimensionality of complex gene-expression space without significant loss of information [54] . Step 2 , Differential expression analysis was performed on annotated genes with Limma using the functions lmFit and eBayes [55] . Gene lists of differentially expressed genes were controlled for false discovery rate ( fdr ) errors using the Benjamini–Hochberg procedure [56] . Step 3 , functional annotation of gene lists containing significantly differentially expressed genes was done with QIAGEN’s Ingenuity Pathway Analysis ( IPA , QIAGEN Redwood City , www . qiagen . com/ingenuity ) . Microarray data have been deposited to NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE85805 . Statistical analysis was performed using Prism 6 for Mac OS X ( v6 . 0f , GraphPad Software ) . Differences between groups were determined by t-test or ANOVA followed by Tukey’s or Dunn’s multiple comparison-test . In some cases data was log-transformed to achieve normal distribution as determined by optical examination of residuals . Where this was not possible a Mann-Whitney or Kruskal-Wallis test was used . Comparison of activation markers of F4/80high / low or Relm-α positive / negative MΦ within one experimental animal were considered as paired observations . Differences were assumed statistically significant for P values of less than 0 . 05 . | Macrophages are specialized cells of the immune system that help to keep the organism healthy by removing dead cells and debris , assisting in wound healing and fighting off many types of infections as well as tumours . To deal with these various tasks , macrophages produce distinct sets of activation molecules , tailored to the specific task at hand . Once a macrophage displays a certain activation profile it will be less suitable and potentially even detrimental to other typical macrophage tasks . However , previous reports have indicated that macrophages can lose their activation state and quickly switch to a different set of molecules should a new task arise . Here we show that in the context of consecutive co-infection with parasitic worms and bacteria the loss of one set of molecules and gain of another is limited and is determined by whether the macrophage originates from the affected tissue or has been newly recruited to the site of infection . Taken together our data indicates that tissue origin of a macrophage has a dominant impact on macrophage activation and treatments aiming to alter macrophage activation status ( e . g . in cancer ) need to consider the source of the cells in order to successfully predict outcome . | [
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"white",... | 2017 | Macrophage origin limits functional plasticity in helminth-bacterial co-infection |
The primary visual cortex ( V1 ) is pre-wired to facilitate the extraction of behaviorally important visual features . Collinear edge detectors in V1 , for instance , mutually enhance each other to improve the perception of lines against a noisy background . The same pre-wiring that facilitates line extraction , however , is detrimental when subjects have to discriminate the brightness of different line segments . How is it possible to improve in one task by unsupervised practicing , without getting worse in the other task ? The classical view of perceptual learning is that practicing modulates the feedforward input stream through synaptic modifications onto or within V1 . However , any rewiring of V1 would deteriorate other perceptual abilities different from the trained one . We propose a general neuronal model showing that perceptual learning can modulate top-down input to V1 in a task-specific way while feedforward and lateral pathways remain intact . Consistent with biological data , the model explains how context-dependent brightness discrimination is improved by a top-down recruitment of recurrent inhibition and a top-down induced increase of the neuronal gain within V1 . Both the top-down modulation of inhibition and of neuronal gain are suggested to be universal features of cortical microcircuits which enable perceptual learning .
Since Plato's Allegory of the Cave ( 360 BC ) and Kant's Critique of Pure Reason ( 1787 ) , it is often suggested that our perception of objects in the outer world can never tell us what they really are . “If men had green glasses in place of their eyes , they would perceive the objects as green , and never be able to tell whether this color was intrinsic to the objects or just of our perception” ( letter of Heinrich von Kleist to his fiancée Wilhelmine von Zengen , 22 March 1801 , in which he describes Kant's ideas , http://www . kleist . org [in German] ) . In a contemporary neuroscientific version of the empiricist's position , one may argue that the perception of visual objects is always distorted by the nonlinearities in the visual pathway , and in particular by the intrinsic circuitry of primary visual cortex ( V1 ) . In fact , any visual input is filtered by the neuronal processing in V1 before reaching consciousness . For instance , collinear edges are enhanced by the intrinsic V1 circuitry [1] , and our brightness perception will never match the physical luminance . Nevertheless , perceptual training without teacher feedback may still improve our brightness discrimination abilities [1] , casting certain doubts about the strict empirical view . How then is it possible to reach more veridical perceptions by just “pure reason , ” i . e . , by intrinsically adapting the cortical dynamics without being told about the mismatch between percept and true physical quality ? We show in a model that top-down modulation of V1 during unsupervised perceptual learning can suppress intrinsic nonlinearities in V1 . The top-down suppression leads to a faithful neuronal representation of the sensory input . The underlying neuronal mechanisms are elaborated in an example of brightness discrimination . In this example , a flanking light bar which is closely aligned in prolongation of a test bar acts as a visual context . This flanking bar biases the brightness perception of the test bar . In the presence of the flanking bar , the test bar is perceived to be brighter than it actually is . Clearly , this enhanced brightness perception is helpful when extracting collinear line elements against some noisy background [2 , 3] . However , when the task consists of comparing the brightness of the test bar with a displaced single reference bar , then the collinear flank distorts the brightness comparison [4] . The brightness of the test bar is overestimated because the underlying neuronal population representing the test bar within V1 is recurrently excited by the corresponding population representing the collinear flanking bar [1] . We show that top-down input can remove this contextual bias by activating recurrent inhibition within V1 . The recurrent inhibition cancels the lateral excitation and linearizes the brightness representation of the test bar , allowing for a faithful perception . An additional top-down induced gain increase in V1 further enhances the sensitivity to brightness differences . Perceptual learning , i . e . , the change of perception following sensory experiences , is typically explained as a modification of either the feed-forward synaptic pathway to V1 [5–7] , or recurrent connections within V1 [8–11] triggered by repeated practicing . Because these synaptic modifications would affect any input stream through V1 , however , perceptual learning would inevitably deteriorate the information processing in other situations . Although negative transfer of learning to other tasks is known to appear ( see , e . g . , [12] ) , perceptual learning is typically task-specific and does not deteriorate perception in other tasks; see , e . g . , the reviews [13 , 14] . While improving in brightness discrimination between a context-modulated test bar and a displaced reference bar , for instance , the edge detection capability is expected to not suffer . In fact , the mutual enhancement of collinear light bars is advantageous for extracting lines in a noisy scene , as required for contour integration in everyday scenes [4] . Hence , models of perceptual learning have to explain how improvement on one task is possible without interference with others . An intriguing possibility is that perceptual learning might be based on modifying a task-dependent top-down input to sensory areas , as opposed to a permanent change of the bottom-up input stream [15–17] . Taking up this idea we show how top-down signaling from a higher cortical area to V1 could modify the neuronal processing in this lower area , consistent with both electrophysiological recordings in V1 and psychophysical experiments on perceptual learning .
We first modeled the in vivo experiment which reveals a nonlinear facilitation of V1 neurons triggered by collinear flankers in their extra-classical receptive field ( see [18] and Figure 1 ) . According to this experiment , the response of a V1 neuron to a light bar in its receptive field is stronger when an additional collinear bar is present nearby . This second flanking bar alone does not evoke any response as it is outside the receptive field . However , if it appears together with the one within the receptive field , the response is almost doubled ( Figure 1A ) . The same nonlinear response properties are also present in the model neuron ( Figure 1B ) . This receives direct input from the stimulus within its receptive field , but only indirect input from the collinear flank outside the receptive field ( Figure 2A ) . The indirect , lateral input by itself may only drive the neuron towards firing threshold , not above . Together with the direct , supra-threshold input , however , it visibly adds to the response of the model neuron . Because the full model network is recurrent and includes a positive feedback loop ( Figure 2A ) , a quantitative description of the different neuronal responses requires some formal treatment . A minimal network model consists of two mutually excitatory neuronal populations , the first of which is directly driven by the test bar , and the second by the flanking bar . To reveal the main idea , we identify each of these populations with a single prototypical neuron reflecting the dynamics of the whole population . In a further simplification , the firing rates of these two prototypical neurons representing the test and flanking bar ( f1 and f2 , respectively ) are considered to be threshold-linear functions of the total synaptic inputs , where g ( =1 ) is the neuronal gain , xi the feedforward input of the corresponding light bar , wij the lateral synaptic strength from neuron j to neuron i ( with i ≠ j ) , and θ the firing threshold . The brackets [z]+ = max ( 0 , z ) denote the identity function with cut-off at 0 . Although the full model takes account of the neuronal dynamics ( see Materials and Methods ) , the steady-state considerations presented here and below are enough to understand the results . When stimulus 1 ( test bar ) is presented alone , say with input strength x1 = 2θ , neuron 1 will respond alone with strength f1 = θ , provided that the synaptic strength from neuron 1 to neuron 2 ( w21 ) is not too strong . In turn , if only stimulus 2 ( flanking bar ) is presented , neuron 1 will not respond because the second neuron ( which then fires with f2 = θ ) will not drive neuron 1 above threshold ( say that w12 = w21 ≈ 0 . 5 ) . However , when both stimuli are present ( x1 = x2 = 2θ ) , the two neurons mutually excite each other and their firing rate is roughly doubled ( fi = g ( xi − θ ) / ( 1 − gwij ) ≈ 2θ , for both i = 1 , 2 ) . This formally explains the strong response increase to two collinear light bars extending across and beyond the receptive field ( Figure 1A and 1B ) . Although the mutual excitation between the collinear edge detectors is beneficial for extracting lines , it may be detrimental for other tasks such as brightness discrimination . How is it possible to suppress the perceptual distortions imposed by the recurrent V1 circuitry ? Cutting off or permanently modifying the lateral connections through learning is not a solution since otherwise the facilitation effect would be lost when needed to extract line segments in a noisy surrounding . However , it is possible to compensate for the lateral excitation in V1 by a top-down recruitment of inhibition . Different wirings are conceivable which yield a cancellation of the lateral excitation . Recurrently connected feedback inhibition through tightly coupled inhibitory networks is a characteristic cortical architecture [19–21] , and it is particularly challenging to test whether such a circuitry may also serve for suppressing the facilitation . In fact , by driving recurrent inhibition within V1 , each neuron can be inhibited by approximately the same amount as it is facilitated by its surrounding excitatory neurons . Since this recurrent inhibition is enabled by the top-down input , the suppression can transiently be turned on when required by the task , without leaving long-lasting modifications of the intrinsic V1 circuitry . To test these ideas , we considered a population of inhibitory neurons driven by both excitatory neuronal populations within V1 and some task population in a higher cortical area . Again , each population is identified by a prototypical neuron . We assume that the firing rate of the inhibitory neuron is a threshold-linear function of the total input with gain one , where f1 + f2 is the total firing rate of the V1 neurons representing the test bar and the flank , , is the effective postsynaptic rate at the synapse projecting from the task neuron to the inhibitory neuron , and θinh is the firing threshold for inhibition ( Figure 2B1 ) . We further assume that the top-down synapses undergo short-term synaptic depression such that the effective postsynaptic current becomes the same for any strong presynaptic firing rate [22 , 23] . Whenever the top-down firing rate from the task neuron is turned on , say with ftask > 25 Hz , then the effective postsynaptic rate reaches some saturation value . We chose the strength of synaptic depression such that this saturating rate roughly cancels the firing threshold , ≈ θinh . Due to saturation , this approximate equality remains true even when the top-down firing rate ftask is strengthened after perceptual learning . Hence , when the top-down drive is strong , the firing rate of the inhibitory neuron ( Equation 2 ) gets linearized , If the inhibitory neuron recurrently projects to the two excitatory neurons with strength k , the latter receive the additional inhibitory current −kfinh = −k ( f1 + f2 ) . Adding this to the total postsynaptic current of the excitatory V1 neurons ( Equation 1 ) leads to the recurrent firing rate equations Next we assume that the strength of the inhibitory and the recurrent excitatory synapses are roughly equal in absolute strength , k = wij . The additional drive from the collinear flank is then canceled by the recurrent inhibition , simplifying Equation 4 to fi = g [xi−kfi−θ]+ . Solving this equation for the postsynaptic firing rates fi then yields The reasoning shows that the response of each excitatory V1 neuron in the presence of strong top-down input is approximately a threshold-linear function of the feed-forward input , independent of the firing rate of the other neurons ( Equation 5 ) . The recruitment of recurrent inhibition virtually breaks up the excitatory recurrent circuitry ( symbolized by Figure 2B2 ) . As a consequence , a particular V1 output neuron in the presence of strong top-down input will only respond to a light bar in its classical receptive field , and it is only marginally affected by an additional light bar in its non-classical surround ( Figure 1C ) . This suppression of the surround modulation induced by focal attention is partially confirmed by single cell recordings in monkeys ( it was confirmed to be the case for two monkeys before learning , and it became pronounced for one of the two monkeys after learning , see [18] ) . Before being ready to explain the perceptual learning results , we need to introduce an additional feature to our model network . Unsupervised training and focal attention has been shown to improve brightness discrimination in two specific ways: ( i ) the bias imposed by a collinear light bar is suppressed and ( ii ) the sensitivity to brightness discrimination is enhanced [1 , 4] . While bias suppression could be explained by the suppression of the recurrent feedback ( Equations 1 to 5 ) , the sensitivity enhancement could be explained by an additional gain increase of V1 neurons . One candidate neuron for a top-down induced gain increase would be the layer 2/3 ( L2/3 ) pyramidal neurons within V1 , the “input neurons” in our model network . However , a gain increase in these neurons would roughly be canceled by the simultaneous suppression through recurrent inhibition we are postulating . In fact , a look at Equation 5 shows that the gain of the local V1 microcircuitry in the presence of the top-down suppression , α = g/ ( 1 + kg ) , saturates quickly when increasing the gain g of the neuronal transfer function . To at least overcome the effective reduction of the circuitry gain when recruiting inhibition ( acting through k ) , we still assume that the top-down input to the L2/3 pyramidal neurons increases their gain g . This gain increase is modeled by a nonlinearly increasing and saturating function of the top-down frequency ftask , similarly to the one measured in vitro [24] . For instance , a top-down activated gain increase from g0 = 1 to g1 = 2 keeps the original network gain α constant when co-activated with recurrent inhibition ( α0 = g0 = 1 and α1 = g1/ ( 1 + kg1 ) = 1 with k = 0 . 5 ) . To nevertheless achieve a net gain increase of the whole network , we assume that L2/3 neurons feed through layer 5 ( L5 ) pyramidal neurons before projecting to higher cortical areas ( see , e . g . , [25 , 26] ) . We incorporate a top-down gain increase also in these L5 pyramidal neurons ( Figure 3A1 ) . Consistent with the experimental findings [24] , the gain of the L5 pyramidal neurons , denoted by gx̃ , is again modeled by a monotonically increasing and saturating function of the top-down firing rate ftask . The overall circuitry gain α then has the form While for top-down input ftask < 8 . 5 Hz we have a gain gx̃ = 1 of the L5 pyramidal neuron , we get an additional gain factor gx̃ = 1 . 5 for ftask = 10 Hz and gx̃ ≈ 3 . 3 for ftask = 45 Hz , for instance . The top-down recruitment of inhibition and the top-down gain increase are the two key elements which explain perceptual learning in the brightness discrimination task considered in [1 , 18] . In this task , a subject ( human or monkey ) has to judge whether one of four randomly chosen test bars is brighter or dimmer than a reference bar ( Figure 3A ) . A preceding cue indicates whether the subject has to attend to one ( focal attention ) or all four test bar locations simultaneously ( distributed attention , Figure 3A1 ) . To investigate the effect of collinear light bars onto brightness perception , collinear flanks were placed outside each of the four test bars in half of the stimulus presentations ( Figure 3A2 ) . No feedback on the correctness of the brightness decision was given , neither in the experiment nor in the model . The model architecture consists of three V1 pyramidal neurons in L2/3 ( again each representing a population of those ) with receptive fields at the position of the relevant test bar , the flanking bar , and the reference bar , respectively ( Figure 3B1 ) . The neurons responding to the test and flanking bar are recurrently connected through direct excitation and shared inhibition , while the neuron responding to the reference bar is indirectly inhibited only by its own drive . Attention acts through a task population in a higher cortical area which itself modulates the gain of the L2/3 and L5 pyramidal neurons and drives the inhibitory neurons within V1 towards firing thresholds . As compared with the top-down induced gain increase , the top-down drive of the inhibitory neuron is assumed to saturate earlier by means of synaptic depression ( see inset of Figure 2B1 ) . The decision about the brightness difference between test and reference bar is modeled as a stochastic function of the difference between the L5 output activities fx̃test − fx̃ref , as it can be implemented with a classical decision making network [27]: the more fx̃test exceeds fx̃ref , the more likely will the test bar be judged to be brighter than the reference bar ( see Figure 3B2 ) . We assume that the comprehension of the task by the subject implies the selection of an appropriate decision network in a higher cortical area . This decision network combines the potentially distorted , but relevant , inputs from the lower area , fx̃test and fx̃ref , while suppressing the irrelevant input from the flank neuron , fx̃flank . We assume that without external feedback about the outcome of the decisions , these bottom-up connections to the decision network are not modified . Prior to brightness discrimination training , the top-down input in the case of distributed attention is too weak to activate inhibition within V1 . The top-down drive is therefore also too weak to suppress the recurrent excitation between the test and the flanking bar ( cf . Equation 1 ) . Due to the unbroken recurrent excitation , the flanking bar enhances the V1 activity and shifts the brightness perception of the test bar towards higher values ( facilitation , see also Figure 1B ) . This brightness shift implies a bias in brightness discrimination in favor of the test bar as compared with the reference bar ( Figures 4A1 and 5A1 , before learning ) . Perceptual learning in our model consists of increasing the drive from attentional centers to the task population through Hebbian modification of the synaptic strength watt ( Figure 3B1 ) . Because in the case of distributed attention the attentional input is only weak , say = 16 Hz , and because we assume that before learning the synaptic strength is weak as well , = 0 . 5 , the task neuron is barely activated , = ≈ 8 Hz . During training , long-term potentiation ( LTP ) of the attention-to-task synapse ( watt , Figure 3B1 ) steadily increases towards = 1 . 0 . At the lower area , the increasing firing rate of the task neuron then drives the inhibitory neuron towards threshold ( → θinh , see Equation 2 ) . As a consequence , the intrinsic V1 circuitry is suppressed ( cf . Equation 5 ) and the perceptual bias is reduced ( Figures 4A1 and 5A1 ) . Simultaneously to the recruitment of inhibition , the training-based increase of the top-down input during distributed attention , , leads to a gain increase of the L2/3 pyramidal neurons from g0 = 1 . 0 to g1 ≈ 2 and in the layer 5 pyramidal neurons from gx̃0 = 1 . 0 to gx̃1 ≈ 3 ( cf . Equation 6 ) . This gain increase causes the threshold in brightness discrimination to drop ( Figures 4A2 and 5A2 ) . Both the reduced brightness facilitation and the reduced discrimination threshold in the case of distributed attention closely reproduce the experimental observations ( Figures 4B and 5B ) . In the case of focal attention , the facilitation and discrimination threshold are already reduced before learning and do not substantially decrease further during the learning process ( Figures 4B and 5B ) . In our model , this arises because focal attention drives the task neuron considerably above the critical frequency for synaptic depression and also above the gain modulation threshold , even before learning ( = ≈ 24 Hz ) . As a consequence , inhibition and gain increase are present right from the beginning , reflecting the corresponding high performance in brightness discrimination . The performance does not further improve during learning due to saturation effects . Because synaptic depression limits the drive of the inhibitory neuron , the bias in brightness discrimination is not further reduced . Similarly , because the gain increase saturates with strong top-down input , the discrimination threshold does not further decrease , in full agreement with the psychophysical data ( Figures 4 and 5 ) . While in our model the unsupervised learning is purely top-down driven , an external feedback may additionally modulate bottom-up pathways to the decision network in the higher cortical area . Assuming that the decision circuitry for distributed and focal attention is the same also for learning with feedback , we would expect interferences between the modifications of the bottom-up and top-down pathways . Since subjects are not aware of their progress during learning [1] , it is in fact likely that the same readout circuitry is used for distributed and focal attention . An interference induced by the plasticity in the bottom-up and top-down pathways is indeed observed in the model . The teacher feedback is used to modify the synaptic strengths of all three types of L5 inputs to the decision network , fx̃test , fx̃flank , and fx̃ref ( Figure 3B1 ) . We apply a specific form of reinforcement learning to these synapses , an error-correcting learning rule which changes the synaptic strengths only when a wrong decision occurs . Upon missing reward , the synapses are modified in an anti-Hebbian way: if the postsynaptic neuron was erroneously active , the activated synapses weaken , and if the postsynaptic neuron was erroneously silent , the activated synapses strengthen . These correction steps enhance the chance that with the next presentation of the same stimulus the decision network will correctly respond—as far as this is possible in the presence of the brightness distortion imposed by the intrinsic V1 circuitry . Simulations show that the facilitation bias is rapidly reduced in the initial phase ( Figure 6A ) . This early progress is enabled by the fast learning of the feed-forward synapses onto the decision network , as compared with the slow learning of the attention-to-task synapses considered before . Because distributed and focal attention are randomly interleaved , the synaptic strengths on the decision network converge to an average between the optimal strength for distributed and focal attention . To compensate for the intrinsic network bias in V1 , the fast feed-forward learning causes the facilitation to undershoot in the presence of focal attention , while staying positive in the presence of distributed attention ( Figure 6A , up to 11 weeks ) . The simultaneous top-down learning eventually leads to a suppression of the perceptional bias , and facilitation slowly vanishes for both attentional states ( in contrast to the learning scenario without external feedback , see Figure 4A1 and Figure 4B1 ) .
The top-down model of perceptual learning has several advantages over models which either change the lateral connections within the sensory area , or which change the feed-forward ( bottom-up ) connections to a read-out population . First , models which intrinsically change the early stimulus representation [8–11] can explain perceptual learning only at the expense of a degradation on other tasks . A task-specific top-down input , instead , can specifically suppress or enhance a certain pre-wiring without interference with other tasks . Second , models which explain perceptual learning by only adapting the read-out connections to a higher cortical area [5 , 6 , 17] have the problem that the specific sensory information required to solve the task may have been suppressed by nonlinearities in the early sensory area , and no learning in the subsequent read-out connections could recover this information . Although these models could explain the task-specificity of perceptual learning by switching the read-out populations for different tasks [7 , 17] , it remains unclear how such a switch should be implemented in neuronal terms . One option would be that the cognitive representation of the task in a higher cortical area would gate the activity to the appropriate read-out population while suppressing the other inappropriate read-out units . However , such a gating would again involve top-down projections , and it appears to be simpler to directly modulate the early stimulus representation by such a top-down signal . Besides solving the task-switching problem , a task-dependent top-down modulation might also explain the longevity of perceptual learning which is not disturbed by repeated practicing of other tasks ( see [16] and the review [29] ) . The top-down modulation is also consistent with the observation that perceptual learning in monkeys did neither change the receptive field size [30] nor the orientation tuning in V1 [31] during the performance of the trained task . We assume that a cognitive understanding of the task implies the selection of a task population in a higher cortical area with appropriate top-down projections to V1 . Similarly , we assume that a decision population in a higher cortical area is selected which is driven by appropriate projections from V1 . Such a pre-wiring must exist because no feedback from the external world about the performance in the perceptual task is given which may first shape the required synaptic connectivity . The synaptic top-down template encompasses the drive of the inhibitory populations together with the drive to the apical trees of the pyramidal neurons in V1 . The bottom-up template selects a read-out network in some higher cortical area with an appropriate weighting of the feed-forward input . Both selection processes could themselves emerge from experience-dependent synaptic modifications during development [32] or during learning . For instance , it is conceivable that during the exposure to similar tasks , certain synaptic templates emerged based on intrinsic reinforcement signals or on a Hebbian type of synaptic plasticity [15 , 16] . These templates might be acquired subconsciously or even without explicitly performing a task [33] . The top-down modulation of the intrinsic V1 circuit during a task allows attention to operate through the same top-down template . In our model , the task neuron projecting down to V1 is directly driven by attention , making attention itself task-specific ( Figure 3B1 ) . Without external feedback , perceptual improvement is only possible in the case of weak , distributed attention ( Figure 4B ) . Since learning in this case consists of strengthening the top-down template , it can be mimicked by increasing the attentional drive . In fact , the performance for distributed attention after learning reached the same level as for focal attention before learning . Learning with focal attention is not further possible because the common top-down pathways saturate . However , additional feedback on the correctness of the response may further lead to a fast reduction in the decision bias by modifying the readout synapses targeting the decision center ( compare Figure 6A with Figure 4A1 ) . In general , the fast initial progress often seen in perceptual learning [29] may reflect the adjustment of bottom-up connections to higher cortical areas , while the slow components of learning may follow the adjustment of top-down connections . We hypothesize that perceptual learning is always accompanied by a top-down modulation of the lower sensory area . The top-down input may act in a twofold manner on the sensory area: ( i ) it may suppress ( or enhance ) the lateral connectivity by driving inhibition and ( ii ) it may modulate the gain of the pyramidal neurons . In functional terms , these top-down templates will ( i ) “de-contextify” ( or “contextify” ) the stimulus representation to suppress ( or enhance ) the perceptual bias and ( ii ) sharpen the stimulus representation to improve the discrimination sensitivity . Depending on the task , the two ingredients may be of different importance . If perceptual learning mainly consists in lowering some discrimination threshold such as in hyper-acuity tasks [16] , a top-down gain increase may be enough . If perceptual learning includes the suppression or enhancement of intrinsic nonlinearities such as in context-enabled contrast discrimination [8 , 34] or in a bisection task [35] , the modulation of the intrinsic circuitry will become crucial . Recent research has started to uncover these top-down templates [22 , 32 , 36] , similarly to the uncovering of the bottom-up templates in terms of the neuron's ( bottom-up ) receptive fields . To implement the suppression of the flanking bar , we made use of a population of electrically coupled interneurons which inhibits a group of pyramidal neurons and receives feedback from these . Such a negative feedback circuitry represents a universal building block of the neocortex [19–21] . The same global inhibition can also enable competition among the pyramidal cells when operating in a high gain regime . This competition may enable winner-take-all behavior as it is used for decision making [27] . In fact , our decision network in the higher cortical area could be implemented by a similar local microcircuit used to linearize V1 and consisting of two ( self- ) excitatory populations which are both recurrently connected through the same inhibitory population ( see Figure 2B1 ) . As we were showing , the same canonical microcircuitry can be modulated to yield the suppression of brightness facilitation . While the top-down recruitment of recurrent inhibition is one way to suppress lateral excitation , other local architectures in V1 yielding the desired suppression are also conceivable . The psychophysical phenomena alone are not constraining enough to postulate a unique neuronal implementation of the suppression effect . To make our additional model assumptions transparent , we recall the three psychophysical results the model explains . ( 1 ) Repeated practicing can reduce the facilitation in brightness discrimination induced by a flanking bar . ( 2 ) Focal attention without practicing can equally reduce facilitation , but the effects of perceptual training and focal attention do not add up when they are combined . ( 3 ) Similarly , repeated practicing and focal attention each may decrease the brightness discrimination threshold , but their combination does not lead to a further decrease . An explanation of these phenomena requires at least three neuronal mechanisms operating on the early sensory area . ( 1 ) To account for the cancellation of lateral excitation during learning , we need to postulate some specific inhibition which is instantiated by the learning process . ( 2 ) Because focal top-down attention is equally effective in canceling lateral excitation as slow perceptual learning is , we also postulate a direct top-down recruitment of this inhibition . ( 3 ) Since the reduction in the brightness discrimination threshold is equivalent to an increase in the signal-to-noise ratio , we postulate a multiplicative enhancement of the sensory signal in the early representation . Again , because the threshold reduction can be induced by attention , the multiplicative modulation must be governed by a higher cortical center . Hence , the top-down recruitment of inhibition in V1 together with the top-down modulation of the neuronal gain represent the minimal number of assumptions which can account for the psychophysical data . Our two additional assumptions that the top-down drive should roughly match the threshold for inhibition , and that the synaptic strength of the feedback inhibition should roughly match the synaptic strength of the lateral excitation , are a consequence of explaining the suppression effect by means of recurrent inhibition . Other ways of implementing the top-down suppression may not need these additional assumptions . For a generalization of the suppression mechanism to multiple neurons and for alternative wirings , see below and Figure S1 . As a first alternative explaining the top-down suppression , we may consider the scenario of non-recurrent lateral inhibition . Each excitatory neuron which laterally projects to a target neuron is postulated to also project through an inhibitory companion neuron onto the same target neuron . Without top-down input , the companion neuron is silent , but in the presence of a top-down depolarization it inhibits the target neuron as strongly as this is excited , effectively canceling the lateral excitation . Besides being highly specific , such a wiring suffers from the same problem of fine-tuning ( see Figure S1 ) . As a second alternative , all excitatory lateral connections onto the target neuron might first be funneled through a specific population of excitatory neurons before they effectively excite the target neuron . This additional population just has to linearly feed through the excitatory input . But top-down input can now easily inhibit this population and cut off any lateral excitation , without affecting the activity of the source neurons . Although this version would require less tuning of inhibition , it makes an even stronger assumption on the lateral excitatory wiring . One advantage of the non-recurrent inhibition , though , is that it would not require the additional top-down gain increase at the intermediate layer 5 neurons ( Figure 3B1 ) . In reality , the different local suppression mechanisms discussed above might act in parallel . Whatever the specific implementation is , the top-down modulation of the suppression mechanism ( s ) remains an appealing paradigm to explain the reduction in brightness facilitation with perceptual training . The fact that top-down input may operate in different ways to achieve the same result reflects the generality and flexibility of this concept . Alternative mechanisms exist also to implement the top-down gain modulation , here required to explain the reduction in the brightness-discrimination threshold . In addition to the suggested dendritic calcium currents [24] , other mechanisms on the level of a single neuron [37–39] or of a recurrent network [40] are conceivable which may yield an appropriate top-down gain modulation . The suggested mechanisms underlying the suppression of the perception bias and the reduction of the discrimination threshold would permit a specific pharmacological modulation of perceptual learning ( see also [41] ) . ( 1 ) Any experimental manipulation which would modulate the top-down input would have behavioral implications . For instance , blocking GABA receptors in vivo in monkeys by local perfusion or in humans by medications would prevent the top-down suppression . After unsupervised practicing , the brightness facilitation for both attentional states would then be still as high as the original facilitation for distributed attention before practicing . ( 2 ) A more specific test of the model would be to activate GABAB receptors by Baclofen to prevent the gain increase of L5 pyramidal neurons [42 , 43] . As a consequence , the discrimination threshold would remain high ( Figure 6B ) , while brightness facilitation may still get suppressed . An interesting option is to lower the excitability of human V1 by repetitive transcranial magnetic stimulation ( rTMS ) [44] . rTMS may recruit inhibition and block the gain increase through GABAA and GABAB receptor activation . Again , this is expected to increase the discrimination threshold while the facilitation may still get suppressed . ( 3 ) Finally , learning in the presence of an external feedback may help to disentangle the contribution of bottom-up and top-down inputs . A teacher feedback may lead to a complete suppression of the facilitation by the flanking bar ( Figure 6A ) . Such a further reduction of the perception bias would be consistent with the effect of the teacher feedback in context-dependent orientation discrimination [17] . However , although the discrimination threshold was further reduced by a teacher feedback in a Vernier task [45] , it was not reduced in the orientation discrimination task [17] nor in our model for brightness discrimination ( simulations , unpublished data ) . In the model , the differential effect of the teacher feedback on the perception bias and the discrimination threshold arises because the teacher signal is assumed to only affect learning in the decision circuitry of the higher cortical area , and not the representation network within the lower sensory area .
To account for Weber's law stating that perception scales logarithmically with the stimulus intensity , the inputs xi into V1 encoding the test , flank and reference bar are chosen to be logarithmic functions of the stimulus brightness , xi = 35log ( Li + 1 . 5 ) , where Li ( i = 1 , 2 , 3 ) denotes the luminance of the test , flank , and reference bar , respectively . The luminance values are set to match the luminance ratios of test bar and the flank bar to the reference bar used in the experiments [1 , 18] . The reference bar luminance is fixed to Lref ≡ L3 =4 , and the test bar luminance is one out of the seven different brightness levels Ltest ≡ L1 = 1 , 2 , … , 7 ( arbitrary units ) . The luminance of the flanking bar is always slightly above the one of the test bar , Lflank = Ltest + 0 . 05 , as chosen in the brightness discrimination experiment ( Figure 3A ) . The firing rates of the prototypical excitatory L2/3 neurons ( each representing homogeneous neuronal population ) are characterized by with [z]+ = max ( 0 , z ) , a time constant τ = 20 ms , and a gain g which is a monotonically increasing function of the top-down firing rate ftask as described below . The prototypical L2/3 pyramidal neuron encoding the test bar ( i = 1 ) and flank bar ( i = 2 ) , respectively , receives the total input current Ii = xi +wijfj + λftask − kfinh ( i , j ∈ {1 , 2} , i ≠ j ) , where xi is the feed-forward input , wij the lateral synaptic strength , λ = 0 . 2 the dendritic attenuation factor for the top-down input projecting to the distal dendrite , and k the strength from the lateral inhibition . The dynamics of the two prototypical inhibitory neurons are defined by with a time constant τinh = 5ms ( for other parameter values see the caption of Figure 1 ) . For the inhibitory neuron which is related to the excitatory L2/3 neurons representing the test and flanking bar , the total input current is given by Iinh = f1 + f2 + wtask ( Figure 3B1 ) . Here , wtask denotes the synaptic strength of the top-down input to the inhibitory neurons ( Figure 3B1 ) and is the synaptic release rate undergoing short-term depression ( see Figure 1 and the definition below ) . Setting = wtask and dfinh/dt = 0 in Equation 8 yields the steady state firing rate finh = [f1 + f2 + − θinh]+ ( cf . also Equation 2 in the main text ) . The firing rate of the L2/3 neuron which encodes the reference stimulus ( f3 ≡ fref ) is governed by the dynamics ( Equation 7 ) with input current I3 = x3 − kfinh . The corresponding inhibitory neuron is again governed by Equation 8 , but with an input current Iinh = f3 + wtask , i . e . , with f1 + f2 replaced by f3 ( with i = 1 , 2 , 3 standing for “test” , “flank” , and “ref” , respectively ) . Finally , the task neuron is driven by an attentional neuron with a firing rate fatt . This attentional input is weak in the case of distributed attention , = 16 Hz , and strong in the case of focal attention , = 48 Hz . The firing rate of the task neuron is proportional to the attentional input , ftask = wattfatt , with watt being the synaptic strength from the attentional center to the task neuron . This top-down weight watt undergoes slow Hebbian modifications ( see Equation 14 below ) . The top-down input from the task neuron to V1 changes the gain of the L2/3 and L5 pyramidal neurons . The gain g of the L2/3 neurons increases with ftask according to with c = 2 . 4 , θg = 8 . 5 Hz , and τg = 0 . 8s . L5 pyramidal neurons receive a single bottom-up somatic input from their co-aligned L2/3 neurons and a top-down dendritic input from the task neuron . The overall somatic current to a L5 neuron is Ĩi = fi + λftask ( i = 1 , 2 , 3 ) , where λ = 0 . 2 is the dendritic attenuation factor and i = 1 , 2 , 3 standing for “test” , “flank” , and “ref” , respectively . The firing rate of the L5 neurons is determined by with the same time constant τ and threshold θ as for the L2/3 pyramidal neurons ( i as above ) . Similarly , the gain gx̃ monotonically increases with ftask according to the same right-hand side of Equation 9 , but with the parameter values c = 1 . 2 and τg = 0 . 4s and θg = 8 . 5Hz . This parameter choice leads to a gain function which is twice as steep and saturates at twice the level of the corresponding function for L2/3 pyramidal cells ( M . Larkum , unpublished data , see also [24] ) We introduced synaptic short-term depression in the top-down projection to the inhibitory neurons ( Figure 2B1 , inset ) . The synaptic release rate at these connections is given by the product of the release probability and the presynaptic firing rate , = prelftask . The release probability itself is a dynamic variable and is proportional to the vesicle recovery probability , prel = uprec , with proportionality constant u interpreted as a fraction of transmitter use per release . The dynamics of the vesicle recovery probability is given by where τrec is the vesicle recovery time constant , see [23] or [46] . We set u = 0 . 4 and τrec = 0 . 1s . In the steady state the release rate becomes and it is reached with an effective time constant τrec/ ( 1 + uτrecftask ) . For presynaptic frequencies ftask beyond the critical input frequency fcrit = 1/ ( uτrec ) ( =25 Hz ) , the release rate saturates at the same value ( of 25 Hz ) . Synaptic depression in the top-down connection to the inhibitory neurons is introduced to achieve a constant drive at high top-down frequencies . According to Equation 8 and its subsequent remarks , the steady state firing rate for the recurrent inhibition among the test and flanking neurons in layer 2/3 is see also Equation 2 . To obtain the linearized firing rate in Equation 3 , finh = f1 + f2 , the effective top-down input wtask must roughly match the firing threshold θinh . This requires that the postsynaptic current generated by the top-down input remains approximately constant . Such a constant drive is in fact achieved by synaptic short-term depression for ftask ≫ fcrit , as expressed by Equation 12 . To obtain the match wtask ≈ θinh , for large presynaptic firing rates we set wtask = 1 . 9 θinhuτrec . To take account of the temporal processing during the experiment underlying Figure 4 , we run the network with the same schedule for the stimulus presentation as in the experiments [1 , 18] . For each presentation , the attentional condition ( focal/distributed ) , the contextual condition ( flank/no flank ) , and the luminance of the test bar ( Li , see above ) are randomly chosen . A virtual “attentional cue” ( Figure 3A1 ) turns on top-down input from the attentional center to the task center , or ( Figure 3B1 ) , representing either “distributed” or “focal” attention and remains active throughout the stimulation protocol up to the final decision . 1 . 5 s after the attentional onset , the stimulus is flashed for 0 . 1 s . Each stimulus consists of a reference bar and a test bar with or without flank . The decision about the brightness difference between test and reference bar is drawn 0 . 9 s after stimulus offset based on the activities of the L5 pyramidal neurons at that time . In the case of unsupervised learning ( i . e . , without feedback ) , the total postsynaptic current entering in the decision function is given by Idec = fx̃test − fx̃ref ( Figure 3A2 ) . To mimic noisy neuronal decision making [27] , the decision ydec = 1 ( test bar judged to be brighter than reference bar ) is chosen with probability p = p ( Idec ) = 0 . 5 ( 1 + erf ( Idec/d ) ) , and the decision ydec = 0 with probability 1 − p , where d = 2/15 and erf ( x ) is the standard error function . After the decision making , the neuronal firing rates are reset to 0 and short-term synaptic depression is put to the recovered state prec = 1 . A “trial” consists of three ( in the experiment it was one to six ) stochastically independent stimulus presentations including decision making . To compare with the experiment [1 , 18] , we trained our model network with 600 ( in the experiment it was 500–800 ) trials per “week” . During the stimulation protocol , the strength of the attention-to-task synapse watt ( Figure 3B1 ) changes according to the Hebbian rule with a factor η = 3·10−8 s , an initial value watt = 0 . 5 , and hard bounds for watt at 0 and 1 . The modification threshold θM ( t ) is itself slowly following the postsynaptic firing rate ftask according to with an adaptation time constant τθ = 60s , a proportionality constant α = 0 . 8 , and an initial value of θM = 10Hz . In the unsupervised learning scenario , watt steadily increased until it reached the upper bound 1 after roughly 10 , 000 trials ( 17 weeks ) . All differential equations were integrated with forward-Euler using a time step of dt = 0 . 3 ms . In the case of supervised learning ( as underlying Figure 6A ) , the current entering in the decision network is given by Idec = wx̃testfx̃test + wx̃flankfx̃flank + wx̃reffx̃ref , where the wx̃i reflect the weights emerging from the L5 pyramidal neurons ( Figure 3B ) . In addition to the top-down weight watt ( Equations 14 and 15 ) , we modified the bottom-up weights wx̃i ( with i = 1 , 2 , and 3 standing for “test” , “flank” , and “ref” , respectively ) according to the perceptron learning rule [47]: whenever the output of the decision unit was correct , no modification of the wx̃i was made , while otherwise the synapses change in an anti-Hebbian way . Formally , we consider a reward signal R with R = 1 if the network decision ydec is correct , and R = 0 otherwise ( where “correct” means that ydec = 1 if the test bar is brighter than the reference bar , Ltest > Lref , and ydec = 0 if the test bar is equal or less bright than the reference bar , Ltest ≤ Lref ) . The synaptic strength wx̃i is then changed according to with learning rate q = 0 . 0001 , a modification threshold θ̃M = 0 . 5 , and i = 1 , 2 , and 3 standing for “test” , “flank” , and “ref” , respectively . Because we assume that the choice of the decision network is appropriate for an unbiased discrimination , we choose initial weights wx̃test = 0 . 5 , wx̃flank = 0 . 0 , and wx̃ref = −0 . 5 . The average reward R increases throughout the simulations from 0 . 5 towards roughly 0 . 9 . To enhance the undershoot of the learning curve for focal attention , only 15% of the presentations were with focal attention and 85% with distributed attention . During the learning procedure , the top-down weight watt is subject to the dynamics in Equation 14 and Equation 15 and steadily increases to a value of 0 . 74 at the end of the learning process ( 12 , 000 trials ) . The brightness facilitation ( shift in brightness perception of the test bar induced by the presence of the flanking bar ) and the discrimination threshold ( just distinguishable brightness difference relative to the absolute brightness ) were extracted from the psychometric curves described below using the probit method ( cf . [1] and [48] ) . After each block of 600 trials , stimuli presented under the same attentional and contextual conditions ( focal/distributed , flank/no flank ) and the same brightness were pooled together . For each pair of attentional and contextual condition , the ratio of positive responses to a particular test luminance , Ltest , was plotted against the logarithm of the relative luminance , log ( Ltest/Lref ) . The four sets of 2-D data points were fitted by a “normal cumulative distribution function” ( approximating the decision probability p as a function of the logarithmic relative luminance ) , yielding the psychometric response curves for the different conditions . Facilitation was identified by the left shift ( at the 50% correctness level ) of the fit for the flank relative to the non-flank condition . The detection threshold was identified by the maximal slope of the fitting curve for the no-flank condition . | Perceptual learning improves sensory stimulus discrimination by repeated practicing . The improved stimulus discrimination is often thought to arise either from modified stimulus representation in the sensory cortex , or from modified readout from the sensory cortex by higher cortical units . Both explanations , the modified sensory representation and the modified readout , have their advantages and disadvantages . Modifying the stimulus representation within the early sensory cortex may lead to an improvement on one discrimination task , but may have long-lasting negative effects on another task . Modifying the task-specific readout by a higher cortical area , on the other hand , prevents this undesirable interplay between tasks . However , it may be difficult for the readout units to compensate for stimulus distortions produced by interactions in the sensory cortex . Here we show that top-down modulation of the early stimulus representation combines the benefits of task specificity and of eliminating inherent distortions . The task specificity naturally arises from distinct task representations in the higher cortical areas which , by top-down signaling , reversibly improve the task-related representation of sensory stimuli . Based on a visual brightness discrimination task , we show that modifying top-down projections alone can explain psychophysical and electrophysiological data on perceptual learning . | [
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] | 2007 | Perceptual Learning via Modification of Cortical Top-Down Signals |
A crucial step in several major evolutionary transitions is the division of labor between components of the emerging higher-level evolutionary unit . Examples include the separation of germ and soma in simple multicellular organisms , appearance of multiple cell types and organs in more complex organisms , and emergence of casts in eusocial insects . How the division of labor was achieved in the face of selfishness of lower-level units is controversial . I present a simple mathematical model describing the evolutionary emergence of the division of labor via developmental plasticity starting with a colony of undifferentiated cells and ending with completely differentiated multicellular organisms . I explore how the plausibility and the dynamics of the division of labor depend on its fitness advantage , mutation rate , costs of developmental plasticity , and the colony size . The model shows that the transition to differentiated multicellularity , which has happened many times in the history of life , can be achieved relatively easily . My approach is expandable in a number of directions including the emergence of multiple cell types , complex organs , or casts of eusocial insects .
I consider a finite population of asexual haploid cells that form undifferentiated multicellular colonies by binary division . Mutation occur during cell divisions . Colonies surviving to the time of reproduction disintegrate; the released cells start new daughter-colonies . Each cell founding a colony goes through divisions so that the final colony size is cells . Each cell is characterized by viability and fertility . The former is a measure of the cell's contribution towards the survival of the colony it belongs to , e . g . via flagellar action [20] , [21] . The latter is defined as the probability that the cell successfully starts a new colony . I assume the existence of two major genes with effects and controlling cell fertility and viability , respectively ( ) . The direct effects of these genes increase the corresponding fitness components . To capture the fundamental trade-offs between cells division and locomotion capabilities [3] , [4] , [22] , I postulate indirect negative effects of on viability and of on fertility . Specifically , fertility and viability are defined using a simple multiplicative model:In the right-hand side of these equations , the first terms account for the direct effect of genes . Positive parameter controls the shape of the relationships between direct genetic effect and the corresponding fitness component . The second terms specify the reduction of a fitness component due to the need to develop/maintain the other trait . Positive parameter specifies the strength of fitness tradeoffs ( which are completely absent if ) . Because direct effects of genes are expected to be at least as strong as indirect effects , it is reasonable to assume that . The population of colonies is subject to density-dependent viability selection; all cells comprising surviving colonies can potentially form their own colonies in the next generation . Following previous work [4] , [15] , the viability of each colony is defined as the average of viabilities of individual cells ( i . e . ) . To describe viability selection at the colony level , I use a version of the Beverton-Holt model in which the probability that a colony survives to the time of reproduction depends on its viability and the overall number of colonies in the population:where is the maximum carrying capacity of the population of colonies and parameter gives the number of “offspring” of each colony . In the deterministic version of the Beverton-Holt model ( which represents a discrete-time analog of the logistic model [23] ) , the population size monotonically approaches the carrying capacity for any positive initial condition . The probability that a cell from a surviving colony does start a daughter colony is given by its fertility . By the model's assumptions , the carrying capacity of a population of identical colonies isso that increasing cell viability and/or fertility increases the number of colonies and cells maintained in the system; if the colony size is very large , . Note that in this model there is a conflict between individual level selection which favors larger values of and colony level selection which favors larger values of . Both and cannot be maximized simultaneously because of the trade-offs . Mutation occurs during the process of cell division resulting in within- and between colony genetic variation . I assume each gene mutates with a small probability per cell division . Note that if a mutation does happen , the expected number of mutant cells per colony is which is approximately [24] . I assume that mutation changes the corresponding allelic effect ( or ) by a value chosen randomly and independently from a truncated Gaussian distribution with zero mean and a constant standard deviation ( with truncation at and ) . This is a version of the standard continuum-of-alleles model [25] . Note that a mutant cell in a colony will benefit if it has a higher value of and/or smaller value of than other cells as this will increase the cell's fertility . However such a cell will decrease the colony's viability . Next I add a possibility for gene regulation . Molecular data suggest that in green algae Volvox carteri , which is a bona fide multicellular organism with a complete division of labor between two cell types [26] , the germ-soma differentiation is controlled by three types of genes [20] , [27] , [28] . First , the gls genes cause asymmetric division resulting in a large number of small cells and a small number of large cells . Then the regA gene acts in small cells supressing their reproductive development , so that they become soma , and the lag gene acts in large cells supressing their somatic development , so that they become germ . Note that the expression of the regA gene has been shown to depend on environmental factors [29] . In the model , I postulate the existence of some dichotomy in the internal and/or external environment of the cells . For example , it can be asymmetry due to the differences in their size ( large and small ) or in their spatial position ( e . g . inner and outer layer of the colony ) leading to differences in some external stimuli ( e . g . chemical or temperature ) . I call the two types of cells the proto-germ cells and the proto-soma cells . I assume that within each colony the proportion of the proto-germ cells is and that of the proto-soma cells is . I further assume the existence of two differentially expressed regulatory genes with effects and , respectively ( ) . The first gene ( analogous in action to the lag gene ) , is expressed in the proto-germ cells suppressing the effect of the “viability gene” from to . The second gene ( analogous in action to the regA gene ) is expressed in the proto-soma cells suppressing the effect of the “fertility gene” from to . These two genes control the developmentally plastic response of the cell to the gradient in the internal and/or external environment . Note that in contrast to other modifiers studied in population genetic models [30]–[32] , the two suppressor genes considered here have direct effect on fitness . This feature is common in theoretical models of phenotypic plasticity [33]–[35] . Since evolving gene suppression mechanisms and developmental plasticity is expected to involve fitness costs [36] , [37] , I assume that fertility of the proto-germ cells and viability of the proto-soma cells are reduced by factors and , respectively . In numerical simulations I used Gaussian functions:The costs grow as suppression becomes more efficient ( i . e . with deviation of and from zero ) ; positive parameter scales the costs of suppression ( larger values correspond to smaller costs ) . Gene effects on reproductive and somatic function as well as fertility and viability of the proto-germ and proto-soma cells in the general model are shown in Table 1 . The initial population of cells have all and values set at so that no gene suppression is present . I allow for mutation in the regulatory genes and describe its effect in a way analogous to that in the major loci . The complete germ-soma differentiation corresponds to and all evolving to so that germ cells have maximum fertility but cannot survive on their own while soma cells have maximum viability but cannot reproduce .
First I studied a variant of the general model in which gene regulation was absent ( i . e . , and values were set to zero ) . I used a multidimensional invasion analysis [38]–[43] and stochastic individual-based numerical simulations ( see Methods for details ) . Both methods show that in this model the major gene effects and relatively rapidly evolve towards intermediate values so that both fitness components and the population size are relatively low ( see Figure 1 ) . The inability to increase fitness is a consequences of fitness trade-offs explicitly accounted for by the model . Analytical approximations show that the equilibrium values of and satisfy to inequalities . As the strength of fitness tradeoffs decreases to , both and approach . As the colony size becomes larger , both equilibrium values converge to . If , then at equilibrium with given by a solution of an algebraic equation . In general , analytical and numerical results show that increasing the strength of selection , the strength of trade-offs , and decreasing the colony size result in decreasing both fitness components and the population size . To analyze the whole model I performed large-scale stochastic individual-based simulations that account for selection , mutation , and random genetic drift ( see Methods ) . For each run , all individuals in the initial population were genetically identical with the major locus effects and set to values chosen randomly and independently from a uniform distribution on and the suppressor effects and set to zero . The simulations show that the initial phase of evolution is typically driven by selection on the major loci whose effects evolve towards the optimum values predicted by our theory when developmental plasticity is absent ( as in Figure 1 ) . After that there are three dynamic possibilities . First , the population stays at a state in which developmental plasticity is absent ( so that and remain close to 0; Figure 2 , first row ) . Second , some developmental plasticity evolves but the resulting degree of differentiation between proto-germ and proto-soma cells is intermediate ( Figure 2 , second row ) . Third , one observes the evolution of strong developmental plasticity and complete germ-soma differentiation ( Figure 2 , third row ) . The last outcome is observed when costs of developmental plasticity are small , mutation rates are high , and fitness trade-offs are strong ( Figure 3 ) . The effects of increasing costs of plasticity and mutation rate on the plausibility of differentiation are intuitive . Indeed , less constraints and more genetic variation typically means more adaptation . But why do fitness trade-offs have such a big effect ? This happens because larger values of imply that fitness advantage of a highly differentiated state is larger . For example , for the parameter values used in the simulations the size of the equilibrium population of undifferentiated colonies is thousand . However , the size of the equilibrium population of completely differentiated colonies will be about , and thousand for and , respectively . That is , the benefit of cell differentiation for the population size ( and fitness ) increases dramatically with . The results shown in Figures 2–3 as well as in Supporting Information ( Text S1 and Figures S1 , S2 , S3 , S4 , S5 , S6 , and S7 ) are for . If , the conditions for complete differentiation are more strict . Neither the proportion of the proto-germ cells nor the colony size affect the results qualitatively . Analytical approximations for the case when the colony size is very large ( i . e . ) allow one to get some additional insights . In particular , one can find the conditions for stability of a population state with no gene regulation ( i . e . , ) towards introduction of mutations with small positive values of and . These conditions are illustrated in Figure 4 which shows that this equilibrium becomes unstable so that some gene suppression evolves if parameters and are sufficiently large and the cost of developmental plasticity is low ( i . e . is not too small ) . Moreover , one can show that if fitness trade-offs are sufficiently strong ( ) then the corresponding dynamic system has an equilibrium in which major effects have maximum possible values ( ) whereas the minor gene effects are . The later value is biologically feasible ( so that ) , if fitness costs of plasticity are sufficiently high ( ) . If , only partical gene suppression evolves . If the costs are relatively low ( ) , the analytical approximations suggest that complete gene suppression evolves ( i . e . , ) . These results are well in line with numerical simulations described above .
The model introduced and analyzed here shows the emergence of complete germ-soma differentiation . This is achieved via the evolution of developmental plasticity resulting in the suppression of somatic function in one subset of the colony's cells and of reproductive function in the remaining cells of the colony . Differential suppression of gene expression is triggered by environmental factors during development . A necessary condition for this process is the existence of sufficiently strong trade-offs between somatic and reproductive functions significantly reducing fitness . Also necessary are sufficiently high mutation rates and sufficiently low costs of developmental plasticity . With parameter values used here , complete germ-soma differentiation can evolve within a million generations . The model proposed here is simple and biologically realistic in capturing the major features of volvocine green algae biology [20] , [26]–[28] that are relevant for the germ-soma differentiation . [The model does not account for the gls genes introducing asymmetry in size between proto-germ and proto-soma cells , but asymmetric division was a late , lineage-specific step in volvocine evolution [44] . ] The results presented clearly show that fitness advantages of the division of labor in the presense of strong genetic relatedness of cells in a colony are sufficient to drive the complete differentiation of cells [2] , provided mutations that altruistically remove lineages from the germ line are expressed conditionally [10] , [45] . Conditionally expressed genes allow the benefits of altruism to go to cells that possess , but do not express , the same allele [10] . In the model , cell differentiation and the division of labor are driven by individual selection maximizing the number of colony-producing offspring of a colony-producing cell . That is , the transition to individuality can be explained in terms of immediate selective advantage to individual replicators [2] . Note that mutant cells that “cheat” by having increased fertility within colonies will tend to lose in competition at the colony level after they develop their own colonies . Therefore , the conflict between individual and colony level selection is largely removed . The division of labor is achieved by using the variation in external and/or internal cell environment as a cue to separate the colony's cells by function and then enhance different functions using different subsets of cells . The colony size has no significant effect on the model dynamics . In contrast , in Volvox the degree of differentiation between germ- and soma-like cells does correlate with the colony size [26]: species with small colonies ( 8–32 cells ) show no cell differentiation , in species with intermediate colonies ( 64–128 cells ) incomplete germ-soma differentiation is observed , and differentiation is complete in species forming large colonies ( 500–5000 cells ) . However there is a number of biological factors not included in the model explicitly but acting in real cells and colonies which should result in a positive relationship between the colony size and the degree of differentiation . First , one can reasonably argue that a sufficiently large colony size is necessary for the existence of sufficiently strong gradients in the external environment to which the regulatory genes can react to . Second , increasing the colony size should result in some spatial heterogeneity between cells in their ability to perform different functions . For example , inner-layer cells are likely to be less important in contributing towards the colony motility than the outer-layer cells . Such heterogeneity should decrease the cost of loosing certain functions for some parts of the colony and make the evolution of cell differentiation easier . Third , the total number of cells performing a particular function in very small colonies may be too small to guarantee an appropriate level of performance especially if the probability of breakage per cell is not small . A potentially important role for developmental plasticity in the evolution of differentiated multicellularity was emphasized earlier by Schlichting ( [46]; see also [29] ) but from a different perspective . Schlichting's argument was that cell differentiation started as a by-product of random environmental effects translated into new phenotypic forms via pre-existing reaction norms . Then later favorable phenotypic differentiation became canalized and stabilized via genetic assimilation process . In contrast , in the scenario considered here developmental plasticity is absent initially and emerges later as a direct result of selection . Few additional points and connections are worth to be made . First , the model assumes the existence of undifferentiated multicellular colonies . Undifferentiated multicellularity has a number of advantages ( e . g . size related ) over single-celled organization and is expected to evolve relatively easily [3] , [16] , [18] , [47] , [48] . Second , empirical data show a strong positive relationship between the number of cells in an organism and a number of cell types [5] , [49] , [50] . The classical explanation of this pattern is that increasing the number of cells changes fitness landscape ( e . g . due to physical constraints ) in such a way that differentiation and specialization become necessary for optimizing the efficiency of organisms [5] , [49] , [51] . In our simple model , the fitness landscape is unaffected by the number of cells in the colony so the model in its current form cannot be used for addressing the question about the relationsips between the number of cells and cell types . Third , the model is also relevant to ongoing work and discussions on the importance and evolution of modularity , i . e . the separability of the design into units that perform independently , at least to a first approximation [52]–[54] . Although there is an emerging agreement that organisms have a modular organization , one of the major open questions is whether modules arise through the action of natural selection or because of biased mutational mechanisms [53] . In the model considered here , the modules ( e . g . germ and soma ) clearly emerge as a result of selection for reduced fitness trade-offs . Finally , I should mention some parallels between the model's structure and dynamics and the arguments on “groundplans” [55]–[57] according to which the patterns of labor division in complex organisms and societies are built upon simple changes in the regulation of conserved ancestral genes affecting reproductive physiology and behavior . The model presented here is expandable in a number of directions including the emergence of multiple cell types , complex organs , or casts of eusocial insects . For example , the emergence of multiple cell types can be modeled by considering additional cell functions and introducing additional regulatory genes . The evolution of casts of eusocial insects can be explored by explicitly accounting for regulatory genes that react to the external stumuli ( e . g , food level or pheromones ) affected by the colony's composition . The majority of existing models of the division of labor in eusocial insects focus on individual worker flexibility in task performance [58] , [59] . In contrast , the approach introduced here concentrates exclusively on genetically predetermined roles that do not change in time . Note that genetic variation present in some insect colonies ( e . g . due to polyandry , [60] ) will result in reduced genetic relatedness and , thus , is expected to make conditions for the evolution of the division of labor more strict . The main result that complete cell differentiation evolves relatively easily and fast supports the view that the transition to differentiated multicellularity , which has happened at least two dozen times in the history of life , is in a sense actually a minor major transition [3] , [8] , [61] , [62] .
It is natural to define fitness as the expected number of offspring colonies in the next generation for a cell starting a colony . Then , for a cell characterized by viability and fertility , fitness is ( 1 ) In the model , the number of colonies of cells with viability and fertility changes approximately according toTherefore the number of colonies evolves towards the carrying capacity ( 2 ) Assuming that the ecological dynamics ( i . e . changes in the population size ) occur on the faster times scale than the evolutionary dynamics , the ( invasion ) fitness of a mutant cell in a resident population is given by eq . 1 with given by eq . 2 corresponding to the resident population . Simplifying , where the approximation is good only if . Note that the derivative of the invasion fitness function ( with respect to a particular independent variable ) evaluated at the resident population values can be written as With only major gene effects and evolving ( and minor gene effects and set at zero ) , the corresponding invasion fitness gradients areAt an equilibrium ( i . e . , at a singularity ) , . From the first equation , it follows that at equilibrium and that as . From the second equation , it follows that at equilibrium and that as . Eliminating the term from the equalities , one finds that at equilibriumwhich is greater than for . If , then with given by a solution of equation which simplifies toNote that stays above decreasing to it only asymptotically as . If , the equilibrium values of and can still be found numerically from the above system of equations . In the general model , fertility and viability of a monomorphic colony can be written aswhere and are fertilities and and are viabilities of the proto-germ and proto-soma cells ( as defined in Table 1 ) , and is the proportion of proto-germ cells in the colony . Multidimensional invasion analysis requires one to consider four invasion fitness gradients: and . Some analytical progress can be achieved if the colony size is very large ( ) . Under this condition , both major locus effects evolve to ( see the previous subsection ) . Then we can study the stability of the equilibrium with no gene regulation ( i . e . , with minor locus effect ) to introduction of mutants with small and . The corresponding invasion fitness gradients are approximated by equations linear in and :where . Assuming equal genetic variation maintained in both genes , standard linear stability analysis shows that an equilibrium with no gene regulation is locally unstable ifand is stable otherwise . Figure 4 in the main text illustrates this result . By considering the four invasion fitness gradients simultaneously ( while still assuming that ) , one can show that if , there exists a singular point at which and . This suggests that if costs of developmental plasticity are not too big ( i . e . , if , then maximum possible gene suppression evolves ( ) . Overwise , the minor gene effects stay at intermediate values ( i . e . , between 0 and 1 ) . Note that with and , the predicted values of and are which is very close to the values observed in numerical simulations with ( see the legend of Figure 4 ) . Unfortunately , similar simple approach cannot be used for an arbitrary because the equilibrium values of the major locus effects cannot be found explicitly . In numerical simulations I used all possible combinations of the following parameters: fitness trade-off coefficients , costs of developmental plasticity ; mutation rates ; number of divisions ( so that the colony size was ) ; proportion of the proto-germ cells . Mutational standard deviation was set to . The maximum carrying capacity was chosen so that the population with no developmental plasticity ( i . e . with ) evolved to a state at which the number of colonies was close to . For example , with , was set to , and for and , respectively . First , I run the model 3 times for each parameter combination each for generations . Then for parameter values resulting in no differentiation , I did one additional run for generations . A gallery of numerical results can be viewed in Supporting Information ( Text S1 and Figures S1 , S2 , S3 , S4 , S5 , S6 , S7 , and S8 ) . | Biological organisms are highly complex and are comprised of many different parts that function to ensure the survival and reproduction of the whole . How and why the complexity has increased in the course of evolution is a question of great scientific and philosophical significance . Biologists have identified a number of major transitions in the evolution of complexity including the origin of chromosomes , eukaryotes , sex , multicellular organisms , and social groups in insects . A crucial step in many of these transitions is the division of labor between components of the emerging higher-level evolutionary unit . How the division of labor was achieved in the face of selfishness of lower-level units is controversial . Here I study the emergence of differentiated cell colonies in which one part of the colony's cells ( germ ) specializes in reproduction and the other part of the colony's cells ( soma ) specializes in survival . Using a mathematical model I show that complete germ-soma differentiation can be achieved relatively easily and fast ( with a million generations ) via the evolution of developmental plasticity . My approach is expandable in a number of directions including the emergence of multiple cell types , complex organs , or casts of eusocial insects . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"evolutionary",
"biology"
] | 2010 | Rapid Transition towards the Division of Labor via Evolution of Developmental Plasticity |
Genomic resources for the domestic dog have improved with the widespread adoption of a 173k SNP array platform and updated reference genome . SNP arrays of this density are sufficient for detecting genetic associations within breeds but are underpowered for finding associations across multiple breeds or in mixed-breed dogs , where linkage disequilibrium rapidly decays between markers , even though such studies would hold particular promise for mapping complex diseases and traits . Here we introduce an imputation reference panel , consisting of 365 diverse , whole-genome sequenced dogs and wolves , which increases the number of markers that can be queried in genome-wide association studies approximately 130-fold . Using previously genotyped dogs , we show the utility of this reference panel in identifying potentially novel associations , including a locus on CFA20 significantly associated with cranial cruciate ligament disease , and fine-mapping for canine body size and blood phenotypes , even when causal loci are not in strong linkage disequilibrium with any single array marker . This reference panel resource will improve future genome-wide association studies for canine complex diseases and other phenotypes .
The modern domestic dog ( Canis lupus familiaris ) consists of over 500 breeds selected for diverse roles and subject to wildly different disease prevalences [1] . A high quality reference genome [2–4] and affordable SNP genotyping arrays [5] have helped make the dog a powerful animal model for studying the genetics of complex traits and diseases . Of 719 genetic traits and disorders in the dog , 420 are potential models of human disease ( https://omi . org/home/ ) . With an average spacing of 1 SNP every 13kb , the CanineHD array ( Illumina , San Diego , CA ) has been successfully implemented in many genome-wide association studies ( GWAS ) , especially within single breeds where linkage disequilibrium ( LD ) often extends beyond 1Mb [for example , see [6 , 7] . However , the results of many complex disease mapping studies in dogs have been underwhelming , with only one or a few significant loci identified [for example , see 8–10] . 57% of the 719 genetic traits and disorders in dogs are complex but the likely causal variant is known for only 27% of these ( https://omia . org/home/ ) . Recently , we used simulations to show that an increase in SNP density to 1 SNP every 2kb would improve power for canine complex trait GWAS [8] . An increase in density can be achieved by the following: adding more SNPs to the CanineHD array , using whole genome sequencing ( WGS ) , or using imputation to predict genotypes through the use of a reference panel created from WGS data . Of these , imputation is the most cost-effective option and has been used successfully in human and cattle GWAS , especially with the recent WGS efforts in these species [11 , 12] . Imputation has also been used in a canine within-breed GWAS of primary hypoadrenocorticism in the Standard poodle , resulting in an approximately 20-fold increase in SNP number , although this did not lead to the identification of a significant association [13] . GWAS of canine morphological traits has been very successful , due to large effect sizes and long regions of LD as a result of recent selection in purebred dogs [14] . Seventeen quantitative trait loci ( QTLs ) associated with body weight , as a proxy for body size , have been identified [5 , 8 , 15–23] , as well as associations for other morphological phenotypes such as ear flop [5 , 16 , 17] and fur type [8 , 24 , 25] . Despite the success of morphological trait mapping , we suggest that imputation can improve the power of GWAS , especially for reducing large intervals for use in fine-mapping . In addition to morphological traits , we aim to improve on complex canine disease associations and blood phenotype associations recently conducted using the CanineHD array . For diseases , we focus on the orthopedic disease of cranial cruciate ligament disease ( CCLD ) , a complex genetic disease that involves the partial to complete rupture of the cranial cruciate ligament and is a well-established model for anterior cruciate ligament rupture in humans [26] . Several recent studies have identified significant associations [26–30] , mostly within high-risk breeds and none of these results overlap . For blood phenotypes , we recently performed an across-breed GWAS of 353 dogs on 39 blood phenotypes ( complete blood count and clinical chemistry panel ) , resulting in 9 phenotypes that yielded significant associations [31] . We posit that improving the density of variants by using an imputation panel will greatly improve the power to identify causal loci for canine complex traits , due to increased LD . We use 365 canine whole genome sequences to create a reference panel of 24 million variants and impute these variants in 6 , 112 dogs previously genotyped on a semi-custom 185k CanineHD array . We show that using an imputation panel increases our power to detect variants affecting complex canine traits–including a common orthopedic disease , CCLD–by identifying potentially novel loci , and by refining intervals for previously-identified QTLs for use in fine-mapping . To our knowledge , this is the first study to use an imputation panel based on WGS for across-breed canine mapping studies .
We used IMPUTE2 to impute the WGS reference panel across the 6 , 112 genotyped dogs resulting in 24 million variants . By comparing 33 , 144 imputed variants to directly genotyped sites on a second custom array , we were able to calculate the accuracy for our imputation panel , which was 88 . 4% overall . Across all sites , purebred dogs had the highest accuracy ( 89 . 7% , n = 276 ) , followed by mixed-breed dogs ( 88 . 6% , n = 13 ) , and then village dogs ( 84 . 2% , n = 86 ) . This result is expected given that 210 of our 365 WGS panel were purebred dogs , and also due to the long-range haplotypes found in purebreds that make calling imputed variants easier . For all three dog types ( purebred , mixed-breed , and village ) , imputation accuracy increased with decreasing minor allele frequency ( MAF ) ( Fig 1A ) , which is an expected result because as MAF decreases , the occurrence of the major allele is the correct call more often . Looking at true heterozygous sites only ( Fig 1C ) , imputation accuracy was lower across most MAFs compared to all sites ( Fig 1A ) . Imputation accuracy increased as MAF increased for heterozygous sites , as there are more heterozygous calls for SNPs with higher MAF . Looking at the imputation accuracy in the four breeds with the highest number of dogs in the WGS panel ( Yorkshire terriers , Maltese , German shepherd dog , and Labrador retriever ) , the overall imputation accuracy was 92 . 7% ( n = 32 ) , a 3% increase from the larger panel of 276 purebred dogs described above . The same trends of increasing accuracy with decreasing MAF and vice versa for heterozygous sites are observed for the four breeds analysis but at higher imputation accuracies ( Fig 1B–1D ) . In general , the larger chromosomes and chromosome X had higher imputation accuracies than the smaller chromosomes , such as 35 , 36 , 37 , and 38 , due to lower recombination rates ( S1 Table ) . For all purebred , mixed-breed , and village dogs , the average imputation accuracy per chromosome was 89 . 1% ( range of 84 . 3–93 . 5% ) , 88 . 0% ( range of 83 . 0–93 . 0% ) , and 83 . 7% ( range of 78 . 5–92 . 4% ) , respectively . We performed two separate GWAS , firstly using the semi-custom CanineHD array data of 185k markers , and secondly using our imputed panel of 24 million variants . The phenotypes used in these GWAS were male breed-average weight , male breed-average height , and individual sex-corrected weight . We were then able to compare the results from the array GWAS and the imputed GWAS using the exact same phenotypes ( see Table 1 for male breed-average weight results ) . In the imputed GWAS for male breed-average weight , we identified 20 significant QTLs , 5 of which were potentially novel , and of the 17 previously identified body size loci only two ( CFA3:62 and CFA12:33 ) were not significant ( S2 Table ) . For the imputed analysis of male breed-average height , we identified 16 significant QTLs , 3 of which were potentially novel , and we saw 13 of the known 17 body size loci ( S2 Table ) . Finally , for the imputed GWAS of individual sex-corrected weight , we found 12 significant QTLs , 2 of which were potentially novel , and we found 10 of the known 17 body size loci ( S2 Table ) . Note that the CFA12:33 QTL was not significant in the male breed-average weight imputed GWAS ( P = 1 . 3×10−8 ) but was significant in the male breed-average height imputed GWAS ( P = 2 . 8×10−22 ) . Using imputed data generally increased the significance of body size associations seen in the array data , especially HMGA2 on CFA10 and fgf4 on CFA18 for height ( Fig 2A , 2B and 2C; S2 Table ) . Most of the respective QTLs from the array GWAS and the imputed GWAS were in LD ( r2 > 0 . 2 ) with the exception of the CFA12 and two CFA26 associations ( Table 1 ) . When imputed variants were not in high LD ( r2 < 0 . 8 ) with array QTLs , the imputed variants generally had stronger effect sizes and lower minor allele frequencies ( Table 1 ) . For most size-associated variants , the male breed-average weight and height effects were roughly isometric , with the exception of CFA18 and CFA12 QTLs , which had a greater effect on height than weight ( Fig 2D ) . Of the seventeen QTLs that have been previously associated with body size [5 , 8 , 15–23 , 32] , only one ( CFA3:62 ) did not reach significance in any of the three imputed analyses ( significance threshold of P = 1×10−8 ) , with P-values of 1 . 1×10−5 , 2 . 1×10−7 and 1 . 8×10−8 for individual sex-corrected weight , male breed-average weight and male breed-average height respectively ( S2 Table ) . GWAS of male breed-average weight provided the most power on average , so we will focus on that phenotype for the rest of the body size analyses . We used the significant body size QTLs from the male-breed average weight GWAS to predict the body weight of individuals , by randomly setting 20% of the body weights in the dataset to missing , then using a Bayesian sparse linear mixed model to predict the missing weights , and finally comparing the predicted weights to the actual weights . Using the 20 QTLs identified from the array GWAS ( see bold in Table 1 ) , we found a correlation coefficient ( r ) of 0 . 851 and this increased to 0 . 869 when we used the 20 QTLs from the imputed GWAS ( see bold in Table 1 ) . Nine body size QTLs have previously been fine-mapped ( IGF1R , STC2 , GHR , SMAD2 , HMGA2 , IGF1 , fgf4 retrogenes on CFA12 and CFA18 , IGSF1 ) [18–21 , 32 , 33] . For each of these , the region in LD with the most significant respective marker in the male breed-average weight imputed GWAS contained the known or putative causal variant ( S1 Fig ) . While the putative causal variants weren’t always the highest associated variant at a locus , they generally had P-values within two orders of magnitude of the most associated marker ( S1 Fig ) , confirming that the imputation panel performs well for the weight GWAS . Unsurprisingly , many of the putative causal variants are not markers on the CanineHD array , including IGF1R ( 3:41 , 849 , 479 ) [21] , STC2 ( 4:39 , 182 , 836 ) , GHR ( 4:67 , 040 , 898 and 4:67 , 040 , 939 ) , and HMGA2 ( 10:8 , 348 , 804 ) [18] . With the array data , the IGF1R QTL ( P = 1 . 4×10−5 for male breed-average weight GWAS ) did not reach significance , but with the imputed data we saw a significant association signal ( P = 1 . 6×10−12 for male breed-average weight GWAS ) , and the causal variant was the 6th most associated SNP ( r2 = 0 . 73 between causal and associated SNP ) ( S1A Fig ) . The STC2 and GHR ( 4:67 , 040 , 898 ) putative causal variants were the most significant variants at those loci in the imputed GWAS ( S1B and S1C Fig ) . Note that there are two putative causal variants for GHR [18] , both in exon 5 , but only one passed our 5% MAF filter . Similarly , the HMGA2 causal variant was in high LD ( r2 = 0 . 91 ) with the most significant marker at this locus in the imputed GWAS ( S1E Fig ) . For IGF1 , SNP5 ( BICF2P971192 , 15:41 , 221 , 438 ) , which is in LD with the SINE element [19] , was the most significant association in the array GWAS . In the imputed GWAS , SNP5 was the 2nd most associated SNP and the SNP that tags the SINE element ( 15:41 , 220 , 982 ) was the 4th most associated SNP , and these SNPs were nearly in complete LD with the most significant marker in the GWAS ( r2 = 0 . 98 and 0 . 97 respectively ) ( S1G Fig ) . The IGSF1 missense mutation [33] was in high LD with the most significant association in the imputed GWAS ( r2 = 0 . 97 ) ( S1I Fig ) . Note that there is a second variant in IGSF1 –an in-frame deletion–that has also been identified [33] . Seventeen QTLs have been previously associated with body size in dogs [8] . Here , using the imputation data , we found a further five QTLs ( CFA5:31 , CFA7:41 , CFA9:12 , CFA26:7 , CFA32:5 ) that passed our significance threshold in a male breed-average weight GWAS . As a conservative control , we performed a further male breed-average weight GWAS in which we included the four most-associated QTLs ( CFA10:8 , CFA15:41 , CFA3:91 , CFA7:43 ) as covariates . The results showed that the potentially novel loci at CFA5:31 , CFA7:41 and CFA32:5 were no longer significant–and four other loci were also not significant in the covariate GWAS: CFA1:56 , CFA11:26 , CFA20:21 , CFA34:18 . Further analyses are required to determine if these are true or spurious associations , but since we cannot rule out that they are spurious , we conclude that we have identified only two potentially novel canine body size QTLs , at CFA9:12 and CFA26:7 . The most significant SNP at CFA9:12 is located about 200kb upstream of the gene growth hormone 1 ( GH1 ) ( Fig 3A ) , which is expressed in the pituitary and has been associated with body size in humans and cattle [34–36] . The non-reference , derived indel that was the most highly associated in our imputed GWAS is found at high frequency in the small breeds Papillon , Yorkshire terrier , and Pomeranian , and also in New Guinea Singing Dogs ( S3A Table ) . The genotype proportions show that the smallest dogs in our dataset have the highest proportion of the derived indel ( S3B Table ) . Using our snpEff annotated variant files , we found two variants in GH1: a splice donor variant in intron 3 ( CFA9:11 , 833 , 343 , c . 288+2_288+3insT ) , and an in-frame deletion in exon 5 ( CFA9:11 , 832 , 437 , c . 573_578delGAAAGA , p . Lys191_Asp ) . Both of these variants were at <5% frequency in the WGS panel but all occurrences were in small-sized breeds ( such as Yorkshire terrier and Maltese ) . The second potentially novel body size QTL is at CFA26:7 ( Fig 3B ) . Investigation of the surrounding region uncovered a couple of potential candidate genes . The first is ANAPC5 , a member of the anaphase-promoting complex gene family that includes ANAPC13 , which has been associated with height in humans [37] . The second candidate gene is the histone H3 demethylase KDM2B ( lysine-specific demethylase 2B ) , which has been associated with body mass index in humans in a CpG methylation study [38] . However , we did not identify any variants in ANAPC5 or KDM2B in the snpEff-annotated files that are in LD with the associated imputation variant . The non-reference , derived allele was found at high frequency in the small breeds Shiba Inu , Papillon , and Chihuahua ( S3A Table ) . The genotype proportions again show that the derived allele is most common in the smallest weight class in our dataset ( S3B Table ) . With the imputation panel , we saw a refinement in several QTL regions–for example , the chromosome 3 association near the genes LCORL and ANAPC13 , both of which have previously been associated with body size [5 , 16 , 36 , 37 , 39] . Using imputed data , this QTL had a more significant and defined association , compared to the CanineHD array data alone ( S2A Fig ) . The QTL interval is about 65kb and 60kb upstream of the genes LCORL and ANAPC13 respectively , suggesting the causal variant is likely regulatory . Another example is the recently identified body size QTL at CFA7:30 Mb , near the gene TBX19 [8] . Here the imputed GWAS results showed a narrower QTL interval of greater significance when compared to the array GWAS ( S2B Fig ) . This region overlaps TBX19 but we did not observe any coding loci in our snpEff annotated variant files that are in LD with the most associated SNP . In order to reduce phenotypic noise , again we included the four most-associated QTLs ( CFA10:8 , CFA15:41 , CFA3:91 , CFA7:43 ) as covariates in the GWAS ( hereafter referred to as “top 4 covariates” ) , and then implemented a region-specific stepwise approach , including further associated SNPs in the region as covariates , until no significant association signal remained . For male breed-average weight , when we regressed out the most significant association for a QTL , we expected the association signal to disappear , as seen with the SMAD2 QTL ( Fig 4A and 4B ) . Our results showed two QTLs ( CFA3:91 , CFA10:8 ) that retain significant association signal after regressing out the most associated locus in the respective region ( Fig 4C–4E and 4F–4H ) . For both CFA3 and CFA10 , the data suggest there may be two independent significant associations in these regions . In the CFA3 region , the initial association signal peak looked regulatory while the residual signal is located in the genes ANAPC13 and LCORL . The residual signal in the CFA10 region lies close to the ear flop association [5 , 16 , 17] ( candidate gene MSRB3 ) but is not in LD with the imputed ear flop locus at CFA10: 8 , 097 , 650 ( r2 = 0 . 147 ) . The variant in this residual signal region may be regulatory , as the significance peak lies approximately 248kb upstream of HMGA2 . We did not identify any coding variants in these two residual signal regions from the snpEff annotations . Two other QTLs ( CFA4:67 and CFA15:41 ) showed evidence of residual signal but these did not reach significance ( P = 2 . 9×10−7 and 2 . 9×10−8 , respectively ) . This residual signal suggested allelic heterogeneity in these regions but could also be due to imperfect tagging in the imputed dataset . As a follow-up analysis , for each of these two QTLs ( CFA3:91 and CFA10:8 ) , we took the most significant SNP from the top 4 covariates GWAS . We used that significant SNP as a covariate in a GWAS to see if we were able to recover the most significant SNP from the initial GWAS with no covariates . For both CFA3 and CFA10 , we did recover the initial associated SNP , suggesting that these are real associations and not midway between two imperfectly tagged SNPs . Using our imputed panel for GWAS on blood phenotypes revealed several potentially novel associations . For example , we saw significant associations with the phenotypes of albumin ( Fig 5A ) and calcium ( Fig 5C ) levels in peripheral blood ( P = 4 . 5×10−10 and 5 . 9×10−9 respectively ) , neither of which were previously identified in the array GWAS [31] ( S4 Table ) . We also identified a potentially novel association with blood glucose level and CFA1 ( Fig 5D ) , located in the gene solute carrier family 22 member 1 ( SLC22A1 ) and about 30kb downstream of the insulin-like growth factor 2 receptor gene ( CI-MPR/IGF2R ) . During gestation , IGF2R binds insulin-like growth factor 2 ( IGF2 ) , the presence of which stimulates the uptake of glucose [40] . The SNP was at highest frequency ( >50% ) in the Samoyed and American Eskimo dog breeds . Of the eight significant associations ( using a threshold of P = 1 . 0×10−8 ) we saw with the imputed data , only two were not novel–alanine aminotransferase ( ALT ) and amylase–although both increased in significance ( Fig 5G and 5H; S4 Table ) . In addition to significant associations , we also saw six associations that nearly meet our significance threshold , that is , P < 1 . 8×10−8 , including three that were not significant using the genotype data ( S4 Table ) . Using a quantitative GWAS design that separates the CCLD cases into partial rupture and complete rupture , we identified a significant association using the imputation data that is not identified using the array data ( Fig 6 ) . This association was located on CFA20: 42 , 827 , 199 ( P = 7 . 9×10−9 ) in the gene LIM Domain-Containing 1 ( LIMD1 ) , which is involved in regulating stress osteoclastogenesis , and osteoblast function and differentiation [41 , 42] . 10% of all dogs with a complete rupture ( n = 84 ) have two copies of the minor allele , C , at this marker , while only 1% of all control dogs ( n = 377 ) and 4% of all dogs with a partial rupture ( n = 141 ) have two copies of the C allele ( S5A Table ) . This locus has an effect on CCLD in some breeds ( such as German shepherd dog and Labrador retriever ) but not others ( such as Golden retriever and Rottweiler ) ( S5B Table ) .
Imputation increases GWAS power by including additional sites that are not well-tagged by any single array marker , and has been successfully implemented in human studies , for example , low-density lipoprotein GWAS [43–45] . Here we present a canine imputation panel of 24 million variants–an approximate 130-fold increase in SNP number and SNP density from the semi-custom CanineHD array–for use in association studies . This panel has an overall accuracy rate of 88 . 4% when compared to genotype data from the same individuals ( 276 purebreds , 86 village dogs , and 13 mixed-breed dogs ) . It is important to note that our WGS reference panel includes only 76 breeds while our genotyping panel contains 185 breeds , so more than half the breeds we are imputing across are not represented in our reference panel . Furthermore , there is an average of only 2 . 7 dogs ( SD 3 . 4 , range 1–16 ) within a single breed in our reference panel . Both of these factors act to reduce the accuracy of our imputation . In the future , panels based on even larger numbers of sequenced individuals would yield even higher accuracy [for example , see 46] , but our current panel based on hundreds of dogs is still a useful way to improve the power of canine mapping studies today . Although the price of WGS is decreasing , it is still more cost-effective to use a panel of WGS individuals to create an imputation dataset based on genotyped samples than it is to directly WGS all samples [47] . Our imputation panel was created using over 350 canine WGSs representing 76 breeds . The inclusion of more breeds , especially diverse breeds ( such as the Parson Russell Terrier ) and rare breeds ( such as the Pumi ) , will improve the accuracy of , and the number of rare variants in , future imputation panels . A recent canine imputation study has shown that imputation accuracy is highest using a multi-breed reference panel ( compared to a breed-specific panel ) , and when there is overlap in breeds between the target and reference panel [48] . Indeed , the overall imputation accuracy rate here increased to 92 . 7% when including only individuals of the four breeds that are most highly represented in our reference panel , highlighting the usefulness of this resource for within-breed studies . Furthermore , human studies have shown that imputation accuracy increases with the size of the reference panel [49 , 50] . With our imputation panel , we improved association mapping for previously studied phenotypes , such as body size . Previous mapping studies of canine body size and other morphological traits using CanineHD array data have identified many significant QTLs . This success is largely the result of selection for body size during the formation of dog breeds , leading to selective sweeps around large-effect loci that facilitated mapping efforts . Nevertheless , using the imputation panel , we were able to identify two additional potentially novel loci ( at CFA9:12 and CFA26:7 ) that influence body size . Using imputation , we were also able to narrow intervals for previously known associated QTLs , and find evidence of possible allelic heterogeneity at two loci . Furthermore , imputation provides a more accurate analysis of the genetic architecture underlying canine body size and , in turn , allows a more accurate prediction for body size in dogs . Imputation is especially helpful in across-breed and/or mixed-breed study designs , where LD breaks down very rapidly making it more difficult to identify associations . Increasing the number and density of queried variants ( as done by imputation ) increases the chance that a variant will be in LD with the causal variant , especially when compared to a within-breed study design . We used our imputation panel for across-breed GWAS of blood phenotypes and the orthopedic disease CCLD . For blood phenotypes , we show several potentially novel associations and the narrowing of associated intervals when compared to array data alone , and for CCLD , we identified a significant association ( at CFA20:42 ) that was not identified with array genotype data . Previously identified loci that influence CCLD risk include five in Labrador retrievers ( CFA1 , 4 , 6 , 23 , 24 ) , three in Newfoundlands ( CFA1 , 3 , 33 ) , one in Rottweilers ( CFA9 ) , and another two from an across-breed GWAS design ( CFA8 , 9 ) using the same phenotyped dogs as this study [26–30] . This CFA20 potentially novel association is especially important , given that CCLD in the domestic dog is an excellent model system for anterior cruciate ligament rupture in humans . Validation of the potentially novel loci identified here using our canine imputation panel would involve firstly confirming the genotypes and then functional studies , both of which are beyond the scope of this research study . Imputation is a type of statistical inference , whereby genotype data are filled in based on observed haplotype patterns from WGS . As we have shown above , this is not 100% accurate , especially in dog breeds not well-represented in our WGS panel and , as a result , some significant associations identified in our imputation panel alone may be spurious . However , in general , we expect the effect of low imputation accuracy to be a reduction in power to detect significant associations , as the LD between an imputed marker and the causal variant will be reduced . In summary , using our canine imputation panel of 24 million variants results in an increase in GWAS power , even for phenotypes that have multiple significant associations . The improvements to canine GWAS , especially for complex phenotypes , will not only further the field of canine genetics , but may also have beneficial implications for human medical genetics–especially for complex diseases , such as cancer and specific orthopedic diseases , for which the domestic dog is a good model organism [51] .
The 365 whole genome sequences include 210 breed dogs ( from 76 breeds ) , 107 village dogs ( from 13 countries ) , and 28 wolves ( S6 Table ) . 88 of these were sequenced at the Cornell University BRC Genomics Facility; others were sourced from public databases ( S6 Table ) . Those sequenced at Cornell were run on an Illumina HiSeq2000 or Illumina HiSeq2500 and the reads were aligned to the CanFam3 . 1 reference genome using BWA version 0 . 7 . 8 [52] . Variants were called using GATK’s HaplotypeCaller [53–55] . Variant quality recalibration was done in GATK v3 . 5-0-g36282e4 using the semi-custom CanineHD variant sites [8] as a training set ( known = false , training = true , truth = true , prior = 12 . 0 ) . We included SNPs in the 99 . 9% tranche and removed sites with minor allele frequency ( MAF ) less than 0 . 5% . Phasing was done using Beagle 4 . 0 version r1399 [56] , following the analysis pipeline used in the 1000 Genomes Project [57] . SHAPEIT v2 . r790 [58] was used to phase the genotype data from 6 , 112 dogs as previously described [8] and then IMPUTE2 version 2 . 3 . 0 [59] was used to impute across these data . Imputation was only performed on the autosomes and chromosome X , not on the Y chromosome or mitochondrial SNPs . The final reference panel consists of 24 . 0 million variants , including 750 , 000 on the X chromosome , of which 20 . 33 million are SNPs and 3 . 67 million are indels . 276 purebred , 86 village , and 13 mixed-breed dogs were also genotyped on a second custom Illumina CanineHD 215k array , which contains 33 , 144 SNPs that are not on the 185k semi-custom CanineHD array but do feature in our imputed dataset . These 33 , 144 SNPs were used to determine the accuracy of our imputation across the 375 total dogs . Accuracy was calculated for each SNP as the number of sites that are correctly called in the imputed dataset divided by the total number of dogs . For example , if a G/C SNP was called G/G in 14 dogs , C/C in 10 dogs , and G/C in the remaining 351 dogs , then the imputation accuracy for that SNP is 93 . 6% . MAF was calculated for each SNP as the number of occurrences of the allele across all village , purebred , and mixed-breed dogs in the genotyped dataset . Imputation accuracy and MAF were plotted in Jupyter notebook [60] using Matplotlib library [61] . In addition , imputation accuracy was also calculated for a subset of these 375 dogs , namely 32 dogs of the four breeds ( Yorkshire terrier , Maltese , German shepherd dog , Labrador retriever ) most highly represented in the WGS reference panel ( with 16 , 13 , 12 , and 10 sequences , respectively ) . This was done to show the increase in imputation accuracy when using a reference panel containing dogs of the same breed as the genotyping dataset . For the genotype data , individuals were run on a semi-custom Illumina CanineHD array of 185k SNPs , and quality control steps were performed as previously described [8] . For the imputed data , IMPUTE2 outputs were converted into PLINK [62] binary format , one for each chromosome . We ran GWAS using a linear mixed model in the program GEMMA v 0 . 94 [63] , with a MAF cut-off of 5% and using the Wald test to determine P-values . All LD plots were created using Matplotlib library [61] in Jupyter notebook [60] . For the imputed panel , GWAS was performed for each canine chromosome ( CFA1-39 ) separately . The kinship matrix calculated using the array data in GEMMA was also used in the imputed GWAS for the same phenotype . The significance threshold was set to P = 1 . 0×10−8 , based on thresholds calculated using the effective number of independent markers for human 1000 Genomes datasets [see 64] and given that LD in the domestic dog is more extensive than in humans [65] . To identify QTLs associated with body size , we ran a GWAS of male breed-average weights ( n = 1926 ) and heights ( n = 1926 ) , and individual sex-corrected weights ( n = 3095 ) , using both the semi-custom 185k CanineHD array data and the imputed panel data . Effect sizes were recorded from the GEMMA output , and MAFs and LD statistics were calculated using PLINK . Using previously published data of 39 phenotypes from complete blood count ( CBC ) and serum chemistry diagnostic panels of 353 dogs from a range of breeds [31] , GWAS were performed using the imputed data in GEMMA , as described above . Results were compared to the published results using the semi-custom CanineHD array data [31] . A quantitative GWAS was performed in GEMMA , as described above , using both the semi-custom CanineHD array data and the imputed data for the phenotype of cranial cruciate ligament disease ( CCLD ) . The across-breed GWAS included 602 dogs total: 377 dogs had no rupture ( controls ) , 141 dogs had a partial rupture , and 84 dogs had a complete rupture–this is the same dataset recently analyzed in a GWAS using FarmCPU [29] . Results from the imputed panel were directly compared to the array data . | Complex traits are controlled by more than one gene and as such are difficult to map . For complex trait mapping in the domestic dog , researchers use the current array of 173 , 000 variants , with only minimal success . Here , we use a method called imputation to increase the number of variants–from 173 , 000 to 24 million–that can be queried in canine association studies . We use sequence data from the whole genomes of 365 dogs and wolves to accurately predict variants , in a separate cohort of dogs , that are not present on the array . Using dog body size , blood phenotypes , and a common orthopedic disease that involves rupture of the cranial cruciate ligament , we show that the increase in variants results in an increase in mapping power , through the identification of new associations and the narrowing of regions of interest . This imputation panel is particularly important because of its usefulness in improving complex trait mapping in the dog , which has significant implications for discovery of variants in humans with similar diseases . | [
"Abstract",
"Introduction",
"Results",
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"analysis",... | 2019 | Imputation of canine genotype array data using 365 whole-genome sequences improves power of genome-wide association studies |
Extracellular capsules constitute the outermost layer of many bacteria , are major virulence factors , and affect antimicrobial therapies . They have been used as epidemiological markers and recently became vaccination targets . Despite the efforts to biochemically serotype capsules in a few model pathogens , little is known of their taxonomic and environmental distribution . We developed , validated , and made available a computational tool , CapsuleFinder , to identify capsules in genomes . The analysis of over 2500 prokaryotic genomes , accessible in a database , revealed that ca . 50% of them—including Archaea—encode a capsule . The Wzx/Wzy-dependent capsular group was by far the most abundant . Surprisingly , a fifth of the genomes encode more than one capsule system—often from different groups—and their non-random co-occurrence suggests the existence of negative and positive epistatic interactions . To understand the role of multiple capsules , we queried more than 6700 metagenomes for the presence of species encoding capsules and showed that their distribution varied between environmental categories and , within the human microbiome , between body locations . Species encoding capsules , and especially those encoding multiple capsules , had larger environmental breadths than the other species . Accordingly , capsules were more frequent in environmental bacteria than in pathogens and , within the latter , they were more frequent among facultative pathogens . Nevertheless , capsules were frequent in clinical samples , and were usually associated with fast-growing bacteria with high infectious doses . Our results suggest that capsules increase the environmental range of bacteria and make them more resilient to environmental perturbations . Capsules might allow opportunistic pathogens to profit from empty ecological niches or environmental perturbations , such as those resulting from antibiotic therapy , to colonize the host . Capsule-associated virulence might thus be a by-product of environmental adaptation . Understanding the role of capsules in natural environments might enlighten their function in pathogenesis .
Extracellular capsules , hereafter named capsules , constitute the outermost layer of some prokaryotic cells where they establish the first contact between the microorganism and its environment . They fullfill a myriad of roles , often linked to colonization and persistence . Their physical properties prevent dessication by retaining moisture near the cell surface , enhance survival in harsh environments , and protect cells from phagocytosis by grazing protozoa [1–4] . Capsules also play an essential role during infection; they downregulate pro-inflammatory cytokines [5] , protect cells against reactive oxygen species generated by the host [6] , and help bacteria to evade phagocytosis by macrophages and complement activation [3] . Capsules also reduce the efficiency of antibiotics [7] and cationic antimicrobial peptides [8] . These medical implications have driven the research on capsules and their roles , leading to the widespread perception that they are mostly associated with virulence [9 , 10] . This triggered the numerous studies on the genetic diversity of capsules in several prominent bacterial pathogens such as Streptococcus pneumoniae [11 , 12] , Escherichia coli [13] , Klebsiella pneumoniae [14 , 15] , Campylobacter jejuni [16] , and Acinetobacter baumanii [17] . Capsules can be synthesized through different genetic pathways ( Fig 1 and reviewed in [18–20] ) . Most capsules are high molecular weight polysaccharides made up of repeat units of oligosaccharides . In capsules synthesized through the Wzx/Wzy-dependent pathway or Group I [20] , the oligosaccharidic repeat unit is linked to an undecaprenyl phosphate acceptor in the cytoplasm by membrane-bound glycosyltransferases . This precursor is then transported across the inner membrane by the Wzx flippase and polymerized nonprocessively in the periplasm by the Wzy polymerase . In contrast , the nascent polysaccharidic chains of Group II and Group III capsules are polymerized in the cytoplasm and linked to a phospholipid acceptor before being transported across the inner membrane by the ATP-binding cassette ( ABC ) transporter . Group II and III capsules will be jointly referred to as ABC-dependent capsules . In spite of these differences , both the Wzx/Wzy- and the ABC- dependent pathways use homologous outer membrane proteins from the polysaccharide export family to transport the capsule across the outer membrane of diderm bacteria [21] . Both pathways are characterized by large operons that have a conserved region encoding the secretion machinery and a variable region encoding numerous polymer-specific enzymes . The latter defines the capsule serotype and includes enzymes for the synthesis of NDP-sugars , glycosidic linkages ( mainly by glycosyl-transferases ) , and sugar modification ( O-acetylation ) . Within-species serotype-diversity prompted the biochemical characterization of the oligosaccharide composition of capsules , ultimately leading to the development of serotype-specific vaccines [16 , 22 , 23] , and serotyping schemes for epidemic strains [24 , 25] . The synthesis of the polysaccharidic Group IV capsules relies on the Wzy polymerase but not on Wzx flippase , and depends on very diverse export machineries , including in certain cases proteins homologous to those of Group I [26 , 27] . Polysaccharidic capsules can also be produced by the synthase-dependent pathway , where a unique processive enzyme is responsible for the all the steps of initiation , polymerization and translocation of the capsule [28] . Some capsules are proteic , instead of polysaccharidic , notably the poly-γ-d-glutamate or PGA capsules produced by Bacillus anthracis [29] . To date very few studies have characterized the frequency and diversity of capsules across bacterial phyla , presumably because they are difficult to identify . Capsular systems have many poorly characterized components and are subject to frequent variation by homologous recombination and horizontal transfer , resulting in rapid genetic turnover [30] . Furthermore , the genetic pathways leading to the synthesis of lipopolysaccharides ( LPS ) , extracellular polysaccharides ( EPS ) , and capsules have many key homologous components that are difficult to disentangle [31 , 32] . Finally , there are few studies on the role of capsules in ecological settings other than the host , limiting the identification of new capsule secretion pathways . The understanding of capsule distribution and evolution across Prokaryotes has been hampered by the lack of computational tools to identify capsule systems in genomes . In order to tackle this limitation , we have built protein profiles to identify the key components of the different capsule biosynthesis pathways and defined models describing their expected frequency and genetic organization . We used them within MacSyFinder , a computational tool that allows the detection of macromolecular systems [33] , to identify capsule systems in more than 2500 complete prokaryotic genomes . We then searched for the presence of species with capsules in more than 6700 metagenomes . We aimed at answering the following questions: How many capsules are there in prokaryotic genomes ? Do multiple capsule groups co-occur and , if so , are there any correlations between capsule groups ? Which Prokaryotes encode capsules ? Which are the genetic and life-history traits associated to capsule prevalence ? What is the environmental distribution of Prokaryotes encoding capsules ? Our results uncovered novel intriguing patterns in the distribution of capsules , which have important biological implications and provide new insights into the evolutionary and ecological role of capsules .
We defined independent and customizable models describing the genetic composition and organization of eight groups and subgroups of capsules ( Fig 1 ) , based on the literature of the best-described experimental capsule systems [18–21 , 26 , 27 , 29 , 34] . This information was complemented with exploratory analyses of the diversity of these systems in other genomes ( see Methods ) . We identified 58 key components ( protein families ) involved in capsule synthesis . The majority of them regard the secretory and polymerization components of each capsule system , as well as the most common polymer-specific enzymes . Each component was associated with a hidden Markov model ( HMM ) protein profile , retrieved from PFAM ( 31 ) or built for the purpose of this study ( 27 ) ( S1 Table ) . The resulting computational tool—CapsuleFinder—uses as input the protein sequences of a genome , searches for the components of capsule systems using the HMM profiles and then delimits the systems based on the information provided in the models . There is no curated database with information on the organisms encoding and/or lacking capsule systems . The literature rarely mentions the absence ( or presence ) of a capsule for non-pathogens . Nevertheless , we sought to validate CapsuleFinder by comparing its results with those mentioned in two lists of some of the best-studied encapsulated Prokaryotes [19 , 35] . We successfully identified capsules in all 11 species that were reported as encapsulated and for which a complete genome sequence was available . To validate a broader set of systems , we randomly picked 100 species from our complete genome database . We then checked the literature for information on the presence of a capsule in the 40 species where a capsule system was detected ( S2 Table ) . There were 28 species for which we could find published reference to the presence or absence of a capsule . Among these , we found published experimental evidence for a capsule in 15 species and some positive information ( from either bioinformatic analyses or evidence in closely-related species ) for 10 others . The literature explicitly mentioned that no capsule had been observed for the remaining three species ( details S2 Table ) . It is difficult to say if these are false positives , which would give a false positive rate of ~8% , or if capsules actually exist in the species and the respective strains or conditions of expression were not yet identified . We have not attempted to quantify the rate of false negatives—cases where we missed an existing capsule—since there have been very few experimental efforts to show that a species lacks a capsule in a variety of environmental conditions . Yet , the analysis of our data showed a small number of cases where we missed some capsule systems and obtained some false positives . These are indicated in S3 Table . Even in the worst case , CapsuleFinder is able to identify all the best-known capsules whilst fetching few putative false positives ( S1 Text ) . We detected 2182 capsule systems in 1304 out of the 2643 genomes ( Fig 2 ) . The complete list of genomes and capsule systems is available in S1 Dataset . Around half ( 49% ) of the genomes , representing 52% of the species , encoded at least one capsule system . Group I capsules were the most frequent , representing ca . 70% of the total . ABC-dependent and synthase-dependent capsules were less frequent ( nearly 10% each ) , and subgroup CPS3 capsules were the most frequent among the latter . Group IV capsules ( 8 . 8% ) , and PGA proteic capsules were rarer ( 3 . 1% ) ( Fig 2 and S1 Dataset ) . We investigated the presence of capsule systems in all major taxonomic divisions of Bacteria and Archaea ( Fig 2 ) . The highly abundant Group I capsule was detected in all bacterial phyla represented by more than 20 available complete genomes ( except Spirochaetes and Tenericutes ) . PGA capsules , even if rare overall , were also present in most phyla . They were particularly abundant in Synergistetes , Planctomycetes , Bacillales and Fusobacteria ( Fig 2 ) . On the other hand , Group IV capsules were almost exclusively identified in γ-Proteobacteria and some subgroups were only identified in the taxa in which they were first described , e . g . , all Group IV_f capsules were identified in Francisella spp . ( Fig 2 ) . We identified at least five out of the eight capsule groups in α- and γ-Proteobacteria and in Actinobacteria . Following previous observations of capsule-like structures in Archaea [38–40] , and even if no experimental evidence has yet been given for their existence , we detected 47 capsule systems in 40 archaeal genomes . They were all synthase-dependent ( both subgroups ) or PGA capsules . Taken together , our results show that capsules are prevalent in Prokaryotes , where their frequency depends on the capsule group and on the phyla . The genetic loci encoding the experimentally studied capsule systems have remarkably different sizes . Since the number of genes in the capsule system is expected to have some impact on the complexity and evolution of capsules , we computed the number of genes of each system identified in our work ( see Materials and methods ) . These values are only approximate , because capsule systems surrounded by genes encoding enzymes involved in sugar metabolism cannot be delimited without ambiguity in the absence of experimental work . The Group I and ABC-dependent capsules were encoded by significantly more genes than the other capsule groups ( S1 Fig ) . Whereas the median Group I and ABC-dependent systems had between 19 and 16 genes , the synthase-dependent HAS ( hyaluronic acid ) capsule was encoded in three genes and the Syn_CPS3 in four ( S4 Table ) . These differences may be affected by the abovementioned inaccuracies in capsule loci delimitation and by the definition of the models . Nevertheless , our results show that some groups of capsules have loci of almost invariable sizes ( all Group IV capsules ) , whereas others showed very significant variation in the number of components ( especially Group I and ABC , see lower slopes in S1 Fig ) . These results give statistical support to the idea that the number of capsule components differs markedly between groups . We then searched to test if genome size was correlated with the number of genes encoding a capsule system . Genomes encoding capsule systems were generally larger than those lacking them ( Wilcoxon rank sum test , P < 0 . 001 ) , but the number of genes in the capsule loci showed no correlation with genome size when controlled for phylogenetic dependence ( S4 Table ) . This suggests that constraints on genome size have no significant effect on the complexity ( number of genes ) of each capsule system . We found that almost half of the genomes encoding capsules have more than one system ( 40% , Fig 3A ) . Strikingly , two environmental cyanobacteria encoded up to eight capsules , and 23 other species encoded between five and seven systems ( S5 Table for details ) . Among these 25 species , all with large genomes ( >4 . 5 Mb ) , we identified very few human-associated bacteria: a commensal Bacteroidetes , and some opportunistic pathogens of the Burkholderia cepacia complex . Instead , most of the 25 genomes were from mutualistic or environmental bacteria , including several α- and β-Proteobacterial rhizobia . The size of the genome was correlated with the number of capsules it encodes ( Spearman’s rho = 0 . 16 , P < 0 . 0001 after phylogenetic correction ) ( Fig 3B ) , and with the sum of all capsule components ( Fig 3B , and S4 Table for phylogenetic corrections ) . Hence , while the number of genes in a capsule system is not associated with genome size , larger genomes tend to encode more capsule systems , and thus have more capsule-associated genes . Nearly half of the genomes with multiple capsule systems encode several occurrences of the same capsule group ( 246 out of 537 ) . We analyzed their sequence similarity to test if they could have arisen by recent large segmental duplications . The systems were typically very divergent: 97% of the intra-genomic comparisons showed less than 80% sequence similarity at the homologous proteins used to identify the group ( see Methods ) . Systems of the same group were not found in tandem , as expected if they had resulted from recent duplications [41] and only eight ( out of 1004 ) pairs of consecutive systems were less than 10 kb apart ( S2 Fig ) . Furthermore , some genomes encoded two ( 238 ) , three ( 50 ) , and up to four ( in E . coli strain REL606 ) different capsule groups ( Fig 3C ) . Hence , multiple capsule systems do not seem to originate from recent segmental duplications . Remarkably , more than half of the genomes encoding an ABC-dependent capsule also encode a Group I capsule ( S6 Table ) , and all genomes coding for Group IV_s and Group IV_e capsules also code for at least one other capsule group . A non-random assortment of capsule groups would suggest epistatic interactions between capsules . To test this possibility , we analyzed the co-occurrence of capsule groups in the light of the underlying phylogenies ( see Materials and methods ) . We used Pagel’s method [42 , 43] to fit models of dependent evolution between capsule groups and compared them with models assuming independent evolution ( see Methods ) . We observed significant co-occurrence of Group I capsules and most of the other capsule groups ( Fig 3D and S7 Table ) , including ABC-dependent capsules and Group IV_s . We also observed frequent co-occurrence between PGA and Group IV_f capsules ( Fig 3D and S7 Table ) . In contrast , several groups of capsules showed unexpectedly low co-occurrence patterns suggesting the existence of negative epistatic interactions . For example , we only identified two co-occurrences of Group I and Syn_HAS . The family of Enterobacteria showed the most frequent co-occurrence of capsules from different groups and subgroups ( Fig 4 , see S8 Table for the complete list of genomes ) . Since it also includes several of the model organisms used to study the capsule—E . coli , S . enterica , K . pneumoniae–we analyzed these genomes more in detail . We detected seven out of the 24 different combinatorial possibilities offered by the four different capsule groups identified in the clade . In the line of the results mentioned in the previous paragraph , we observed a clear pattern of correlation between Group IV_s and Group I capsules in enterobacterial genomes ( Fig 3 ) . We observed that closely related genomes often encode different capsule systems . For instance , within the phylogenetic group B1 of E . coli , the two enteroaggregative pathotypes ( E . coli 55989 and E . coli O104 ) encode the same capsule groups , which differ from all the others of the same phylogroup . Similarly , the two only commensal strains ( ED1a and SE15 ) of the phylogroup B2 share the same capsular combination , which is different from all other B2 genotypes ( S8 Table ) . Finally , E . coli from phylogroup A , comprising a majority of commensal bacteria , often have at least three different capsule groups , which is significantly more than other clades including many pathogens , such as Shigella sp . and E . coli B2 ( two capsule systems per genome , on average ) . These results revealed an association between capsule groups and bacteria-host interactions . To conclude , the rapid genetic turnover of capsule systems within closely-related genomes [44] suggests that they can rapidly change to face environmental or lifestyle changes . The observation that multiple capsules are more frequently observed in commensals , mutualists or environmental bacteria seems at odds with the hypothesis of a tight association between capsules and pathogenesis . We classified bacterial species according to the degree of host-association they commonly exhibit ( S1 Dataset , see Methods for criteria and [45 , 46] ) and found that the probability of encoding a capsule depends on the lifestyle of the bacteria ( Fig 5A ) , even when accounting for genome size ( S9 Table ) . We then first tested whether free living species were more likely to code capsules than pathogens . We found that , indeed , capsules were slightly rarer in pathogenic species as opposed to free living species ( Fig 5A and S4 Fig ) . The lower frequency of capsules in pathogens remains qualitatively similar when commensals and mutualists ( or both ) are grouped together with free living species . Additionally , we observed no difference in genome size between pathogenic bacteria encoding a capsule system and the others , suggesting that the association between the presence of capsule and pathogenesis is independent of genome size . Many of the pathogenic bacteria in our dataset are facultative or opportunistic . These bacteria typically have environmental reservoirs and larger genomes than obligatory symbionts ( pathogens or mutualists ) [47 , 48] . We observed that many facultative pathogens encode capsules , in contrast to most obligate pathogens , independently of the differences in genome size between the groups ( Fig 5B and S1 Dataset ) . The difference between obligatory and facultative pathogens remained statistically significant when controlling for phylogenetic structure ( see Methods , Fig 5 and S5 Fig ) . Whereas very few obligate pathogens encoded a capsule , amongst which Shigella flexnerii and Mycoplasma mycoides , a small majority of the facultative pathogens encoded a capsule ( Fig 5B ) . This result does not change qualitatively when only human pathogens are taken into account . Facultative pathogens tend to start infections only at high infectious dose ( ID50 ) , to be motile , and to grow fast under optimal growth conditions [49] . These characteristics also tend to be associated with a lack of ability to kill professional phagocytes of the immune system or to survive in the intracellular milieu of these cells [49] . Since capsules may provide some resistance to phagocytosis , we enquired on the possible association between the capsule , minimum doubling time , and ID50 ( measured in humans as available for only 39 species , [49] ) . We observed that bacterial species that encode a capsule system ( Csp+ ) , show significantly lower minimum doubling times ( Fig 5C and S4 Fig ) , higher ID50 , are more likely to be motile , and are less likely to be able survive phagocytosis than those that do not encode a capsule ( Csp- ) ( Fig 5 , S3 and S4 Figs ) . Whereas the first association was significant even when controlling for genome size and pathogenicity ( S9 Table ) , and phylogenetic dependence ( S5 Fig ) , the two latter associations were not statistically significant due to lack of statistical power ( there is little data available for these traits ) . Overall , our results indicate that capsules are more readily associated with facultative pathogens with high infection doses and short minimal generation times . We analyzed microbiome data to confirm that capsule systems are frequent in environmental bacteria and in facultative pathogens ( that often have environmental reservoirs ) . Unfortunately , loci encoding capsule systems are too long and complex to be identifiable in the sequences of metagenomes . To circumvent this difficulty , we identified the presence of the species for which we had at least one complete genome in a large number of publicly available metagenomics datasets ( 16S rRNA ) . We used this information to quantify the abundance of each species and , using the species' complete genomes as a proxy , to predict the presence of capsules in these environments . Specifically , we searched for the presence of Csp+ in 16S datasets from four classes and numerous sub-classes of environments ( Fig 6A ) . This allowed both the qualitative and quantitative identification of bacterial species in 6700 environmental 16S datasets ( S8 Table , see Methods ) . We computed the abundance of Csp+ relative to Csp- species in the 16S datasets in qualitative ( number of species ) and quantitative ( number of 16S sequences ) ways ( see Methods ) . The percentage of Csp+ was similar in the 16S ( 53% out of 1197 bacterial species ) and in the genome ( 52% ) datasets . Csp+ were more frequently present and quantitatively more abundant than Csp- in all four classes of environments , even if this trend was not always significant ( Fig 6A and S6A Fig ) . Capsules allow Prokaryotes to withstand a series of stresses , from environmental disruptions to protozoa grazing , and are expected to be associated with broader environmental ranges . Indeed , Csp+ species were present in significantly more environmental subclasses than Csp- ( Fig 6B ) . Importantly , the number of environmental subclasses for a given species increased with its average number of capsules per genome ( Partial Spearman test , P < 0 . 001 , after correction for genome size ) . These results show that bacteria encoding capsule systems are able to colonize a larger variety of environments . The vast majority of previous studies focused on capsules of bacterial pathogens . To disentangle the relation between capsule and pathogenesis , we analyzed the presence of Csp+ species in human-associated datasets . We first checked that we were able to identify well-known pathogens in the host-associated environments . Indeed , we detected pathogens with Group I capsules , such as K . pneumoniae and S . pneumoniae , as well as pathogens with ABC capsule systems , namely Neisseria meningitidis , in samples of the human microbiome , and sometimes also in other environments ( S10 Table ) . The total abundance of species encoding capsules within the human host varied between body locations ( Fig 6C ) , and was higher overall than within the complete genome database ( 57% , binomial test , P = 0 . 005 ) . Csp+ species were more abundant than Csp- in all locations , and especially in the gut microbiota , which encompasses the largest fraction of bacteria in the human body . Likewise , clinical samples over-represented Csp+ species . Interestingly , we observed that the relative abundance of Csp+ and Csp- was strongly dependent on the human body sites ( ANCOVA , P < 0 . 001 , S6B Fig ) . Taken together , our results show that even if capsules are relatively rare among obligatory pathogens , they are very frequent in human microbiota where they are frequently associated with clinical conditions .
Capsules play important roles in bacterial virulence , but their study has been hampered by the lack of computational tools to identify them in genomes . Our tool , CapsuleFinder , identifies the eight major groups and subgroups of capsule systems in bacterial and archaeal genomes and is thus complementary to software designed to analyze very specific capsule systems , e . g . , the recently released Blast-based tool to identify capsular serotypes in Klebsiella spp . ( Kaptive , [50] ) . The models in CapsuleFinder can be modified to either increase specificity ( obtain systems closer to the experimental models ) or sensitivity ( to detect more distantly related systems ) . This can be done by changing the number , type and genetic organization of the components that are required to identify a system . Users can also add novel models and protein profiles to improve the tool , e . g . , to account for novel experimental data . If enough experimental serotype data is available for a given species , then the models can be specified in order to infer a putative serotype for the strains . The construction of our models was based on previous experimental studies restricted to a relatively small number of model organisms from Proteobacteria and Firmicutes . Capsules , like many extracellular structures [51] , are subject to rapid evolution and reorganization via recombination , complicating their detect from a small number of taxonomically restricted reference systems . In spite of this , we were able to identify them in many phyla of Prokaryotes—even in Archaea—with few putative false positives . Hence , we expect to have identified the majority of capsules of known groups in the complete genome database . The entire collection of capsule systems can be consulted in our database ( http://macsydb . web . pasteur . fr/capsuledb/_design/capsuledb/index . html and S1 Dataset ) . Further , the identification of capsule systems by CapsuleFinder opens the way for their comparative analysis , including the study of how horizontal transfer leads to serotype switching across bacteria [52 , 53] . Our analysis showed that a majority of Prokaryotes encodes at least one capsule system ( Fig 2 ) . Group I , PGA and Syn_CPS3 are the most widespread across the Bacteria whereas other groups were restricted to a few taxa , namely Group IV and Syn_HAS . Importantly , we found capsule systems in all phyla for which more than ten genomes were available . Future work will be necessary to assess if poorly sampled phyla—Chrysiogenetes , Deferribacteres , and Elusimicrobia—are effectively devoid of known capsule groups or if they encode novel groups of capsules . It will also be interesting to analyse capsule prevalence in newly discovered uncultivable phyla characterized by single-cell genomics since they may reveal novel capsule groups ( or variants of existing ones ) [37 , 54] . Given our results in the phyla with higher representation in the database , capsules might occur across all prokaryote phyla . Capsule-like structures have been described in Archaea [38–40] , where a previous bioinformatic study revealed the presence of proteins similar to those involved in the synthesis of the PGA capsule in one species [29] . We identified PGA capsule systems and also Syn_CPS3 systems in many genomes of Archaea . These two groups of systems have few components and we couldn't find data suggesting that they allow extensive serotypic variation . However , the lack of more complex capsule groups , should be subject to caution owing to the lack of experimental data . Furthermore , our tools to identify capsules were based on bacterial systems . Alternatively , the peculiarities of the cellular envelope of Archaea may explain the absence of certain capsule groups in the phyla . Most Archaea have a S-layer composed of glycans that might affect secretion or cell surface association of certain capsules . Our method may underestimate the number of capsule systems of the same group co-occurring in a genome owing to strict localization rules in our models to avoid false positives . For example , not all Group I Bacteroides thetaiotaomicron were detected because some operons lacked the minimum mandatory genes required to identify the gene cluster as a capsule system ( S3 Table ) . This suggests that some structural elements involved in capsule secretion might be shared between different systems . Yet , to date , the existence of genomes encoding multiple capsules of the same group had previously been documented in only a few species , namely Bacteroides spp [55 , 56] . In B . fragilis , a key commensal of the gut microbiome , there are several Group I capsule systems , some of which are implicated in the formation of intra-abdominal abscesses [57] . This species encodes a DNA inversion mechanism that combinatorically switches the expression of the different systems [58] , producing extremely diverse capsule structures that are thought to increase bacterial fitness in the intestinal milieu by virtue of their immunomodulatory properties [59] . In this case , capsule variation seems to evolve as a response to the rapid change of the human immune system [58] . Bacteria may also encode multiple capsules from different groups , as described for the PGA and Syn_HAS capsules encoded in different plasmids of Bacillus cereus biovar anthracis[60] . Co-expression of different capsule groups is thus possible , implicating that capsules will physically interact in the cell envelope . Our data suggests that capsule combinations can be even more complex , since this same strain encodes a Group I capsule in the chromosome , and some enterobacteria encode up to four different groups of capsules . The non-random patterns of co-occurrence of different capsule groups observed in this study suggest that capsule repertoires are affected by epistatic interactions ( Fig 3D ) . The nature of these interactions depends on whether the different capsules are expressed at the same time , thereby producing combinatorial diversity , or at different moments , e . g . , in response to different environmental cues . Positive epistasis may result from the synergistic combination of the properties of the different capsules , e . g . , different capsules may provide a broader range of environmental protections and capsule switching ( or variation in the proportions of each capsule group ) may facilitate escaping grazing protozoa , professional phagocytes of the immune system , or bacteriophages . Negative epistasis associated with co-expressed capsules may result from problems in accommodating different capsule structures in the cell envelope . Negative epistasis between capsules that are not co-expressed could be caused indirectly by the effects of the genetic background , e . g . , because some groups of capsules are more compatible with certain membrane structures ( pili , flagella , secretion systems ) than others . The mechanisms leading to the acquisition of multiple capsules will have to be studied in detail in the future , but our results already provide some clues . We observed that many genomes encode capsules of different groups , that capsules of the same group are very divergent in sequence and are encoded in distant regions in the genome ( or in different replicons ) . This suggests that capsules were independently acquired by multiple events of horizontal gene transfer . This fits the abundant literature showing that capsules vary rapidly within species by recombination and horizontal transfer [61–63] . It also explains why most capsule systems are encoded in a single locus , since this facilitates transfer [64] . Finally , the outcome of capsule transfer is likely to depend on the environmental challenges faced by the bacteria and will be affected by the abovementioned epistatic interactions . A substantial part of the previous literature on capsule systems has focused on bacterial pathogens and on the role of capsules as virulence factors . For instance , it has been shown that acquisition of certain capsule types by horizontal gene transfer in Neisseria meningitidis allowed the bacteria to increase in pathogenicity and going from non-pathogenic carriage to infectious state [52 , 53] . It was thus surprising that non-pathogens are more likely to encode capsules , and that , among pathogens , the ones establishing obligatory antagonistic interactions with their hosts typically lacked a capsule . The abundance of capsules across most phyla and environmental classes , and their rarity among obligatory pathogens , suggest they play important roles beyond pathogenesis . Indeed , the capsule also constitutes an advantage for commensal bacteria of the gut . To colonize the gut , the bacteria have to first withstand the harsh conditions of the stomach and then grow and multiply in the duodenum and colon , in the presence of bile salts . In Bifidobacterium longum , capsule expression would enhance survival in the stomach and allow growth under high concentrations of detergent-like bile salts in the duodenum [65] . Similarly , a study performed in yeast has shown that although capsules from environmental and pathogenic strains display similar composition and features , they fulfil different roles [66] . Capsules are an example of the ability of bacteria to evolve structures serving multiple purposes in different environments . Like other virulence factors , such as some iron capture proteins , while evolving as an adaptation to an environment they also confer an advantage during pathogenesis ( exaption ) , either during colonization or transmission across hosts [47 , 67] . Our data also shows that the presence of capsule systems , and especially multiple systems , is associated with broader environmental ranges . The ability to express different capsules , or combinations of them , can result in heterogeneity in the surface charge of bacterial cells which can in term influence important phenotypes such as cellular adhesion to tissues or surfaces , susceptibility to certain cationic peptides , etc . In the aforementioned B . cereus strain , the co-expression of the two capsules did not increase virulence in two different animal models , but rather favoured bacterial colonization and dissemination [60] . Similarly , previous studies in soil-borne nitrogen-fixing bacteria indicated that bacterial exopolysaccharides and lipopolysaccharides that can be similar to capsules are involved in species-specific interactions between the bacteria and the host [68] . This is consistent with our observation that capsule multiplicity increases environmental breadth , and suggests that it may also increase host range . Taken together , our study revealed an unsuspected prevalence of capsules in Prokaryotes , especially in environmental bacteria and facultative pathogens . Our results are in line with the multitude of roles proposed for capsules and are not consistent with the idea that capsules evolved to facilitate pathogenesis . Instead , they highlight that capsules might have an important role in facilitating bacterial adaptation to novel or changing environments . Interestingly , we found many capsule systems in soil bacteria , from which probably originated capsulated opportunistic multi-resistant bacteria such as Klebsiella pneumoniae , Enterococcus faecium , and Acinetobacter baumanii [69–72] . Capsules may have thus evolved primarily as an adaptation to a range of different environments , and this facilitated subsequent ecological transitions towards host colonization and pathogenesis .
We built a model for each group of capsule with the information we could obtain from the literature . We specified models with mandatory ( biologically essential components for a putative functional system , a majority of which , if not all , are required to identify and classify the systems ) , accessory ( non-essential components used to improve the annotation of the system ) , and forbidden components ( e . g . , those found in other capsule groups and not in the focal one , thus helpful to discriminate between the capsule groups , see below the example of Group IV capsules ) . Of note , due to the low conservation of some mandatory elements , for example Wzy polymerases , in some instances a system could be validated even if a certain number of mandatory components were not detected . This is controlled by the option min_mandatory_genes_required . The parameters used for the minimum quorum of mandatory genes were set based on the analysis of experimental systems and on our previous experiences with the development of similar models for protein secretion systems and CRISPR-Cas systems [33 , 36] . While these systems are very different , they have in common that certain components that are thought to be biologically necessary may not be identifiable by sequence analysis either because they evolve too fast , or because they can be replaced by analogues lacking sequence homology . Additionally , we specified that components should be encoded in a single locus ( defined as a series of genes respecting a maximal pre-specified distance between consecutive elements ) . When the available experimental data suggested that it was relevant to allow components to be encoded elsewhere in the genome , we defined them as loners in the models . Models were written in plain text , using a specific XML grammar , and can be modified by the user ( see http://macsyfinder . readthedocs . io/en/latest/ for details ) . For simplicity , we named the components after the protein names in the species that served as a biological model for each group of capsule . The names of the homologs to these proteins in other species with experimentally validated systems are listed in S1 Table . Polymer-specific enzymes were regarded as accessory in the models because they can be homologous to enzymes of other cellular processes [18] . We used MacSyFinder to search for capsule systems [33] . This program takes as input a proteome , a set of hidden Markov models ( HMM ) protein profiles ( one for each component of the system , see below ) , and models describing the number of components and their genetic organization ( Fig 1 ) . MacSyFinder identifies the individual components of each capsule system using hmmsearch from the HMMER package v3 . 1b2 [86] . A component was retained for further analysis when its alignment covered more than 50% of the length of the profile and obtained an e-value smaller than 0 . 001 . We used 58 different HMM protein profiles in our searches ( S1 Table ) , 31 retrieved from the PFAM 28 . 0 database ( http://pfam . xfam . org , [87] , last accessed November 2015 ) and 27 built in this study . Each protein profile was constructed as follows ( except when explicitly stated otherwise ) . We started from a well-described and experimentally-validated component of a system and used BLASTP v 2 . 2 . 28 [88] ( default settings , -v 4000 , e-value < 10−4 ) to search for homologs among complete genomes . To reduce the redundancy of the dataset ( i . e . , to remove very closely related proteins ) , we performed an all-against-all BLASTP v 2 . 2 . 28 analysis and clustered the proteins with at least 80% sequence similarity using SiLiX v1 . 2 . 9 ( http://lbbe . univ-lyon1 . fr/SiLiX , default settings ) [89] . We selected the longest sequence from each family as a representative . The set of representative sequences was then used to produce a multiple alignment with MAFFT v7 . 215 using the L-INS-i option and 1000 cycles of iterative refinement [90] . The alignment was manually trimmed to remove poorly aligned regions at the extremities , using SEAVIEW [91] . The HMM profile was then built from the trimmed alignment using hmmbuild ( defaults parameters ) from the HMMER package v3 . 1b2 [86] . We validated the method to identify capsule systems using two published lists of capsulated bacterial pathogens [19 , 35] . Since these lists were very short , and not necessarily meant to be exhaustive , we made a complementary validation on a random set of species from our dataset . We used the R function sample to randomly draw 100 species from a curated list of 1241 species in our database ( this list did not include genomes for which a genus but not a species was defined , such as Glacieola sp . ) . We identified capsule systems in 40 of the 100 species . We then sought to confirm the presence of capsule in the latter ( they include 52 . 5% of free-living , 30% facultative pathogens , 12 . 5% commensals and 5% of mutualists ) by analyzing the primary scientific literature . For those species for which we did not detect a capsule system , we did not seek further validation as negative results are not systematically reported . We identified the core genome of 131 enterobacterial genomes belonging mostly to E . coli and Salmonella spp . , but also Shigella spp . , Citrobacter , Cronobacter , Klebsiella , and Enterobacter ( see S8 Table for the complete list of genomes ) . We followed a previously published methodology [92] . Briefly , orthologs were identified as bidirectional best hits , using end-gap free global alignment , between the proteome of E . coli K12 MG1655 and each of the 130 other proteomes . We discarded hits with less than 60% similarity in amino acid sequence or more than 20% difference in protein length . The list of orthologs for every pairwise comparison was then curated to take into account the high conservation of gene neighborhood at this phylogenetic scale [93] . We defined positional orthologs as bidirectional best hits adjacent to at least four other pairs of bidirectional best hits within a neighborhood of 10 genes ( five genes upstream and five downstream ) . The core genome was defined as the intersection of pairwise lists of positional orthologs and consisted of 759 gene families . To control for phylogenetic independence of data at the genome-level , we aligned the 16S rRNA using secondary structure models with the program SSU_Align v0 . 1 [94] of 2440 bacterial genomes . The alignment was trimmed with trimAl v1 . 4 [95] using the option -noallgaps to delete only the gap positions but not the regions that are poorly conserved . The 16S rRNA phylogenetic tree was infered using IQTREE v . 1 . 4 . 3 [96] under the GTR+I+G4 model with the options–wbtl ( to conserve all optimal trees and their branch lengths ) , and–bb 1000 to run the ultrafast bootstrap option with 1000 replicates . Two hundred and eleven genomes from our database were excluded from the final phylogenetic tree because identical 16S sequences were already present in the multiple alignment . When data was analyzed at the species level , a 16S rRNA gene per species was chosen by the Bash function RANDOM ( from all the available genomes of the species ) from the secondary structure alignment and a new phylogenetic tree constructed as above . To build the core-genome phylogenetic tree of the Enterobacteria , we aligned each core gene family at the amino acid level with MAFFT v7 . 215 ( default options ) [90] , trimmed non-informative positions with BMGE v1 . 12 ( default options and—t AA ) [97] , and concatenated the alignments . The tree of the concatenate was built using IQTREE v . 1 . 3 . 10 under the GTR+I+G4 model [96] . In both trees , the model used was the one minimizing the Bayesian Information Criterion ( BIC ) among all models available ( option -m TEST in IQTREE ) . All phylogenetic corrections were done using the 16S rRNA tree of Bacteria . We restricted our phylogenetic controls to Bacteria , because the inclusion of Archaea reduced very much the phylogenetic signal ( resulting from a shorter multiple alignment ) and clumped together many species’ 16S sequences . The presence of phylogenetic signal in the evolution of traits was estimated with Pagel’s lambda using the phylosig function of the phytools package v . 0 . 5–20 for R [42] and the aforementioned 16S rRNA phylogenetic tree . To estimate the phylogenetic signal across capsule groups , instead of using the 16S rRNA tree , we built new trees comprising only the 16S rRNA sequences of the genomes for which we detected the given capsule groups . To control for the effect of the uncertainty in phylogenetic inference on the key positive results , we produced 1000 bootstrap trees ( options -wbtl -bb 1000 in IQTREE ) and randomly selected 100 of those trees . We then ran each key analysis ( those in the figures , either GEE , fitPagel or phylosig functions ) using the different trees . The distribution of the 100 P values of each analysis is presented in S5 Fig . We tested the significance of the co-occurrence of capsule groups , with the default method ( fitMk ) of the fitPagel function from the phytools package ( v0 . 5–52 maps v3 . 1 . 0 ) . This function assumes an ARD—all rates different , which allows different rates at all transitions- substitution model for both characters and gives the probability that they are independent ( the rates of transitions of each character are independent of the other character ) . We controlled the associations between traits for phylogenetic dependence whenever one of their lambda’s P values was less than 0 . 05 . We used the pic function to make independent contrast analysis of continuous data and the compar . gee function to analyze associations between discrete and continuous variables using generalized estimation equations ( GEE ) . Both were computed with the functions included in the ape v . 3 . 5 package for R [98] . We also controlled associations for the effect of genome size by fitting linear regression models using aov from R . We selected from MG-RAST the metagenomes matching at least one species of our complete genome database and obtained from four environmental categories ( subclasses indicated in S8 Table ) : ( i ) water ( fresh , marine and spring water ) , ( ii ) soil ( agricultural , dessert , forest , tundra and grasslands ) , ( iii ) air ( indoor , mammal ) , and ( iv ) host-associated ( human , other mammals , arthropods , aquatic organisms and plant ) . These categories are broad and heterogeneous ( they put together many different environments ) . They are used to provide a very coarse-grained classification of the type of environment of each species . We used 16S rRNA assembled reads to identify and quantify the presence of species from the complete genome dataset in the environmental samples . All analyses were performed at the species level rather than at the strain level because 16S rRNA does not allow resolving phylogenetic structure below the species level . For consistency with previous analyses , Archaea were also excluded from the 16S environmental datasets . First , for each metagenome we identified the 16S matching each of the species in our database using BLASTN v 2 . 2 . 28 ( selected hits with more than 97% sequence identity and with alignments covering at least 90% of the query sequence ) . The relative abundance of each species was then calculated by dividing the number of 16S rRNA sequences in each metagenome by the total number of sequences . This information was used to draw the frequency of species with capsule systems in each environmental category and subcategory . To validate the analysis , we searched for well-known pathogens and quantified the frequency in which they appeared across metagenomes of each environmental subcategory ( S11 Table ) . Sequence identities and similarities were calculated with needle function ( default settings ) included in the EMBOSS 6 . 6 package . Phylogenetic trees were produced with iTol v3 . 0 [99] . Statistical analysis and graphs were done with R version 3 . 2 . 0 and the packages ggplot2 and RColorBrewer , unless stated otherwise . PMCMR [100] , stats and NCstats [101] packages for R were used for post hoc pairwise multiple comparisons of mean ranks and data manipulation . We have made publicly available the methods to detect capsules . CapsuleFinder can be used locally using the program MacSyFinder [33] , freely available for download at https://github . com/gem-pasteur/macsyfinder . We recommend the use of our models without the option "all' ( as recommended in the documentation of the program ) . It can also be queried on a dedicated webserver within the Galaxy platform ( https://galaxy . pasteur . fr/root ? tool_id=toolshed . pasteur . fr/repos/odoppelt/capsulefinder/CapsuleFinder/1 . 0 . 2 ) . The protein profiles and capsule models used in this study are accessible at https://research . pasteur . fr/fr/tool/capsulefinder/ . The models are written in a simple XML grammar in plain text files to allow user modifications ( see documentation in http://macsyfinder . readthedocs . io/en/latest/ ) . The results of MacSyFinder can be visualized with MacSyView , available online at http://macsyview . web . pasteur . fr . The capsules detected in this study , their genomic localization and organization are collected in an accessible database , CapsuleDB , http://macsydb . web . pasteur . fr/capsuledb/_design/capsuledb/index . html . | Extracellular capsules protect bacterial cells from external aggressions such as antibiotics or desiccation , but can also be targeted by vaccines . Since little was known about their frequency across Prokaryotes , we created and made freely available a computational tool , CapsuleFinder , to identify them from genomic data . Surprisingly , its use showed that many bacterial strains , especially those with the largest genomes , encode several capsules . The frequencies of the different combinations of capsule groups depended strongly on the phyla and the groups themselves , suggesting the existence of epistatic interactions between capsules . Bacteria encoding capsule systems were found in many natural environments , and were frequent in the human microbiome . In contrast to their frequent association with virulence , we found many more capsules in non-pathogens or facultative pathogens than among obligatory pathogens . We suggest that capsules increase the environmental breadth of bacteria thereby facilitating host colonization by opportunistic pathogens . | [
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"managem... | 2017 | Abundance and co-occurrence of extracellular capsules increase environmental breadth: Implications for the emergence of pathogens |
Group B Streptococcus ( GBS ) is the leading cause of neonatal pneumonia , septicemia , and meningitis . We have previously shown that in adult mice GBS glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) is an extracellular virulence factor that induces production of the immunosuppressive cytokine interleukin-10 ( IL-10 ) by the host early upon bacterial infection . Here , we investigate whether immunity to neonatal GBS infection could be achieved through maternal vaccination against bacterial GAPDH . Female BALB/c mice were immunized with rGAPDH and the progeny was infected with a lethal inoculum of GBS strains . Neonatal mice born from mothers immunized with rGAPDH were protected against infection with GBS strains , including the ST-17 highly virulent clone . A similar protective effect was observed in newborns passively immunized with anti-rGAPDH IgG antibodies , or F ( ab' ) 2 fragments , indicating that protection achieved with rGAPDH vaccination is independent of opsonophagocytic killing of bacteria . Protection against lethal GBS infection through rGAPDH maternal vaccination was due to neutralization of IL-10 production soon after infection . Consequently , IL-10 deficient ( IL-10−/− ) mice pups were as resistant to GBS infection as pups born from vaccinated mothers . We observed that protection was correlated with increased neutrophil trafficking to infected organs . Thus , anti-rGAPDH or anti-IL-10R treatment of mice pups before GBS infection resulted in increased neutrophil numbers and lower bacterial load in infected organs , as compared to newborn mice treated with the respective control antibodies . We showed that mothers immunized with rGAPDH produce neutralizing antibodies that are sufficient to decrease IL-10 production and induce neutrophil recruitment into infected tissues in newborn mice . These results uncover a novel mechanism for GBS virulence in a neonatal host that could be neutralized by vaccination or immunotherapy . As GBS GAPDH is a structurally conserved enzyme that is metabolically essential for bacterial growth in media containing glucose as the sole carbon source ( i . e . , the blood ) , this protein constitutes a powerful candidate for the development of a human vaccine against this pathogen .
Streptococcus agalactiae , also named Group B Streptococcus ( GBS ) , is a Gram-positive encapsulated commensal bacterium of the human intestine that colonizes the vagina of up to 30% of healthy women . This bacterium is the leading cause of neonatal pneumonia , septicemia , and meningitis [1] , [2] , [3] , [4] . Neonatal GBS infections are acquired through maternal transmission and may result in early-onset disease ( EOD ) , which occurs within the first week of life , or in late-onset disease ( LOD ) , that occurs after the first week and accounts for most meningitis cases and deaths [3] , [5] , [6] . Despite early antimicrobial treatment and improvement in neonatal intensive care , up to 10% of neonatal invasive GBS infections are lethal and 25 to 35% of surviving infants with meningitis experience permanent neurological sequelae [3] . Because recommendations for intrapartum antibiotic prophylaxis ( IAP ) for mothers in labor at risk for GBS infection have been widely implemented in many countries , the incidence of EOD has declined to <1/1 , 000 births , but the incidence of LOD has slowly increased in the last decade [7] . An unexpected burden of case fatalities among children aged less than 90 days caused by GBS infection was recently reported in different European countries [8] , [9] , [10] . Moreover , recent reports described the emergence of antibiotic-resistant GBS strains likely caused by the widespread use of IAP [11] , [12] . Maternal vaccination is the best alternative to IAP to deal with GBS neonatal infections . Vaccines to prevent GBS disease have been initially developed by coupling capsular polysaccharide ( CPS ) antigens to immunogenic protein carriers . Glycoconjugate vaccines against nine GBS serotypes have been shown to be immunogenic in animals , but the existence of distinct epitope-specific capsular serotypes has hampered the development of a global GBS vaccine [5] , [13] . Moreover , glycoconjugated vaccines directed against the ten known serotypes of GBS would not protect against infections by nontypeable GBS isolates that are increasingly being reported [14] , [15] , [16] , [17] . The sequencing of numerous GBS genomes has accelerated advances in vaccine development and new protein antigens have been revealed using reverse vaccinology [5] , [18] , [19] . To avoid the selection of mutants that escape immune recognition , the ideal human GBS vaccine should be directed against structurally conserved antigens that are essential for GBS virulence and/or growth , but none of the hitherto described candidate antigens fulfills these requisites . The causes for the neonatal susceptibility to GBS infections are still poorly understood . Newborn immune system is not completely developed at birth , and undergoes an age-dependent maturation until fully developed . Thus , invasive infections in the first days of life pose serious threats for the newborn due to accentuated deficiencies in both innate and adaptive arms of the immune responses . Cases of early-onset GBS sepsis are usually characterized by an unexpectedly low number of neutrophils in infected tissues [20] , [21] , [22] , [23] . This is commonly explained by the reduced neutrophil chemotaxis and impaired granulocyte maturation observed in neonates [24] , [25] , [26] . Of interest , high concentration of plasma and cord blood IL-10 in preterm neonates evaluated for sepsis was associated with mortality and is considered as an early indicator of prognosis [27] , [28] . We have previously shown that the essential housekeeping enzyme glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) also acts as a GBS extracellular virulence factor that induces rapid production of interleukin-10 ( IL-10 ) by the host [29] . Adult C57BL/6 mice ( resistant to GBS infection ) infected with a GBS mutant strain that over-express GAPDH ( oeGAPDH ) had increased bacterial colonization compared to mice infected with wild-type ( WT ) GBS . Increased bacterial burden in oeGAPDH infected C57BL/6 mice was accompanied by elevated serum levels of IL-10 . Consequently , acquired susceptibility of C57BL/6 mice to oeGAPDH infection was completely reverted in IL-10-deficient animals [29] . This suggested that an exacerbated production of IL-10 during GBS infection might facilitate pathogen immune evasion . We demonstrate here that maternal immunization with rGAPDH confers protection against GBS infection in neonatal mice by abrogating the early IL-10 production detected upon the bacterial challenge . We also demonstrate that blocking GAPDH-induced early IL-10 production restores the recruitment of neutrophils in infected organs , which is essential for pathogen elimination and host protection against GBS infection . Since GBS GAPDH is a structurally conserved enzyme that is metabolically essential for bacterial growth in blood , it constitutes an attractive target for the development of a human vaccine .
GAPDH , a key enzyme of the glycolytic pathway , is structurally conserved in all 8 published GBS genomes ( identity>99 . 8% ) . Anti-rGAPDH IgG antibodies purified from sera of rGAPDH immunized mice or rabbits were thus used to demonstrate the presence of GAPDH in culture supernatants of ten unrelated GBS clinical isolates ( Figure 1A ) belonging to different serotypes and/or MLSTypes ( Table S1 ) . GBS GAPDH displays 44 . 7 , 45 . 8 and 44 . 0% amino acid identity with rabbit , mice , and human GAPDH , respectively ( Figure 1B ) . However , western blot and ELISA analysis revealed that rabbits and mice antibodies directed against GBS rGAPDH do not react with human , mouse , or rabbit GAPDH ( Figure 2A and 2B ) . To favor the production of antibodies recognizing linear buried epitopes , mice were immunized with heat-denaturated rGAPDH ( ΔT_rGAPDH ) . Anti-ΔT_rGAPDH antibodies purified from the sera of these animals did not show any cross-reactivity against mouse ( self cross-reactivity ) or human GAPDH when analyzed by western blot and ELISA ( Figure 2C and 2E ) . These results are consistent with the fact that the longest identical stretches observed between eukaryotic and prokaryotic GADPH sequences are only 10-aminoacid long ( Figure 1B ) . To test whether maternal immunization with rGAPDH conferred protection to the offspring against GBS infection , female BALB/c mice were immunized with rGAPDH in alum adjuvant . Control mice were sham-immunized with the adjuvant alone . Pups born from sham-immunized or rGAPDH-immunized females were infected intraperitoneally ( i . p . ) 48 h after birth with 5×106 colony-forming units ( CFU ) of serotype III virulent GBS strain NEM316 . All but one mouse born from rGAPDH-immunized mothers survived the infection ( 95% survival ) whereas 22 out of 27 infected pups succumbed to GBS challenge in the control group ( 18 . 5% survival ) ( Figure 3A ) . Most of the cases of GBS meningitis and LOD are caused by a serotype III hyper virulent clone , defined by multilocus sequence typing as ST-17 [30] , [31] , [32] . To better assess the effectiveness of maternal vaccination with rGAPDH , pups born from sham- or rGAPDH-immunized progenitors were i . p . infected 48 h after birth with 106 CFU of BM110 , a serotype III GBS hyper virulent strain ST-17 ( Table S1 ) . All ST-17 GBS challenged neonates born from sham-immunized mothers died whereas mortality rate dropped to 21 . 4% in neonates born from rGAPDH-immunized mothers ( Figure 3B ) . The protective effect conferred by rGAPDH maternal immunization was also observed in neonate mice infected by the subcutaneous ( s . c . ) route with 2 . 5×104 BM110 CFU . As shown in Figure 3C , none of the mice born from sham-immunized mothers survived this infectious challenge whereas only 23% of mice born from rGAPDH-immunized mothers died . Altogether , these results show that maternal vaccination with rGAPDH protected the offspring against GBS infections , including those caused by the hyper virulent strain BM110 . Pups born from rGAPDH-immunized mothers presented increased serum titers of anti-rGAPDH IgG antibodies when compared with those born from sham-immunized mothers ( Figure S1 ) . To evaluate the importance of these maternal antibodies in the newborn protection against GBS infection , neonatal mice were passively immunized with purified anti-rGAPDH IgG antibodies 12 h prior to GBS challenge . The passive antibody transfer conferred protection against infection caused by the virulent NEM316 or hyper virulent BM110 strains ( Figure 4A and 4B ) . Anti-rGAPDH IgG antibodies conferred a similar protection to neonate mice infected by the s . c . route ( data not shown ) . As described by others [33] , we observed that GAPDH is present at the cell surface of GBS strains ( Figure S2 ) and , it was therefore conceivable that protection conferred by anti-rGAPDH antibodies could be due to an enhanced opsonophagocytosis-mediated killing of GBS . However , anti-rGAPDH IgG antibodies did not enhanced in vitro phagocytosis or complement-mediated killing of GBS BM110 cells ( Figure 4C ) . This indicated that protection conferred by anti-rGAPDH antibodies was not mediated by these mechanisms . Furthermore , complete protection against GBS infection was observed in neonate mice treated with purified anti-rGAPDH F ( ab' ) 2 fragments 12 h before i . p . infection with BM110 strain . In contrast , all pups that received the same amount of control F ( ab' ) 2 fragments died within the first 3 days upon the infectious challenge ( Figure 4D ) . Altogether , these results demonstrate that enhanced opsonophagocytic killing or complement activation did not mediate the observed protective effect of anti-rGAPDH antibodies . We have previously described a rise in IL-10 serum levels in adult mice treated with rGAPDH [29] . As shown in Figure S3 , a similar increase in serum IL-10 levels was detected in newborn mice 1 h after i . p . injection of rGAPDH . Inactivation of rGAPDH enzymatic activity did not reduce this effect ( Figure S3 ) . This result indicates that IL-10 production induced by GBS GAPDH is independent of the dehydrogenase activity . We have also described that adult mice infected with GBS oeGAPDH mutant strain presented higher serum IL-10 levels than counterparts infected with WT GBS [29] . Thus , we also quantified the levels of serum IL-10 in mice pups at early times after GBS infection . As shown in Figure 5 , infection of newborn mice with GBS WT strain NEM316 resulted in a rapid increase of serum IL-10 concentration . Maternal rGAPDH vaccination or treatment with anti-rGAPDH F ( ab' ) 2 fragments completely abrogated the elevated amount of IL-10 found in the sera of infected pups born from sham-immunized mothers or treated with control F ( ab' ) 2 ( Figure 5A and 5B ) . Altogether , these results strongly suggest that the elevated IL-10 serum levels detected upon infection were due to GBS GAPDH . The results presented above indicate that newborn susceptibility to GBS infection is most probably associated with early IL-10 production induced by GAPDH . To confirm this hypothesis , IL-10 deficient ( IL-10−/− ) pups and WT controls were infected with this bacterium . In agreement with our hypothesis , IL-10−/− pups were more resistant ( 78% ) to GBS infection compared to WT controls ( 10% ) ( Figure 6A ) . To demonstrate further the essential role of IL-10 in neonatal susceptibility to GBS , newborn mice were treated with anti-IL-10 receptor ( IL-10R ) mAb 12 h before NEM316 or BM110 GBS challenge . As expected , most pups treated with anti-IL-10R mAb survived ( 86% or 82% , respectively ) while all control pups died ( Figure 6B and 6C ) . No additional protection was observed when newborn mice were treated simultaneously with anti-IL10R mAb and anti-rGAPDH IgG ( Figure 7 ) . Altogether , these results indicate that protection achieved using anti-GAPDH antibodies is due to inhibition of host IL-10 production . Several studies have shown that GBS can survive for prolonged periods within the phagolysosome of macrophages [34] , [35] , [36] , [37] . Interestingly , we observed that the simultaneous addition of anti-rGAPDH IgG's , or anti-IL10R mAb , and GBS cells to bone marrow-derived macrophages ( BMMφ ) cultures inhibits the bacterial survival ( Table 1 ) . This result , combined with those shown in Figure S3 , indicates that the inability of the macrophages to kill the intracellular GBS is due to IL-10 production induced by GBS GAPDH . A plausible explanation for the observed protection induced by maternal vaccination with rGAPDH could be that GAPDH-mediated early IL-10 production , elicited upon the GBS challenge in newborns , inhibits the initiation of a host-protective inflammatory response . Neutrophil recruitment is an early event associated with protection against bacterial infection in neonates . Moreover , lack of neutrophil recruitment into infected organs has already been associated with neonatal susceptibility against GBS infections [21] , [22] , [23] , [38] . To confirm that neutrophil recruitment into infected organs is an essential event for newborn protection against GBS infection , neutrophils of newborn mice were depleted by treatment with anti-Ly6G ( 1A8 clone ) monoclonal antibodies ( Figure S4 ) . We observed that either blocking IL-10 signaling with neutralizing antibodies or anti-rGAPDH antibody treatment was not sufficient to protect neutropenic pups infected with a lethal inoculum of GBS ( Figure 8 ) . In addition , we assessed the numbers and frequency of neutrophils in the liver and lungs of GBS NEM316-infected pups previously treated with anti-rGAPDH IgG or anti-IL10R mAb . The frequency and the total number of neutrophils quantified 18 h after GBS challenge in the analyzed organs of infected pups treated with control IgG ( or isotype control ) was as low as with the non-infected pups ( data not shown ) . Treatment with either anti-rGAPDH IgG or anti-IL-10R mAb prior to GBS infection significantly increased the neutrophil recruitment in organs ( Figure 9A and 9B ) . Consistently , the organs of pups treated with anti-IL10R mAb or anti-rGAPDH IgG contained significantly less bacteria than those of untreated pups ( Figure 9C ) . These results indicate that an efficient neutrophil recruitment into infected organs is crucial for neonatal protection against GBS infection whereas impaired neutrophil recruitment facilitates GBS colonization . Moreover , no bacterial colonization was detected three weeks after GBS infection in the brain or in any other organ of pups born from rGAPDH-immunized mothers and infected with NEM316 or with the ST-17 hyper-virulent strain BM110 ( data not shown ) . These results indicate that rGAPDH-maternal vaccination is also effective in preventing LOD .
Newborns are highly susceptible to infectious disease and deficiencies of key components of the complement cascade combined with the inability to produce high amounts of antibodies against T-independent antigens greatly impairs their ability to respond to encapsulated bacteria [39] . Absence of previous interactions with environmental microbes also implies that no immunological memory exists against specific antigens , which means that the acquired immune protection of newborns relies mainly on antibodies passively transferred from their mothers [40] . In addition , previous reports indicate that neonatal innate immune cells are less efficient in producing Th1-type inflammatory cytokines , but more competent in producing the immunosuppressive cytokine IL-10 upon Toll-like receptor ( TLR ) engagement by microbial products [41] , [42] , [43] , [44] . Moreover , newborn mice leukocytes are highly committed to produce increased amounts of IL-10 [44] , [45] , as also shown in human neonates [41] , [43] , [46] . As report in this work , high levels of serum IL-10 could be detected in the sera of GBS infected newborn mice early ( 1 h and 4 h ) upon infection . This result is in agreement with a previous report by Cusumano et al . [47] , showing that elevated levels of plasma IL-10 were detected in newborn mice 24 h and 48 h upon GBS challenge . These authors suggested a host protective role for IL-10 in the outcome of neonatal GBS sepsis as pre-treatment of newborn mice with recombinant IL-10 improved their survival upon a lethal s . c . GBS challenge [47] . Nevertheless , they also showed that a therapeutic administration of this cytokine ( 24 h after the bacterial challenge ) did not improve survival . This would limit its use in human therapies because neonatal GBS infection is usually acquired before or during labor [3] , [5] , [6] . Our results revealed that blocking IL-10 signaling through anti-IL-10R mAb administration was sufficient to confer protection against a bacterial challenge using either the s . c . or i . p . routes . Thus , they contrastingly indicate that IL-10 has a deleterious effect in the newborn host resistance to GBS infection . The increased resistance of IL-10-deficient neonates to GBS infection reported here constitutes further support for a deleterious effect of IL-10 in host resistance to GBS . As shown here , the GBS GAPDH induced host IL-10 production detected early after bacterial infection . IL-10 is produced by multiple cell types and inhibits leukocyte activation , pro-inflammatory cytokine production and down-regulates the expression of anti-microbial molecules on activated phagocytes [48] , [49] , [50] , [51] . IL-10 also inhibits production of CC and CXC chemokines by activated monocytes [52] , [53] , [54] . Since these chemokines are implicated in the recruitment of leukocytes during inflammation , IL-10 production indirectly inhibits leukocyte trafficking to inflamed tissues [50] , [55] , [56] . IL-10 production was already associated with host susceptibility against different pathogens [24] , [45] , [57] , [58] , [59] , [60] , [61] . We show here that treatment of pups with either anti-IL10R mAb or anti-rGAPDH IgG prior to the GBS challenge increased the neutrophil recruitment in liver and lungs that is triggered upon infection . Neutrophil recruitment is a crucial event in the host effector immune response to GBS [21] , [62] , [63] and , consequently , lack of neutrophil infiltration in infected sites has been reported in cases of severe early-onset GBS sepsis [21] , [22] , [23] , [38] . Thus , neutralization of GAPDH , and hence blockade of the induced IL-10 production , allowed an effective immune response at an early stage of infection that prevented death of pups . Moreover , pups protected by maternal immunization with rGAPDH presented no GBS CFU in the brain , lungs , and liver 3 weeks upon the infectious challenge . This indicates that protection achieved by this vaccination strategy might prevent LOD . The recruitment of neutrophils into infected tissues is very important to restrain bacterial replication . Thus , qualitative and quantitative deficiencies in the neutrophils of newborns may explain the observed susceptibility to GBS infections . Indeed , newborn neutrophils have reduced adhesion capabilities due to reduced expression of adhesion molecules [24] , [25] and they produced a limited number of microbicidal molecules . Moreover , the number of these cells is also reduced when compared to adults due to insufficiencies on neonatal granulocyte lineage development [26] . As a consequence , the intra-cellular and extracellular killing of pathogens is greatly impaired in neonates [64] . However , our results indicate that , despite these serious functional defects , the neutrophils of neonates can control the GBS infections as long as the inhibitory effect of IL-10 is blocked . Importantly , IL-10 blockade with a specific mAb did not significantly decreased the elevated serum TNF-α levels detected upon GBS infection in neonate mice [47] . This further suggests that impairment of neutrophil recruitment rather than inhibition of pro-inflammatory cytokines could be the prominent effect of IL-10 produced in the course of neonatal GBS infection . Neonatal sepsis is a pathological condition associated with elevated levels of pro-inflammatory cytokines , including IL-1β , TNF-α , and IL-6 . However , it has been previously described that cord blood or plasma IL-10 concentration is significantly increased in neonatal sepsis , constituting an early indicator of prognosis [27] , [28] . Of interest , it was also reported that high IL-10 levels are found in children at initial phases of fulminant septic shock [65] , [66] . This indicates that early IL-10 production , instead of being a physiological attempt to counterbalance the elevated levels of pro-inflammatory cytokines , could be a predisposing factor for disease . Our results are in accordance with this hypothesis and they provide the first evidence that the lack of neutrophil recruitment in infected organs combined with elevated cord blood IL-10 concentration may account for neonatal susceptibility to GBS infection . Hence , the discovery of GAPDH as an extracellular virulence factor of GBS that induces an early IL-10 production by the infected host could be a significant contribution to our understanding of the pathology of neonatal infections . GAPDH is a promising candidate for a human GBS vaccine because it is an essential metabolic enzyme that also plays a critical role in virulence . Our results show that maternal vaccination with rGAPDH protects the offspring against GBS lethal infection , including those caused by the hyper virulent ST-17 clone , which is responsible for most cases of neonatal meningitis [31] , [32] , [38] . As a consequence , maternal rGAPDH vaccination might efficiently protect against both EOD and LOD [5] , [67] , [68] . We demonstrated that passive immunization of neonates with GAPDH-specific IgG antibodies is sufficient to confer protection against GBS infection . Importantly , rGAPDH maternal vaccination prevents the early production of IL-10 in GBS infected pups and similar protective effect was obtained when GAPDH-specific antibody F ( ab' ) 2 fragments were used instead of whole IgG . These results indicate that neutralization of GAPDH-mediated IL-10 production , rather than complement activation or bacterial opsonophagocytosis , accounts for the observed protection . The extracellular GAPDH was detected at the bacterial surface and in culture supernatants of GBS isolates , which suggests that neutralization of its biological activity by antibody binding should not be sterically impaired by surface capsular polysaccharides . Recently , Margarit et al . showed that pili proteins could be used as a human vaccine to prevent GBS infections but , due to sequence variability , a combination of 3 antigens was required to confer protection against 94% of contemporary GBS strains [19] . It is likely that under selective pressure this vaccine will select GBS variants expressing new pili antigens , as shown for Neisseria gonorrhoeae [69] , [70] , [71] , [72] , [73] . In contrast , since it is an essential and highly conserved metabolic enzyme , GAPDH is unlikely to accumulate rapidly escape mutations or rearrangements under such a selective immune pressure . Taken together , our results demonstrate that extracellular GAPDH confers a selective advantage to GBS for survival in the infected host . In particular , GBS GAPDH acts on the host immune system to elicit IL-10 production thereby favoring bacterial colonization and survival . As we demonstrated that GBS GAPDH was still able to induce host IL-10 production upon exposure to an oxidative agent , this mechanism may still operate within the highly oxidative environment resulting from the host inflammatory response . Our data highlight the critical role played by this immunosuppressive cytokine in determining susceptibility to GBS infection at an early time after birth . Our results also show that GBS-associated pathology can be counteracted either by rGAPDH vaccination or IL-10 neutralization . In the future , it will be essential to explore the use of either strategy to induce protection towards other human neonatal pathogens .
Relevant characteristics of the GBS strains used in this study are summarized in Table S1 . Escherichia coli BL21 ( DE3 ) strain ( Novagen ) and the pET28a plasmid ( Novagen ) were used for production of recombinant GAPDH ( rGAPDH ) as described previously [29] . GBS was grown in Todd-Hewitt broth or agar ( Difco Laboratories ) containing 0 . 001 mg/mL of colistin sulphate and 0 . 5 µg/mL of oxalinic acid ( Streptococcus Selective Supplement , Oxoid ) and E . coli was cultured on Luria-Bertani medium . Bacteria were grown at 37°C . Male and female BALB/c mice ( 6-8 weeks old ) were purchased from Charles River . IL-10-deficient BALB/c ( IL-10−/− ) mice were kindly provided by Dr . A . O'Garra ( National Institute for Medical Research , London , U . K . ) . New Zealand White rabbits were purchased from Charles River . Animals were kept at the animal facilities of the Institute Abel Salazar during the time of the experiments . This study was carried out in strict accordance with the recommendations of the European Convention for the Protection of Vertebrate Animals used for Experimental and Other Scientific Purposes ( ETS 123 ) and 86/609/EEC Directive and Portuguese rules ( DL 129/92 ) . The animal experimental protocol was approved by the competent national authority Direcção Geral de Veterinária ( DGV ) ( Protocol Permit Number: 0420/000/000/2008 ) . All animal experiments were planned in order to minimize mice suffering . Recombinant GAPDH ( rGAPDH ) was purified as described in detail previously [29] . Enzymatically inactive rGAPDH ( inact-rGAPDH ) was obtained by pretreatment of the enzyme with 500 µM H2O2 . The lack of enzymatic activity upon inactivation was confirmed using a previously described enzymatic assay for GAPDH [29] . Recombinant GAPDH was used for maternal immunization assays . Female mice were injected intraperitoneally ( i . p . ) twice , with a 3-week intervening period , with 200 µL of a preparation containing 25 µg of rGAPDH in a 1:20 PBS/alum suspension ( Aluminium hydroxide Gel; a kind gift of Dr Erik Lindblad , Biosector , Frederickssund , Denmark ) . The sham-immunized control animals received 200 µL of a 1:20 PBS/alum suspension . Immediately after the second injection , female and male mice were paired . Females were monitored closely during gestation and the day of delivery was recorded . Serum anti-rGAPDH antibody titers were determined by ELISA as previously described [29] . Antibody treatments were performed in newborn BALB/c mice 12 h prior to GBS infection . For passive immunizations , pups were injected i . p . with 100 µg of anti-rGAPDH IgG antibodies or anti-rGAPDH F ( ab' ) 2 fragments . Control animals received the same amount of control IgG's or F ( ab' ) 2 fragments issued from control IgG's . For IL-10 signaling blocking , 100 µg of anti-IL10R antibodies ( 1B1 . 3a , Schering-Plough Corporation ) were administered i . p . and control animals received the same amount of matched isotype control antibody . Newborn mice were infected i . p . with 5×106 cells of GBS NEM316 or 106 cells of GBS BM110 ( ST-17 ) , 48 h after birth in a maximum volume of 40 µL . Subcutaneous ( s . c . ) infections were performed 48 h with 2 . 5×104 cells of GBS BM110 after birth in a total volume of 20 µL . Survival curves were determined in a 12-day experiment period and newborns were kept with their mothers during the entire time of the experiment . The liver , lungs , and brain of infected pups were aseptically removed at indicated time points and homogenized in PBS and serial dilutions of homogenized organs were plated on Todd-Hewitt agar to enumerate bacterial CFU . Adult mice or rabbit were immunized twice with 25 µg of rGAPDH in a PBS/alum suspension as described above and sera were collected 10 days after the second immunization . Pooled serum samples were applied to a Protein G HP affinity column ( HiTrap , GE Healthcare Bio-Sciences AB ) and purified IgG antibodies were then passed through an affinity column with immobilized rGAPDH ( Hi-trap NHS-activated HP , GE Healthcare Bio-Sciences AB ) . Control IgGs were obtained from sera of mice or rabbits sham-immunized with a PBS/alum suspension and purified on a Protein G HP affinity column . Purified IgG antibody fractions were further equilibrated in PBS and stored at -80°C in frozen aliquots . GBS-specific antiserum was obtained from mice immunized i . p . twice ( with a 3-week interval ) with isopropanol-fixed 105 GBS cells plus alum ( total volume ) . Serum from immunized animals ( Anti-GBS serum ) was obtained from retro-orbital bleeding 10 days after the second immunization . F ( ab' ) 2 fragments from anti-rGAPDH or control IgGs were obtained using IgG1 F ( ab ) and F ( ab' ) 2 Preparation Kit ( Pierce ) used according to manufacturer's instructions . Bone marrow-derived macrophages ( BMM ) purified as described previously [74] were plated in 96-well plates ( 105 BMM/ well ) and stimulated for 30 min at 37°C 5%CO2 with 106 CFU of GBS BM110 ( or NEM316 ) in the medium alone ( RPMI ) , the medium containing 25 µg/mL of anti-rGAPDH IgG's , or medium with 10% of serum containing anti-GBS IgG antibodies . After this incubation period , the plates were washed three times with HBSS to remove extracellular bacteria . To enumerate intracellular GBS CFU , 10% saponin ( 1∶100 dilution ) was added to wells and serial dilutions of supernatant were plated onto agar plates . Blood from adult mice was collected in heparinated tubes and diluted 1:1 in HBSS . 106 GBS NEM316 ( or BM110 ) CFU with 25 µg/mL of anti-rGAPDH IgG's or 10% of serum containing anti-GBS IgG antibodies were then added . Rabbit serum ( 5% ) was added to the mixture as a source of complement . After 2 h of incubation at 37°C , serial dilutions of the mixture were plated onto agar plates to evaluate complement-mediated GBS killing . The BMM , obtained as described previously [74] , were infected with GBS strains NEM316 and BM110 at a macrophage:bacteria ratio of 1:10 in RPMI containing 10% FCS . Microplates were incubated for 2 h at 37°C in 5% CO2 for GBS phagocytosis . After this period , culture supernatants of infected macrophages were removed by aspiration and cells were washed three times ( 10 min for each wash ) with HBSS containing penicillin ( 100 IU/mL ) and streptomycin ( 50 µg/mL ) to kill extracellular bacteria . Infected macrophages were further incubated in RPMI medium containing 10% FCS and the same concentrations of antibiotics . To quantify intracellular GBS , the supernatants containing antibiotics were removed , the cells were washed with antibiotic-free HBSS , lysed with saponin ( 0 , 1% final concentration ) , and the CFU were estimated by plating serial dilutions of the lysate onto agar plates . Neutrophil recruitment in liver and lungs of infected pups was evaluated by flow cytometry analysis . Briefly , 18 h after GBS infection , the organs were collected , gently homogenized in HBSS ( Sigma ) , and passed through glass wool to remove cellular aggregates . PerCP/Cy5 . 5 anti-mouse Ly-6G antibody ( clone 1A8; Biolegend ) was used for neutrophil detection . Cells were analyzed by an Epics XL cytometer ( Beckman Coulter ) . Newborn mice were depleted of neutrophils by treatment with anti-Ly6G antibodies ( clone 1A8 , Biolegend ) . Antibody treatment was performed twice , 12 h before and immediately after GBS challenge . Each pup was injected with a total of 80 µg of anti-Ly-6G antibodies . IL-10 was quantitated in the serum of newborn mice with an ELISA kit ( eBioscience ) used according to the manufacturer's instruction . The presence of GAPDH in the culture supernatants of GBS strains was visualized by Western-blot analysis . Extracellular proteins were isolated as described previously [29] . The reactivity of purified anti-rGAPDH IgG antibodies obtained from the serum of rGAPDH immunized mice or rabbits against self or human GAPDH , was determined by Western-blot analysis or ELISA . Human , rabbit , and mouse GAPDH were purified from human erythrocytes , rabbit erythrocytes or mouse muscle as previously described [75] , [76] . Student's T test was used to analyze the differences between groups . Survival studies were analyzed with the log-rank test . A P value<0 . 05 was considered statistically significant . | Streptococcus agalactiae ( Group B streptococcus , GBS ) is the leading infectious cause of morbidity and mortality among neonates . However , there is still no satisfactory explanation of why neonates are so susceptible to GBS infections . Intrapartum antibiotic prophylaxis ( IAP ) was implemented in many countries but led to the emergence of antibiotic-resistant GBS strains . Therefore , maternal vaccination represents an attractive alternative to IAP . Here , we show that the high susceptibility of newborn mice to GBS infections is associated with their propensity to produce elevated amounts of immunosuppressive cytokine IL-10 . We also demonstrate that IL-10 impairs neutrophil recruitment into infected organs thus preventing bacterial clearance . We identified extracellular GAPDH as the GBS factor that induces the high IL-10 production detected early upon neonatal infection . We show that maternal vaccination with recombinant GAPDH confers robust protective immunity against lethal infection with a GBS hyper-virulent strain in mice offspring . This protection can also be obtained either by antibody neutralization of GBS GAPDH or by blocking IL-10 binding to its receptor . As GBS GAPDH is an essential protein for bacterial growth , it is present in all GBS strains and thus constitutes an appropriate target antigen for a global effective vaccine against this pathogen . | [
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] | 2011 | Inhibition of IL-10 Production by Maternal Antibodies against Group B Streptococcus GAPDH Confers Immunity to Offspring by Favoring Neutrophil Recruitment |
Are the information processing steps that support short-term sensory memory common to all the senses ? Systematic , psychophysical comparison requires identical experimental paradigms and comparable stimuli , which can be challenging to obtain across modalities . Participants performed a recognition memory task with auditory and visual stimuli that were comparable in complexity and in their neural representations at early stages of cortical processing . The visual stimuli were static and moving Gaussian-windowed , oriented , sinusoidal gratings ( Gabor patches ) ; the auditory stimuli were broadband sounds whose frequency content varied sinusoidally over time ( moving ripples ) . Parallel effects on recognition memory were seen for number of items to be remembered , retention interval , and serial position . Further , regardless of modality , predicting an item's recognizability requires taking account of ( 1 ) the probe's similarity to the remembered list items ( summed similarity ) , and ( 2 ) the similarity between the items in memory ( inter-item homogeneity ) . A model incorporating both these factors gives a good fit to recognition memory data for auditory as well as visual stimuli . In addition , we present the first demonstration of the orthogonality of summed similarity and inter-item homogeneity effects . These data imply that auditory and visual representations undergo very similar transformations while they are encoded and retrieved from memory .
In the past decade , cognitive science has spawned some powerful computational models for both the large-scale and detailed structure of many fundamental phenomena , including categorization and recognition . These models have enjoyed considerable success , particularly in accounting for recognition of simple visual stimuli , such as sinusoidal gratings and chromatic patches [1–3] , and more complex visual stimuli , such as realistic synthetic human faces [4] . By exploiting stimuli whose properties can be easily manipulated , but resist consistent verbal rehearsal strategies [5] , researchers can formulate and test detailed predictions about visual recognition memory . To date , this effort has focused on vision , raising the possibility that the properties of recognition memory revealed thus far might be modality specific and therefore of limited generality . There are several prerequisites that must be satisfied before another sensory modality can be addressed in a comparable fashion . First , a suitable task must be found; second , a family of stimuli must be identified that can be parametrically varied along dimensions thought to be encoded in memory . In addition , baseline memory performance must be comparable across modalities , and the effect of early perceptual processing on the stimulus representations must be similar . Failure to satisfy any of these prerequisites would undermine inter-modal comparisons of memory . We decided to use Sternberg's recognition memory task , which had been used previously with visual stimuli and whose properties were well understood [6] . We then identified a family of auditory stimuli—moving ripple sounds—whose attributes resembled ones that had proven useful in modeling visual recognition memory . These auditory stimuli vary sinusoidally in both time and in frequency content , and are generated by superimposing sets of tones whose intensities are sinusoidally modulated . Until now , these stimuli have been mainly used to characterize the spectro-temporal response fields of neurons in mammalian primary auditory cortex [7–9] , but because their spectro-temporal properties resemble those of human speech [7 , 10] , moving ripple stimuli are well suited to probe human speech perception and memory with minimal contamination by semantic properties or by the strong boundaries between existing perceptual categories [11] . This selection of stimuli was influenced by previous attempts to compare auditory and visual memory . Some of those attempts used auditory and visual stimuli that differed substantially in their early sensory processing , but shared semantic representations [12] . For example , Conrad and Hull's classic study compared memory for a list of digits presented either visually or as spoken items [13] . Initial processing differs tremendously for the two types of inputs , indicating that differences in memory may be due to the divergent initial processing . Further , with stimuli like these , once the items have been encoded into verbal form for storage in memory , shared semantic processes may obscure any fundamental differences in memory for the two modalities . Other experiments use stimuli that arguably are free from semantic influences , yet still fail to equate the early stages of processing required by the stimuli [14] . We examined short-term memory for auditory and visual stimuli whose early sensory processing is comparable . Finding comparable stimuli across modalities is difficult , as it may initially seem incontrovertible that the brain operates differently upon auditory and visual inputs . Certainly the initial stages of processing by the modalities' respective receptors differ from one another in many ways . However , the transformations performed by the nervous system on the information generated by the auditory and visual receptors appear to be very similar [7 , 15] . Starting from each modality's sensory receptors and continuing to the modality's respective processing networks within the cerebral cortex , analogs between hearing and vision have been noted by several researchers [7 , 9 , 16 , 17] . To take a few examples , adjacent sensory receptors in the cochlea of the ear detect neighboring frequencies of sound the same way adjacent sensory receptors in the retina of the eye respond to light from neighboring locations in space . This analogy extends to the retinotopic/tonotopic structure and receptive fields of auditory and visual cortex . Both moving ripples and Gabor patches vary sinusoidally along the dimensions that primary sensory neurons encode . These stimuli are described in Figure 1 . Moving further along the processing hierarchy , it appears that primary auditory cortex responds to moving ripple stimuli analogously to the way primary visual cortex responds to Gabor patches: a few neurons respond robustly to the stimulus , but most are relatively quiet [9] . The sets of stimuli , therefore , are very well matched in terms of early sensory processing . In addition , to decrease reliance upon verbal rehearsal , these unfamiliar stimuli can be varied continuously , and do not support readily available verbal or semantic labels [5] . So we should expect results to be minimally influenced by semantic relationships among stimuli . Finally , to promote comparability in the difficulty of the memory task with auditory or visual stimuli , we adopted a strategy introduced by Zhou and colleagues [18] . Recognizing that the similarity relationships among visual stimuli strongly influenced recognition memory , those researchers adjusted each participants' memory test stimuli according to that participant's discrimination threshold . Their aim was to minimize individual differences on the memory task . We took the procedure one step further , adjusting stimuli separately within each modality according to each participant's discrimination threshold for that modality . This was meant to equate for both auditory and visual modalities the powerful influence that similarity exerts on memory . We present the results of two experiments . Experiment 1 assessed several basic properties of recognition memory for ripple stimuli and memory for Gabor patches; Experiment 2 used ripple stimuli to isolate the effects of summed probe-item similarity and inter-item homogeneity . The design of Experiment 2 was meant to orthogonalize these two potential influences on recognition memory , allowing the effects of summed similarity and inter-item homogeneity to be explored independently . A previously proposed model for visual memory , the Noisy Exemplar Model ( NEMo ) was fit to the data [1] . Because so many trials were required for each case , and because the NEMo has been shown previously to fit data for visual stimuli quite well [1] , only auditory stimuli were used in Experiment 2 .
Experiment 1 measured short-term recognition memory for moving ripple stimuli and for both moving as well as stationary Gabor patches . We used a variant of Sternberg's recognition task [6 , 19] . On each trial , one to four stimuli were sequentially presented , followed after some retention interval by a probe . The participants' task was to identify whether the probe matched any of the items presented in the list , pressing a button to indicate their choice . The use of the Sternberg paradigm for auditory stimuli allows comparisons to the many studies that have used the same paradigm with visual stimuli [1 , 19 , 20] . Both moving and static visual Gabor patches were tested because although moving Gabor patches change in time similarly to the ripple sounds , their stationary counterparts have been extensively studied in psychophysical examinations of memory [1] . We examined several basic properties of short-term memory for auditory and visual stimuli: the effect of the number of stimuli that must be remembered ( list length ) , the interval over which those stimuli must be remembered ( retention interval ) , and the serial position of the stimulus matching a probe . Each participant's data from trials of a given list length and retention interval were averaged to obtain a proportion correct for that combination of conditions . These were compared across participants using standard parametric statistics . Proportion correct measures were used rather than , for example , d′ measures because in this case , the assumption that variances associated with target ( probe matches a list item ) and lure ( probe does not match a list item ) trials are identical is probably not defensible , as the range of summed probe-item similarities for target trials is much smaller than for lures ( as by definition , Target trials always include a stimulus that is identical to the probe , with a similarity equal to 1 ) [21] . Previous studies have shown that short-term recognition memory for visual stimuli can be understood using the NEMo introduced by Kahana and Sekuler [1] . Experiment 2 directly tested this model's predictions for memory for moving ripple sounds , and compared these results to previous results obtained with visual stimuli . This experiment was crafted so that the key assumptions of the model , effects of inter-item homogeneity and summed similarity , could be explored in a model-free way , while also allowing data to be fit to NEMo for a more quantitative assessment of these effects . The next section explains the logic of the experimental design . Figures 5 and 6 show that inter-item homogeneity and summed probe-item similarity both affect memory for complex sounds . This result is analogous to that observed for visual stimuli [1 , 2 , 4 , 18] . By fitting the same computational models to these auditory data and visual memory data , we can more sensitively examine whether the cognitive processing undergone by auditory and visual representations are similar . The data for Experiment 2 were fit to the models described in the Methods section: a three-parameter model that does not take into account inter-item homogeneity , a four-parameter model that adopts values describing perceptual similarity based on participants' performance when list length is 1 ( Figure 7 ) , and a five-parameter model including inter-item homogeneity effects , and not assuming that perceptual similarity can be based on participants performance for list length 1 . Table 1 shows the parameter values produced by model fits to the combined data for 12 participants . Fits were made for individual participants as well , and the parameters are similar in the individual participant fits and the fits to the average . The τ and σ parameters showed most variability across participants . Models in which the τ parameter was estimated from an independent dataset in which study lists comprised just one item are indicated . The value for A calculated from data with list length 1 was 0 . 93 for the data averaged over participants , with individual participant values ranging from 0 . 88 to 1 . 02 . The value for τ calculated from data with list length 1 ranged from 0 . 43 to 1 . 41 across participants . When τ was allowed to vary in the five-parameter model , the value ranged from 0 . 97 to 3 ( the maximum of the allowed range ) across participants . The parameter τ and the criterion C have somewhat of a reciprocal relationship mathematically , and so their values depend on one another: as C decreases , τ increases . Interestingly , the α parameter was not significantly less than 1 , indicating that in this experiment , when participants had to remember only two stimuli , both stimuli were remembered equally well . When participants must maintain more stimuli in memory , however , they are more likely to forget stimuli presented earlier in the list , as is shown in Figure 4 . Note that the β parameter remained negative and with a similar value regardless of model . Note that in both the four- and five-parameter models , β ∼ −1 . This result is similar to that found by Kahana and Sekuler [1] . Results indicated that models must incorporate inter-item homogeneity in order to fit the data well . The three-parameter model that did not incorporate inter-item homogeneity ( as shown in Figure 8A ) accounted for only 51% of the variance ( r2 ) , and had an Akaike information criterion ( AIC ) value ( see Methods ) of 1 , 010 ( higher AIC values indicate worse fit [27] ) . On the other hand , the four-parameter model accounted for 78% of the variance ( r2 ) , and had a considerably lower AIC value of 652 . The five-parameter model , allowing τ to vary according to the list length 2 data , accounted for 81% of the variance ( r2 ) and had a slightly higher AIC value of 696 , indicating that the addition of this extra parameter does not make the model more generalizable .
In our hands , direct comparison between auditory and visual memory revealed the two to be strikingly similar . The list length manipulation effectively changed the memory load participants had to bear , and has been used in experiments on vision [6] and hearing [11] . The current experiment reveals that the effect of load does not depend on the modality of the stimulus by comparing the stimulus types using the same participants and same experimental paradigm . The effects of retention interval on recognition memory are also quite similar across stimulus types , as seen in Figure 3 . Memory for auditory and visual stimuli decreased only modestly with retention interval . This result is consistent with previous studies of visual memory [20] . The effects of serial position on recognition memory were found to be quite similar across modality , as seen in Figure 4 . Although this is consistent with some studies [31] , there is an apparent contradiction in the literature: some researchers have found serial position curves of different shapes for auditory and visual experiments [32] . Many such experiments rely on auditory stimuli that are phonological in nature , and others use different experimental paradigms or stimulus types for auditory and visual experiments . A study by Ward and colleagues [31] implies that the auditory versus visual difference seen in other studies can be explained by the differing experimental methods used . When experimental methods are held constant , little or no serial position difference was seen between the two modalities , consistent with our data . Although there was no significant interaction of the effect of serial position with stimulus type , there is a trend toward a larger recency effect for the auditory stimuli than for the visual stimuli ( Figure 4 ) . The origin of this recency effect has been debated [33] . One idea put forward by Baddeley and Hitch [33] implies that the recency effect may be due to implicit learning of the items ( similar to priming ) followed by explicit retrieval of the residual memory . Many studies using various types of stimuli in free-recall tasks have shown a “primacy effect” in which serial position 1 shows a better proportion correct than serial position 2 [34] . No such primacy effect is evident in our data , as can be seen in Figure 6 . The lack of a prominent primacy effect is consistent with some previous experiments using this paradigm [1 , 35] , whereas other experiments using the same paradigm , but different stimuli , have found modest primacy effects [36] . Previous experiments have shown that these effects are sensitive to the delay between the stimulus items and probe [14 , 37] . The absence of a primacy effect may be due to specifics of timing , the difficulty of rehearsing these stimuli , or an interaction of the stimuli and recognition memory task employed . Figure 5 shows that summed probe-item similarity correlates very strongly with whether a probe will be judged as new . Because the similarity of the probe to the closest item is identical in each pair , the data imply that participants use information from all stimuli when making a judgment , not just information about the stimulus closest to the probe [1 , 25] . This gives credence to an exemplar model of memory , rather than a prototype model [25] , and is entirely consistent with the results found in the visual domain [1 , 4 , 18] . These data indicate strongly that inter-item homogeneity plays a role in memory for sounds . When items in a list are more similar to each other , participants are less likely to say that a probe was a member of the list . This result was robust through direct data comparison ( Figure 6 ) as well as by model fitting , which gave a more sensitive measure of the effect of inter-item homogeneity . As noted earlier , these results are consistent with experiments that examine memory for visual stimuli , including gratings and faces [1 , 4] . In fact , some older experiments using sound stimuli are consistent with this inter-item homogeneity effect . In one experiment , participants were required to remember a tone stimulus during presentation of distracter tones , and performed much worse when the distracter tones were presented both higher and lower than the remembered stimuli ( low homogeneity between the remembered tone and the distracters ) , as opposed to the case when distracters were presented only higher or only lower than the remembered stimulus ( higher overall homogeneity between the remembered tone and distracters ) [38] . The similarity across stimulus type implies that the origin of the inter-item homogeneity effect is a process common to both auditory and visual memory . The strikingly similar patterns of memory observed for the auditory and visual stimuli imply that the information-processing steps involved in memory for each stimulus type are similar . Previous research has shown that sensory-specific cortex is re-activated during memory for a sensation [39 , 40] . Further , lesions of some auditory-specific cortex results in impairment specifically to auditory memory [41] . The current data imply that the effects of inter-item homogeneity and summed probe-item similarity on memory either arise from non-sensory–specific cortex , or that the mechanisms in each sensory-specific region are very similar . The data presented here show that memory for visual and auditory stimuli obey many of the same principles . In both modalities , recognition performance changes in similar ways in response to variation in list length , retention interval , and serial position . Further , memory performance depends not only on the summed similarity between a probe and the remembered items , but also on the similarity of remembered items to one another . Memory performance data for both modalities are fit well by the NEMo . These results imply that auditory and visual short-term memory employ similar mechanisms . Previous studies have examined how auditory and visual items are encoded into memory , implicating some structures in both visual and auditory working memory [42 , 43] . Behaviorally , visual and auditory stimuli can interfere with each other , indicating some shared processing [44] . On the other hand , some memory information is processed in sensory-specific cortex , indicating that the transformations performed on such information may differ between modalities [39 , 40 , 45] . Our data imply that , regardless of whether the processing is performed by the same brain area or not , similar processing is performed on auditory and visual stimuli as they are maintained and retrieved from memory . For centuries , people have pondered possible parallels between their experiences of light and sound [17] . Belief that the two modalities were parallel probably influenced Sir Isaac Newton's conclusion that the visible spectrum contained seven colors , the same number of tone intervals in a musical octave [46] . ( Newton observed: “And possibly colour may be distinguished into its principle degrees , red , orange , yellow , green , blue , indigo and deep violet , on the same ground that sound within an eighth is graduated into tones . ” [46] ) Today , 300 years after Newton , understanding of the neural signals supporting vision and hearing has advanced sufficiently that we have been able to formulate and test hypotheses about fundamental relationships between the characteristics of short-term memory for each modality .
Moving ripple sounds: moving ripple stimuli varied sinusoidally in both time ( with a period w cycles per second [cps] ) and frequency content ( with a period Ω cycles per octave ) . The sounds were generated by superimposing sounds at many frequencies whose intensity at any time , and for any frequency ( f ) , was defined by where g = log ( f/f0 ) , t is time , ψ is the phase of the ripple , and D is modulation depth . ( D0 represents the baseline intensity , and is set to 1 in the equation to avoid negative intensity values . ) f0 is the lowest allowed frequency . In these experiments , the parameter space was simplified by allowing only one parameter ( w ) to vary . Other parameters took the following fixed values: Ω = 1 , D0 = 0 . 9 , f0 = 200 Hz , and ψ was varied randomly between 0 and π/2 for each stimulus . Frequencies ranged over three octaves above f0 , that is , from 200 to 1 , 600 Hz . Choices for these parameters were made so that a range of stimuli with parameters close to these could be discriminated , as suggested by existing psychophysical data [10 , 47 , 48] , and in pilot experiments of our own . Each stimulus contained 20 logarithmically spaced frequencies per octave . Levels for each frequency were identical , but psychophysical loudness varied . However , the same group of frequencies was used for every stimulus , so the time-averaged loudness should be nearly identical for each of the stimuli . Equation 1 describes for each frequency f , a sinusoidal modulation of the level around some mean , at a rate of w cps . This produces a spectral profile that drifts in time , so that different frequencies are at their peaks at different times . Figure 1 illustrates the dynamic spectrum of a moving ripple , with modulation in both time ( w , horizontal axis ) and frequency content ( Ω , vertical axis ) . For all stimuli , duration was fixed at 1 s . The level of the stimulus was ramped on and off gradually and linearly over 10 ms at the beginning and end of each stimulus . Frequencies at the spectral edges of the stimulus were treated identically to frequencies in the middle of the frequency range . Two examples of auditory stimuli with different w values are given in Audios S1 and S2 , and correspond to the stimuli schematized in Figure 1A and 1B . Visual stimuli: visual stimuli were Gabor patches , created and displayed using Matlab and extensions from the Psychtoolbox [49] . The CRT monitor was calibrated using Eye-One Match hardware and software from GretagMacbeth ( http://www . gretagmacbeth . com/index . htm ) . The Gabor patches' mean luminance matched that of the background; the peak contrast of a Gabor patch was 0 . 2 . Patches were windowed with a two-dimensional Gaussian envelope with a standard deviation of 1 . 4 degrees . Before windowing , the visual stimuli were generated according to the following equation: where s represents the luminance of the stimulus at any y ( vertical ) position and time , t . Note that these stimuli were aligned horizontally and moved only vertically; the luminance did not change with horizontal position . ψ is the phase of the grating , which varied randomly between 0 and π/2 for each stimulus . D is modulation depth . ( D0 is the mean luminance , set to a mid-gray level on the monitor . ) In these experiments , the parameter space was simplified by allowing only one parameter to vary at a time . In blocks with moving gratings , the wv parameter varied; in blocks with static gratings , the spatial frequency , Ωv , parameter varied . Other parameters took the following fixed values: D0 = 0 . 9 and f0 = 200 Hz . All moving gratings had a spatial frequency , Ωv , of 0 . 72 cycles per degree , and moved with speeds that ranged upward from 1 . 5 cps ( 2 . 1 degrees per second ) . For static gratings , stimuli did not move ( wv = 0 ) , and had spatial frequencies , Ωv , with a minimum of 0 . 36 cycles per degree . An example of a moving grating is shown in Video S1 , and an example of a static grating is shown in Figure 1C . Parameter values were chosen based on pilot experiments and previous data so that a range of stimuli with parameters near these would be discriminable . Stimuli were tailored to each participant in an initial session , JND thresholds to achieve 70% correct were estimated using the QUEST algorithm [50] as implemented in the Psychtoolbox [49] . Participants were presented with two stimuli sequentially and responded indicating which stimulus was “faster” ( in the case of moving ripples or moving gratings ) or “thinner” ( in the case of stationary gratings ) . Thresholds for each stimulus type were estimated in separate blocks . These JND values were used to create an array of ten stimuli for each participant , in which each stimulus differed from its nearest neighbor by one JND . All stimuli were chosen from this array , and were thus separated from one another by an integer number of JNDs . The timing of stimulus presentation during threshold measurements was the same as that used in the later memory tests for a list with a single item . Stimuli were thus individually tailored for each participant , so that the task was of similar difficulty for all participants , and somewhat similar difficulty across modality [18] . The lowest value that each stimulus could take was the same for all participants . Other stimulus values were allowed to vary by participant in order to equate discriminability across participants . In Experiment 1 , for the static grating stimulus type , in which the spatial frequency , Ωv , changed , the lowest Ωv value was 0 . 36 cycles per degree . For the moving grating stimulus type , in which temporal frequency , wv , changed , the lowest wv value was 0 . 025 cps . For the moving ripple sounds , the lowest possible ripple velocity , w , was 6 cps . In Experiment 2 , the lowest ripple velocity , w , was 7 cps . In order to minimize the possibility that participants could memorize all stimuli , a second , “jittered” set of stimuli was created and then used on half the trials chosen randomly . This list of stimuli started at 0 . 5 JND above the base value , and increased in units of 1 JND to create a second array of ten stimuli . For data analysis , we do not distinguish between trials on which the two arrays were used . We experimentally manipulate the physical difference between any two stimuli , here measured in JND . However , the perceptual similarity is traditionally referred to in models that take perception into account . Therefore , when discussing physical stimuli , we refer to their difference ( in JND ) , but later , when discussing fits to models , it is the related perceptual similarity that is relevant . Participants: participants for all experiments were between the ages of 18 to 30 y , and were recruited from the Brandeis student population . They participated for payment of $8 per session plus a performance-based bonus . Using a MAICO MA39 audiometer , participants' hearing thresholds were measured at 250 , 500 , 750 , 1 , 000 , 2 , 000 , 3 , 000 , 4 , 000 , and 6 , 000 Hz . Each participant had normal or better hearing , that is , thresholds under 20 dBHL ( decibels hearing level ) at each frequency . Fourteen participants participated in seven total sessions each . In an initial session , hearing was tested and vision was tested to be 20/20 or better ( using a Snellen eye chart ) , participants performed 30 practice trials for each stimulus type , and JND thresholds were measured at a 70% accuracy level for each stimulus type . Each of the subsequent six sessions lasted approximately 1 h , and consisted of 504 trials . A session began with 15 practice trials , whose results were not included in subsequent data analysis . For each participant , successive sessions were separated by at least 3 h , and all sessions were completed within 2 . 5 wk . Apparatus and sound levels: participants listened to ripple sounds through Sennheiser Pro HD 280 headphones . All stimuli were produced by Apple Macintosh iMac computers and Matlab , using extensions from the Psychtoolbox [49] . Sound levels for this system were measured using a Knowles electronic mannequin for acoustic research , in order to define the stimulus intensity at the participant's eardrum . Levels for all stimuli in Experiment 2 were 79 dBSPL ( decibels sound pressure level ) , well above our participants' hearing thresholds , and levels for stimuli in Experiment 1 were similar ( with the same code and hardware settings , but a different computer ) . This experiment examined and compared some basic characteristics of short-term memory for moving ripple sounds and for Gabor patches . Using Sternberg's recognition memory paradigm , we examined recognition's dependence on the number of items to be remembered , the interval over which the items had to be retained , and the serial position of the to-be-remembered item [6] . The experiment used static visual gratings ( in which the spatial frequency of the gratings , Ωv , varied ) , moving visual gratings ( in which the speed of the gratings , wv , varied ) , and moving ripple sounds ( in which the temporal frequency , w , of the ripples varied ) . Stimulus presentation: trials were presented in blocks such that only one stimulus type ( moving ripple sounds , static gratings , or moving visual gratings ) was presented per block . During presentation of either visual or auditory list stimuli , participants fixated on a “+” in the center of a computer screen . Each stimulus , auditory or visual , lasted for 1 s . After the last item from a list was presented , a short beep sounded , and the “+” was replaced by the text “ . . . ” , indicating that the participant should wait for the probe . The text “ ? ” was presented onscreen during presentation of the probe ( for sound stimuli only ) and after the probe presentation , before the participant made a response . Participants were instructed to be as quick and accurate with responses as possible . Stimuli were presented in blocks of 84 trials of a given stimulus type . Six total blocks were presented per session . The first two trials of each block were not used for analysis to allow for task-switching effects . Stimuli for each list were chosen from a set created as described above for each participant based on their own JND threshold . Trials with different list lengths and retention intervals were randomly interleaved . Twenty-four trials of each possible serial position were presented to each participant , for each stimulus type . Effect of retention interval was examined by having participants perform trials in which a single stimulus was followed by a probe , after a retention interval of 0 . 6 , 1 . 9 , 3 . 2 , 4 . 5 , or 9 . 7 s; 24 trials of each retention interval were performed by each participant . Equal numbers of trials in which the probe matched a list item ( target ) , and trials in which the probe did not match ( lure ) were performed . Trials were self-paced , with each beginning only when participants indicated with a key press that they were ready . Participants were alerted with a high or low tone whether they got the current trial correct or incorrect , and were updated after each trial as to their percent correct . For every percentage point above 70% , participants received an extra $0 . 25 reward above their base payment of $56 . Participants and stimulus presentation: on each trial , a list of one or two ripple stimuli ( s1 , s2 ) were presented , followed by a probe ( p ) . As in Experiment 1 , the participants' task was to identify whether the probe stimulus matched any of the items presented in the list , and press a button to indicate a choice . During list presentation , participants fixated on a “+” in the center of a computer screen . This was replaced by a “ ? ” during the presentation of the probe item . Twelve participants participated in each of eight sessions , following an initial session in which hearing was tested , JND thresholds for the w parameter ( cps ) were measured , and 200 practice trials were performed . Sessions were approximately 1 h each , and consisted of 586 trials . At the beginning of every session , each participant completed at least 30 practice trials that were excluded from data analysis . Each session began at least 6 h from the previous session , and all sessions were completed within 3 wk . All other details are as described for auditory stimuli in Experiment 1 . Summed probe-item similarity: in order to examine the effect of summed probe-item similarity independently of other confounds , such as the similarity of the probe to the closest item or the inter-item homogeneity , stimulus conditions were created that varied summed probe-item similarity while other factors were held constant . Two pairs of conditions were created that were similar in all respects , but the summed probe-item similarity varied between the two conditions in the pair . Figure 5A shows the relationships between stimuli for each condition . All figures indicate relationships between stimuli in terms of their differences in units of JND . Pairs of conditions ( labeled a & b on one side , and c & d on the other ) were created with identical inter-item homogeneities , and identical similarities between the probe and the item closest to it . However , each pair has one low and one high summed probe-item similarity ( pair a & b , for example , both have inter-item difference = 2 JND , but summed probe-item differences of 2 and 4 JND units , respectively ) . Figures 5 and 6 indicate only the relationship among the stimuli in units of JND , not their physical values . Part A in these figures illustrates the case when s1 < s2 , equally often s1 > s2 . Also in conditions b and d , the probe , p , is equally likely to be greater than or less than the stimuli s1 and s2 . The conditions as shown in the figures do not specify exactly the stimulus values for a trial . Eight cases of each condition were chosen randomly from all possible configurations that satisfy the condition , given ten stimuli in the array . This made 64 lure cases . Twenty repetitions of each case were performed by each participant , interleaved among the other trial types . For each lure case , analogous target cases were created where the probe matched one of the stimuli . Each target case matched a different lure case in either inter-item homogeneity ( in conditions a–d ) , or summed probe-item similarity ( in conditions e–h , explained below ) . Inter-item homogeneity: stimulus conditions with high and low inter-item homogeneity were created according to Figure 6A , which follows the same conventions as Figure 5A . Relationships between stimuli for each condition are shown in terms of their physical differences , in units of JND . Two sets of paired high and low homogeneity conditions were created; both members of a pair had the same inter-item homogeneity and similarity between the stimulus and the closest probe . Computational modeling of results: fitting computational models to experimental data can help determine what information processing steps are involved in short-term memory . Previous experiments in the visual domain found that a NEMo , including effects of summed probe-item similarity as well as inter-item homogeneity , fit data for short-term visual memory well [1 , 2 , 4] . The NEMo model was applied only to the data from the 128 auditory memory cases whose list length was two items , because only those trials incorporated information about inter-item homogeneity , important to the model . The NEMo assumes that given a list of L items and a probe item , p , the participant will respond that “Yes , the probe is a member of the list” if the quantity: exceeds a threshold criterion value , C . The first term depends on the summed similarity between the probe and the items on the list . α is defined as 1 for the most recent stimulus; its value for a less-recent stimulus determines the degree of forgetting of that stimulus . It should take on values less than 1 if the earlier item is forgotten more readily . η , as defined in Equation 4 , measures the perceptual similarity between any two stimuli , as a function of τ , which defines how quickly perceptual similarity drops with physical distance: The parameter A in Equation 4 defines the maximum similarity between two stimuli . ε defines the noise in the memory representation of the stimulus ( hence the label “Noisy Exemplar” ) . The parameter ε is a normally distributed random variable with variance σ2 . Note that the similarities incorporated in the model depend on the noisy values of the remembered stimuli . The second term in Equation 3 involves the homogeneity of the list , that is , the similarity between the remembered list items . β is a parameter determining the direction and amplitude of the effect of list homogeneity . If β < 0 , as was found in earlier experiments using visual stimuli , a given lure will be more tempting when s1 and s2 are widely separated; conversely , if β > 0 , a lure will be less tempting when s1 and s2 are widely separated . If β = 0 , the model does not depend on inter-item homogeneity , and is a close variant of Nosofsky's Generalized Context Model [24] . The parameter A , as defined in Equation 4 , was set to 1 . This model allows five parameters , σ , α , β , C , and τ , to vary . Two additional similar models were also examined . A second model assumes that the similarity between items can be predicted from participants' probability of confusing two items in a trial of list length 1 . This model adopts values for τ and A for each participant based on the fit of Equation 4 to their data with list length 1 . This model is identical to that above , but simpler , allowing only four parameters ( σ , α , β , and C ) to vary based on the data with list length 2 . A third model is identical to the second , but in it , β is forced to be 0 , which means that only three parameters are free to vary: σ , α , and C . Note that this last model does not take into account any possible influence of inter-item homogeneity . Models are labeled according to the number of parameters varied in each: five , four , and three . Model fits: models were fit to participants' accuracy data by means of a genetic algorithm . Such a method was chosen because it is robust to the presence of local minima [51] . The parameter spaces involved in this experiment are relatively complex , so the genetic algorithm approach was particularly attractive . To summarize our implementation of a genetic algorithm , 3 , 000 “individuals” were generated , each a vector of randomly chosen values for each of model's parameters . The ranges for each parameter were: 0 < σ < 5 , −3 < τ < 3 , 0 < α <1 , −2 < β < 2 , and 0 < C < 2 . Three thousand trials were simulated for each individual , each with a randomly chosen value for ε given the parameter σ . When the value in Expression 3 exceeds C , the simulation produced a Yes response . The proportion of Yes responses for each case was calculated . The fitness of each individual was computed by calculating the log likelihood that the predicted and observed data came from the same distribution . Log likelihood was chosen because it is more robust to non-normal data than is a least-squares error method [27] . The 10% most fit individuals are maintained to the next generation . These act as “parents” to the next generation: the parameters for the 3 , 000 individuals of the next generation come from combinations of pairs of parents and mutations . This procedure was repeated for 25 generations . Best-fit parameters typically did not change past the 20th generation , indicating stable parameter values had been obtained . Model comparison: in order to compare the three models described above , the predicted data and observed data were plotted against each other , and a measurement of the variance accounted for by the model , r2 , was calculated . However , when comparing two models with different complexities , for example , with different numbers of parameters , the important distinction between models is their generalizability to new data , that is , the likelihood that the model will fit another set of similar data . The AIC is a measure of model fitness that takes into account both how well the data fit the model and the number of parameters in the model . See the work of Myung et al . [27] for more information about AIC and calculation techniques . Thus , both the AIC and r2 values were used to discriminate between different models . | Memories are not exact representations of the past . But can we say that all our senses are equally reliable ( or unreliable ) sources for memory ? We performed a series of experiments to test that proposition . Sound and light are processed by different receptors and neural pathways in the brain . Previous comparisons of auditory and visual memory have done little to place on equal footing the stimuli that will be remembered , limiting the ability to truly compare the two processes . However , using current knowledge of how these sensations are represented in the nervous system , we created auditory and visual stimuli of similar complexity and that undergo similar initial processing by the nervous system . We then used these well-matched stimuli to examine memory for studied lists of either auditory or visual items . Using behavioral measures and a computational model for list memory , we show that memory representations are altered similarly for both hearing and vision . We found that auditory and visual memory exhibit striking parallels in terms of how memory is affected by all the parameters we changed in this experiment . These results imply that auditory and visual short-term memory employ similar mechanisms . | [
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When listening to music , humans can easily identify and move to the beat . Numerous experimental studies have identified brain regions that may be involved with beat perception and representation . Several theoretical and algorithmic approaches have been proposed to account for this ability . Related to , but different from the issue of how we perceive a beat , is the question of how we learn to generate and hold a beat . In this paper , we introduce a neuronal framework for a beat generator that is capable of learning isochronous rhythms over a range of frequencies that are relevant to music and speech . Our approach combines ideas from error-correction and entrainment models to investigate the dynamics of how a biophysically-based neuronal network model synchronizes its period and phase to match that of an external stimulus . The model makes novel use of on-going faster gamma rhythms to form a set of discrete clocks that provide estimates , but not exact information , of how well the beat generator spike times match those of a stimulus sequence . The beat generator is endowed with plasticity allowing it to quickly learn and thereby adjust its spike times to achieve synchronization . Our model makes generalizable predictions about the existence of asymmetries in the synchronization process , as well as specific predictions about resynchronization times after changes in stimulus tempo or phase . Analysis of the model demonstrates that accurate rhythmic time keeping can be achieved over a range of frequencies relevant to music , in a manner that is robust to changes in parameters and to the presence of noise .
Humans have the ability to estimate and keep track of time over a variety of timescales in a host of different contexts ranging from sub-seconds to tens of seconds or more [1 , 2] . On the millisecond to second time scale , for example , numerous studies have shown that humans can accurately discriminate shorter intervals from longer intervals [3 , 4] . On a longer timescale , we utilize a form of time estimation that can span hours , days or years [5] . Many such examples involve the brain making a calculation over a single event , so-called “interval timing” [6 , 7] . Humans can also track timing that involves multiple or repeated events . For example , we instinctively move to the beat of a piece of music through a form of sensorimotor synchronization , so-called beat-based timing [8–11] . Doing so involves identifying an underlying beat within a piece of music and coordinating the frequency and timing of one’s movements to match this beat . Understanding how humans perceive a beat has been an active area of research for quite some time . Beat perception refers to our ability to extract a periodic time structure from a piece of music . It is a psychological process in which beats can be perceived at specific frequencies , even when the musical stimulus does not specifically contain that frequency [12] . In a recent study by Nozaradan et al . [13] , brain activity was found to entrain to the beat frequency of a musical rhythm . Additionally , participants with strong neural entrainment exhibited the best performance when asked to tap to the rhythm [13] . Various parts of the brain have been identified as being active during beat perception . Grahn and Brett reported that basal ganglia and the supplementary motor area showed increased activity for beat-based tasks , and as such , postulated that these areas mediate beat perception [14] . Interestingly , fMRI studies of participants asked to lie still with no movement while listening to music revealed that the putamen , supplementary motor area , and premotor cortex are active [15] . Thus although no external movement may be occurring , various motor areas are nevertheless active when the brain is representing a passage of time . From the theoretical perspective , error-correction [16–22] , entrainment [12 , 23 , 24] , and Bayesian [25–27] models have been proposed to account for the ability to perceive a beat . Many beat perception studies have involved finger tapping while listening to a piece of music or a metronome [13 , 28–32] . However , humans can also mentally conjure a beat in the absence of motor movement and external stimuli . These observations , in part , lead us to ask what neural mechanisms might be responsible for detecting , learning and generating a beat . We define beat generation as the brain’s process of construction and maintenance of a clock-like mechanism that can produce repeatable , essentially constant , time intervals that demarcate a beat . In this formalism , the brain may be monitoring the firing times of neurons involved in beat generation to match event times of an external source . Beyond such reactive synchronization , we suggest that beat generation can occur as a strictly internally created and self-driven phenomenon in the absence of external stimuli . Models of beat generation should take into consideration several empirically observed features taken from human finger tapping studies in the presence of isochronous tones ( evenly spaced in time ) . First , the model’s output should rapidly synchronize with the external tone sequence . Second , a model should mimic the human ability to continue tapping even after the stimulus is removed [33] , a property known as synchronization-continuation . Third , a model should quickly resynchronize after a tempo change or perturbation ( deviant or phase shift ) to the tone sequence . Fourth , ideally a model should be capable of addressing the phenomenon of negative mean asynchrony ( NMA ) , the reported tendency of humans to tap on average prior to tone onsets . Although the cause of , extent of , and even the existence of NMA are still in dispute , new models can potentially provide insight into the phenomenon . Two primary modeling frameworks for addressing the above mentioned properties have been proposed: entrainment models and error-correction models . Entrainment models rely on principles of dynamical systems . Mathematically , entrainment refers to an external forcing that sychronizes a set of oscillators to a specific frequency . In the context of beat perception , entrainment models posit the existence of oscillators that resonate and entrain to the underlying periodicity creating an oscillation whose spectral profile matches that of the sound sequence . These models have been used to explain various beat-related phenomena including the emergence of pulse and meter [24] and the missing pulse percept [12] . These oscillator models are typically abstract mathematical formulations and , although generic in structure , presuppose a formulation in which the system is poised near to oscillatory-destabilization of a steady state , a Hopf bifurcation [24] . Error-correction models , on the other hand , are formulated at an algorithmic level to understand how a motor movement , such as a finger tap , can be synchronized to an isochronous tone sequence [18–22] . Errors between the current tap and tone times and between the current intertap time and stimulus period are used to adjust the timing of the next finger tap . Error-correction models provide different algorithmic ways in which to make an adjustment ( see [29] for a review ) , but typically do not propose mechanisms for how a set of neurons would estimate and correct for the error . In this paper , we introduce a neuromechanistic framework that can be used to construct neuronal network models that are capable of learning and retaining isochronous rhythms . In its simplest form , the network consists of a single , biophysically-based , beat generator neuron ( BG ) , a periodic brief stimulus and a time-interval computation mechanism based on counting cycles of a gamma oscillation . The BG does not directly receive input from the external stimulus and is thus not being entrained by it . Instead , the BG learns ( within a few cycles ) the frequency of the stimulus thereby allowing the BG to continue oscillating at this frequency , even in the absence of the stimulus . Our approach combines ideas from entrainment based , information processing based and interval timing based models . In part , it extends the heuristic two-process model [18–21] to a neural setting , by developing a neural system that learns the period and phase of the periodic stimulus , in order to bring its spikes into alignment with the stimulus tones . A central feature of our model is the concept of a gamma counter . Gamma rhythms ( 30-90 Hz ) are ubiquitous throughout the human nervous system [33 , 34] . Here we utilize roughly 40 Hz gamma oscillations to form two discrete-time clocks that count the number of gamma cycles between specific events . This idea is similar in spirit to pacemaker-accumulator models , which also use counting mechanisms [6 , 7 , 35] . In our case , one clock counts gamma cycles between successive onsets of a stimulus , sometimes called the interonset interval in behavioral studies . Another neuronal clock counts gamma cycles between successive spikes of the BG , the interbeat interval . A comparison is made via a putative gamma count comparator ( GCC ) between these two counts and this information is sent to the BG , consistent with recent studies of neuronal circuits that can count discrete events and compare counts with stored information [36–38] . Our BG neuron possesses plasticity and uses the difference in count to adjust an intrinsic parameter so that it learns the interonset interval of the stimulus . The gamma counters also provide information to the BG about its firing phase relative to stimulus onset times , thereby allowing for the possibility of synchronization with zero-phase difference of the BG spikes with the stimulus . The coupling of the stimulus and BG counts through the GCC is both nonlinear and non-periodic . This contrasts with coupling between stimulus and oscillator in many entrainment and information processing models where either the phase or period is directly targeted for change . Further , in such models either the coupling is periodic or the update rules are linear or vice versa [39] . Our model updates are neither periodic or linear . We note that the neuronal clocks that count cycles need not operate exclusively in the range of 40 Hz . The comparison mechanism that we describe will work for any sufficiently fast frequency oscillator . In this paper , we will show how the BG model learns and holds an isochronous beat over a wide range of frequencies that includes the band of 0 . 5 Hz to 8 Hz which is relevant for beat generation and perception . Using mathematical analysis and a continuous time clock , we explain first how the BG learns to period match . We then show how this extends to both period and phase for the discrete time clock counters . As will be seen , the discrete time clocks give rise to a natural bounded variability of spike times of the BG even when it is holding a beat . We will test our model using standard paradigms which parallel behavioral studies that are designed to study specific aspects of beat perception and production , such as NMA [13 , 40] , synchronization-continuation [41] and fast resynchronization times [42 , 43] . The BG’s mean firing time exhibits negative mean asynchrony and we describe how this NMA can be manipulated . The model resynchronizes rapidly in response to perturbations . For an abrupt change in tempo we predict and explain with the model an asymmetry in the resynchronization time for increases versus decreases in tempo . As with tempo changes , our model predicts an asymmetry in the resynchronization time due to a phase advance versus delay of the stimulus sequence . An asymmetry also arises for a transient perturbation , a single deviant onset timing in the stimulus sequence . We explain these effects by understanding how our model incorporates linear , but discrete step , error-correction to invoke non-linear changes in frequency of the BG . In turn , we develop a set of testable predictions for human behavior that help to contrast our proposed model framework from existing ones .
The BG in our model can be described using biophysical , conductance based equations and is required to have only two specific properties . The first is that it possesses a sharp ( voltage ) threshold . A spike of the BG occurs when the voltage increases through this threshold . The second requirement is that the BG has a slow process that governs the time between its spikes . A simple model that possesses these basic properties is the leaky integrate and fire ( LIF ) model which we first use to describe our analytic findings . Our simulation studies utilize a biophysical model motivated by models of delta waves in sleep , as we require a similar frequency range for the BG . To that end , we chose voltage-gated currents similar to those from an idealized model for sleep spindle rhythms of thalamo-cortical relay cells , namely for the slow wave of relay cells in burst mode [44] . The slow wave can be generated by the interplay of a T-type calcium current , and Ih current and a leak current . Here , the BG has a persistent sodium INaP , T-type calcium ICaT , sag Ih and leak IL currents . In the text , we refer to this as the INaP model; for a full description and the equations see Appendix . The analytic results also hold for the INaP model , but as the analysis is more complicated , showing so is outside the scope of this paper . For either the LIF or INaP models , parameters are chosen that allow for a wide range of intrinsic frequencies of the BG up through 8 Hz as Ibias is varied as quantified by a neuron model’s Ibias versus frequency relationship which is presented in the Results . This range of frequencies is appropriate for speech and music . The time between successive spikes of the BG is called the interbeat interval and is denoted by IBIBG . The voltage in the LIF model evolves according to v ′ = I bias - v τ ( 1 ) where v is a dimensionless variable representing voltage , Ibias is the drive to the neuron and τ is the membrane time constant . The LIF model has a spike and reset condition which makes it discontinuous . When the voltage reaches one at t = ts , it is instantaneously reset to the value 0; if v ( t s - ) = 1 , then v ( t s + ) = 0 . When Ibias > 1 oscillations exist . In this case , the LIF model is rhythmic with period given by T = τ log e ( I bias I bias - 1 ) . ( 2 ) The period of BG given by Eq ( 2 ) can be adjusted to any positive value by appropriately adjusting Ibias . For both the LIF and INaP models , the specific nature of the stimulus is not modeled , only the onset is of interest here . We limit our simulations to a range between 1 and 6 Hz , which corresponds to an interstimulus interval ranging from 1 s down to 166 ms . There is no theoretical or practical problem to extend the model outside of this range , as further addressed in the Discussion . We utilize a neuron S to faithfully transform the stimulus sequence into spikes . The interonset interval , IOIS , is then defined as the time between successive S spikes . The model for S is not important provided that it is set to be an excitable neuron that fires quickly in response to input; see the Appendix for equations . The gamma count comparator , GCC , in our model utilizes two generic oscillators with frequency sufficiently larger than that of both the stimulus and the BG . Here it is taken to lie in the gamma range at roughly 40 Hz ( Fig 1B ) . We choose the oscillators to be identical , though this is not a requirement of the model . To avoid integer values , both have a frequency of 36 . 06 Hz ( period 27 . 73 ms ) ; see the Appendix for details . We let γBG be a variable that counts the number of gamma cycles between consecutive BG spikes and γS be a variable that counts the number of gamma cycles between consecutive spikes of S . At each spike of BG or S , the appropriate counter is reset to zero . We stress that γBG and γS are integers , but , in general , the periods that they are estimating are not integer multiples of a gamma cycle ( 27 . 73 ms ) . Hence , although the stimulus period may be constant , the gamma counts may vary from cycle to cycle . The difference γBG − γS provides an estimate of how different or aligned the frequencies of BG and S are . For example , if S oscillates at 5 Hz and the BG is initially oscillating at 3 Hz , then the GCC would count roughly 8 cycles between S spikes and 13 cycles between BG spikes . In this case , the GCC determines that the BG is oscillating too slowly and sends a speed up signal to the BG . Alternatively if the BG were initially oscillating at 6 Hz , then the GCC counts roughly 6 cycles and sends a slow down signal . In general , speeding up or slowing down of the BG is achieved by changing Ibias . At each spike of the BG , the period learning rule adjusts Ibias by a fraction of the difference between the gamma oscillator count of cycles between S and BG . This learning rule ( LRT ) assigns at each BG spike LR T : I bias → I bias + δ T ( γ B G - γ S ) , ( 3 ) where the parameter δT is independent of period . This simple rule is enough to align the frequencies of S and BG , the details of which will be explored through the derivation and analysis of a one-dimensional map . However , this frequency matching rule does not provide the beat generator with any information about its firing phase relative to stimulus onset . To align the phase of the BG to the stimulus onset , we formulate a second learning rule . We define the current count of the BG , CCBG , as the number of gamma cycles from the last BG spike to the current S spike; see Fig 1B . Then at each S spike define ϕ = CCBG/γS to be the phase of BG firing . We use the phase to determine if the BG fires “before” or “after” S at each cycle . In a rhythmically active network , the concept of whether BG fired before S is somewhat ambiguous . We define the BG to be “before” the stimulus if it fires in the second half of the stimulus period ϕ ∈ ( 0 , 0 . 5 ) . In this case we say that the BG is too fast and needs to slow down . Conversely , if ϕ ∈ ( 0 . 5 , 1 ) , the BG is said to fire “after” S and needs to be sped up . At each S spike , we update Ibias with the second part of the learning rule ( LRϕ ) LR ϕ : I bias → I bias + δ ϕ q ( ϕ ) ϕ | 1 - ϕ | ( 4 ) where δϕ is independent of period and phase and q ( ϕ ) = sgn ( ϕ − 0 . 5 ) , with q ( 0 . 5 ) <0 . Thus if ϕ = 0 ( or 1 ) , there is no change to Ibias . But if the BG fires before S ( ϕ ∈ ( 0 , 0 . 5 ) ) , then q ( ϕ ) <0 and Ibias is decreased to slow down the BG . The opposite occurs if the BG fires after S . The absolute value keeps the last term positive as ϕ can become larger than 1 . For example , during transitions from high to low frequency , CCBG can exceed γS . The quadratic nature of LRϕ is chosen so that the maximum change occurs for phases near 0 . 5 . With this two-part learning rule , the BG learns both the period and phase of S . Both parts of the rule are implemented concurrently so that the process of period and phase alignment occurs simultaneously . The two rules LRT and LRϕ target the value Ibias . In the Results section , we describe how these changes to Ibias , in turn , affect the frequency of the BG which then affects the period and phase of oscillations . Given the discreteness of our gamma counters , the BG learns to fire a spike within a suitably short window of time of the stimulus onset , an interval equal to plus or minus one gamma cycle . We define this concept as one gamma cycle accuracy . For the earlier described choice of parameters , this amounts to ±27 . 73 ms from stimulus onset . We address two important and related concepts: synchronization to the beat and holding a beat . In our model , synchronization to the beat refers to the process by which the BG brings its spike times within one gamma cycle accuracy of a specific stimulus frequency . Holding a beat refers to the ability of the BG to maintain synchronized firing at a specific frequency over a specified stretch of time . We will say that BG has synchronized to the stimulus if three consecutive BG spikes each fall within one gamma cycle accuracy in time of a stimulus onset . The BG is said to be holding a beat for as long as it continues to remain synchronized with the stimulus onset . In the presence of an isochronous stimulus , the BG displays what we shall call stationary behavior . This refers to the pattern of spike times of the BG in response to a fixed frequency stimulus . Despite there being no source of noise in our model , the discrete nature of the gamma count comparator allows the BG’s spike times during stationary behavior to naturally display variability . Thus , during stationary behavior , while the BG’s spike times typically land within one gamma cycle accuracy of stimulus onset they can also fall outside this window . The variability of the BG’s spikes arises because the gamma counters and learning rules adjust Ibias in discrete steps whenever γS ≠ γBG or ϕ ≠ 0 . What this means is that during stationary behavior , the BG does not converge to a limit cycle oscillation ( periodic orbit ) . The variables that govern the dynamics of the BG do not periodically return to the same values , but instead can vary by small amounts from cycle-to-cycle . In practice , these small differences affect the exact spike times of the BG , creating the variability . Furthermore , as the gamma counts are not exact representations of the period , they may be equal even when IOIS and IBIBG are unequal . This amounts to additional variability in the BG’s spike times relative to the spike times of S . We will determine the time that it takes for the BG to resynchronize its spikes to stimulus onset after a change to the stimulus . Resynchronization is declared similarly to synchronization in that the BG is required to fire three consecutive spikes each of which must lie within one gamma cycle accuracy of a stimulus onset . The resynchronization time is then taken as the time of the first synchronized spike . In all studies , we begin with the BG displaying stationary behavior at a specific frequency . Because of the variability present in stationary behavior , the resynchronization times will depend on the initial conditions at the moment that the change to the stimulus profile is enacted . We will compute mean resynchronization times and standard deviations over 50 realizations , each of which differs by a small change in the initial condition of the BG at the time that the stimulus profile is changed . All simulations are carried out in MATLAB with a standard Euler solver ( Euler-Maruyama when noise is introduced ) .
An oscillatory neuronal model spikes with a period that is quantifiable by its frequency versus Ibias relation ( f-I ) . This relationship is obtained from the reciprocal of ( 2 ) for the LIF model and computed numerically for the INaP models ( Fig 2A ) . The blue ( red ) curve depicts the f-I curve for the LIF ( INaP ) model . In the LIF model , the interspike interval is governed by the difference between Ibias and the spiking threshold , as well as the parameter τ . In the INaP model , the interspike interval is determined by an interplay of the various non-linear currents ( Fig 2B ) . In particular , the ICaT and IL currents provide basic excitability to the model , the INaP current allows for spikes once a voltage threshold is crossed and the Ih current provides a slow depolarization of the membrane allowing the neuron’s voltage to gradually reach spiking threshold . Thus the primary determinant of the interspike interval is the time constant of the Ih current . An important point regarding the f-I relations is that they are both strictly increasing . Hence , there is exactly one value of Ibias that yields a specific frequency . The learning rules we use make discrete changes to Ibias . Thus , there is little chance of adjusting Ibias to the exactly correct value . Instead , the learning rules adjust Ibias so that it stays within a small window of the correct one . The frequency relations increase steeply from frequency equal to zero . Therefore , at low frequencies , larger changes in frequency can result from small changes in Ibias . The same is also true for the INaP model at frequencies in the 3 to 8 Hz range . It is important to note that any implementation of a BG model with a monotone increasing f-I relation will produce the qualitative results described below . However , the quantitative details will certainly depend on the slope and non-linearities of these relations that are produced by different ionic currents and parameters . For example , changes to Ibias in the LIF at frequencies above 1 Hz lead to linear changes in BG frequency . Alternatively , for the INaP model a change of Ibias from say 5 to 10 produces a smaller change in BG frequency than changes from 10 to 15 . The BG learns to oscillate at a frequency by adjusting its bias current through the set of plasticity rules LRT and LRϕ ( Fig 3 ) . The BG is initially set to oscillate at 2 Hz with Ibias = 9 . 06 . At t = 0 ms , we adjust the stimulus frequency to 4 . 65 Hz and activate the period learning rule LRT ( Fig 3A ) . Notice how the cycle period of the BG increases on a cycle-by-cycle basis until it matches the stimulus period . This results from the value of Ibias iteratively increasing over the transition , based on the difference γBG − γS . The first change to Ibias does not occur until t = 500 ms , which is the time the BG naturally would fire when oscillating at 2 Hz , since LRT updates ( purple curve in the bottom panel of Fig 3A ) are only made at spikes of the BG . At around t = 2 . 25 s , the value of Ibias falls within one gamma cycle accuracy as depicted by the blue band but continues to adjust . Note that Ibias does not settle down to a constant value . Instead it changes by ±δT whenever |γBG − γS| = 1 . Additionally , since LRT contains no phase information , the spikes of the BG are not synchronized in phase with those of S . At t = 4 . 2 s ( 20 cycles of the stimulus ) , the stimulus is completely removed , and the BG continues to oscillate at roughly 4 . 65 Hz . This shows that the BG has learned the new frequency and does not require periodic input to make it spike . There are still adjustments to Ibias because the BG continues to compare γBG with the last stored value of γS . This example demonstrates how the BG oscillates at a learned frequency rather than through entrainment to external input . When both learning rules operate together , the BG learns both the correct period and phase . Starting with the same initial conditions as in Fig 3A , now at t = 0 ms both LRϕ and LRT are turned on ( Fig 3B ) . Note the very rapid synchronization of the BG’s spikes with the stimulus onset times . The middle panel shows how Ibias grows much more quickly when both rules are applied . The first update is due to the phase learning rule at the third stimulus spike , at t = 433 . 5 ms , which is earlier than in the previous example . This causes enough of an increase in Ibias for the BG to immediately fire , which causes an update due the period learning rule . The lower panel shows how the two rules LRT and LRϕ contribute to the change in Ibias . Note that the learning is not sequential with period learning preceding phase learning or vice versa . Rather , period and phase learning occurs concurrently . Below we shall describe in more detail each of these learning rules and their role in synchronizing the BG with the stimulus . The dynamics of how the period learning rule LRT matches the interbeat interval of the BG , IBIBG , with the interonset interval of S , IOIS , can be explained in terms of an event-based map . Each spike of the BG is treated as an event and we define a map that updates the bias current Ibias on a cycle-by-cycle basis . To derive the map , we first use exact time differences , in effect , equivalent to a continuous time-keeping mechanism . This will allow the map to possess a parameter-dependent asymptotically stable fixed point . For simplicity of presentation , we use the LIF model to derive the specifics of the map . We will then discuss how those findings inform simulations of the INaP model for the discrete gamma count case . Assume that the stimulus sequence occurs with a fixed period T* corresponding to a specified IOIS , and that the BG is initially oscillating with an interbeat interval of T0 . This IBIBG corresponds to a specific value I0 of Ibias given by solving ( 2 ) . Ibias is then updated to I1 by comparing T0 to T* . In turn , this produces a new cycle period T1 and so on . In general , the continuous time version of LRT updates Ibias at each firing of the BG as follows: I n + 1 = I n + δ T ( T n - T * ) = I n + δ T ( τ log e ( I n I n - 1 ) - T * ) , ( 5 ) where the second line is obtained by substituting Eq ( 2 ) evaluated at In for Tn . Error-correction models also take the form of an iteration scheme , but typically target the next cycle period for adjustment , i . e . Tn and Tn+1 would replace In and In+1 , respectively , in the first equation of ( 5 ) . In contrast , the adjustment in our model is made to the biophysical parameter Ibias ( In ) which then has a subsequent effect on the cycle period ( Tn ) . Eq ( 5 ) defines a one-dimensional map which can be expressed as In+1 = f ( In ) , where f ( I ) denotes the right-hand side . A fixed point of the map satisfies I* = f ( I* ) whose stability can be determined by checking the condition |f ′ ( I* ) | < 1 . A fixed point of the map corresponds to a case where the IBIBG of the BG is equal to the IOIS of S . Stability of the fixed point implies that the learning rule is convergent . Note that for any T* , there is a unique fixed point of the map which satisfies I* = 1/ ( 1 − exp ( −T*/τ ) ) . This means that any stimulus period can in practice be learned by the BG , provided that the fixed point is stable . A simple calculation shows that |f ′ ( I* ) | < 1 provided 0 < δT < 2I* ( I* − 1 ) /τ . For fixed δT , as the stimulus frequency gets smaller , I* converges to 1 , and as a result the term 2I* ( I* − 1 ) /τ goes to zero . This expression provides the insight that convergence for lower stimulus frequencies requires taking smaller increments in the learning rule . This finding carries over to any f-I relation that is steeply sloped at low frequencies . Parameter dependence and the ensuing dynamics of the map are readily illustrated graphically ( Fig 4 ) . The one-dimensional map has a vertical asymptote at I = 1 , a local minima at I = ( 1 + 1 + 4 δ T τ ) / 2 and a slant asymptote of I − δT/T* . The graph intersects the diagonal at exactly one point , and the slope of the intersection determines the stability as calculated above . For increasing stimulus frequency , with δT and τ fixed , the map’s graph shifts upward ( Fig 4A ) and the fixed point moves to larger values of Ibias . Note , for low stimulus frequency ( here , 1 Hz ) the fixed point is unstable . The update parameter δT does not change the value of the fixed point I* , but affects the stability ( Fig 4B ) . As δT increases , the slope at the intersection decreases through 0 , then through -1 , at which point stability is lost . If the stimulus frequency changes ( eg , 2 Hz to 5 Hz ) , Ibias changes dynamically as the BG learns the new rhythm . The learning trajectory corresponds to the cobweb diagram on the map ( Fig 4C , black dashed lines and arrows ) . Each adjustment of Ibias occurs at a spike of BG and allows it to speed up for the next cycle . In this example , it takes only a few cycles for the BG to learn the new rhythm . The transition from 5 to 2 Hz ( Fig 4C , red dashed lines and arrows ) demonstrates the asymmetry in convergence for similar sized changes of opposite directions . Here the convergence for the decrease in frequency occurs over fewer cycles because the value of δT chosen yields a fixed point at 2 Hz with near zero slope . The smaller in magnitude the slope , the less the number of cycles needed to converge . This result suggests that certain preferred frequencies can exist for specific choices of parameters . In contrast to the idealized continuous-time learning rule , the gamma count-based case does not lead to updates that converge to zero . An interesting illustration is seen after an IOIS has been learned and the stimulus is turned off . Small updating persists ( e . g . , Fig 3A , bottom panel ) . Just after the turn-off , the IBIBG is less than the last stored IOIS ( γBG < γS ) . So the BG is too fast , and at the next BG spike the period rule LRT activates and decreases Ibias by δT producing a new , longer IBIBG . Not immediately , but after a while ( just after t = 5s ) a difference in gamma counts again arises . This time LRT increases Ibias , shortening IBIBG and so on . These changes are all due to LRT as the phase learning rule LRϕ can never be invoked since the stimulus is no longer present . The phase learning rule LRϕ considers the current BG gamma count , CCBG , at each firing of S . As a result , the BG has information about its phase at each stimulus onset . We use a learning rule function ϕ|1 − ϕ| that has maximal effect at ϕ = 0 . 5 and no effect at ϕ = 0 and 1 . This is similar to a logistic function that attracts dynamics towards ϕ = 1; see also [45] for a similar mathematical rule used in a different biological context . In our case ϕ = 0 is equivalent ϕ = 1 , so our learning rule LRϕ utilizes a sign changing function q ( ϕ ) = sgn ( ϕ − 0 . 5 ) , q ( 0 . 5 ) = −1 to stabilize ϕ = 0 as well . This will allow convergence via either phase increase or decrease towards synchrony . At each S spike-time , the BG is sped up ( if ϕ ∈ ( 0 . 5 , 1 ) ) or slowed down ( if ϕ ∈ ( 0 , 0 . 5 ) ) by adjusting Ibias until the phase reaches a neighborhood of 0 or 1 . This , in conjunction with LRT which equalizes the IOIS and IBIBG , brings about synchronization . Note that when the BG fires within one gamma cycle accuracy of S , ϕ = 0 or 1 . In that case , there is no update to Ibias . Thus as with LRT , because of the discreteness of the learning rule updates , the value of Ibias is brought into close proximity of the value of Ibias that produces a specified rhythm but need not become exact . The rapid synchronization results shown earlier in Fig 3 hold for a large range of stimulus frequencies . Under certain conditions , it is possible to derive a two dimensional map that tracks how Ibias and ϕ change on a cycle-by-cycle basis . Though it is outside the scope of this paper , an analysis of the map shows that stable period and phase matching can be achieved for each IOIS , if δT and δϕ are not too large . In practice these two parameters should be chosen so that the changes due to LRT , δT ( γBG − γS ) , and LRϕ , δϕ ϕ|1 − ϕ| , are of the same order of magnitude . If the IBIBG is at least one gamma cycle longer than the IOIS , then ϕ > 1 . We could have restricted the phase to be less than one by periodically extended LRϕ beyond the unit interval , but this would allow for stable fixed points at ϕ = 2 , 3 , 4 , etc . Instead , the learning rule utilizes an absolute value around the term 1 − ϕ to keep it positive . This introduces an asymmetry in the resets of Ibias . For example if ϕ = 0 . 1 then ϕ ( 1 − ϕ ) = 0 . 09 but if ϕ = 1 . 1 , then ϕ|1 − ϕ| = 0 . 11 . Thus when the BG fires after the S spike after a long IBIBG ( e . g . ϕ = 1 . 1 ) then Ibias is increased more than it is decreased if it fires before the S spike ( e . g . ϕ = 0 . 1 ) . As a result , the learning rule favors the BG firing before the stimulus onset , as if in anticipation . This is more pronounced at lower frequencies where the slope of the f-I curve is much steeper than linear . This issue is explored in more detail in the following sections . The phase learning rule LRϕ adjusts Ibias as opposed to directly affecting the phase of the BG , for example , via a perturbation and reset due to the phase response curve ( PRC ) . Using a PRC to adjust phase would lead to a situation of entrainment rather than learning . Indeed , with a PRC , bringing the value of Ibias to within one gamma cycle accuracy to achieve a specific frequency is rarely reached . Thus if the stimulus were to be removed , a BG with a PRC-based phase rule would fail to continue spiking at the correct target frequency , i . e . it would fail in a synchronization-continuation task . To hold a beat , the BG must fire spikes within a time window of one gamma cycle accuracy of stimulus onset . As discussed earlier , the discreteness of the gamma counters and comparator causes the BG spike times to naturally display variability . Thus the BG must at each firing compare its period and phase relative to stimulus onset times and make necessary corrections . Holding a beat is an example of stationary behavior of the BG in response to a constant frequency stimulus ( Fig 5 ) . In this typical example , here shown at 2 Hz , each spike of the BG is aligned to the closest spike of S and then a timing error equal to the BG spike time minus S spike time is computed . The value of Ibias hovers around the dashed black line Ibias = 9 . 06 which is the value that produces exactly a 2 Hz oscillation ( Fig 5A , upper ) . The spike times of the BG jitter around those of S , and thus , the timing error is poised around zero ( Fig 5A , lower ) . While holding a beat , these differences fall within a single gamma cycle time window ( dashed gray lines at ±27 . 73 ms ) . During some time windows ( pink shaded region in Fig 5B ) no updating of Ibias occurs ( lower time course ) , but the timing differences progressively decrease and become more negative . The BG spike times are drifting relative to onset times because the IBIBG is slightly smaller than IOIS , but close enough that γBG = γS . The drift represents the fact that during this interval , the BG is not entrained to the stimulus . The slope of the timing error in this interval is shallower when Ibias is closer to 9 . 06 , as shown for example in the time window between t = 72 and 76 s . During some intervals ( e . g . shaded blue region ) , the update rules are actively trying to keep the period and phase of the BG aligned with S . Although the timing errors are not large in this case , the counts γBG and CCBG differ from γS thus causing LRT and LRϕ to be invoked . During either drifting or corrective behavior , the BG spike times occur closely in time with the stimulus onsets . Note that drifting and corrective behaviour continue to occur after the stimulus is turned off , but only the period rule will be active ( see Fig 3 ) . Many studies have pointed towards drifting dynamics in human tapping experiments [46–48] . Dunlap first noted this behavior , stating that the errors tend to get progressively more negative/positive , until a correction occurs and causes a change in direction [46] . Although the dynamics of the BG are deterministic , they are sensitive in quantitative detail to changes in initial conditions . This is because the learning rules LRT and LRϕ ignore timing differences less than one gamma cycle . To get a more general sense of the fluctuations in BG firing times , we ran a simulation for 1000 stimulus cycles and calculated error distribution plots ( spike time of BG minus spike time of S ) . This was performed at six different stimulus frequencies in steps of 1 Hz ( Fig 6 ) . There are several points to note . First , at all frequencies , the error distribution shows negative mean asynchrony [49 , 50] . In other words , the actual time of the beat generators firing , on average , preceded the time of the stimulus onset . Second , the variance in the error distribution shows some frequency dependence , particularly with the standard deviation increasing at slower frequencies . Further , the standard deviation increases as the frequency decreases down to 0 . 5 Hz . We also found that the standard deviation increases with frequency in the 6-8 Hz range . Accurately tapping at rates above ∼ 4 Hz is extremely difficult , hence , no tapping studies exist for this frequency range to either corroborate or contradict our result . However , Drake et al . [51] found a U-shaped dependence of the subject’s tempo discrimination ability on frequency in the range 1-10 Hz , consistent with our result . We additionally note that increasing δT and δϕ leads to increased variability and larger NMA at all frequencies . In our model , the interplay of the BG neuron’s f-I curve and the learning rules , LRT and LRϕ , are responsible for the frequency dependent results . In particular , at both low ( ∼ 0 . 5Hz ) and high ( ∼ 5-8Hz ) frequencies the f-I curve is steeper than in the intermediate range . Hence , equidistant changes of Ibias around a mean value result in different changes in instantaneous frequency . The negative mean asynchrony arises from the non-linearity in the f-I curve and the asymmetry in the phase learning rule LRϕ . As states earlier , LRϕ pushes the BG to fire before the stimulus . As demonstrated in Fig 3 , the BG is able to quickly learn a new frequency . This learning can be quantified as a resynchronization of the BG’s spike times with the new stimulus onset times . As previously stated , we declare the BG to be resynchronized if three consecutive spikes each fall within one gamma cycle accuracy of an S spike . We computed the resynchronization times as a function of several parameters including initial and final stimulus frequency ( Fig 7 shows one example ) . From a fixed initial stimulus frequency , we changed the stimulus frequency to different values within the range 1 to 6 Hz and computed resynchronization times . In one such case , the stimulus frequency is decreased from 3 to 2 Hz ( Fig 7A ) . The change is applied at t = 0 s ( gold star ) and the BG takes about four seconds ( eight stimulus cycles ) to synchronize to the new frequency ( depicted by the shaded region ) . During the transient , the learning rules LRT and LRϕ drive Ibias down in order to slow the BG down ( lower panels of Fig 7A ) . Adjustments due to LRT occur whenever the BG spikes . For the first second after the change in frequency , BG spikes at roughly its initial 3 Hz rate . The S neuron spikes at t = 0 . 5 s , which resets γS . But the BG is not aware of this new larger value of γS until it fires at around t = 0 . 66 s . At this point , γS > γBG and the period learning rule LRT decreases Ibias . Adjustments due to LRϕ occur whenever the stimulus neuron S spikes , which now occur at the slower 2 Hz rate . These adjustments depend on the current phase of the BG and are seen at times to increase Ibias , but at other times to decrease it . Within two seconds , both rules have succeeded in bringing Ibias within one gamma cycle accuracy of the 2 Hz target value ( dashed black line inside blue band in middle panel ) . Aligning the spike times then takes a few more seconds . In contrast , an increase in stimulus frequency can lead to much shorter resynchronization times ( Fig 7B ) . In the transition from a 3 to 4 Hz stimulus frequency , the BG only takes about one and a half seconds ( six stimulus cycles ) to synchronize . The phase learning rule LRϕ plays a more prominent role as it is invoked more often due to the increase in stimulus frequency . These examples illustrate two important properties of the resynchronization process . First , the two learning rules act concurrently to adjust Ibias , but are asynchronous in that LRT adjustments occur at different times than those of LRϕ ( see the lower panels of either Fig 7 ) . Second , adjustments to Ibias are not periodically applied , they occur at a BG or S spike , and only the S spikes occur periodically . Resynchronization times increase with decreasing frequency , but are nearly constant and mostly flat for increasing frequency ( Fig 7C ) . Decrements from initial to final frequency lead to slower convergence than equally-sized increments . This follows from the slope of the f-I curve being steeper while increasing from 3 Hz than when decreasing . For the slowest stimulus frequency ( 1 Hz ) oscillation , we have included a broader measure of synchrony , defined by BG spike times falling within 5% of the interonset times , i . e . ± 50 ms around S spike times . This definition is consistent with the stationary behavior shown in Fig 6 where many of the BG spikes fall outside of one gamma cycle accuracy . With this broader measure of resynchronization , the average number of cycles and standard deviation of the resynchronization to 1 Hz rhythm are reduced . Although resynchronization times are longer for frequencies decrements , the number of stimulus cycles for resynchronization do not show major differences for increments and decrements , except for the 1 Hz case ( the mean number of cycles for resynchronization are reported beside each data point ) . The resynchronization process occurs stereotypically depending on whether there is an increase or decrease in frequency ( Fig 7D ) . We calculated the average cycle-by-cycle time differences for 50 realizations of the resynchronization process from 3 Hz to the target frequency with the standard deviation shown in the shaded region . Decreases ( increases ) in frequency show initial time errors that are positive ( negative ) . This is due to our spike alignment process ( see Fig 7 caption ) . Each curve is non-monotonic and , except for the 6 Hz curve , has an under- or over-shoot that transiently takes the curve outside the band of one gamma cycle accuracy ( horizontal grey line ) . The average resynchronization times in Panel C are shown as the time at which the curve reenters this band , i . e . the time at which the timing errors become consistently less than one gamma period . Consistent with our prior results , the standard deviation bands are largest for the 1 Hz curve and relatively similar and small for the other curves . Resynchronization also occurs when a phase shift of the stimulus sequence occurs . Now consider the 2 Hz case for which the IOIS is 500 ms . A phase advance will occur if we shorten one IOIS to be less than 500 ms and then return the remainder of sequence to the original IOIS of 500 ms . A phase delay is the opposite , where a single IOIS is elongated . We define the phase ψ of the shift to lie within ( −0 . 5 , 0 . 5 ) where negative values represent advances and positive values represent delays . If the phase shift falls within one gamma cycle of the normal onset time , the BG is likely to initially ignore it since no change in the gamma counts will occur . But for larger valued phase shifts , resynchronization will need to occur ( Fig 8 ) . As an example , resynchronization for a positive phase shift at ψ = 0 . 4 ( Fig 8A ) is much quicker than the corresponding negative phase shift ψ = −0 . 4 ( Fig 8B ) . The reason for this is how LRT changes Ibias in either case . A negative phase shift causes the BG to increase its frequency in response to the temporarily shorter IOIS , followed by a return to a lower frequency . A positive phase shift causes the opposite , a transient decrease in the BG frequency followed by an increase . As we have shown earlier , resynchronization times are shorter when the target frequency is larger ( Fig 7 ) . Hence , the model predicts that resynchronization times should be shorter for positive phase shifts ( Fig 8C red ) . The mean timing errors ( standard deviation shaded ) for different phase shifts ( Fig 8D ) are stereotypical in much the same way as the timing errors for tempo changes . In the current context , the timing errors start out large and then systematically reduce until they fall within one gamma cycle accuracy . The graph clearly shows that the resynchronization after positive phase shifts is faster than after negative shifts , as negative phase shifts exhibit an overshoot . Another case where we see resynchronization is the introduction of a temporal deviant where a single S spike occurs at an unexpected early or late time . Unlike phase shifts in which a single IOIS changes , a single deviant causes both the IOIS before and the IOIS after the deviant to change . An early ( late ) deviant causes a shorter ( longer ) interonset interval , followed by a longer ( shorter ) than normal one , followed by a return to the standard IOIS . The model’s response is different for early versus late deviants . For an early deviant , the phase learning rule LRϕ is invoked at the time of the early deviant . This is then followed by the period learning rule LRT at the next BG spike . Both of these signal the BG to speed up . For a late deviant , however , the BG spikes when it normally would have . At that time , it has no new information about its phase or about the value of γS . Therefore there is no change to Ibias . When the late deviant arrives , it now causes LRϕ to send a slow down signal to BG . But any potential changes due to LRT have to wait one full BG cycle to be invoked . Thus , the model reacts quicker to an early deviant than to a late deviant . Thus we predict shorter resynchronization times for earlier deviants ( Fig 8C blue ) . Additionally , because of the need to adjust to two different IOISs , we predict that resynchronization times due to deviants will be longer than those for comparably sized phase shifts where only a single IOIS is changed . The model results are robust to perturbations and can operate over a range of parameters . To assess this we ran several simulations , where we varied intrinsic parameters of the BG and the gamma counter speeds . For example , the maximal conductance for IL , INaP and ICaT was varied by up to 10% and we measured the subsequent performance across a range of periods . This did not affect the ability of the BG to learn the correct period and phase , because the f-I relation remained qualitatively unchanged . At a quantitative level though , the range of Ibias values which yield gamma cycle accuracy will differ and the BG neuron may have a different preferred frequency . These results indicate that the BG does not require fine tuning of parameters to learn a rhythm . Next we allowed the speed of the gamma counters for S and BG to be different . We kept the gamma counter for IOIS at 36 . 06 Hz while we varied the gamma counter frequency for the BG counter by up to 10% . In all cases , across a range of frequencies there was little qualitative difference in the BG’s ability to learn the correct frequency . This is not surprising as the discreteness of the gamma counts allows for similar values of the counts despite there being differences in counting speed . Note that a faster gamma counter for the BG tends to lead to earlier firing times relative to stimulus onset times for the parameters we have chosen . In this case , LRT tends to increase Ibias since γBG is larger than γS . On the other hand LRϕ decreases the same quantity and it is the parameter-dependent balance between the two rules that determines how much earlier on average the BG fires . A slower BG gamma counter has the opposite effect . These results imply that the extent of NMA can be manipulated by changing the counter frequency of the BG and can even be transformed to a positive mean asynchrony if the BG gamma counter is too slow . To assess the effects of noise , we introduced stochasticity into the gamma counters ( see Appendix for details ) . This acts to jitter the gamma periods , but for modest noise this will only cause the gamma count to discretely change by at most plus/minus 1 . Since the BG is monitoring its period and phase at each spike and stimulus event , it quickly adjusts to counteract these potential changes . We also see an increase in the standard deviation of the timing error , across all frequencies , during stationary behavior , as well as an increase in NMA . While this widened the distributions ( as seen in Fig 6 ) , approximately 90% of the timing errors remain within one gamma cycle accuracy , apart from at 1 Hz where only 60% of the distribution lies within one gamma cycle accuracy ( 80% lie within 5% accuracy ) . Finally , although not explicitly modeled here , one could introduce intrinsic noise in the BG , for example a noisy spike threshold or ionic conductance . This small amount of noise would not change the IBIBG by more that a single gamma cycle and , as above , should not change the BG’s ability to synchronize to the external rhythm . In general , noise makes the model fit better with tapping data , exhibiting more variability and larger NMA . Given that human tapping data contains both motor and time keeper noise that our model does not attempt to disentangle ( our model does not have an explicit movement component ) , we did not address this further .
Beat perception as described in many previous studies [52 , 53] refers to the ability of an individual to discern and identify a basic periodic structure within a piece of music . Beat perception involves listening to an external sound source as a precursor to trying to discern and synchronize with the beat . Alternatively , we might ask how do we ( humans ) learn and then later reproduce a beat in the absence of any external cues . Such issues and questions lead us to consider what neuronal mechanisms might be responsible for producing an internal representation of the beat . At its most basic level , we refer to this as beat generation , and a neuronal system that does so we call a beat generator . Different than beat perception , beat generation is envisioned to be able to occur in the absence of an external cue . A BG is a neural realization of an internal clock that can be used as a metronomic standard by other internally driven processes that depend on time measurements . While demonstration of a beat involves a motor action ( tapping , clapping , vocalizing , head bobbing ) , the BG could include a general representation of a motor rhythmicity but the specific motor expression ( say , foot tapping ) may not be an integral part of the BG . Our formulation proposes that time measurement for beat perception and the beat generator model are oscillator-based . In this view , a beat can be learned and stored as a neuronal oscillator ( cell or circuit ) . The frequency range of interest , 1-6 Hz , is relatively low compared to many other neuronal rhythms , but similar to those seen in sleep . We rely on faster ( gamma-like ) oscillators to provide clocklike ticks and we assume two counters and a comparator circuit can be used for adjusting the BG period and phase to match with the stimulus . Conceptually , counting and comparing with a target period are essential features of the algorithmic ( or sometimes called , information processing timekeeper ) approach , falling into the class of error-correction strategies; see [16 , 17 , 20–22 , 54] for examples of two-process models . These models suggest mechanisms used by humans to bring their movements into alignment with a rhythmic stimulus . They do not , however , provide a biological framework for these mechanisms . We provided a neuronal implementation of the BG in the form of an oscillator with a tunable biophysical knob and two learning rules; the BG is a continuous-time dynamical system , a realizable neuronal oscillator . It does not require a separate reset mechanism . The implementation also does not require a separate knob for phase correction; the two learning rules both make adjustments/corrections to the same parameter , Ibias , and they are ongoing whenever a stimulus is present . We propose this BG as the internal clock—an oscillator that learns a beat and keeps it . A different class of oscillator models for beat perception relies on large networks of neuronal units [12 , 24 , 28] . The units’ intrinsic frequencies span the range of those that are relevant in speech and music . In the neural entrainment models of Large and collaborators , different units within the ensemble respond by phase-locking to the periodic stimulus . Units with intrinsic frequencies near that of the metronome will entrain 1:1 while those with higher intrinsic frequencies entrain with different patterns , such as 2:1 . Dominant responses are found at harmonics and sub-harmonics of the external input . Amplitude , but not precise timing relative to stimulus features ( say , stimulus onset times ) , are described in these models . The framework is general although the identities of neuronal mechanisms ( synaptic coupling or spike generation ) are not apparent as the description is local , based on small amplitude perturbation schemes around a steady state and the coupling is assumed to be weak . The approach is nonlinear and provides interpretations beyond those of linear models , e . g . it identifies a beat for complex input patterns even if the beat/pulse is not explicitly a component of the stimulus [12] . Our model cannot be described as entrainment in the classical sense . Entrainment occurs when an intrinsically oscillatory system is periodically forced by an external stimulus to oscillate and , in the present context , to phase lock at the forcing frequency ( or some subharmonic ) that may differ from its endogenous frequency . Our BG neuron is not entrained by the stimulus but rather it learns the frequency of the stimulus . The BG’s frequency is adapted indirectly through the control parameter in order to match with the stimulus . The influence of the stimulus on the BG diminishes as learning proceeds . In fact , in the continuous time version when the frequency and phase are eventually learned , the BG no longer requires the stimulus; it will oscillate autonomously at the learned frequency if the stimulus is removed or until the stimulus properties change . In the discrete time version , even after the stimulus and BG periods and phase agree ( to within a gamma period accuracy ) modest adjustments are ongoing to maintain the rhythm . In contrast , for an entrainment model , the oscillator’s parameters are fixed . The stimulus does not lead to a change in the oscillator’s intrinsic properties . For a transient perturbation , the dynamics of resynchronization are according to an entrainment unit’s phase response curve , which instantaneously changes the current phase of the oscillator . In contrast , the BG’s response to transient inputs impacts the parameter Ibias invoked by adjustments according to either or both of the period and phase learning rules . Our model is further distinguished from entrainment models in that the BG strives for zero phase difference but in an entrainment setting there is typically a phase difference between the stimulus and the units . Finally , for an entrainment model the coupling from stimulus to oscillator is periodic . In our model , the influence of a periodic stimulus is delivered both periodically ( via LRϕ ) and aperiodically ( via LRT ) . Although humans can learn to accurately estimate time intervals [1] , little is known about the neural mechanisms used to generate these estimates . For beat generation , we are positing an ability to estimate time intervals ( e . g . , between stimulus onset events ) in real time in an ongoing and flexible manner . We introduced the idea of gamma counters to perform such measurements . These counters provide a rough estimate of elapsed time that can be used to compare the internal representation of an interval with that of an external cue . The model then produces a finer representation of the interval by adjusting the BG’s spike time and period . There is growing evidence for the existence of counting mechanisms within neuronal systems . For example , Rose and collaborators have demonstrated that neurons in the auditory mid-brain of anurans ( frogs and toads ) count sound pulses in order to make mating decisions [36 , 55] . These neurons have been called ‘interval counting neurons’ because they respond only after a threshold number of pulses have been counted provide that those pulses are spaced in time intervals of specific lengths [37] . In a very different context , it has been recently demonstrated that mossy fiber terminals in rat hippocampus have the ability to count action potentials , an ability cited as improving the reliabilty and accuracy of information transfer [38] . The discreteness of the gamma counter , used in our model , leads to variability in the BG spike times , allowing the model to exhibit negative mean asynchrony ( NMA ) . This is consistent with finger tapping experiments which show that humans display variability in their tap times relative to an isochronous stimulus and tend to , on average , tap before the stimulus 13] . As discussed earlier , the NMA as shown in Fig 6 , is rather modest , however , changes in parameters will lead to larger NMA . In contrast , replacing the gamma frequency oscillators with continuous time clocks , which exactly determine time intervals , leads to perfect phase alignment , ϕ = 0 ( no NMA ) . Thus , our work posits the existence of discrete time clocks as a potential source of intertap time variability . The gamma counters also provide an upper bound on the stimulus frequency which can be reliably learned by the BG neuron . For the 36 Hz clocks used here , this limit is roughly 9 Hz . After this point the phase rule overcorrects , transiently increasing Ibias to a value corresponding to a much larger frequency . We stress that this upper bound is dependent on the specific gamma frequency , and faster clocks may be used to keep track of shorter intervals . An interesting experimental study would be to look at the EEG power spectrum while subjects listen to periodic stimuli and monitor whether the gamma band activity changes with stimulus presentation rate . Many interval timing models involve accumulation ( continuous time or counting of pacemaker cycles ) with adjustment of threshold or ramp speed [6 , 7] to match the desired time interval . Applications to periodic beat phenomena , say the metronome case , would include instantaneous resetting and some form of phase adjustment/correction [56 , 57] . Algorithmic models may not specifically identify the accumulator as such , but instead refer to counters or elapsed time . Our BG model shares some features with interval models for beat production ( as described in [9] and [58] ) , as the BG relies on counters and accumulators . Additionally , as described earlier , it shares features with entrainment models , as the BG is a nonlinear oscillator . In short , the BG is a hybrid . Interval- and oscillator-based models are related . Even if not explicitly stated as such , in an interval model , the accumulator and its reset are equivalent to highly idealized models for neuronal integration , the so-called integrate-and-fire ( IF ) class of models [59] . For steady input , the state variable rises toward a target value ( that is above the event threshold ) , rising linearly for a non-leaky IF model and with a decreasing slope for a leaky IF model ( LIF ) , and is reset once the state variable exceeds the threshold . These IF/LIF models are dynamical system oscillators , and are also nonlinear by way of the reset mechanism . However , the time constant/integration rate required for beat applications is much longer/slower than in typical applications of IF models for neuronal computations where timescales of 10-30 ms are more common . These models have entrainment and phase-locking properties [60 , 61] and they typically show a phase difference from the stimulus . Extended in this way , periodic in time , such an interval model can be recast as an entrainment model ( see also [39] ) . As noted by Loehr et al . [39] , differences between such interval and continuous oscillator models do appear in some circumstances . Adding a plasticity mechanism , say for the threshold or input drive , then allows learning of a period . We described how one may analyze the dynamics of such an LIF oscillator-like interval model in terms of a map ( Fig 4 ) . One could additionally add a phase correction mechanism as in two-process models in order to achieve zero-phase difference . This can be achieved in a LIF model , for example , by adjusting the reset condition after reaching threshold or by utilizing phase response curves . Our mechanism for phase correction differs from these approaches in that we target the excitability parameter Ibias for adjustment . This has the advantage that the BG learns the correct phase and period allowing it to continue to hold a beat after the stimulus is removed , similar to other two-process interval models . The effects of noise on time estimation/production have been studied with interval models , cast as first passage time problems for accumulator models ( drift-diffusion models ) [62–64] . In that context , the issue of scalar timing is of significance [5 , 63 , 65 , 66] , however the time intervals of interest are typically longer than what one would find in a musical context . Wing and Kristofferson [16 , 17] considered effects of noise and contrasted sensory noise with motor sources of noise , concluding that timekeeper noise was frequency dependent but motor noise was not . Whether or not scalar timing holds for short rhythmic intervals is unsettled . A number of tempo discrimination studies have failed to produce any evidence for frequency dependent errors for periods below 1000 ms [51 , 67] . However , Collyer et al . [68] reports scalar timing in the distribution of tap times when tapping to an isochronous rhythm . A distinctive interval model was developed by Matell and Meck [69 , 70]—the striatal beat frequency ( SBF ) model . In this neuromechanistic description , the basic units are neuronal oscillators with different fixed frequencies . All oscillators are reset at t = 0; differences in frequencies of convergent units will eventually lead to collective near-coincidence ( so-called beating phenomenon of non-identical oscillators ) at a time that through learned choices ( synapses onto coincidence detector units ) can match the desired interval . It may be extended to the periodic case and considered for beat generation as discussed in [56 , 57] although the brain regions involved may be different for explicit time estimation than for rhythmic prediction/reproduction [71 , 72] . We consider here only the case of isochronous inputs . A natural next step is to consider more complex , non-isochronous stimulus sequences . Additionally , we have side-stepped questions of perception in order to focus solely on timing . Our BG model does not recognize variations in pitch or sound level . For example , if stimulus events were alternating in , say , sound level ( as in [73] ) , our model , as is , would not capture the effects . An extension of our model involving pairs of stimulus and beat generator clocks for each sound level could conceivably address this shortcoming . We have chosen a particular biophysical instantiation for the BG . The capabilities of learning and holding a beat over a range of frequencies depends only on the monotonic frequency dependence of the control ( “learnable” ) parameter and would not be compromised by variation of biophysical parameters . Some features of the BG dynamics ( say , the degree and signatures of asymmetries in resynchronization for speeding up or slowing down ) can be expected to depend on the specifics of , say , the relationship between Ibias and the intrinsic frequency , but we have not explored this in detail . The learning rules LRT and LRϕ utilized in our study both target the excitability parameter Ibias with a simple goal to either speed up or slow down the BG so that it synchronizes with the stimulus . Alternatively , the drive could be provided as the summed synaptic input from a population of neurons afferent to the BG . The synaptic weights onto the BG and/or internal to the afferent population could be plastic and affected by our learning rules which in spirit are similar to spike time dependent plasticity rules [74] . Our model assumes significant increments of drive at each learning step , leading to fast learning . This may be relatable at a population scale to balanced network models , where fast learning can be achieved with smaller step changes due to the large number of synapses [75] . Currently , during synchronization continuation , our BG model retains its estimate of the most recent stimulus period , γS . We have not yet included a slow decay of this memory or a slow degradation of the BG rhythm . It is plausible that the addition of noise could lead to this slow drift after the stimulus is removed since , as we showed , noise does introduce additional variablity during stationary behavior . We have not ascribed a location for the BG within a specific brain region . As a result , we have not addressed issues of sensorimotor synchronization ( SMS ) where sensory processing of a beat must be coordinated with the motor action that demonstrates the beat ( e . g . finger tapping ) . Several models for SMS in the context of beat perception already exist , for example the two-layer error-correction model of Vorberg and Wing [76] and the entrainment model of Large et al . [12] described earlier . Van der Steen and Keller have developed the Adaptation and Anticipation Model ( ADAM ) [22] , a type of algorithmic error-correction SMS model , and they noted a need for an extended ADAM that would incorporate dynamical systems principles . Our model could certainly be a starting point for such an endeavor . Patel and Iversen [77] proposed the Action Simulation for Auditory Prediction ( ASAP ) hypothesis . In their conceptual model , the motor system primes the auditory system to be able to process auditory input . In particular , ASAP proposes that the motor system is required for beat perception . Generally , these studies raise questions about whether the causal roles of sensory and motor systems can be disambiguated in the context of beat perception and beat generation . Addressing such questions from a modeling perspective is a natural next step . Our model framework allows us to make several predictions , which are summarized here . First , the BG model succeeds at synchronization continuation [78]; it can hold a beat after the sound stimulus terminates . The BG will continue to oscillate with fine adjustments of its period as needed , according to LRT , to match that of the most recently stored IOIS of the stimulus ( as in Fig 3 ) . Error-correction models would also continue oscillating at the last stored frequency [20 , 21] . In contrast , for an entrainment model , without a plasticity mechanism , the oscillators are likely to return to their original intrinsic frequencies after the stimulus is removed . However , Large et al . [12] have illustrated using their two-layer model , that the network can hold a beat if the units within the motor layer , have bistable properties , i . e . a unit may have a steady state ( damped resonator ) coexistent with an oscillator state . The main difference between our approach and others is that period error correction ( LRT ) occurs at the BG spike times rather than at stimulus onsets . As such , even after stimulus removal , comparison between the BG period and last stored stimulus period continues . Second , the time course of adjusting to a sudden tempo change occurs over seconds and has more or less monotonic phases of slowing down or speeding up ( Fig 7 ) . If the new sound stimulus is stopped during this transition , we predict from the model that the BG will still learn the new beat frequency . However , the phase of the BG will differ depending on when during the transition the stimulus is removed . This could be detected using EEG or perhaps a finger-tapping demonstration . This prediction differs from those made by traditional error-correction models which will cease making updates after the stimulus is turned off . Hence , these models may not reach the new frequency , and instead settle at an intermediate frequency . An entrainment model also relies on the external input to match its frequency . Thus if the sound stimulus is stopped during the transition , an entrainment model is likely to return to its basal frequency , and not learn the new one . Third , resynchronization should be faster after a phase shift of the rhythmic stimulus than after a single timing-deviant sound event . Lastly , the model predicts an asymmetry in the resynchronization process after phase shifts ( advance versus delay ) , deviants ( early versus late ) and tempo changes in the stimulus sequence . We hesitate to assign whether the resynchronization time will be shorter or longer in these cases , as this property depends on the intrinsic dynamics of the BG as defined by its f-Ibias relationship . Similarly , the specific responses of an entrainment model for these cases depends on the phase response properties of the underlying oscillator model . Typically , these are one time adjustments to the phase of the oscillator , followed by a transient return to the entrained solution . Thus rather than elucidating concrete differences in predictions of our BG model versus others , instead consideration of different models may help to narrow the choice of biophysical currents that produce Ibias-f or phase response properties that allow neuron spike times to match empirical data . There are several questions that we plan to address in our future modeling and behavioral studies . How sensitively do timing errors depend on variability of the gamma counters and , say , on stimulus frequency ? To what degree can the BG model track modulations of the beat frequency ? Different candidate beat generators that possess different ionic currents will have f-I relationships that are different than the ones presented here . How sensitively do the quantitative results described here depend on the shape of these curves ? When considering an ensemble of beat generating neurons , how does coupling between these neurons shape the dynamics of learning ? How could the model be enhanced to become predictive , to not just track modulation but to predict dynamic trends ? Going beyond isochronous timing only , we plan to consider more complex rhythms . For example , suppose we consider the effect of shifting identically the timing of alternate stimulus tones . Eventually , after a sequence of modest shifts , the beat frequency would be halved although the number of stimulus events would be maintained but with a different temporal pattern . How is the transition of frequency halving executed dynamically ? Perhaps there is a regime of shift values where beat determination is ambiguous , a possible regime of bistability . A different manipulation toward a complex stimulus could involve parametrically changing the sound intensity or pitch of alternate tones . Such cases will bring us toward questions of perception and auditory streaming together with beat perception . The questions surrounding how we perceive and keep a beat are easy to pose but developing models for beat perception and generation present challenges . Our model is a first-pass attempt at formulating and analyzing a neuromechanistic model that can learn a beat . Our approach stems from a neurobiological and dynamical systems perspective to develop neuronal system-based models for beat learning and generation . The essential features involve neuro-based elapsed timekeepers , time difference comparators and a neural oscillator ( cellular or circuit level ) with some plasticity and learning rules . Looking ahead , one hopes for development of more general beat and rhythm pattern generators ( for complex rhythmic sounds , music pieces ) that can be stored in a silent mode and are both recallable and replayable . | Music is integral to human experience and is appreciated across a wide range of cultures . Although many features distinguish different musical traditions , rhythm is central to nearly all . Most humans can detect and move along to the beat through finger or foot tapping , hand clapping or other bodily movements . But many people have a hard time “keeping a beat” , or say they have “no sense of rhythm” . There appears to be a disconnect between our ability to perceive a beat versus our ability to produce a beat , as a drummer would do as part of a musical group . Producing a beat requires beat generation , the process by which we learn how to keep track of the specific time intervals between beats , as well as executing the motor movement needed to produce the sound associated with a beat . In this paper , we begin to explore neural mechanisms that may be responsible for our ability to generate and keep a beat . We develop a computational model that includes different neurons and shows how they cooperate to learn a beat and keep it , even after the stimulus is removed , across a range of frequencies relevant to music . Our dynamical systems model leads to predictions for how the brain may react when learning a beat . Our findings and techniques should be widely applicable to those interested in understanding how the brain processes time , particularly in the context of music . | [
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... | 2019 | A neuromechanistic model for rhythmic beat generation |
The type I interferon ( IFN ) activated transcriptional response is a critical antiviral defense mechanism , yet its role in bacterial pathogenesis remains less well characterized . Using an intracellular pathogen Listeria monocytogenes ( Lm ) as a model bacterial pathogen , we sought to identify the roles of individual interferon-stimulated genes ( ISGs ) in context of bacterial infection . Previously , IFN has been implicated in both restricting and promoting Lm growth and immune stimulatory functions in vivo . Here we adapted a gain-of-function flow cytometry based approach to screen a library of more than 350 human ISGs for inhibitors and enhancers of Lm infection . We identify 6 genes , including UNC93B1 , MYD88 , AQP9 , and TRIM14 that potently inhibit Lm infection . These inhibitors act through both transcription-mediated ( MYD88 ) and non-transcriptional mechanisms ( TRIM14 ) . Further , we identify and characterize the human high affinity immunoglobulin receptor FcγRIa as an enhancer of Lm internalization . Our results reveal that FcγRIa promotes Lm uptake in the absence of known host Lm internalization receptors ( E-cadherin and c-Met ) as well as bacterial surface internalins ( InlA and InlB ) . Additionally , FcγRIa-mediated uptake occurs independently of Lm opsonization or canonical FcγRIa signaling . Finally , we established the contribution of FcγRIa to Lm infection in phagocytic cells , thus potentially linking the IFN response to a novel bacterial uptake pathway . Together , these studies provide an experimental and conceptual basis for deciphering the role of IFN in bacterial defense and virulence at single-gene resolution .
Mammalian cells encode numerous pattern recognition receptors ( PRRs ) that sense invading pathogens and initiate innate immune responses through cytokine and chemokine production [1] . With viral pathogens , the type I interferon ( IFN ) family of cytokines serves as a first line of defense and is essential for controlling virus replication and pathogenesis . The IFN-induced antiviral response results from the transcription of hundreds of interferon-stimulated genes ( ISGs ) , many of which inhibit different steps of the viral life cycle [2 , 3] . Although less studied , the type I IFN response is also induced by many bacterial pathogens including Legionella pneumophila , Helicobacter pylori , Francisella tularensis , Yersinia pseudotuberculosis , Mycobacterium tuberculosis , and Listeria monocytogenes [4] . However , the role of type I IFN in bacterial infection remains unclear and systematic studies to uncover the breadth of ISGs targeting a bacterial pathogen have not been carried out . We chose to clarify these aspects of IFN biology by using Listeria monocytogenes ( herein referred to as Lm ) as a model pathogen as its cellular life cycle has been described in detail and it exhibits a complex relationship with the mammalian IFN response system [5] . Lm is a Gram-positive food-borne pathogen that causes severe and life threatening disease in immunocompromised individuals , pregnant women , elderly and children [6] . Upon invasion of enterocytes , hepatocytes , or phagocytes , Lm gains access to the cytoplasm by lysing the primary phagosome . Lm rapidly replicates in the cytoplasm and spreads to adjacent cells via actin-based protrusion machinery [7] . Recent studies show that Lm stimulates the type I IFN response by secreting cyclic diadenosine monophosphate ( c-di-AMP ) that activates the Stimulator of Interferon Genes ( STING ) . Activation of STING results in IRF3 phosphorylation and transcription of IFN genes [8 , 9] . Notably , STING-deficient mice fail to produce IFNβ in response to Lm infection [10] . While the relationship between IFN and in vivo Lm infection has been firmly established , some discrepancies do exist between these studies . Early work showed that IFNβ increases the tolerance of mice to intravenous systemic Lm infection [11] . Similarly , Ifnar1 is required for resistance of mice to Lm invasion through the intestinal tract , further demonstrating a protective effect of IFN for a natural route of infection [12] . However , more recent studies indicate that mice lacking a functional type I IFN receptor ( Ifnar1-/- ) display greater resistance to intravenous Lm infection , suggesting that IFN exacerbates systemic Lm infection [13–15] . The type I IFN response has also been found to suppress adaptive immunity against Lm , since Sting-deficient mice exhibit greater numbers of cytotoxic lymphocytes and show protection from Lm reinfection after immunization [16] . These various effects of type I IFN on Lm infection likely reflect the different routes of Lm infection and the pleiotropic roles of IFN in distinct tissue environments or cellular populations encountered by the pathogen . Nevertheless , it is clear that type I IFN plays a significant role in shaping the host-pathogen interaction in vivo . Because IFN induces a robust transcriptional response , the regulatory role of IFN in bacterial infection likely depends on the cellular expression of ISGs . However , the functions of most ISGs in immunity have not yet been elucidated due to the technical challenges of studying complex transcriptional responses at single-gene resolution . Recently , overexpression screens have been designed to study individual ISG functions [17–20] . While these approaches have proven to be highly successful for identifying genes that potently suppress invasion , replication , or egress of a wide variety of viruses , similar screening methodologies have not yet been adapted for bacterial pathogens . Here , we performed a gain-of-function screen of over 350 human type I IFN ISGs to identify genes that regulate Lm infection . This screen revealed potent bacterial restriction factors including MYD88 , UNC93B1 , TRIM14 , AQP9 , and MAP3K14 . We demonstrated that the signaling adaptor MYD88 restricts Lm infection through the stimulation of a robust host gene expression program . In contrast , TRIM14 inhibited Lm infection through a non-transcriptional mechanism , thus suggesting that IFN stimulates diverse antibacterial properties . Importantly , we identified the human high affinity immunoglobulin receptor FcγRIa ( CD64 ) as an IgG-independent enhancer of Lm internalization and established its role in the entry of Lm into phagocytic cells . Taken together , these findings reveal effector molecules involved in the complex relationship between Lm and the IFN response system , and open new avenues for exploring the cell-autonomous immune regulation of other bacterial pathogens .
We sought to employ a gain-of-function screening approach to identify ISGs that regulate Lm infection of host cells . We first optimized the screening conditions by determining the suitability of the host cell type previously used for ISG screens [17 , 18] and by defining the optimal conditions of Lm infection . Since Lm is known to potently induce IFN expression [5] , we chose human STAT1-deficient fibroblasts as the primary host cell type for infection [21] . These cells lack functional STAT1 , have defective IFN responses , and therefore limit the spurious activation of ISGs during bacterial infection . To screen hundreds of ISGs in a single experiment , we optimized Lm infection of STAT1-deficient fibroblasts for compatibility with multicolor flow cytometry with auto-sampling functionality [17] . GFP-expressing Listeria monocytogenes 10403s ( GFP-Lm ) was added to fibroblasts at a multiplicity of infection ( MOI ) 5 in the absence of antibiotics . GFP-Lm was incubated for 90 minutes with host cells prior to adding gentamicin-containing media to eliminate non-internalized extracellular bacteria . Approximately 10% of host cells were infected by GFP-Lm at this time point . Cells were then incubated for 1 , 2 , 4 and 6 hours , providing a temporal evaluation of infection . As shown in Fig 1A , we observed an increase in the percentage of GFP-positive host cells over time indicating that GFP-Lm readily infects STAT1-deficient fibroblasts . Lm infection progresses through a series of well-defined stages including entry , vacuole escape , cytoplasmic replication , and cell-to-cell spread ( Fig 1A ) . Since ISGs could potentially affect any stage of the Lm lifecycle , we assessed the ability of flow cytometry to identify blocks in each of these distinct stages . STAT1-deficient fibroblasts were infected with mutant Lm strains that lack key virulence factors that are critical for cellular entry ( Lm ΔinlAΔinlB ) , phagosomal escape ( Lm ΔhlyΔplcAΔplcB ) , and cell-to-cell spread ( Lm ΔactA ) . As expected , Lm ΔinlAΔinlB , lacking the major Lm invasion proteins InlA and InlB , exhibited a severe infection defect observed as early as 1 hour following initial infection ( Fig 1B ) . In contrast , Lm ΔhlyΔplcAΔplcB mutant lacking listeriolysin O ( LLO ) and phospholipases required for phagosomal rupture and escape , invaded cells similar to wild type Lm , yet the percentage of infected cells and their bacterial burden ( intensity of the GFP signal ) did not increase over the time course of infection ( Fig 1C ) . This observation is consistent with the Lm ΔhlyΔplcAΔplcB phenotype in which the pathogen trapped in the phagosome survives but is unable to replicate [22–25] . Finally , Lm ΔactA lacking the ActA protein required for intracellular actin-based motility invaded cells initially but failed to spread from cell to cell . Importantly , the GFP fluorescence intensity of infected STAT1-deficient fibroblasts increased over the course of infection due to bacterial replication and accumulation in initially invaded cells ( Fig 1D ) . Thus , flow cytometry is a well-suited method to measure Lm infection of STAT1-deficient fibroblasts as it is capable of detecting specific infection defects that may arise due to ISG expression . We next asked whether ISGs that control viral infection also regulate bacteria . Briefly , STAT1-deficient fibroblasts were transduced with bicistronic lentiviral vectors driving constitutive expression of an ISG and a red fluorescent protein TagRFP ( Fig 2A ) . Cells expressing ISGs in a one-gene one-well format were then challenged with a GFP-Lm and resulting infection was analyzed by flow cytometry ( Fig 2B ) . Infection rates were quantified as a percentage of GFP-positive host cells ( a measure of infection ) among the RFP-positive cell population ( a measure of ISG expression ) . Firefly luciferase ( Fluc ) , which did not affect Lm infection ( S1 Fig ) , was used as a negative control . A panel of ISGs that enhance ( MCOLN2 , LY6E ) or inhibit ( IFI6 , RTP4 , TREX1 , IRF2 , IRF7 , P2RY6 , and IFITM3 ) yellow fever virus ( YFV ) had no effect on Lm infection ( Fig 2C ) . In addition , in the absence of IFN signaling the cytosolic DNA and RNA sensors MB21D1 ( cGAS ) and DDX58 ( RIG-I ) [26 , 27] as well as OASL , an ISG that inhibits hepatitis C virus [17 , 28] did not inhibit Lm ( Fig 2C ) . Thus , effects of ISGs can be differentiated between model bacterial and viral pathogens . Next , we expressed a library of over 350 ISGs in a one-gene to one-well format [17] and performed Lm infection as described above . Infectivity of Lm obtained from the average of two screen replicates is shown as a dot plot in Fig 2D ( S1 Table ) . The majority of ISGs had little effect on Lm infection with the cellular bacterial burdens falling within two standard deviations of the population mean ( Z-score less than 2 , Fig 2D ) . We considered inhibitors of Lm infection as those ISGs that restricted infection with Z-score greater than 2 . Six ISGs fulfilled these criteria including PKRD2 , UNC93B1 , MYD88 , AQP9 , MAP3K14 , and TRIM14 ( Fig 2D ) . In addition , two genes FCGR1A and SCO2 enhanced Lm infection with Z-score greater than 2 . Repeat trials with independent lentiviral preparations confirmed statistically significant inhibitory effects for all six ISGs and an enhancing effect for FCGR1A ( Fig 2E ) . The identification of UNC93B1 and MYD88 as cell-intrinsic inhibitors of Lm provided positive validation of the ISG screen . These genes are key components of the immune response to pathogens and function to properly target Toll-like Receptors ( TLR ) to subcellular compartments and to propagate NF-κB signal transduction , respectively [29 , 30] . Consistent with the initial ISG screen , a dose-response to infection revealed that MYD88 and UNC93B1 reduced Lm infectivity ( defined as the MOI of Lm required to achieve 50% cellular infection during the course of 8 hours ) by 9 . 7-fold and 5 . 7-fold compared to firefly luciferase control ( Fig 3A ) . To determine if the anti-Lm activity of MYD88 results from increased NF-κB transcriptional response as predicted , we isolated mRNA from STAT1-deficient fibroblasts ectopically expressing MYD88 . RNA-seq revealed 107 genes that were upregulated over 2-fold ( S2 Table ) and included NF-κB signature genes involved in inflammation ( e . g . IL6 , IL8 , IL1B , CXCL1-3 , CXCL5 , CCL2 ) , signal transduction ( e . g . NFKB1/2 , RELB , IRAK2 , C1QTNF1 ) , cell adhesion ( e . g . ICAM-1 , LAMB3 , MMPs ) , and complement activation ( C3 ) ( S2 Table ) . To then establish the role of NF-κB activation in the observed antibacterial activity of MYD88 , we tested the function of a naturally occurring single nucleotide polymorphism of MYD88 ( rs1319438 ) that confers a S34Y substitution . While this mutation does not affect the interaction of MYD88 with IRAK1 , IRAK4 , and Mal , it disrupts MYD88 signaling and NF-κB activation by preventing oligomeric Myddosome complex formation required for downstream signaling ( Fig 3B ) [31 , 32] . The cellular expression level of MYD88 S34Y was comparable to wild type MYD88 ( Fig 3C ) , however the mutated protein failed to activate NF-κB ( Fig 3D ) . More importantly , MYD88 S34Y did not inhibit Lm infection ( Fig 3E ) . These findings indicate that MYD88-dependent suppression of Lm infection results from strong NF-κB transcriptional activation and currently unknown effector mechanisms . The ISG screen also revealed AQP9 , PKRD2 , MAP3K14 , and TRIM14 as potent inhibitors of Lm infection , suggesting that these proteins may harbor novel antibacterial activities . Aquaporin 9 , encoded by AQP9 , is a transmembrane channel involved in water and small solute transport [33] , whereas PRKD2 and MAP3K14 are kinases implicated in membrane trafficking and immune signaling , respectively [34 , 35] . It is currently unclear how expression of these genes blocks Lm infection . Among the newly identified anti-listerial ISGs , TRIM14 exhibited the greatest inhibitory activity ( Fig 2E ) . Interestingly , this protein has recently been linked to antiviral defense through several independent mechanisms ( [36–38] ) but has not been previously implicated in antibacterial immunity . TRIM14 is a member of the tripartite motif-containing ( TRIM ) gene superfamily that includes proteins involved in innate immunity , transcriptional regulation , cell proliferation , and apoptosis [39] . While several family members exhibit anti-viral functions [40] , their role in bacterial pathogenesis remains poorly understood . We found that expression of IFN-inducible TRIM5 , TRIM21 , TRIM25 , TRIM34 , and TRIM38 had no effect on Lm infection ( Fig 4A ) , suggesting that TRIM14 is a unique anti-bacterial effector among the IFN-stimulated TRIMs . As shown in Fig 4B , domain architecture of TRIM14 is distinct from other family members as it is does not encode the RING E3-ligase domain typically found within the N-terminal tripartite motif , and is therefore likely to function through an alternative mechanism [39] . We next asked if TRIM14-mediated antibacterial activity could be attributed to one of its structural domains . Based on the available crystal structures of truncated TRIM proteins [41 , 42] , we generated TRIM14 constructs consisting of either the B-box with coiled-coil ( residues 1–255 ) or the PRY/SPRY domain ( residues 158–442 ) . Notably , separate domains of TRIM14 had no effect on Lm infection ( Fig 4C ) , indicating that the full-length TRIM14 is required for the anti-bacterial activity . TRIM14 was recently reported to play an important role in IFN and NF-κB activation during viral infection [36 , 37] . It associates with the mitochondria wherefrom it links MAVS and NEMO to NF-κB and IRF3-activated transcription [37] . It has also been shown to positively regulate type I IFN signaling by inhibiting cGAS degradation [36] . However , our studies were performed in STAT1-deficient fibroblasts that cannot be activated by IFN , suggesting that the anti-Lm activity of TRIM14 is not associated with this ascribed function . Furthermore , a critical lysine in TRIM14 , K365 , was shown to be required for IFN activation by TRIM14 in mitochondria [37] . However , we found that K365 was not necessary for the antibacterial function of TRIM14 ( Fig 4C ) . Finally , ectopic expression of TRIM14 in STAT1-deficient fibroblasts induced the expression of 36 genes by over 2-fold and only 5 genes had greater than 5-fold increase compared to over 100-fold increase in TRIM14 . Ingenuity Pathway Analysis failed to identify possible upstream transcriptional regulators ( Fig 4D and S3 Table ) , and the observed transcriptional response did not exhibit an NF-κB or IRF3 signature as would be predicted if TRIM14 regulated MAVS and NEMO as previously reported . To then determine if TRIM14 functioned through a transcription-independent mechanism during infection , we compared host mRNA produced during Lm infection ( 6 hours ) in cells expressing TRIM14 or luciferase as a control . Lm infection altered expression of hundreds of genes in luciferase-expressing cells ( S4 Table ) . As expected , TNFα , NF-κB and IL1A were among the strongest predicted upstream regulators ( Fig 4E and S4 Table ) . Interestingly , expression of TRIM14 did not alter the host transcriptional response to Lm infection ( S4 Table ) , further suggesting that TRIM14 has a direct anti-bacterial function in host cells . Taken together , these results indicate that the gain-of-function ISG screening technique can resolve direct mechanisms of inhibition of bacteria , similar to what has been demonstrated for viruses [17] . In addition to anti-bacterial ISGs , we identified a high affinity immunoglobulin receptor FcγRIa ( CD64 ) as an enhancer of Lm infection . A dose-response experiment indicated that FcγRIa potentiated Lm infectivity by over 100-fold ( Fig 5A ) . Consistent with the flow cytometry measurements , a greater number of individual bacteria were found in the cytoplasm of FcγRIa-expressing cells ( Fig 5B ) and FcγRIa increased the total number of cell surface protrusions emanating from infected cells ( Fig 5C ) . Because FcγRIa is a cell surface expressed protein , we hypothesized that it may enhance Lm infection by promoting primary internalization into host cells or secondary cell-to-cell spread . To distinguish between these possibilities , we visualized Lm infection foci , which are formed from Lm invasion of a single host cell followed by rapid cell-to-cell transmission . Cell monolayers were infected with very low doses of Lm ( at MOI 0 . 015 , 0 . 05 , 0 . 1 ) and the formation of foci was evaluated 30 hours post infection ( see Materials and Methods ) . Cellular expression of FcγRIa increased the total number of infection foci compared to control ( Fig 6A ) . However , the diameter and surface area of individual foci were not altered in presence of FcγRIa ( Fig 6B ) . Thus , FcγRIa enhances efficiency of primary Lm invasion , yet has little effect on secondary cell-to-cell spread . We next asked if FcγRIa potentiated Lm entry by coordinating interactions with the host Lm internalization receptors E-cadherin or c-Met [43] . We introduced frameshift mutations into CDH1 ( encoding E-cadherin ) and MET ( encoding c-Met ) by CRISPR/Cas9 resulting in non-coding genetic disruption of these loci ( S2A–S2F Fig ) . As expected , the invasive capacity of Lm was significantly attenuated in CDH1/MET-deficient cells ( S3G Fig ) , which could be restored by ectopic expression of either CDH1 or MET ( Fig 6C ) . Remarkably , ectopic expression of FcγRIa in CDH1/MET-deficient cells increased Lm infection to levels comparable with MET complementation ( Fig 6C and 6E ) . In addition , mutant Lm ΔinlAΔinlB lacking the invasins InlA and InlB that directly bind host surface proteins E-cadherin and c-Met , respectively , readily infected CDH1/MET-deficient cells expressing FcγRIa ( Fig 6D , 6F and 6G ) . Therefore , FcγRIa supports bacterial uptake independently of “classic” host Lm internalization receptors E-cadherin and c-Met as well as bacterial invasins InlA and InlB . Fcγ receptors bind the Fc ( antigen non-specific ) region of IgG antibodies produced as a part of adaptive response to infection in mammals . The human Fcγ receptor family includes activating receptors FcγRIa , FcγRIIa , FcγRIIc , FcγRIIIa and FcγRIIIb , as well as an inhibitory receptor FcγRIIb . Crosslinking of activating Fcγ receptors by IgG typically results in the phagocytosis of opsonized particles and cellular activation , facilitating destruction of the pathogens and induction of inflammation , respectively [44] . In humans , FcγRIa is constitutively expressed on monocytes and macrophages , and its expression is upregulated by type I and II interferons and other signaling molecules , such as IL-10 [45] . It consists of three extracellular immunoglobulin ( Ig ) -like domains , a single transmembrane domain , and a short cytoplasmic tail that does not contain any known signaling motifs . During receptor engagement with IgGs , FcγRIa recruits the accessory immunoreceptor tyrosine-based activation motif ( ITAM ) -containing γ-chain ( FcεRIg ) . Clustering of the FcγRIa with γ-chain triggers intracellular signaling cascades involving Syk and Src family kinases necessary for FcγRIa-mediated particle phagocytosis [46 , 47] . Additionally , FcγRIa has been shown to interact with FcγRIIa , using its ITAM-motif to signal in the absence of the γ-chain [48] . To compare the mechanism of Lm invasion to the classic IgG-coated particle uptake though FcγRIa alone in the absence of possible crosstalk with other Fcγ receptors [44 , 48] , we developed a model of Fc-receptor functions in a non-phagocytic cell type [46 , 49–51] . We first reconstituted IgG-coated particle internalization via FcγRIa . U-2 OS cells were transduced with a lentivirus expressing FcγRIa , or Fluc as a negative control . Latex beads were coated with human IgG and labeled with anti-human secondary antibody conjugated to Alexa Fluor 488 ( green ) . The IgG opsonized particles were incubated with U-2 OS cells for 1 . 5 h at 37°C and then shifted to 4°C to inhibit further uptake . Cell-surface bound beads were differentiated from internalized beads by incubating samples with anti-human secondary antibody conjugated to DyLight 405 ( blue ) without cell permeabilization ( Fig 7A ) . Under these conditions , internalized beads are protected from the secondary antibody and are visualized as green beads by fluorescence microscopy . In contrast , surface-bound beads are labeled with both green and blue secondary antibodies . As expected , luciferase-expressing U-2 OS cells showed no interaction with IgG-coated beads . In contrast , FcγRIa recruited IgG-beads to the cell surface , but revealed low levels of bead internalization ( Fig 7B and 7C ) . This may be anticipated since U-2 OS cells do not express endogenous γ-chain ( FcεR1g ) . Indeed , co-expression of FcγRIa with the γ-chain ( FcεR1g ) fully reconstituted FcγRIa-mediated internalization of IgG-coated beads ( Fig 7C ) . We also cloned and tested another Fcγ receptor–FcγRIIa–a low-affinity immunoglobulin receptor that possesses its own internal ITAM motif and therefore , does not require interaction with the γ-chain for particle internalization . FcγRIIa mediated similar high levels of IgG-coated bead phagocytosis ( Fig 7B and 7C ) . Thus , we have established a robust and simplified cellular system to study the function of individual human Fcγ receptors in context of both particle opsonization and pathogenic Lm infection . As shown in Fig 5A , Lm readily invaded U-2 OS cells expressing FcγRIa . Surprisingly , this phenotype did not require co-expression of the ITAM-containing γ-chain , suggesting that Lm internalization by FcγRIa occurs through a distinct mechanism compared to IgG-coated particle uptake . It has been previously reported that FcγRIa interacts with the γ-chain exclusively through the transmembrane domain [52] . We therefore asked if this region of FcγRIa was necessary for Lm internalization . We targeted the extracellular Ig-like domains of FcγRIa to the cell surface via a GPI-anchor signal of LFA-3 ( FcγRIa-GPI ) [53] . This chimeric protein was expressed on the cell surface similar to the wild type FcγRIa ( Fig 8A ) . Notably , as shown in Fig 8B , FcγRIa-GPI induced the same level of Lm infection as the wild type protein ( 3 . 03 fold ) , further confirming that FcγRIa does not interact with the γ-chain during Lm internalization . Additionally , FcγRIa does not require interaction with any other signaling protein through the transmembrane domain . The ability of Lm to be internalized by FcγRIa in the absence of the signaling γ-chain suggested that the recognition of Lm might also occur independently of IgG opsonization . Several lines of evidence support this conclusion . First , Lm infection was not enhanced by expression of other members of the Fc receptor family ( Fig 8C ) , including FcγRIIa that , as shown in Fig 7C , was able to internalize IgG-coated beads . Second , FcγRIa potentiated Lm invasion in serum-free ( and therefore , IgG-free ) conditions ( Fig 8D ) . Third , reducing the FcγRIa affinity for all types of IgG up to 100-fold by introducing an H174E mutation in the D2 Ig-like domain [54] did not affect its ability to enhance Lm infection ( Fig 8E ) . Finally , FcγRIa had no effect on the infection rate of other intracellular bacteria Shigella flexneri or Salmonella Typhimurium ( Fig 8F and S3A and S3B Fig ) . Therefore , internalization through FcγRIa is independent of non-specific pathogen opsonization with serum IgG . Together , these data indicate that Lm invades cells independently of the well-established route of phagocytosis , which involves IgG opsonization and ITAM-mediated intracellular signaling through the FcγRIa-γ-chain complex . Since our data revealed a novel mechanism of FcγRIa function that included Lm internalization independent of IgG opsonization , we hypothesized that Lm might express a cell surface factor required for Lm-FcγRIa interaction . To determine if this molecule was a general feature of Listeria genus or specific to pathogenic Lm , we assessed the ability of FcγRIa to confer invasiveness to a closely related but non-pathogenic species L . innocua . While L . innocua strains are genetically heterogeneous and may encode various combinations of genes shared with Lm [55 , 56] , L . innocua CLIP 11262 has been fully sequenced and annotated . It has been shown to lack all major genes required for Lm pathogenesis ( inlA , inlB , hly , actA , etc . ) [57] . As show in Fig 8G , in the absence of FcγRIa , invasion rates of non-pathogenic L . innocua CLIP 11262 were 78 . 43-fold lower than those of invasion-deficient Lm ΔinlAΔinlB . Importantly , while FcγRIa expression increased internalization of Lm ΔinlAΔinlB , it did not allow invasion of the CLIP 11262 strain ( Fig 8G ) . Similarly , FcγRIa did not increase internalization of an unsequenced L . innocua strain DUP-104 ( Fig 8G ) , suggesting that the bacterial ligand is specific to Lm and is not shared with non-pathogenic Listeria strains . To determine the contribution of FcγRIa to Lm infection in a naturally phagocytic human cell type that expresses endogenous FcγRIa , we disrupted cell surface expression of FCGR1A in THP-1 human monocytes using a lentiviral CRISPR/Cas9 system [58] ( Fig 9A and 9B ) . Lm infected 54 . 45 ± 2 . 19% wild-type THP-1 cells compared to 44 . 48 ± 2 . 98% of FCGR1A-deficient cells ( Fig 9C ) representing a statistically significant decrease in infection ( p = 0 . 0095 , n = 3 ) . These data suggest that Lm is internalized through multiple pathways with 18 . 13 ± 8 . 1% ( n = 3 ) of the total host cell infection mediated by FcγRIa ( Fig 9D ) . To then determine if the observed decrease in Lm infection was due to the newly defined mechanism of FcγRIa-Lm interaction described above rather than a general defect in IgG-coated pathogen internalization , we performed experiments in serum-free conditions . A significant reduction in Lm infection was observed in FCGR1A-deficient THP-1 ( 44 . 52 ± 1% infected wild type cells compared to 38 . 44 ± 0 . 69% of FCGR1A-deficient cells; p = 0 . 0010 , n = 3 ) ( Fig 9C ) with a 13 . 63 ± 2 . 52% ( n = 3 ) relative contribution of FcγRIa under these conditions ( Fig 9D ) . Thus , endogenous FcγRIa contributes to Lm invasion of phagocytic monocytes independently of IgG opsonization similar to what was observed in the reconstituted cellular system . Having established that human FcγRIa enhances Lm infection , we next asked whether mammalian FcγRIa orthologs exhibit similar functions . Species-specific FcγRIa coding sequences were commercially synthesized , and included ( 1 ) mouse ( naturally resistant to oral Lm infection ) , ( 2 ) sheep and rabbit ( known to be susceptible to Lm ) , and ( 3 ) panda ( uncharacterized susceptibility to Lm infection ) . All FcγRIa orthologs were expressed on the cell surface of U-2 OS cells as determine by IgG-coated latex bead binding assays ( Fig 10A ) . We then co-expressed these receptors with the γ-chain and tested whether they were fully functional in human cells by measuring the rates of IgG-opsonized particle internalization . All tested FcγRIa induced similar levels of IgG-bead phagocytosis ( Fig 10B ) , suggesting that they were indeed functioning as internalization receptors for opsonized particles . Next , we assessed the ability of non-primate FcγRIa to potentiate internalization of Lm . FcγRIa of mouse , sheep and panda failed to enhance Lm infection ( Fig 10C ) . Moreover , murine FcγRIa did not affect Lm infection even when co-expressed with the γ-chain in murine cells ( Fig 10D ) . Unexpectedly , rabbit FcγRIa was found to potentiate Lm internalization in the absence of the γ-chain ( Fig 10D ) . Rabbit is a natural host for Lm and exhibits severe listeriosis upon infection [59] . Analysis of the multiple sequence alignment of FcγRIa from these species did not pinpoint a single residue or a motif that was common between human and rabbit yet divergent from other FcγRIa proteins tested , suggesting a more complex interaction between host and pathogen molecules ( S4 Fig ) . Nevertheless , these data indicate that FcγRIa-Lm interaction is not only pathogen-specific ( Fig 8F ) , but also demonstrates host protein tropism .
The host type I interferon response is stimulated by numerous bacterial pathogens . However , the roles of individual ISGs in restricting bacterial infection are not well characterized . To address this gap in the knowledge of IFN biology , we adapted a gain-of-function screening approach to identify cellular regulators of Lm infection among approximately 350 type I ISGs . The screen revealed strong cell-autonomous inhibitors of Lm infection , such as TRIM14 , AQP9 , MYD88 , UNC93B1 and MAP3K14 . Interestingly , we also identified the human immunoglobulin receptor FcγRIa as an enhancer of Lm internalization , suggesting an intriguing possibility that bacterial pathogens have evolved virulence factors to directly hijack the IFN response system . We identified type I IFN-stimulated inhibitors of Lm infection that function through the upregulation of complex gene expression profiles ( e . g . MYD88 ) and/or through direct anti-microbial mechanisms ( e . g . TRIM14 ) . These ISGs may contribute to the regulation of Lm in a wide variety of tissue environments . For example , upregulation and activation of MYD88 in TLR-expressing lymphocytes would result in the expression of NF-κB-regulated genes with broad antibacterial activity . Our data indeed suggest that a MYD88-induced transcription program suppresses Lm infection through NF-κB activation ( Fig 3C–3E ) . Notably , Lm has been previously reported to counteract host defense systems , including interfering with NF-κB activation , thus dampening the overall inflammatory response to infection [60] . Our findings now indicate that inhibition of NF-κB by Lm may protect the pathogen from previously unknown cell-autonomous immune mechanisms . Further studies are needed to confirm this speculation . Another strong inhibitory ISG , TRIM14 is widely expressed throughout the body , including organs targeted by Lm , such as intestine and liver [37 , 61] . However , this is not the first study to implicate TRIM14 in anti-microbial defense . Recent studies characterized TRIM14 as an antiviral protein that activates both NF-κB and type I IFN through bridging MAVS and NEMO proteins as well as inhibiting cGAS degradation [36 , 37] . Interestingly our data support an alternative mechanism for the function of TRIM14 . We found that TRIM14 inhibited Lm infection in cells with defective IFN responses and that ectopic expression of TRIM14 did not alter the host transcriptional profile induced by Lm ( Fig 4 ) . Further studies are needed to reveal the precise inhibitory mechanisms of TRIM14 as well as other antilisterial ISGs including PRKD2 , AQP9 , and MAP3K14 identified here . Perhaps the most surprising discovery of this work is that the immunoglobulin receptor FcγRIa mediates Lm uptake , contributing to Lm invasion of phagocytic monocytes and macrophages . This finding is particularly insightful since these cells are not only an important target of Lm infection , but also aid the transmission of Lm to peripheral tissues during infection [62] . Currently , the precise molecular mechanisms of Lm internalization in phagocytic cells have not been characterized in detail and are believed to be mediated by C3bi and C1q complement receptors and phagocyte scavenger receptors [63 , 64] . However , our studies now suggest that Lm hijacks an alternative pathway to invade phagocytic cells through an immunoglobulin-independent interaction with FcγRIa . While studies presented here have elucidated many key aspects of the internalization process ( see below ) , several questions remain unanswered: ( 1 ) what is the nature of the IgG-independent interaction between Lm and FcγRIa resulting in Lm uptake by the host cells , ( 2 ) what is the cellular mechanism of FcγRIa-mediated Lm internalization; and , finally , ( 3 ) what are the consequences of this interaction for both pathogen and host in terms of pathogen proliferation and disease outcomes . In this study , we provide compelling evidence that Lm is internalized by FcγRIa independently of IgG opsonization ( Fig 8 ) . The most direct explanation for these findings is that Lm directly engages FcγRIa at the surface of immune cells . However , the identity of the bacterial surface protein involved in the interaction remains unclear . FcγRIa was unable to induce invasion of a non-pathogenic L . innocua ( Fig 8G ) , thus narrowing down the search for the ligand to a small subset of Lm-specific surface proteins [57] . Importantly , we have ruled out the involvement of the most well-characterized Lm invasion factors—internalins InlA and InlB ( Fig 6 ) , known to act individually or in concert to trigger Lm entry into a wide variety of non-phagocytic cells [43 , 65] . Other Lm-specific proteins previously implicated in the entry of Lm into target cells , virulence factor ActA , as well as less-characterized Vip , LapB , and Auto may be involved in FcγRIa interaction [43 , 66–68] . Both biochemical studies on candidate bacterial surface proteins as well as unbiased genetic screens will help determine if the FcγRIa ligand is a known invasion protein or a novel factor previously not implicated in Lm infection . Notably , a similar IgG-independent interaction has been demonstrated between FcγRIa and Escherichia coli K1 [69] . These bacteria have been shown to invade macrophages as a result of the interaction of the bacterial Outer membrane protein A ( OmpA ) with FcγRIa . It therefore appears that targeting IgG-independent functions of FcγRIa may be a general pathogenic strategy to evade immune clearance during systemic infection . While work presented in this study clearly indicates that FcγRIa facilitates entry of Lm into host cells , the cellular signaling mechanisms required for this process remain unknown . Since FcγRIa itself does not contain any known signaling motifs , the FcγRIa-mediated phagocytosis of IgG-coated particles requires receptor interaction with the ITAM-domain containing γ-chain , which in turn mediates downstream signaling , triggering cytoskeleton rearrangement and particle internalization [46 , 70] . Interaction of FcγRIa with the γ-chain occurs exclusively through the transmembrane domain of the receptor [52] . However , we found that GPI-anchored FcγRIa preserved its ability to internalize Lm in the absence of the transmembrane domain ( Fig 8B ) , indicating that both transmembrane and intracellular domains of FcγRIa were dispensable for this process . Thus , our data reveal the existence of an alternative non-canonical mechanism of FcγRIa internalization . It is currently unclear if FcγRIa-mediated uptake of Lm resembles the extensively characterized mechanism of Lm uptake by non-phagocytic cells through E-cadherin and c-Met receptors . Lm-induced clustering of these receptors leads to the recruitment of clathrin-mediated endocytosis machinery , actin cytoskeleton organization , and modulation of the phosphoinositide metabolism at the site of bacterial adhesion , resulting in the engulfment of the pathogen by zipper-like mechanism [71] . It will be of interest to define the involvement of actin , clathrin , and intracellular signaling pathways in the FcγRIa-mediated Lm entry . It is intriguing to speculate on the potential role of FcγRIa in Lm pathogenesis . We found that a small but reproducible percentage of THP-1 infection ( ~18% ) was dependent on cell surface expression of endogenous FcγRIa ( Fig 9 ) . Therefore , our data reveal the existence of at least two distinct pathways for Lm invasion including a canonical phagocytic pathway and a novel FcγRIa-mediated pathway described here . We hypothesize that Lm may have evolved surface molecules to engage the FcγRIa internalization pathway and bypass cell-mediated killing induced by other phagocytic routes of internalization . Consistent with this idea , Lm did not specifically engage the major phagocytic Fcγ receptor FcγRIIa involved in pathogen clearance in neutrophils and monocytes ( Fig 8C ) . In addition , previous studies have demonstrated fundamental differences in intracellular signaling pathways , receptor trafficking , antigen presentation , and kinetics of oxidative burst triggered by high-affinity IgG receptors ( FcγRIa ) compared to low affinity receptors ( FcγRIIa ) [72] . Thus , the ability of Lm to exploit the high affinity IgG receptor rather than being phagocytosed through the canonical opsonization pathway by FcγRIIa , may provide an opportunity for invaded Lm to produce phagosome rupture factors and escape into the cytoplasm . While this scenario has not yet been substantiated in vivo , the challenge for future studies will be to examine Lm internalization by FcγRIa in primary human cells revealing the role of FcγRIa in Lm pathogenesis . In conclusion , our flow cytometry based screening approach not only uncovered type I IFN stimulated suppressors of Lm infection but also revealed a novel Lm uptake pathway , which may play an important role in human Lm infection and disease pathogenesis . This work also opens up new experimental avenues to examine the role of IFNs , and potentially other immune modulatory transcriptional programs , in the pathogenesis of a wide range of bacterial species , including both intracellular bacteria that replicate in either vacuoles or cytoplasmic environment , and extracellular bacteria that may be affected by secreted ISGs .
Listeria monocytogenes 10403s constitutively expressing Green Fluorescent Protein ( GFP ) , L . monocytogenes DP-L2319 ( 10403s Δhly ΔplcA ΔplcB ) , and DP-L3078 ( 10403s ΔactA ) strains were a gift from Dan Portnoy ( UC Berkeley ) . L . monocytogenes 10403s ΔinlA ΔinlB , expressing GFP ( LM 131 ) was kindly provided by Manuel Amieva ( Stanford ) . To generate Lm 10403s Δhly ΔplcA ΔplcB and ΔactA pactA::GFP strains pPL2-GFP construct was chemically transformed into E . coli SM10 , followed by conjugation with the DP-2319 strain . GFPmut2 was PCR amplified from the genomic DNA of Listeria strain LM124 and then cloned downstream of the actA proximal promoter ( 200bp upstream ) in the pPL2 vector . pPL2 was used to integrate genes at the tRNAArg locus of the Listeria chromosome [73] . L . innocua strains DUP-104 [LCDC 81–861] and BAA-680 ( CLIP 11262 ) ( genome sequencing strain ) were obtained from ATCC . Additionally , Shigella flexneri strain M90T ( serotype 5 ) with pBBRMCS1-GFP plasmid and GFP-expressing Salmonella Typhimurium str . SL1344 expressing pBBR1MCS 6Y GFP were used . STAT1-deficient fibroblasts ( an SV40 large T antigen immortalized skin fibroblast line , kindly provided by Jean-Laurent Casanova , Rockefeller University ) were grown in RPMI Medium 1640 ( Gibco , Thermo Fisher Scientific ) , supplemented with 10% Fetal Bovine Serum ( FBS ) ( Gibco , Thermo Fisher Scientific ) and non-essential amino acids ( NEAA ) ( Gibco , Thermo Fisher Scientific ) . HEK293A ( Jack Dixon , UC San Diego ) , HEK293T ( Paul Bieniasz , Aaron Diamond AIDS Research Center ) , U-2 OS ( ATCC ) , and MEF ( Charles Rice , Rockefeller University ) cells were maintained in Dulbecco's Modified Eagle Medium ( DMEM ) ( Gibco , Thermo Fisher Scientific ) , supplemented with 10% FBS and NEAA . THP-1 cells ( ATCC ) were cultured in RPMI Medium 1640 , ATCC modification ( Gibco , Thermo Fisher Scientific ) , supplemented with 10% FBS and NEAA . cDNA for human FCGR1B , FCGR2B , FCGR3A , FCGR3B , FCER1A , FCER2A , FCER1G , FCAR1 were obtained from the Ultimate ORF Clones ( 96-well plate ) collection ( Life Technologies ) as Gateway-compatible pENTR clones . cDNA for human FCGR2A was a gift from Dr . Eric Hansen ( UTSW ) . These genes were amplified by PCR with primers encoding attB sites . Polymerase chain reaction ( PCR ) products were purified with the QIAquick PCR Purification Kit ( Qiagen ) and then recombined into a pDONR221 vector using BP Clonase II Enzyme mix ( Life Technologies ) . BP reactions were transformed into chemically competent DH5a Escherichia coli , and colonies verified by sequencing . Resulting pENTR clones were further recombined into a pTRIP . CMV . IVSb . ires . TagRFP Destination vector [17] using LR Clonase II Enzyme mix ( Life Technologies ) . LR reactions were transformed into DH5α cells and verified by sequencing . pLenti CMV Puro DEST ( w118-1 ) for generation of stable cell lines was a gift from Eric Campeau ( Addgene plasmid #17452 ) [74] . FLUC , FCGR1A , and FCER1G ( referred to as γ-chain ) were introduced using LR Clonase II Enzyme mix ( Life Technologies ) as described above . Point mutations and truncations were generated by PCR of the corresponding pENTR clones using a QuikChange II XL Site-Directed Mutagenesis Kit ( Agilent ) and primers designed according to manufacturer’s instructions . Glycosylphosphatidylinositol ( GPI ) anchored FcγRIa ( previously described in [53] was generated by overlap extension PCR , using FCGR1A and LFA3 , obtained from the Ultimate ORF Clones ( 96 well plate ) collection ( Life Technologies ) , as templates . Sheep ( NM_001139452 . 1 ) , rabbit ( XM_008264510 . 1 ) , and panda ( XM_011217915 . 1 ) FCGR1A cDNA were codon optimized for expression in human cells using Codon Optimization Tool ( Integrated DNA Technologies ) and synthesized as gBlocks Gene Fragments ( Integrated DNA Technologies ) with addition of attB sites . Mouse ( NM_010186 ) FCGR1A cDNA was synthesized as a pENTR clone ( GeneCopoeia , Inc ) . Genes were recombined into pDONR221 and subsequently into expression vector pTRIP . CMV . IVSb . ires . TagRFP Destination vectors as described above . pX335-U6-Chimeric_BB-CBh-hSpCas9n ( D10A ) was a gift from Feng Zhang ( Addgene plasmid # 42335 ) [75] . LentiCRISPR v2 was a gift from Feng Zhang ( Addgene plasmid # 52961 ) [58] . Lentiviral pseudoparticles were generated as previously described [17] . Lentiviral transduction was performed as previously described [17] . Briefly , cells were seeded in 24-well tissue culture plates at a density of 7x104 cells per well and transduced the following day with lentiviral pseudoparticles via spinoculation at 1 , 000 x g for 45 min in medium containing 3% FBS , 20mM HEPES and 4 μg/ml polybrene . 6 h after spinoculation , pseudoparticle-containing media was removed and replaced with full cell culture medium , containing 10% FBS and NEAA . For subsequent bacterial infection , cells were split 1:2 48h after transduction . For generation of stable expressing cell lines using pLenti CMV Puro DEST ( w118-1 ) , cells were transduced with the lentivirus and selected for puromycin resistance for 7 days 48h after transduction . Listeria monocytogenes was inoculated from a frozen stock and grown for 13 h at 30°C in brain–heart infusion media ( BHI ) ( Difco , BD Biosciences ) without shaking . 1 ml of bacteria was then washed in phosphate buffer saline ( PBS ) and resuspended in 1ml of PBS . A 1:10 dilution of the bacterial suspension was used to read the optical density at 600 nm ( OD600 ) . Bacteria were then added to each well of cells to achieve multiplicity of infection ( MOI ) of 10 , unless otherwise stated , and incubated for 90 min at 37°C , 5% CO2 ( unless otherwise noted ) . Culture media was then removed and replaced with media supplemented with 25 μg/ml gentamicin ( Quality Biological ) and cells incubated at 37°C , 5% CO2 for the indicated period of time . STAT1-deficient fibroblasts were infected with Lm for 6 h , HEK293A –for 4 h , MEF– 3 . 5 h , THP-1–6 h , unless otherwise stated in figure legend , U-2 OS–see specific figure legends . L . innocua infection was performed following a similar protocol with MOI of 10 , invasion was measured as described in “Measuring intracellular bacterial burden” ( see below ) 1 h following 1 . 5 h initial infection . For Lm infection of THP-1 cells , 8x104 cells were seeded per well in 96-well tissue culture plates in 10% FBS/RPMI or serum-free RPMI . 24 h later later Lm infection was performed as described above ( MOI = 5 ) . Following 1 . 5 h initial invasion time , gentamicin-containing media was added to the wells ( final concentration 30 μg/ml ) and infection was allowed to proceed for 6 h . Contribution of FcγRIa to Lm infection in each independent experiment was calculated using the following equation: [ ( percent infected wild type cells ) – ( percent infected FCGR1A-deficient cells ) / [ ( percent infected wild type cells ) ] x 100% . To visualize bacterial infection by epifluorescence microscopy , cells were washed once in PBS , fixed in 3 . 7% formaldehyde in PBS for 10 min at room temperature . Cells were then washed three times in PBS and incubated for 2 min in 4' , 6-diamidino-2-phenylindole ( DAPI ) solution . Shigella flexneri strain M90T was inoculated from a frozen stock and grown overnight at 30°C in BHI medium ( Difco , BD Biosciences ) . Bacteria were then back-diluted 1:50 and incubated at 37°C until reaching OD600 ≈ 0 . 5–0 . 6 . Bacteria were then washed in 1×PBS and incubated at 37°C for 15 min in 0 . 003% Congo red . Bacteria were added to each well to achieve MOI = 10 and centrifuged at 1000 x g for 10 min at room temperature to facilitate bacterial adherence . The plates were then incubated for 90 min at 37°C , 5% CO2 . The media was removed and replaced with media supplemented with 50 μg/ml gentamicin ( Quality Biological ) and cells incubated at 37°C , 5% CO2 for 4 . 5 h . Cells were washed once with PBS before collecting for flow cytometry analysis . Salmonella Typhimurium strain SL1344 was inoculated from a frozen stock and grown at 37°C in BHI ( Difco , BD Biosciences ) in a glass flask with high aeration overnight , then subcultured ( 1:30 ) and grown for 3 h at 37°C . 1 ml of bacterial suspension was then washed in PBS and resuspended in 1ml of PBS . 1:10 dilution of the bacterial suspension was used to read the optical density at 600 nm ( OD600 ) . Bacteria were added to each well to achieve MOI = 100 and incubated for 1 h at 37°C , 5% CO2 , washed three times with PBS and incubated at 37°C , 5% CO2 in medium supplemented with 100 μg/ml gentamicin ( Quality Biological ) and cells incubated at 37°C , 5% CO2 for 8 h . Cells were washed again with PBS before collecting for flow cytometry analysis . YFV-17D-Venus infection was performed as previously described [17] . For flow cytometry analysis , cells were detached from the tissue culture plate by incubating in 150μl of Accumax Cell Dissociation Solution ( Innovative Cell Technologies , Inc . ) for 5 min at 37°C , transferred to V-bottom 96-well plates , pelleted by centrifugation at 800 x g for 5 min , resuspended in 1% paraformaldehyde ( PFA ) and incubated at 4°C for at least 30 min . Fixed cells were then pelleted at 800 x g for 5 min and resuspended in 150μl of 1×PBS containing 3% FBS . Plates were stored at 4°C if flow cytometry was not carried out immediately . Samples were analyzed using a Stratedigm S1000 flow cytometer equipped with 405nm , 488nm and 561nm lasers . Data was analyzed using FlowJo Software ( Treestar ) . Following Lm or L . innocua infection , mammalian cells were washed three times with 1×PBS and then lysed by incubating in 0 . 5% Triton X-100 for 5 min at room temperature , followed by vigorous pipetting to complete the lysis . Intracellular bacterial burden was determined by plating serial dilutions of suspension on BHI-agar plates , incubating at 37°C , and counting bacterial colony forming units ( CFU ) the next day . Additionally , serial dilutions of bacterial culture used for infection were plated to obtain the inoculated CFU . Invasion was quantified by using the following equation: [CFU recovered per well/CFU inoculated per well] x 100% = invasion and normalized to control values , if needed . To detect surface expression of FcγRIa V450 Mouse anti-Human CD64 ( BD 561202 ) and V450 Mouse IgG1 , κ Isotype control ( BD 560373 ) antibodies were used according to the manufacturer’s protocol . Briefly , adherent cell ( 4x105 cells per well ) were washed once with PBS , detached from the surface by incubating in 150μl of Accumax Cell Dissociation Solution ( Innovative Cell Technologies , Inc . ) for 5 min at 37°C , transferred to V-bottom 96-well plates , pelleted by centrifugation at 300 x g for 5 min , washed once PBS and staining buffer ( 2% FBS in 1×PBS ) . Cells were then resuspended in 50μl of staining buffer and 2 . 5μl of fluorescently tagged antibody was added . Cells were incubated for 30 min at room temperature , in the dark . After incubation , cells were washed twice in staining buffer , resuspended in 150μl of staining buffer and analyzed immediately by flow cytometry . Cells were washed once with PBS and lysed using RIPA Lysis and Extraction Buffer ( Pierce , Thermo Fisher Scientific ) supplemented with Protease Inhibitor Cocktail ( Sigma ) . Total protein concentration was determined using the BCA Protein Assay Kit ( Pierce , Thermo Fisher Scientific ) . Proteins were separated on SDS-PAGE and transferred to 0 . 45 μm nitrocellulose membranes ( Biorad ) . Membranes were then blocked with 5% ( w/v ) skim milk ( Difco , BD ) in Tris-buffered saline with 0 . 1% Tween 20 ( TBST ) for 1 h at room temperature and immunoblotted with primary antibodies in TBST containing 5% nonfat milk at 4°C overnight , followed by incubation with appropriate secondary antibodies coupled to horseradish peroxidase ( HRP ) for 1 h at room temperature . Proteins were detected using ECL Western Blotting Substrate ( Pierce , Thermo Fisher Scientific ) . The following antibodies were used in this study: anti- MYD88 ( AF2928 , R&D Systems ) , anti-E-cadherin ( BD 610181 , BD Biosciences ) , anti-c-Met ( CST 4560 , Cell Signaling Technology ) , anti-actin ( a-2066 , Sigma Aldrich ) , goat anti-rabbit ( 31460 , Thermo Fisher Scientific ) , donkey anti-goat ( sc-2020 , Santa Cruz Biotech ) , goat anti-mouse ( 115-035-146 , Jackson ImmunoResearch ) . RNA was isolated from STAT1-deficient fibroblasts , ectopically expressing the gene of interest , using an RNeasy Mini Kit ( Qiagen ) according to the manufacturer’s instructions . For each condition , two independent replicates were prepared . Further procedures were performed at the UTSW Next Generation Sequencing Core ( McDermott Center ) . The quality of the total RNA samples was first confirmed on a 2100 Bioanalyzer ( Agilent ) using the total RNA 600 Nano Kit ( Agilent ) and amount of RNA quantified using the Qubit RNA Assay kit ( Life Technologies ) . 4μg of total RNA with an RNA Integrity Number ( RIN score ) above 8 , were further processed as described in TruSeq Stranded mRNA Sample Preparation Guide ( Illumina ) . Samples were fragmented at a lower temperature than recommended ( 80°C for 4 min instead of 94°C for 8 min ) to obtain 400-800bp libraries . Additionally , 12 PCR cycles were performed , instead of 15 cycles recommended by the protocol . Resulting libraries were analyzed on 2100 Bioanalyzer ( Agilent ) using DNA High Sensitivity Kit ( Agilent ) and quantified using Qubit . Sequencing was performed on Illumina Hiseq2500 with 100 bp paired end reads . Further procedures were performed at the UTSW Bioinformatics Core ( McDermott Center ) . Sequencing reads were trimmed to remove adaptor sequences and low quality bases using fastq-mcf ( v1 . 1 . 2–806 , https://expressionanalysis . github . io/ea-utils/ ) . Filtered reads were then mapped to human genome ( hg19 ) using Tophat ( v2 . 0 . 10 ) [76] , guided by igenome annotations ( https://ccb . jhu . edu/software/tophat/igenomes . shtml ) . Duplicate reads were marked but not removed . Expression abundance estimate and differential expression test were performed using Cufflinks/Cuffdiff ( v2 . 1 . 1 ) software [76] . Differential expression was considered as statistically significant when q-value was lower than 0 . 05 , fold change was greater than 2 , and FPKM value of at least one sample was greater than 0 . 01 . The upstream regulator analyses were generated through the use of QIAGEN’s Ingenuity Pathway Analysis ( IPA , QIAGEN Redwood City , www . qiagen . com/ingenuity ) . Guides targeting exon 3 of CDH1 and exon 3 of MET were designed using the Optimized CRISPR Design Tool ( http://crispr . mit . edu/ ) , and cloned into the pX335-U6-Chimeric_BB-CBh-hSpCas9n ( D10A ) vector as previously described [77] . For each guide pair , 4x105 HEK293A cells were seeded in a 6-well plate , and the following day were transfected with 1 μg of GFP-N3 , and 1 μg of the positive and negative guides , according to the FuGENE 6 ( Promega ) protocol . Approximately 48 h post transfection , fluorescence-activated cell sorting ( FACS ) was used to deposit single GFP-positive cells into 96-well plates . Approximately 2 weeks after sorting , colonies were transferred to 24-well plates in duplicate , and screened for reduced GFP-Lm infection . Whole cell lysates of putatively edited clones were prepared in RIPA buffer , and western blot against either E-cadherin ( BD 610181 ) or c-Met ( CST 4560 ) was carried out according to a standard protocol . Further , DNA from the samples with substantially lower infection than the wild type control and no detectable E-cadherin or c-Met as determined by western blot was extracted using the Quick Extract kit . PCR using Phusion High-Fidelity DNA Polymerase ( NEB ) was carried out genomic primers to genotype the indels for CDH1 and MET by cloning into the Zero Blunt cloning vector ( Life Technologies ) with subsequent Sanger sequencing at the UTSW Sequencing Core . CDH1 guides: 1: ATAGGCTGTCCTTTGTCGAC; 2: CTCGACACCCGATTCAAAGT MET guides: 1: GTATGCTCCACAATCACTTC; 2: GGCTACACACTGGTTATCAC Two guides targeting exon 3 of FCGR1A were designed using the Optimized CRISPR Design Tool ( http://crispr . mit . edu/ ) , and cloned into lentiCRISPR v2 vector as previously described [58] . Lentiviruses were generated as described above and used to transduce THP-1 cells . Lentivirally transduced cells were selected in 2 μg/ml puromycin for 7 days 48h after transduction . The absence of FcγRIa on the cell surface in the generated cell line was confirmed by antibody staining as described above . FCGR1A guides: 1: CTGGGAGCAGCTCTACACAG; 2: CACTGTGTAGAGCTGCTCCC . In vitro phagocytosis assay was performed as described previously [78] . U-2 OS cells , stably expressing Fluc or FcγRIa were first transduced with lentivirus coexpressing TagRFP and Fluc , FcγRIIa or FcεRIg ( γ-chain ) , to generate desired gene combinations . 48 h after transduction , cells were plated at 7x104 cells/ml in 4-well chamber slides ( Falcon ) . The day before the assay latex beads ( 3 . 87μm in diameter ) ( Bangs Laboratories , PS05N/6749 ) were opsonized with human IgG by washing a 10% slurry of beads in 1×PBS and mixing overnight with 1 . 5 mg/ml human IgG ( Jackson ImmunoResearch ) . The day of the experiment , beads were washed in 1×PBS and labeled with an Alexa Fluor 488 AffiniPure Donkey Anti-Human IgG ( H+L ) ( green ) antibody ( Jackson ImmunoResearch ) , while rotating at room temperature for 1h . Following secondary labeling , beads were washed , resuspended in DMEM and added to cells in chamber slides . Slides were centrifuged at 300 x g for 1 min , and then placed 37°C for 90 min . After the incubation , slides were placed on ice and washed with ice-cold medium to inhibit further phagocytosis . Extracellular beads were then labeled with DyLight 405 AffiniPure Donkey Anti-Human IgG ( H+L ) ( blue ) antibody ( Jackson ImmunoResearch ) for 10 min on ice . Cells were washed 5 times with ice-cold 1×PBS and fixed with 3 . 7% PFA for 20 min at room temperature . Next , cells were washed with 1×PBS and incubated with 100 mM glycine for 10 min at room temperature to quench PFA . All samples were washed twice with 1×PBS and chamber removed from the slide . When the excess liquid dried , the coverslips were mounted on the samples with ProLong Gold reagent ( Molecular Probes , Life Technologies ) . Samples were observed with a fluorescent microscope Zeiss Observer Z1 . Numbers of green and blue beads were counted for 80 Red Fluorescent Protein ( TagRFP ) -positive cells per sample , two technical replicates per gene . Phagocytosis efficiency was measured as a percentage of internalized beads , determined by subtracting the number of extracellular ( blue ) beads from the total ( green ) beads , divided by the number of total ( green ) beads . Cells were plated at 1 . 4x105 cells/well in a 12-well plate and transduced the next day with FcγRIa or Fluc-encoding lentiviruses as described above . Two days post-transduction , cells were infected with wild type Lm for 1 hr ( MOI = 0 . 015 , 0 . 05 , 0 . 1 ) , washed with medium , supplemented with 50 μg/ml gentamicin ( Quality Biological ) , and then gently overlaid with 1 . 5ml/well of DMEM , containing with 10% FBS , 0 . 4% agarose , and 20 μg/ml gentamicin ( Quality Biological ) . The overlay was allowed to stabilize for 15 min at room temperature , when plates were moved back to an incubator at 37°C . Foci of Lm infection were visualized 30 h after initial infection by adding 200μl of 5mg/ml ( 3- ( 4 , 5-dimethylthiazolyl-2 ) -2 , 5-diphenyltetrazolium bromide ( tetrazolium MTT ) ( Sigma ) solution to each well and incubating at 37°C for 3 h . Plates were scanned and foci of infection quantified using ImageJ software . Cells were plated at 1 . 4x105 cells/well in a 12-well plate and transduced the next day with lentiviruses as described above . Two days after transduction cells were split 1:2 on circular glass coverslips in 12-well plates , and the next day infected with Lm , according to the standard protocol . After infection samples were fixed in 2 . 5% glutaraldehyde in 0 . 1 M cacodylate buffer for a minimum of 2 h . Further procedures were performed at the UTSW Electron Microscopy Facility . Fixed cells were rinsed in the fixation buffer and fixed with Osmium tetroxide as secondary fixative . After several water rinses they were dehydrated in serial concentrations ( 50% , 70% , 85% , 95% , 100% ) , and critical point dried . The samples were coated for 30s with gold palladium and viewed in the Zeiss Sigma VP FE scanning electron microscope . Images were acquired using the Secondary Electron 2 ( SE2 ) detector . STAT1-deficient fibroblasts were seeded into 48-well plates at a density of 2 . 5x104 per well and transduced the following day with lentivirus expressing the gene of interest . 24 h later cells were transfected with 200 ng of the reporter plasmid pNF-kB–luciferase and 150 ng normalization vector pLacZ ( to correct for transfection efficiency using a beta-galactosidase assay ) . 24 h after transfection , cells were lysed and luciferase was measured according to manufacturer protocol ( Luciferase Assay System , Promega ) . LacZ expression was measured in a β-Galactosidase Activity Assay with ortho-Nitrophenyl-β-galactoside ( ONPG ) , and used to normalize luciferase values for each sample . All experiments were performed in as three independent replicates , unless otherwise stated . For experiments where only two groups of samples were compared , unpaired t-test was used to determine if difference between groups was statistically significant . To determine statistical significance in experiments with three or more groups of samples , one-way analysis of variance ( ANOVA ) with Dunnett’s procedure for multiple comparisons was used . Data analysis was performed in GraphPad Prism software . | While the type I interferon response is known to be activated by both viruses and bacteria , it has mostly been characterized in terms of its antiviral properties . Listeria monocytogenes , an opportunistic Gram-positive bacterial pathogen with up to 50% mortality rate and a variety of clinical manifestations , is a potent activator of interferon secretion . In mouse models , interferon has been previously implicated in both restricting and promoting L . monocytogenes infection . Here , we utilized a high-throughput flow-cytometry based approach to screen a library of human interferon I stimulated genes ( ISGs ) and identified regulators of L . monocytogenes infection . These include inhibitors that act through both transcriptional ( MYD88 ) and transcription-independent ( TRIM14 ) mechanisms . Strikingly , expression of the human high affinity immunoglobulin receptor FcγRIa ( CD64 ) was found to potently enhance L . monocytogenes infection . Both biochemical and cellular studies indicate that FcγRIa increases primary invasion of L . monocytogenes through a previously uncharacterized IgG-independent internalization mechanism . Together , these studies provide an important insight into the complex role of interferon response in bacterial virulence and host defense . | [
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"gen... | 2016 | Cell-Based Screen Identifies Human Interferon-Stimulated Regulators of Listeria monocytogenes Infection |
The human cytosolic sulfotransfases ( hSULTs ) comprise a family of 12 phase II enzymes involved in the metabolism of drugs and hormones , the bioactivation of carcinogens , and the detoxification of xenobiotics . Knowledge of the structural and mechanistic basis of substrate specificity and activity is crucial for understanding steroid and hormone metabolism , drug sensitivity , pharmacogenomics , and response to environmental toxins . We have determined the crystal structures of five hSULTs for which structural information was lacking , and screened nine of the 12 hSULTs for binding and activity toward a panel of potential substrates and inhibitors , revealing unique “chemical fingerprints” for each protein . The family-wide analysis of the screening and structural data provides a comprehensive , high-level view of the determinants of substrate binding , the mechanisms of inhibition by substrates and environmental toxins , and the functions of the orphan family members SULT1C3 and SULT4A1 . Evidence is provided for structural “priming” of the enzyme active site by cofactor binding , which influences the spectrum of small molecules that can bind to each enzyme . The data help explain substrate promiscuity in this family and , at the same time , reveal new similarities between hSULT family members that were previously unrecognized by sequence or structure comparison alone .
Cytosolic sulfotransferases ( SULTs ) comprise a family of enzymes that catalyze the transfer of a sulfonate group from 3′-phosphoadenosine 5′-phosphosulfate ( PAPS ) to an acceptor group of the substrate ( Figure 1 ) . In doing so , SULTs modulate the activities of a large array of small endogenous and foreign chemicals , including drugs , toxic compounds , steroid hormones , and neurotransmitters . Because sulfonated molecules are highly soluble in water and easily excreted from the organism , SULTs are often referred to as enzymes of chemical defence . In some cases , however , SULTs activate certain compounds from food and the environment into mutagenic and carcinogenic metabolites [1] . To date , 13 human cytosolic sulfotransferase ( hSULT ) genes have been identified; they partition into four families [2 , 3]: SULT1 , SULT2 , SULT4 , and SULT6 . Although the family members share considerable sequence and structural similarity , they appear to have different biological functions . The SULT1 family comprises nine members divided into four subfamilies ( 1A1 , 1A2 , 1A3 , and 1A4; 1C1 , 1C2 , and 1C3; 1B1; and 1E1 ) . The SULT1A3 and SULT1A4 genes appear to have arisen from a segmental duplication and encode the same protein [4] . Members of the SULT1 family have been shown to sulfonate simple phenols , estradiol , and thyroid hormones , as well as environmental xenobiotics and drugs . The SULT2 family has two genes , encoding three proteins ( SULT2A1 , SULT2B1a , and SULT2B1b ) , which catalyze sulfonation of hydroxyl groups of steroids , such as androsterone , allopregnanolone , and dehydroepiandrosterone ( DHEA ) . SULT4A1 is the only member of the SULT4 family . The fact that it is highly conserved and expressed primarily in the brain suggests an important function; however , no activity or function has been identified for this gene [5] . Finally the SULT6B1 gene is expressed in the testis of primates , but neither the protein nor its enzymatic activity has been characterized [3] . Recent progress in the structural biology and characterization of the catalytic mechanism of hSULTs has established that many family members have distinct , but overlapping , substrate specificities and that the enzymes have a sequential catalytic mechanism that is susceptible to substrate inhibition [6 , 7] . Nevertheless , only a few of the human enzymes have been subjected to detailed structural and mechanistic studies [6 , 8–16] , and there are no reports of a systematic comparison among all the hSULTs . Understanding the structural and mechanistic basis for specificity among hSULTs is essential to elucidate their role in the metabolism of regulatory hormones , drugs , and carcinogens , and may assist in chemical risk assessment and the design of more-effective therapeutics . Here we report the crystal structures of five of the 12 structurally unique hSULTs . These structures , combined with those previously reported for six other hSULTs , allowed a comprehensive comparison of both global and local structural features . We further screened nine hSULTs for binding activity toward a set of 90 potential substrates and inhibitors , and eight hSULTs for enzymatic activity toward 31 potential substrates in order to better understand the relationship between binding specificity , activity , and structure within the hSULT family . These data , combined with detailed structural analysis of substrate binding sites , reveal relationships between family members not previously apparent from sequence analysis . “Chemical fingerprints” of the spectrum of small molecules that bind in the presence and absence of the cofactor product , 3′-phosphoadenosine 5′-phosphate ( PAP ) , demonstrate a marked change in the small molecule binding profile upon PAP binding . This result , combined with the structural data , suggests PAPS has a strong influence on which compounds may bind in the substrate binding site and raises the possibility that the enzymes might be inhibited by chemically related compounds that are not productive substrates . The binding studies also provide insight into potential functions of the under-characterized SULT1C subfamily and of SULT4A1 , an orphan member of the SULT family expressed primarily in the brain .
The crystal structures of SULT1C3 bound to PAP , apo SULT1C2 , a ternary complex of SULT1C2 bound to PAP , and the environmental toxin , pentachlorophenol ( PCP ) , and SULT4A1 were solved at 3 . 2 , 2 . 0 , 1 . 8 , and 2 . 2 Å , respectively ( Figure S1 and Table S1 ) . We also recently reported the structures of SULT1B1 and SULT1C1 bound to PAP at 2 . 1 and 1 . 8 Å , respectively [17] . The structures of a single subunit of each of these normally dimeric proteins are presented in Figure 2 along with a representative structure of each of five other SULT family members previously reported in the literature [8 , 10–15] . Six additional SULT structures , which are available in the Protein Data Bank , are presented in Figure S2 . As expected , all SULTs share the same basic fold: a central four-stranded parallel β-sheet surrounded by α-helices and three loops that are often disordered ( dashed lines ) in the absence of PAP and/or substrate . These disordered segments comprise a 13-residue loop ( shown in gold ) , a 4~10 residue loop ( cyan ) , and a large 32~46 residue loop ( green and magenta ) . These loops have been mapped onto the aligned protein sequences in Figure 3 using the same colouring scheme . The degree of disorder and the exact conformation of these loops vary considerably across the family , but in general , the presence of ligands ( cofactor and/or substrate ) is coupled with increased order , namely , the formation of helices α4-α5 ( gold ) , and α14-α15 ( green ) . In some cases , partial stabilization can be attributed to molecular packing in the crystal , as in , for example , the stabilization of α14 ( green ) in apo SULT1C2 . The binding site for PAP or PAPS ( PAP ( S ) ) is nearly identical in all structures bound to these ligands , with highly conserved residues contributing to the binding pocket ( highlighted in red in Figures 2 and 3 ) . It is interesting to note that the SULT6B1 sequence in the protein databases lacks the N-terminal region , which encodes a β-sheet thought to be an important structural component of the SULT fold ( Figure 3 ) . We note that the recombinant SULT6B1 did not express in our attempts to purify it from bacteria . It is generally agreed that sulfonation takes place via a sequential mechanism in which a ternary enzyme complex is first formed , followed by reaction and release of products [7] . However , both random and ordered binding of the substrate and cofactor molecules have been reported , and the detailed kinetic mechanism ( or mechanisms ) of the sulfonate transfer reaction is the subject of continuing research ( reviewed in [7] ) . Comparison of all the available structural data provides insight into the order of substrate and cofactor binding . The structures provide evidence for both binary complexes ( enzyme/substrate and enzyme/cofactor ) consistent with a random bi-bi mechanism and ruling out an ordered mechanism in which binding of substrate requires binding of cofactor ( or vice versa ) . This is in agreement with a detailed kinetic analysis for SULT1E1 [18] . However , a closer inspection of the structures also suggests that binding of substrates may not be completely uncoupled from binding of the cofactor . In all the structures with the co-factor product , PAP , α14-α15 , and the C-terminal segment of the largest flexible loop ( green in Figure 2 ) are ordered . This region contributes three absolutely conserved residues necessary for PAPS binding , T228 , R258 , and G260 ( SULT1A1 numbering and red in Figure 2 ) . Importantly , although the other loops ( cyan , gold , and magenta ) do not contribute directly to PAPS binding , they are more likely to be partially ordered in the presence of PAP ( S ) . The PAP ( S ) -induced ordering of α14-α15 and residues 256–262 ( green and red ) may also restrict the conformations available to the intervening substrate-binding magenta loop when PAP is bound . Thus , the structural data suggest that PAPS binding tends to prime the cyan , gold , and magenta loops for binding to the substrate . On the other hand , the structure of SULT2A1 bound to androsterone [13] ( Figure 2L ) hints that binding of substrates does not prime the PAPS binding loops . In this structure , the substrate-binding cyan and gold loops are ordered , but the magenta loop and adjacent PAPS binding residues ( green and red portion of the loop ) are disordered . Thus , although in this case , substrate and PAP ( S ) molecules can each bind independently to the enzyme as in a random bi-bi mechanism , there may be some degree of cooperativity between substrate binding and prior cofactor binding , but not vice versa . Given that the estimated cellular concentration of PAPS is well above that of most substrates , this may be relevant to the catalytic mechanism . The family-wide structural comparison also suggests an additional or alternative explanation for the well-documented substrate inhibitory effect . Previously reported cases of substrate inhibition have been attributed either to two substrate molecules occupying the active site at the same time [8 , 19] or to the ability of substrates to bind in unproductive orientations at higher concentrations [6 , 14] . Examination of the structures in Figure 2 suggests a third or alternative mechanism; at high concentrations , substrates may bind in a mode in which the binding loops are incompatible with PAPS binding . This case is exemplified by the structure of SULT2A1 with DHEA [14] . As shown in Figure 2J , this structure can accommodate two substrate orientations at roughly 30° to one another . Comparison with other hSULT structures strongly suggests that SULT2A1 in this structure adopts a non-productive conformation . A portion of the green-and-magenta loop that contributes two residues for PAP ( S ) binding is folded into a helix , orienting the crucial PAPS binding residues away from the cofactor binding pocket . This helix conformation is not an intrinsic feature of SULT2A1 , because in the SULT2A1–PAP complex , this region adopts a conformation similar to that in other SULT–PAP structures ( compare Figure 2K and 2J with this region highlighted by a question mark ) . Thus , it appears that the structure adopted by SULT2A1 with two molecules of DHEA is incompatible with PAP ( S ) binding and that this conformation is induced by the substrate . This is further evidence of “communication” between the substrate binding site and the PAPS binding site . In order to predict and understand the fate of xenobiotics and drug candidates in humans , it is essential to better understand the selectivity and specificity of binding and activity within the hSULT family . Although detailed analyses of individual structures have been very informative in this regard [6 , 8–12 , 16] , we sought to compare all active sites relative to the spectrum of small molecules that can bind to each site . However , several of the proteins whose structures were solved in this study have not been previously characterized , and it was difficult to directly compare data from the literature due to differences in experimental conditions . Therefore , in order to evaluate specificity and selectivity in a consistent manner , nine purified , recombinant hSULTs were screened for binding to a library of 90 small molecules ( Table S2 ) that comprised known substrates , inhibitors , related hormones , bioamines , and drugs [20 , 21] . In order to profile the entire hSULT family , we made use of the well-known fact that equilibrium binding of a ligand increases the thermal stability of a protein in a manner proportional to the concentration and binding affinity of the ligand [20 , 21] . In a multi-well format , the thermal stability of each hSULT was monitored as a function of temperature and in the presence or absence of compounds ( Figure 4 ) . In the absence of compounds , well-behaved , sigmoidal thermo-denaturation/aggregation profiles were obtained for hSULTs 1A1 , 1A3 , 1B1 , 1E1 , 1C1 , 1C2 , 1C3 , 2A1 , and 4A1 . SULT2B1b did not denature within the range of temperature used for this type of analysis ( up to 80 °C ) . In this screening format , compounds that stabilize a protein by more than 2° C are scored as positives ( Table 1 ) . It was not possible to assay binding to ligands in the presence of the sulfonated co-factor , PAPS , because the sulfonate transfer reaction would have taken place . However , except for SULT4A1 , PAPS and PAP had equivalent stabilizing affects on all hSULTs . Thus , PAP was used as a substitute for PAPS in considering the effect of cofactor upon substrate binding , and screens for the binding of ligands were performed in the absence and presence of a saturating amount of PAP . Based on these binding results , a set of 20 compounds that bound to at least one hSULT plus 11 additional related compounds or known substrates were used as a pool of potential substrates for enzymatic activity of hSULTs 1A1 , 1A3 , 1B1 , 1C1 , 1C2 , 1C3 , 1E1 , and 2A1 ( Table 2 ) . We monitored the conversion of PAPS to PAP by high-performance liquid chromatography ( HPLC ) as a tractable method of screening multiple proteins against multiple substrates ( eight proteins and 31 substrates in this study ) , and the results serve as a convenient first approximation of enzymatic activities . We note that due to the relatively low sensitivity of this method , we were not able to reliably assay substrates at nanomolar concentrations , and therefore , some of the results may be complicated by substrate-mediated inhibition . Indeed , inspection of Table 2 shows that in some cases , the highest levels of activity were observed at lower substrate concentrations . This is especially true of SULT1A1 , an enzyme for which significant substrate inhibition has been noted previously [8] . The combined ligand binding and activity screens revealed a unique “chemical fingerprint” for each hSULT ( Tables 1 and 2 ) . First , as expected from previous studies , there was considerable overlap in the substrate specificity for enzymatic activity . For example , all hSULTs assayed here were able to sulfonate a number of phenolic compounds such as naphthols and/or alkylphenols . However , within each substrate profile , there were also elements of specificity . For example , SULT1A1 and SULT1A3 were the only two hSULTs that showed significant activity toward catecholamines compared to other substrates , with SULT1A3 being more specific for dopamine , as expected from previous studies [22–26] . SULT1A3 was also the only protein to bind dopamine in the binding assays , consistent with its designation as human dopamine sulfotransferase [25] . It is interesting to note that in the past , SULT1A1 and 1A3 have been distinguished from one another in tissue fractions by the higher sensitivity of SULT1A1 to inhibition by 2 , 6-dichloro-4-nitrophenol ( DCNP ) [27] . Although we did not measure inhibition by this compound , we note that SULT1A1 bound DCNP in the presence of PAP , whereas SULT1A3 did not ( Table 1 ) . Six hSULTs ( 1C1 , 1C2 , 1E1 , 1B1 , 1A1 , and 1A3 ) had enzymatic activity toward resveratrol , a polyphenolic compound present in grapes and wine , with possible anticancer and cardioprotective activities [28] . The activity profiles for resveratrol also displayed evidence of substrate inhibition by this compound for SULTs 1C1 , 1C2 , 1A1 , and 1E1 . Acetaminophen was a substrate for SULTs 1A1 , 1E1 , 1A3 , 1C2 , and 1B1 , but not SULT1C3 and SULT2A1 . Substrates for SULT1C3 have not been reported previously . Our data indicate that this recently identified member of the hSULT family is able to sulfonate p-nitrophenol , 1-naphthol , 2-ethylphenol , 2-n-propylphenol , and 2-sec-butylphenol , as well as the steroid-related compounds , α-zearalenol and lithocholic acid . SULT1C3 appeared to be most active with α-zearalenol ( 4 . 1 nmol/min/mg ) and 2-ethylphenol ( 2 . 2 nmol/min/mg ) . These data suggest SULT1C3 may contribute to the metabolism of steroid and phenolic compounds . Finally , SULTs 2A1 and 1E1 , which are reported to metabolize steroids [29–32] , both bound to and sulfonated multiple steroid and steroid-like compounds with different apparent specificities . These data show that despite the limitations of our rapid screening method , the enzymatic activity data in Table 2 reflects , to a first approximation , the expected relative substrate activities reported in the literature and reveal new activities toward pharmacologically important compounds . The binding data , on the other hand , suggest a more complicated situation . The ligand binding profiles were remarkably different in the presence and absence of PAP ( with the exception of SULT4A1 , which will be discussed separately below ) . Some known substrates for the well-characterized SULTs appeared to bind only in the presence of PAP—for example , dopamine for SULT1A3 [24 , 25] , and 1-naphthol for SULT1B1 [33]—whereas many previously unreported compounds bound to these and other family members in the absence of PAP . It is interesting to note that not all known substrates , nor all of those with reactivity in our activity screens , were found to bind to the enzyme in the presence of PAP . For example , resveratrol was a substrate for SULT1A3 and SULT1C2 , but did not stabilize either protein in the binding assays , either in the presence or absence of PAP . There are several reasons that may account for these observations . First , the enzymatic screen is likely more sensitive than the ligand binding assay in which compounds with Km or Kd values in the high micromolar range are less likely to be detected [20 , 21] . Second , some ternary complexes may not be significantly stabilized relative to the PAP–enzyme complex ( especially at elevated temperatures ) . Finally , the presence of the sulfonate group of PAPS may also contribute to binding of substrates , and these cases may not be detected in our binding assay . Interestingly , binding of SULT1C1 , 1B1 , and 1E1 to resveratrol was only observed in the absence of PAP , but all are active toward this substrate . Nevertheless , the radically different binding profiles observed in the presence and absence of PAP are consistent with the structure-based mechanisms proposed above . Specifically , PAP ( and presumably PAPS ) appears to prime the substrate binding loops for subsequent binding to certain substrates , whereas in the absence of cofactor , the loops are free to bind alternative ligands ( perhaps only at high concentrations ) , or non-productive ligand-bound conformations may exist . This priming of the substrate binding loops is likely made possible by flexibility of the binding loops observed in the structure . This structural plasticity may allow a reconfiguration of substrate binding loops in the absence of PAP ( S ) in order to bind a different chemical class of compound . For example , both SULT1C3 and SULT1B1 were stabilized by catecholamines in the absence of PAP , but neither showed significant activity toward this class of compounds . This raises the possibility that certain endogenous and/or exogenous compounds , such as those that bind in the absence of PAP , may act as competitive inhibitors of SULTs by occupying the substrate binding pocket and preventing a productive PAPS binding conformation , as for SULT2A1 ( Figure 2J ) . Excluding SULT4A1 , three compounds bound to all hSULTs: adenosine 5′- ( β , γ-imido ) triphosphate ( AMP-PNP ) , a non-hydrolysable ATP analog; pyridoxal 5-phosphate ( PLP ) , a competitive inhibitor for sulfotransferases [34]; and quercetin , a potent inhibitor of SULT1A1 and SULT1E1 [35] . These compounds had been known to inhibit one or more sulfotransferases , but our data suggest that they may be universal SULT inhibitors . AMP-PNP and PLP bound only in the absence of saturating amounts of PAP , suggesting that they occupy the PAP binding site , as might be expected from their structural similarity to PAP . Quercetin , found in many fresh fruits and vegetables , is a flavonoid with anti-tumour and anti-inflammatory activities . It is possible that some of its favourable physiological effects may be related to inhibition of hSULT activity . Additional inhibitors bound to only a subset of the hSULTs , including 3 , 5-dibromo-4-hydroxy-benzoic acid ( 6 , 8-dichloro-4-oxo-4H-chromen-3-ylmethylene ) hydrazide ( DBHD ) , 3 , 5-dibromo-4-hydroxy-benzoic acid ( 6-chloro-4-oxo-4H-chromen-3-ylmethylene ) -hydrazide ( DBHM ) , and PCP . The binding profiles in Table 1 raise the possibility that compounds that bind to hSULTs only in the absence of PAP may inhibit hSULT activity . In order to investigate this possibility , we assayed the activity of SULT1B1 in the presence of increasing concentrations of five compounds found in our screens . These five include known inhibitors ( quercetin , DBHD , PLP , and PCP ) , as well as isoprenaline , which binds to SULT1B1 only in the absence of PAP and is a poor substrate for this enzyme ( Tables 1 and 2 ) . As shown in Figure 5 , PCP and DBHD were strong inhibitors of SULT1B1 , whereas PLP and quercetin had intermediate effects . Isoprenaline , however , had no inhibitory effect on the activity of SULT1B1 with 1-naphthol , and it was sulfonated by SULT1B1 ( Table 2 ) , indicating that not all compounds that bind in the absence of PAP are necessarily inhibitors . PCP is a significant environmental toxin due to its common use as a wood preservative and its use in the pulp and paper industry . Its chemical structure is related to hydroxylated metabolites of polychlorinated biphenols ( OH-PCBs ) whose endocrine-disruptive properties may be related to the inhibition of estradiol sulfonation by SULT1E1 [36] . The mechanism of inhibition of SULT1E1 by OH-PCBs and related compounds has been proposed to take place via both allosteric [36 , 37] and competitive [12 , 37] mechanisms . Our binding data suggest that PCP may be a competitive inhibitor of SULTs 1B1 , 1C2 , and 1A1 ( Table 1 ) . For 1C2 and 1A1 , PAP was required for binding; and for 1B1 , PCP binds much better in the presence of PAP , suggesting that PCP binds in a substrate-like conformation facilitated by PAP ( S ) . In order to better understand the mechanism of inhibition by PCP , we determined the structure of SULT1C2 bound to PAP and PCP ( Figure S3 ) . The structure reveals that the protein undergoes a disorder–order transition upon PCP and PAP binding . Helices α4 , α5 , and αı5 , and loops α5-α6 and α15-α16 are ordered only in the ternary complex , but not in the apo SULT1C2 structure ( Figure 2B and 2H ) . PCP is found in the substrate binding pocket and therefore appears to be a competitive inhibitor , consistent with crystallographic analysis of SULT1E1 bound to PAP and 3 , 5 , 3′ , 5′-tetrachlorobiphenol [12] . Comparison of the SULT1C2-PAP-PCP and SULT1E1-PAP-estradiol structures ( Figure 6 ) revealed two structural features that may be relevant in explaining the mechanism of PCP inhibition . In the co-crystal structures , the phenol moieties of PCP and estradiol share the same relative position and orientation , positioning the phenolic OH within hydrogen-bond distance of the catalytic histidine . This histidine is thought to be deprotonated and made catalytically competent to accept the phenolic hydrogen from estradiol , facilitating nucleophilic attack on the sulfonate of PAPS . Our structure shows that PCP appears to be in a catalytically competent conformation . However , PCP and estradiol differ dramatically in the acidity of their hydroxyl groups; the estimated pKa for estradiol is approximately 15 , whereas that for PCP is 4 . 5 . Thus , although PCP appears to bind in a catalytically competent conformation , the phenolic oxygen of PCP may be too weak a nucleophile to attack the sulfonate of PAPS . The inhibitory effects of OH-PCBs and PCP have been interpreted previously in terms of variations in the bound conformation of the halogenated compounds relative to that of estradiol [12 , 36] . Although steric and conformational factors clearly play a role , our structure of SULT1C2 with PCP and PAP suggests a key role for the electronic nature of the halogenated phenols . We examined the calculated pKa values for the series of 21 4-hydroxyl–substituted PCBs for which Kester et al . [36] reported 50% inhibitory concentration ( IC50 ) values . The results show a strong correlation between the calculated acidity of these phenols with IC50 values , with the most acidic compounds having the strongest inhibitory effect ( Figure S4 ) . Under the conditions of our screens , PAP bound to all hSULTs except for SULT4A1 . In order to rule out the possibility that SULT4A1 simply has a much weaker affinity for PAP , we performed titration experiments for several of the proteins with increasing concentrations of PAP ranging from 90 μM to 90 mM ( Figure 7A ) . SULT1C1 , SULT1C2 , SULT1C3 , and SULT1B1 showed similar saturation binding curves , reaching saturation at about 100 μM . However , PAP when added at concentrations as high as 90 mM did not stabilize SULT4A1 . SULT4A1 is one of the hSULTs that is most divergent in sequence , and examination of the binding pocket revealed two significant differences that are predicted to affect PAP binding . First , a Trp in α3 that is conserved in all other hSULTs and stacks with the adenine ring of PAPS ( Trp53 in SULT1A1 ) is replaced with a Leu in SULT4A1 ( Figure 3 ) . Second , the magenta PAP binding loop is much shorter in SULT4A1 than in the other hSULTs , and lacks the conserved Lys residue that separates the key PAPS binding residues Arg258 and Gly260 ( SULT1A1 numbering ) , which results in these residues being out of register . Taken together , SULT4A1 has a slightly smaller PAPS binding pocket that is predicted to be unable to accommodate the cofactor . Interestingly , some residual electron density was observed in the PAP binding pocket of recombinant SULT4A1 . Presumably this derives from a bound small molecule that was co-crystallized , although we were not able to identify it either by modelling atoms into the electron density or using mass spectrometry . To test the possibility that SULT4A1 might use an alternate sulfonate donor , we tested several potential alternates such as adenosine phosphosulfate , 4-nitrocatechol sulfate , 4-acetylphenyl sulfate , estrone 3-sulfate , indoxyl sulfate , and 4-methylumbelliferyl sulfate . None of these compounds stabilized SULT4A1 against thermal aggregation ( unpublished data ) . These data strongly suggest that SULT4A1 may not have significant catalytic activity in vivo . Indeed , although very weak activity has been reported in one case [38] , other groups have failed to observe activity for human SULT4A1 [5 , 39] . Significantly , this protein , which is expressed primarily in the brain , binds to 2-hydroxylestradiol , thyroid hormone , T4 ( 3 , 3′ , 5 , 5′-tetraiodo-L-thyronine ) , and the catecholamines norepinephrine , epinephrine , and isoprenaline ( but not dopamine ) , suggesting that SULT4A1 may modulate the bioactivity of these compounds via a mechanism distinct from sulfonation . Of note , SULT4A1 did not bind any simple phenolic compounds under the conditions tested here . Examination of the chemical fingerprints reflected in Tables 1 and 2 suggests that subsets of the hSULTs can be clustered based on the chemical properties of the compounds that they bind—relationships that are not evident from global sequence comparison . To explore alternative activity or structure-based classifications of hSULTs in more detail , we performed average hierarchical clustering on the experimental data in an attempt to identify correlations between the local sequence or structural features of the substrate binding pockets and activity profiles among the hSULTs . Figure 8 shows the clustering of similarity matrices for each parameter , viewed using trees . Considering only global sequence similarity , the hSULTs cluster according to their nomenclature and phylogenetic relationships ( Figure 8A ) . Considering only the nine proteins for which we have binding or enzymatic data , SULTs 1A1 and 1A3 are most closely related , with a global sequence identity of 95% . The three SULT1C proteins cluster with average sequence identities close to 55% , as do SULT1E1 and 1B1 . SULT2 , −4 , and −6 subfamilies are relative outliers with sequence identities to all other SULTs considered here around 35% or less . It is well known that related enzymes with sequence identities below 40% have often evolved to have different substrate specificities [40] , and therefore , we would expect most of the SULTs to sulfonate different substrates , except perhaps 1A1 and 1A3 . In keeping with this concept , the closely related SULTs 1A1 and 1A3 are clearly the most similar within the substrate binding site , as measured by both local sequence and structure comparisons ( Figure 8B and 8C ) . However , the clustering of the other more distantly related SULTs is sufficiently different at the level of local sequence and structure , such that the SULT1C proteins are no longer clustered together and the outliers are different . These comparisons show that the local sequence and structures of the substrate binding sites do not correspond to the global sequence relationships . These results also illustrate why members of the same subfamily do not metabolize the same classes of compounds . Although the clustering of hSULTs presented here is likely influenced by the limited subset of compounds used in our binding and activity assays , the results provide an initial view of the family-wide activity-based classifications . The trees that cluster the eight or nine proteins according to their binding and activity profiles ( Figure 8D–8F ) show a remarkably different clustering from the sequence- and structure-based trees . First , 1A3 and 1A1 no longer cluster together , despite their strong global sequence similarity ( 95% identity ) . Inspection of Table 1 shows that 1A1 strongly binds most of the phenols and acidic compounds , once PAP is bound , whereas 1A3 shows absolutely no binding of these substrates . These SULTs are also distinguished by their differential reactivity toward catecholamines , with SULT1A3 able to bind dopamine and having higher relative activity toward catecholamines compared to phenols , as previously noted [28] . Comparison of the residues in the substrate binding loops of SULT1A1 and SULT1A3 revealed that all residues are identical ( and in identical positions in the structures ) except for the eight residues shown in Figure 9 . These changes map to two loops ( residues 84–89 and 143–148 ) and residue 247 ( SULT1A1 numbering ) . Importantly , SULT1A3 , which does not bind acidic compounds , has acidic instead of hydrophobic residues at three of these positions . The net result is a much more negatively charged pocket for SULT1A3 compared to SULT1A1 . This difference would disfavour binding of compounds with a net negative charge to SULT1A3 and favour interactions with the amino group of catecholamines . Thus , the strong local sequence and structure similarities in 1A1 and 1A3 are manifested in their similar ability to bind similar inhibitors and sulfonate catecholamines ( as a class ) , but small , local sequence changes in the substrate binding site have enhanced the ability of SULT1A3 to bind catecholamines such as dopamine [28] , and completely changed its ability to bind acidic compounds . The influence of residue 146 on the specificity of SULT1A1 compared to SULT1A3 ( Ala vs . Glu ) has been previously noted [28]; however , the results presented here suggest that additional differences in the binding loops also contribute to specificity . SULT1B1 and 1C2 cluster together in the trees of local structure , activity , and binding in the presence of PAP . These groupings reflect their common ability to bind acidic compounds ( and the acidic phenols PCP and 2 , 6-dichloro-4-nitrophenol ) , with promiscuous activity profiles toward phenols . Thus , SULT1B1 and 1C2 appear to be much more closely related in terms of structure and activity than their global sequences would indicate . Finally , Figure 8F shows that the clustering of hSULTs again differs when considering the binding of compounds in the absence of PAP . In this case , none of the previously noted similarities is evident . Many of the compounds in Table 1 are inhibitors of hSULT activity . The differential clustering in the absence of PAP may reflect possible unrelated configurations of binding-site residues when bound to these compounds or the tendency for more disorder in the absence of PAP . The clustering of both binding profiles ( Figure 8E and 8F ) places SULT4A1 as the furthest outlier , analogous to its position in the global sequence comparison . Although this is consistent with the inability of SULT4A1 to bind PAP and catalyze sulfonation , it is interesting to note that this is not due to a radical difference in local binding-site sequence or structure , because SULT4A1 clusters with SULT1C1 when considering these local factors ( Figure 8B and 8C ) . As noted above , apparently small differences of one to two residues in the PAP binding site are likely responsible for this behaviour .
An important challenge in structural biology , chemical biology , and drug discovery is to relate changes in local sequence and structure to the binding and activity profiles of homologous enzymes in order to better predict or explain substrate and inhibitor specificity within an enzyme family . In the case of the hSULTs , this is particularly desirable in order to predict the fate of xenobiotics , hormones , and drug candidates in humans . Like other phase II detoxification enzymes , hSULTs are known to have broad and overlapping substrate specificities . We and others have shown that this promiscuity derives from the considerable flexibility or plasticity of the hSULT binding sites and therefore , a full understanding of specificity will require multiple three-dimensional structures for each hSULT in complex with substrates and inhibitors , as well as knowledge of the full spectrum of small molecules that bind in both productive and non-productive conformations . Our structural and chemical profiling data prepare the foundation for such detailed studies . Here we also reveal a previously unrecognized structural role for the cofactor PAP ( S ) ; namely priming of the often disordered substrate binding loops for interaction with substrates . The “magenta loop” ( Figure 2 ) , which contributes to both PAPS and substrate binding pockets , can also , in some instances , adopt an inactive conformation and may explain substrate-induced inhibition and/or inhibition by compounds that bind in the absence of PAPS , as well as by known inhibitors . The flexibility of the substrate binding loops in hSULTs likely contributes to the wide repertoire of compounds that can be accommodated in the substrate binding pocket , only a subset of which lead to productive sulfonation . Our results provide insight into mechanisms of inhibition of hSULTs . In addition to structural mechanisms of substrate inhibition ( above ) , we identified three compounds ( PLP , AMP-PNP , and quercetin ) that appear to be broad-spectrum hSULT inhibitors , and may also inhibit other non-cytosolic sulfotransferases . We have also provided insight into how PCP and possibly other polychlorinated phenolic compounds can inhibit hSULTs . These compounds , which are known endocrine disruptors , appear to bind in a manner very similar to other productive substrates , but are unreactive , at least in part , due to their weakly acidic properties . Our analyses have also provided insight into the functions of the less well-characterized SULT1C subfamily and SULT4A1 . We have identified a number of novel substrates , inhibitors , and compounds that bind to these SULTs in the absence of PAP . These data combined with activity assays revealed that SULT1C3 can bind catecholamines and phenolic compounds , but only the latter are substrates . SULT4A1 is inactive as an enzyme in our experiments , likely due to its inability to bind PAPS or other sulfonate donors . This orphan SULT likely has an important function in the brain , nevertheless , because it is highly conserved and binds well to the neurotransmitters epinephrine and norepinephrine . The approach outlined here in which simple , medium throughput binding and activity screens can be used to profile properties of purified enzymes has proven extremely useful for identification of novel substrates and analysis of specificity across a human protein family . We demonstrate that the relationship between sequence/structure and function within this small family is remarkably complex , and differences in activity can reflect just a few amino acid changes at critical locations within the protein's active site . Global sequence/structure comparisons provide good clues for broad functional classification , but cannot simply define an enzyme's cognate substrate or class of substrates . For the sulfotransferases , which have a large and flexible binding site , it is clearly necessary to perform much more detailed studies to understand both the binding and catalytic activities in terms of local structure . The actual cellular activity of hSULTs will depend on the spectrum of compounds available to a given enzyme and their relative concentrations . The tissue-specific and developmental variation in both hSULT expression and the cellular milieu of small molecules complicates further attempts to predict activities . Ultimately , detailed enzymatic characterization of all purified hSULTs , as well as cellular assays , will be needed to fully understand this family . The data presented here form a basis for further detailed biochemical and structural studies of both active and inactive enzyme–small molecule complexes in order to fully understand the role of hSULTs in the metabolic fate of endogenous substrates , as well as drugs and toxic compounds .
The SULT1B1 , SULT1C1 , SULT1C2 , SULT1C3 , and SULT4A1 genes were amplified by PCR from the Mammalian Gene Collection clones and subcloned into a modified pET28a-LIC vector . Expression and purification of recombinant proteins was as described by Dombrovski et al . [17] . Purified recombinant proteins contained an additional Gly-Ser dipeptide at the N-terminus . Additional details are provided at http://www . sgc . utoronto . ca in the structure gallery for each protein . Purified SULT1B1 , SULT1C1 , and SULT1C3 were crystallized in the presence of 2 mM PAP using the hanging drop method at 20 °C by mixing: for SULT1B1—2 μl of the protein solution with 2 μl of the reservoir solution containing 0 . 1 M Bis-Tris ( pH 6 . 5 ) , 0 . 2 M ammonium sulfate . and 16%–20% polyethylene glycol 4000; for SULT1C1—2 μl of the protein solution with 2 μl of the reservoir solution containing 0 . 1 M K2HPO4 and 12%–16% polyethylene glycol 3350; and for SULT1C3—2 μl of the protein solution with 2 μl of the reservoir solution containing 18% polyethylene glycol 3350 , 0 . 2 M ammonium formate , 0 . 1 M Bis-Tris ( pH 6 . 5 ) . To obtain crystals of SULT1C2-PAP-PCP ternary complex , 10 mg/ml of purified SULT1C2 was mixed with 2 mM PAP and 2 mM PCP in 20 mM MES-NaOH buffer ( pH 6 . 5 ) , and incubated on ice for 30 min . SULT1C2-PAP-PCP complex was crystallized using the sitting drop method at 20 °C by mixing 0 . 8 μl of the protein-cofactor-inhibitor mix with 0 . 8 μl of the reservoir solution containing 25% polyethylene glycol 3350 , 0 . 2 M lithium sulfate , 0 . 1 M Bis-Tris ( pH 6 . 5 ) . SULT4A1 and SULT1C2 crystals were obtained by using the hanging drop method at 20 °C by mixing 2 μl of the protein solution with 2 μl of the reservoir solution containing 20% polyethylene glycol 4000 , 0 . 2 M ammonium tartrate , and 14%–20% polyethylene glycol 3350 , 0 . 2 M lithium citrate , 0 . 1 M sodium citrate ( pH 4 . 6 ) , respectively . A library of 90 compounds was created for screening sulfotransferases . These compounds were known substrates , products , and inhibitors of sulfotransferases , their analogs , and compounds with high similarity to known inhibitors identified from the literature and public databases ( http://www . rcsb . org and http://www . brenda . uni-koeln . de ) . Certain substrates , such as controlled substances , were not included , and some additional compounds were selected through chemical similarity to known SULT substrates and inhibitors using the ChemNavigator search engine ( http://www . chemnavigator . com/ ) . The compounds were dissolved in 100% DMSO at 100 mM concentration and subsequently diluted stepwise to 10 mM and 1 mM in Hepes buffer ( 100 mM Hepes , 150 mM NaCl [pH 7 . 5] ) . The full list of compounds in the library is included in Table S2 . Screening for ligand binding was performed in 50-μl volume with a final concentration of 1 mM of compound per well , in 384-well plates . The concentration of protein was the same for all wells at 0 . 4 mg/ml . Ligand binding was detected by monitoring the increase in thermostability of proteins in the presence of ligands . Protein thermostability at pH 7 . 5 was studied using StarGazer technology that monitors protein stability by its aggregation properties [20 , 21] . Protein samples at 0 . 4 mg/ml were heated from 27 °C to 80 °C at the rate of 0 . 5 °C per min in clear-bottom 384-well plates ( Nunc , http://www . nuncbrand . com/ ) in 50 μl of 100 mM Hepes ( pH 7 . 5 ) and 150 mM NaCl . Protein aggregation was monitored by capturing images of scattered light every 30 s with a CCD ( Charge-Coupled Device ) camera . The pixel intensities in a pre-selected region of each well were integrated to generate a value representative of the total amount of scattered light in that region . These total intensities were then plotted against temperature for each sample well and fit to the Boltzman equation by nonlinear regression . The point of inflection of each “denaturation” curve was identified as Tagg ( aggregation temperature ) . The increase in stability of the protein in the presence of a ligand is shown as ΔTagg . Enzyme assays were performed using a HPLC-based method that we developed for sulfotransferase activity assay by modifying the protocol previously used to monitor ADP production and ATP hydrolysis by a purified bacterial ATPase [41] . SULTs at 1–5 μM were assayed in the presence of 0 . 1 to 0 . 5 mM PAPS and different concentrations of each substrate in 100 mM Hepes ( pH 7 . 5 ) by incubating the reaction at 37 °C for a period of time from 15 to 120 min depending on how fast PAPS was converted to PAP . The Km values for characterized sulfotransferases are in the range of nanomolar to millimolar concentrations [24 , 42] , with a significant variation in catalytic efficiency and substrate specificity . Based on these observations and considering possible substrate inhibition [19 , 43] , we tested all sulfotransferases at substrate concentrations of 10 , 25 , and 100 μM . The reactions were stopped by adding two volumes of urea ( final concentration of 5 . 3 M ) , and the mixture was filtered through a 5-kDa molecular weight cutoff Amicon Ultrafree-MC filter ( Millipore , Bedford , Massachusetts , United States ) to remove the protein . The ratio of PAP and PAPS was determined after separating them on HPLC using a 4 . 5 mm × 50 mm WP QUAT , a strong ion-exchange column ( J . T . Baker , Phillipsburg , New Jersey , United States ) , using a gradient of triethylamine bicarbonate from 20 to 500 mM applied at 2 ml/min for 7 min . The progress of the reaction was monitored by reading the absorbance at 259 nm , and the amount of PAP produced was determined by integration of the resolved peaks using the HPLC software ( Waters , http://www . waters . com/ ) . All values in Table 2 were corrected for the rate of conversion of PAPS to PAP in the presence of enzyme , but no substrate . This background activity is reported for each SULT in the last row of Table 2 , and the values are the average of three independent measurements . Sequences used to generate Figures 3 and 8 are as follows: SULT1A1 , SULT1A2 , SULT1A3 , SULT1A4 , SULT1B1 , SULT1C1 , SULT1C2 , SULT1C3 , SULT1E1 , SULT2A1 , SULT2B1 , SULT4A1 , and SULT6B1 . We created a multiple sequence alignment of the above-mentioned sequences using HMMer [44] and the pfam [45] Sulfotransferase_1 ( PF00685 . 15 ) Hidden Markov Model . Sequence similarity is measured using the Tanimoto coefficient of residues in common in the HMM-based alignment . The calculation of local sequence similarities involves the detection of binding site residues [46] , and their subsequent mapping onto the HMM-based alignment . Cofactor ( PAP/PAPS ) binding residues were also mapped onto the alignment and excluded from all pairwise comparisons and similarity calculations . Substrate binding-site structural similarities were detected using a two-stage graph-matching process providing a one-to-one chemical and spatial correspondence between atoms in clefts . The method considers all non-hydrogen atoms and can use large sets of atoms as input allowing larger , over-predicted and apo-form binding sites to be analyzed . In the first stage , the two clefts are superimposed [47] via the detection of the largest clique [48] in a Cα association graph corresponding to the largest subset of identical residues in equivalent spatial locations . The first stage is used to constrain the construction of the second-stage all-atom association graph . The second-stage graph-matching results in the detection of the largest subset of heavy atoms of equivalent atoms types [49] , and spatial positions . Pairwise local structural similarity was calculated as a Tanimoto coefficient based on the size of the largest clique in the second graph-matching stage . Dissimilarity matrices were derived from the similarity measures described above . Pairwise experimental catalytic and binding-profile dissimilarity matrices were calculated as the L2 distance of the vectors with the corresponding experimental measurements . Hierarchical clustering was used to create the clustering trees shown in Figure 8 . The correlation between the cophenetic matrix and the original dissimilarity matrix was used to choose the linkage method that results in the most accurate representation of the original data [50] . Average linkage was found to be the clustering method of choice in all instances . To assess the extent to which the acidity of the hydroxyl moiety of hydroxylated polychlorinated biphenols is related to their inhibitory strength , we computed the pKa values of the 4-hydroxyl group for a series of hydroxylated PCB analogs . This group of compounds and their inhibitory effect on SULT1E1 were previously reported [36] . To this end , we used the pKa calculator in the PC stand-alone version of the ACD ( http://www . acdlabs . com ) suite of programs . Two clusters of compounds , namely 4-OH- ( 2 , 3 , 4 , 5 , 6 ) Cl and 4-OH- ( 3 , 5 ) Cl , are identified as outliers upon computing a linear regression on the relationship ( R2 = 0 . 57 ) .
The National Center for Biotechnology Information ( http://www . ncbi . nlm . nih . gov ) Reference Sequence ( RefSeq ) accession numbers for the proteins referred to in this paper are SULT1A1 ( NP_803880 ) , SULT1A2 ( NP_001045 ) , SULT1A3 ( NP_003157 ) , SULT1A4 ( NP_001017389 ) , SULT1B1 ( NP_055280 ) , SULT1C1 ( NP_789795 ) , SULT1C2 ( NP_006579 ) , SULT1C3 ( NP_001008743 ) , SULT1E1 ( NP_005411 ) , SULT2A1 ( NP_003158 ) , SULT2B1 ( NP_814444 ) , SULT4A1 ( NP_055166 ) , and SULT6B1 ( NP_001027549 ) . The Mammalian Gene Collection ( http://mgc . nci . nih . gov ) clones discussed in this paper are SULT1B1 ( gi: 29550928 ) , SULT1C1 ( gi: 4507305 ) , SULT1C2 ( gi: 28830308 ) , SULT1C3 ( gi: 56847626 ) , and SULT4A1 ( gi: 7657633 ) . The Protein Data Bank ( http://www . pdb . org ) IDs for the proteins discussed in this paper are SULT1A3 ( 1CJM ) , SULT1C2 ( 2AD1 ) , SULT4A1 ( 1ZD1 ) , SULT1C1 with PAP ( 2ETG ) , SULT1B1 with PAP ( 1XV1 ) , SULT1C3 with PAP ( 2H8K ) , SULT1A1 with PAP and p-nitrophenol ( 1LS6 ) , SULT2B1b with PAP and pregnenolone ( 1Q20 ) , SULT1E1 with PAP and 3 , 5 , 3′ , 5′-tetrachloro-biphenyl-4 , 4′-diol ( 1G3M ) , SULT2A1 with DHEA ( 1J99 ) , SULT2A1 with PAP ( 1EFH ) , SULT1C2 with PAP and PCP ( 2GWH ) , SULT1A3 with PAP and dopamine ( 2A3R ) , SULT1A1 with PAP and estradiol ( 2D06 ) , SULT2A1 with androsterone ( 1OV4 ) , SULT2B1a with PAP and 2-[N-cyclohexylamino]ethane sulfonic acid ( 1Q1Q ) , and SULT2B1b with PAP and DHEA ( 1Q22 ) . | We metabolize many hormones , drugs , and bioactive chemicals and toxins from the environment . One family of enzymes that participate in the metabolic process consists of the cytosolic sulfotransferases , or SULTs . SULTs have a variety of mechanisms of action—sometimes they inactivate the biological activity of the chemical ( e . g . , in the case of estrogen ) . At other times , the enzymes make the chemical more toxic ( e . g . , for certain carcinogens ) . Humans have 12 distinct SULT enzymes . Determining how each of these human enzymes recognizes and distinguishes between the thousands of chemicals we confront each day is essential for understanding hormone regulation , assessing environmental risk , and eventually developing better , more-effective drugs . We have studied the human SULT family of enzymes to profile which small molecules are recognized by each enzyme . We also visualized and compared the detailed structural features that determine which enzyme interacts with which molecule . By studying the entire family , we discovered new ways in which chemicals interact with each enzyme . Furthermore , we identified new inhibitors and inhibitory mechanisms . Finally , we discovered functions for many of the human enzymes that were previously uncharacterized . | [
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In all vertebrates hearing and touch represent two distinct sensory systems that both rely on the transformation of mechanical force into electrical signals . There is an extensive literature describing single gene mutations in humans that cause hearing impairment , but there are essentially none for touch . Here we first asked if touch sensitivity is a heritable trait and second whether there are common genes that influence different mechanosensory senses like hearing and touch in humans . Using a classical twin study design we demonstrate that touch sensitivity and touch acuity are highly heritable traits . Quantitative phenotypic measures of different mechanosensory systems revealed significant correlations between touch and hearing acuity in a healthy human population . Thus mutations in genes causing deafness genes could conceivably negatively influence touch sensitivity . In agreement with this hypothesis we found that a proportion of a cohort of congenitally deaf young adults display significantly impaired measures of touch sensitivity compared to controls . In contrast , blind individuals showed enhanced , not diminished touch acuity . Finally , by examining a cohort of patients with Usher syndrome , a genetically well-characterized deaf-blindness syndrome , we could show that recessive pathogenic mutations in the USH2A gene influence touch acuity . Control Usher syndrome cohorts lacking demonstrable pathogenic USH2A mutations showed no impairment in touch acuity . Our study thus provides comprehensive evidence that there are common genetic elements that contribute to touch and hearing and has identified one of these genes as USH2A .
All animals are equipped with a range of specialized sensory cells whose prime function is to detect mechanical force . The most familiar of these sensory systems are hearing and touch , but mechanosensory cells also detect important stimuli that are not consciously perceived , for example , changes in blood pressure . We reasoned that since the prime function of different sensory cells is to detect mechanical force they may utilize a common set of mechanosensory proteins for this function . According to this hypothesis genetic variation affecting the function of mechanosensory proteins would be predicted to quantitatively change more than one mechanosensory trait . Genetics has been very successfully used to characterize new molecules that are essential for human hearing [1] . There are over 60 known genes linked to sensorineural hearing loss , and a similar number of loci linked to hearing impairment exist for which the underlying genetic defect has not been identified ( for an updated list see http://hereditaryhearingloss . org ) . Non-syndromic sensorineural hearing loss is commonly caused by single gene mutations , which primarily affect the function of the sensory hair cells that detect movement of the basilar membrane induced by sound . Sensorineural deafness often manifests from birth and some of the responsible genes encode components of the mechanotransduction apparatus of the hair cell that transforms mechanical force into electrical signals [2]–[4] . In contrast to hearing , virtually nothing is known about the genetics of touch . Indeed there are , to our knowledge , no reported cases of non-syndromic reduced or absent touch sensitivity present from birth in humans . Impaired detection of high frequency vibration ( >80 Hz ) in humans was recently shown to be associated with pathogenic mutations in the transcription factor c-Maf ( MIM:177075 , MIM refers to the OMIM database ) and may be due to a failure in the development of specific mechanoreceptors associated with Pacinian corpuscles [5] . In contrast , congenital complete insensitivity to pain has been recognized for many years [6] and there are now a small group of genes ( NTRK1; MIM:191315 , NGFB; MIM:162030 , and SCN9A; MIM:603415 ) , mutation of which is known to cause this condition [7]–[10] . Impaired touch sensitivity has been described as one symptom of several severe inherited or acquired neurological disorders ranging from large fiber neuropathy to Charcot-Marie tooth disease [11] , [12] , however such neurological diseases are often associated with structural changes in the peripheral nervous system . The peripheral sensory nervous system consists of primary sensory neurons located in the cranial and dorsal root ganglia ( DRG ) and these are the most numerous sensory cells of the body . Thus virtually every somatic tissue of the body , skin , muscle , and visceral organs is innervated by the axons of sensory neurons , which can form mechanosensitive endings in these tissues . The skin represents the largest of our sensory organs and is innervated by a variety of sensory neuron types that can be characterized as low threshold mechanoreceptors [13] . Very little is known about the molecular basis of mechanotransduction in somatic mechanoreceptors , but transduction in these neurons may be accomplished by a multi-protein complex similar to that described for touch receptor neurons ( TRNs ) in the nematode worm C . elegans [4]–[6] . We have previously shown that STOML3 ( Swissprot Q6PE84 ) is required for normal touch-driven behavior in the mouse , and STOML3 is a membrane protein that is required for the function of mechanosensitive ion channels in DRG neurons [4] . But evidence that Stoml3 mutations are causative for impairments in human touch is so far lacking . But assuming that touch sensitivity is a complex genetic trait , it should be possible to detect a heritable component in the normal variation of touch-related traits , as has been shown before for other sensory traits , such as pain sensitivity and hearing [14] , [15] . Here we show that there is a significant genetic component to touch sensitivity in humans by determining the heritability of touch traits , assessed by quantitative sensory testing , in a classical twin study . In accordance with our hypothesis that there are common genetic factors underlying different mechanosensitive systems , we found that quantitative measures of mechanosensory traits—that is , touch acuity , hearing acuity , and baroreflex function—are positively correlated with each other in a healthy human population . We also examined a cohort of people suffering from congenital hearing loss and found touch sensitivity to be poorer in these individuals compared to a control cohort . To investigate the role of single sensorineural deafness genes in cutaneous touch sensitivity , we assessed touch acuity and sensitivity in people suffering from Usher syndrome . We found touch sensitivity to be impaired in a cohort of individuals carrying pathogenic mutations in the USH2A gene ( MIM:608400 ) , but not in other cases of Usher syndrome . Our study thus provides comprehensive evidence that there are common genetic elements that contribute to touch and hearing and has identified one of these genes as USH2A .
In general it has been noted that sensory performance decreases with age and is also influenced by sex [21]–[24] . Each of the phenotypes that we measured exhibited some variability that might be partially accounted for by the age of the participant or his or her sex . Indeed , the participants' performance in all tests , with the exception of baroreflex sequence frequency , showed significant deterioration with age ( Figures S1 , S2 , S3 , S4 and Table S1 ) . In order to more reliably compare phenotypic data from participants who ranged in age from 14–68 years , we determined the best mathematical fit for each set of phenotypic data ( Table S1 ) . The mean age of the entire control cohort was 27 . 0±0 . 7 years , and the median age was 24 years ( n = 352 ) . In most cases the changes in the sensory trait was best fit by a second order polynomial function , in two cases the age dependence was best described by a linear fit ( baroreflex sequence frequency and heat pain threshold ) , and in one case ( baroreflex slope ) the data were best fit by an exponential decay function . Here with increasing age the baroreflex sequence slope tends to become shallower , which reflects weaker engagement of the reflex by changes in blood pressure . For subsequent analyses the phenotypes measured were normalized on the basis of the mathematical fits to the mean age of the entire cohort . Of the 13 sensory traits measured , significant sex differences were found for six traits , and in every case women performed better than men when age-matched cohorts were compared ( Figure S5 ) . The sensory performance of women was significantly better than that of men for tactile acuity , otoacoustic emission reproducibility and strength , baroreflex sequence frequency , as well as cold and warmth detection thresholds ( Figure S5 , Table S2 ) . The absolute magnitude of the sex differences measured in the sensory traits was relatively modest compared to the influence of age . About the same number of woman as men were included in the entire study; 223 males ( 43% ) and 295 females ( 57% ) , but the recruitment for the twin study was biased towards females ( see below ) . The heritability h2 of the investigated sensory traits—that is , the component of the variation of the respective trait that can be accounted for by additive genetic effects—was determined in a classical twin study . Of the 100 twin pairs who participated in the study , 38 were monozygotic female pairs , 28 monozygotic male pairs , 25 dizygotic female twin pairs , and 9 dizygotic male twin pairs; no mixed sex twins were recruited . The ages of the twins in our cohort ranged from 18 to 68 years , with a mean age of 29 . 7±1 . 14 . Zygosity was confirmed using a chip-based method with 9 , 080 informative autosomal SNPs derived from the so-called Immunochip [25] , [26] . Most twin pairs were tested for all parameters , but in some cases this was not possible for organizational reasons . Heritability values were estimated by structural equation modeling; the model employed was an ACE model , in which the variance of the trait is determined by additive genetic effects A , the common environment C , and the unique environment E [27] . The values for the twins were previously corrected for age effects ( see Table S1 ) before being subjected to structural equation modeling . Correcting for both age and sex effects before modeling or treating age and sex as fixed effects in the structural equation modeling did not lead to major changes in heritability estimates ( unpublished data ) . If there are common genetic variants that can influence all mechanosensory traits , we would expect to see a correlation between different mechanosensory traits ( mechanosensory intermodal comparison ) . As a prerequisite for an intermodal correlation , one should observe strong correlations between different measures of one sensory system , such as between tactile acuity and vibration detection threshold ( intramodal comparison ) . This was indeed the case as all measures of one sensory system showed a significant correlation with each other , although in many cases this correlation coefficient ( r ) was surprisingly low , but ranged from r = 0 . 21 for VDT and tactile acuity to r = 0 . 65 for otoacoustic emission strength against otoacoustic emission reproducibility ( Figure 5 ) . Notable was the fact that strong and significant correlations were found between cold detection threshold and warmth detection threshold ( r = −0 . 23 ) as well as between cold pain and heat pain threshold ( r = −0 . 60 ) . We then reasoned that if a group of gene variants positively influences one trait like touch , they may also positively influence another mechanosensory trait in which they also play a functional role ( e . g . , hearing ) . Put simply the question is , If someone has good hearing , are they also more likely to have good touch sensitivity ? We made such comparisons for all the phenotypic parameters measured from our twin cohort as well as from an additional cohort of healthy individuals . Significant intermodal correlations between mechanosensory traits were detected between tactile acuity and hearing acuity with r = 0 . 16 ( p<0 . 05 ) and tactile acuity and EOAE reproducibility with r = −0 . 16 ( p<0 . 05 ) . Additionally , we noted a significant intermodal correlation between EOAE strength and baroreflex sequence frequency ( Figure 5 ) . There was just one case of a significant correlation between a mechanosensory and non-mechanosensory trait ( i . e . , between hearing acuity and warmth detection threshold , with r = 0 . 16 , p<0 . 05 ) ( Figure 5 ) . It is possible that because women often perform better than males in some sensory tests ( e . g . , EOAE strength and warmth detection ) ( see above ) , then intermodal correlations may be strengthened because of the presence of females in the cohort . In order to test this idea in cases where a significant sex difference was found , we made a mathematical correction of the raw data so that the male value was corrected to be equivalent to the female value . This was done by making the corrected male value = ( mean female value−mean male value ) +measured male value . After the raw data were adjusted in this way and intermodal correlations tested again , the only statistically significant correlation that remained was between tactile acuity and hearing acuity with r = 0 . 15 ( p<0 . 05 ) ( Figure S6 ) . The fact that fewer significant correlations are found between mechanosensory traits after a correction for the person's sex does not necessarily mean that the lost intermodal correlations are not due to common genetic determinants . The evidence thus suggests that there may be genetic factors that have a common influence on more than one mechanosensory trait . The presence of statistically significant intermodal and intramodal correlations between the different sensory traits was not corrected for multiple testing . However , for the analysis shown in Figure 5 we calculated a false discovery rate ( FDR ) based on the p values obtained for intramodal , intermodal mechanosensory , and intermodal non-mechanosensory correlations [28] . This calculation revealed a very low FDR for intramodal correlations ( 0 . 004 ) but also indicated that the FDR for intermodal non-mechanosensory correlations was much higher ( 0 . 83 ) than for intermodal mechanosensory correlations ( 0 . 14 ) ( see Figure 5 ) . Thus in order to more rigorously test the hypothesis that common genes influence both hearing and touch sensitivity , we chose to study the touch sensitivity of individuals likely to carry serious genetic lesion ( s ) that affect hearing . We asked if touch sensitivity might be affected in some forms of hereditary hearing loss . We tested touch sensitivity in a cohort of individuals aged between 14 and 20 years who were recruited for this study at a school for the hearing impaired . In total , 39 individuals were assessed for tactile acuity and 29 of these individuals were also tested for vibration detection threshold . All the participants suffered from severe congenital hearing impairment or hearing loss . It has been estimated that in around 70% of individuals suffering from severe hearing impairment from birth , there is an underlying genetic lesion [29] . Compared to the age-corrected control cohort , both vibration detection thresholds and tactile acuity were significantly elevated in hearing impaired individuals ( Figure 6 ) . The mean vibration detection threshold in the hearing impaired cohort was 8 . 93±0 . 44 JNDs compared to 7 . 40±0 . 13 JNDs ( p<0 . 001; t test ) in the control cohort ( corresponding to stimulus amplitudes of 2 . 23 µm and 1 . 32 µm , respectively , Table S5 ) . The mean tactile acuity was 1 . 84±0 . 09 mm in the hearing impaired cohort compared to 1 . 63±0 . 02 mm in the control cohort ( p<0 . 01; t test ) . In both cases it appeared plausible from the distribution of individual values that the difference was primarily due to the presence of a subset of individuals with an exceptionally poor touch performance in the hearing impaired cohort ( Figure 6 ) . Thus of the 39 individuals tested for tactile acuity , five ( 13% ) had very poor tactile acuity ( defined as acuity >2 . 44 mm = mean of the control cohort plus 2 standard deviations ) , and of the 29 individuals who were also tested for VDT , two individuals ( 7% ) performed poorly ( defined as JND>11 . 8 = mean of the control cohort plus 2 standard deviations ) . The two individuals with high VDTs were not the same individuals as those with poor tactile acuity as defined above . It might be argued that the above differences may have resulted from the age corrections performed on the control data . However , we also compared data from hearing impaired individuals with a young sub-population of the control cohort , the mean age of which was not significantly different from the hearing impaired individuals ( mean age controls 17 . 1 years , n = 141; mean age hearing impaired 16 . 3 years , n = 39 ) . Comparing these data from the young cohort to our data from the hearing impaired individuals revealed that both measures of touch sensitivity were significantly different from each other ( hearing impaired JND 8 . 1±0 . 4 compared to control JND 6 . 6±0 . 2; hearing impaired tactile acuity 1 . 7±0 . 1 compared to control tactile acuity was 1 . 5±0 . 1; Student's t test p<0 . 01 and p<0 . 05 , respectively ) . Our data so far strongly suggested that genetic factors that influence hearing may also influence touch . Ideally one would like to identify such genes by measuring touch performance in patients with single gene mutations that cause deafness and decrease touch sensitivity . We decided to recruit patients with Usher syndrome to participate in our study . Usher syndrome is characterized by early onset deafness with late onset retinitis pigmentosa leading to tunnel vision and blindness and can be classified into three clinical sub-types , with type 1 ( USH1 ) typically being the most severe and type 3 ( USH3 ) the least severe . There are nine known Usher genes , mutations in which cause the disease [30]; interestingly all known Usher gene products have been localized to the stereocilia of inner ear hair cells where mechanoelectric transduction takes place [31] . Around 60% of Usher patients suffer from the type 2 syndrome in which hearing loss is comparatively mild , with retinitis pigmentosa onset normally in the second decade . We examined patients from two cohorts of Usher patients for touch sensitivity; one cohort was obtained as part of a special consultation for Usher patients from all over Germany at the Audiology and Phoniatrics Clinic of the Charité , and the second cohort was recruited from a genotyped registry of patients diagnosed with Usher syndrome in Valencia , Spain [32]–[34] . In most cases individuals were genotyped using a microarray-based chip for the Usher genes [35] . Often this led to the identification of one mutated allele , but not the second allele ( see Table S4 ) . Thus the data presented here are derived from individuals with compound heterozygous and homozygous pathogenic USH2A mutations ( n = 18 ) , individuals with only one identified USH2A mutant allele ( n = 18 ) , as well as individuals with clinically diagnosed Usher syndrome type 2 in which no genotype has been determined ( n = 29 ) . Interestingly , we observed that the mean tactile acuity threshold was significantly elevated in patients proven to carry compound heterozygous or homozygous pathogenic mutations in the USHA gene; thus tactile acuity was 1 . 88±0 . 14 mm ( n = 19 ) compared to 1 . 63±0 . 02 mm for controls ( p<0 . 05; Student's t test ) ( Figure 7B ) . In addition the mean vibration detection threshold in those patients tested ( n = 17 ) was also significantly elevated , ( 8 . 50±0 . 40 compared to 7 . 40±0 . 13 JNDs in the control cohort; Figure 7A ) . Interestingly , in patients with a clinically diagnosed Usher syndrome type 2 , for which the underlying mutation was unknown ( n = 26 ) , we found no evidence of impaired vibration sensitivity ( Figure 7C ) . This Usher syndrome type 2 cohort did not display impaired tactile acuity but rather performed significantly better than controls in the tactile acuity task ( Figure 7D ) . We analyzed tactile acuity in all individuals with two or just one genotyped USH2A mutant allele ( n = 36 ) and found that tactile acuity in this mixed cohort was slightly attenuated ( acuity = 1 . 76±0 . 10 mm , but this did not reach the criterion for statistical significance when compared to controls , p = 0 . 089 ) . In Usher patients with compound heterozygous or homozygous pathogenic USH2A mutations ( n = 15 ) , we also routinely tested temperature sensitivity traits . We found that warmth and cold detection thresholds as well were not significantly altered in these patients compared to the control cohort ( Figure S7 ) . Heat pain threshold was slightly but significantly altered so that patients with compound heterozygous or homozygous pathogenic USH2A mutations had heat pain thresholds of 43 . 6±0 . 7°C compared to 44 . 9±0 . 2 in the control cohort ( p<0 . 05 , Student's t test ) . When we examined temperature traits in patients with two or just one genotyped USH2A mutant allele ( n = 32 ) , we noted that this cohort performed significantly better in the warmth detection task ( warmth detection threshold 0 . 94±0 . 09 Δ°C compared to 1 . 31±0 . 05 Δ°C in controls , p<0 . 01 ) ; all other temperature traits did not differ from controls . It should be noted that the effect on warmth detection was only apparent in a mixed cohort in which the second mutation was not always known; thus mutations in genes other than USH2A might conceivably cause this effect . Usher type 2 patients for which the underlying mutation was not known showed normal performance in the temperature detection task ( unpublished data ) . It is often assumed that the loss of one sensory modality is associated with a learned increase in the acuity of other sensory modalities . Wecarried out a study to assess the effects of blindness per se on touch sensitivity as assessed by measuring tactile acuity and vibration detection threshold . The cohort studied here ( n = 57 ) was recruited at an occupational training center for blind people and the cause of blindness was sometimes presumed to be genetic but in many individuals had been caused by accidents or other non-genetic causes . The severity of visual impairment varied in the tested individuals , but was in all cases so severe that the test persons were using the Braille system to read . In agreement with previous studies of tactile acuity we found that the mean acuity of the index finger was significantly better in the blind group [36]–[38]; mean tactile acuity was 1 . 38±0 . 05 mm compared to 1 . 63±0 . 02 in the control cohort ( p<0 . 001 , t test ) ( Figure 8B ) . In the same blind cohort , vibration detection threshold was not found to be different compared to the control cohort ( Figure 8A ) . The enhanced acuity observed in blind patients was not limited to the preferred reading finger as measurements from the contralateral finger showed that the mean acuity was not significantly different from that of the main Braille reading finger ( acuity of the main Braille reading finger was 1 . 18±0 . 06 mm compared to 1 . 26 mm±0 . 08 in the contralateral index finger ) . These results are in broad agreement with the view that tactile acuity can be improved , possibly through learning mechanisms involving cortical plasticity . Importantly , for the present study the results show that vibration detection thresholds are probably not sensitive to such learning effects .
We show here using two psychophysical measures that touch performance varies considerably in the normal human population . Importantly , we could show in a classical twin study that a large part of touch performance variability ( between 27% and 52% ) can be accounted for by genetic factors . We also confirmed and extended published work showing that human performance traits that depend on mechanosensory systems ( e . g . hearing and baroreflex sensitivity ) are also partly genetically determined [39]–[43] . We have gone one step further and provide evidence that there are common genetic determinants that influence both hearing and touch . We provide three key sets of experimental data to support this conclusion . First , we show that in a normal human population there is a significant correlation between touch and hearing performance , put simply if you have good hearing there is a higher chance that you will have good touch acuity . These so called intermodal correlations were fascinating but do not establish a causal link between hearing and touch . To this end we next examined a cohort of individuals with congenital hearing impairment . A high proportion of these hearing impaired young adults displayed very poor touch performance . In the third set of experiments with a cohort of Usher syndrome patients , we identified a single gene , USH2A or Usherin , mutations in which are associated with poor touch acuity as well as congenital hearing loss and adult onset blindness . Patients with Usher syndrome in which the underlying mutation was not known did not show reduced touch performance . As well as identifying a gene that influences both hearing and touch acuity , we can conclude that there is probably a larger set of as yet unidentified genes that influence both touch and hearing . The functional characterization of such genes will help in the molecular characterization of normal and abnormal touch sensation . Classical twin studies are used to estimate the heritable component that contributes to phenotypic variation in complex traits . We show that two measures of touch sensation , tactile acuity ( h2 = 0 . 27 ) and vibration detection threshold ( h2 = 0 . 52 ) , are heritable ( Figure 1 ) . This finding is important as it allowed us to ask the question of whether common genetic factors may affect hearing and touch ( see below ) . As part of our twin study we also measured temperature sensation . A striking feature of the cutaneous sensory system in humans is the capacity to perceive tiny increases ( warming ) and decreases ( cooling ) of the skin; thus changes of a fraction of a degree can be perceived when a surface area of ∼9 cm2 is warmed or cooled ( Figure 4 ) . The heritability of temperature sensation has to our knowledge not been examined , and we find significant heritability both for warmth and cold detection thresholds ( h2 = 0 . 37 and h2 = 0 . 40 , respectively ) . Twin studies have previously been used to show that hearing acuity is highly heritable in middle-aged and elderly persons [41] , [42] , [44]; we have extended these finding to confirm the heritability of hearing acuity in a much younger cohort of twins than previously studied ( average age 29 years ) . Hearing acuity is likely influenced by genetic factors that can act at any point along the auditory pathway; therefore , we and others have also measured evoked otoacoustic emissions ( EOAE ) , which is a more direct measure of the mechanosensory function of outer hair cells [45] . High heritability values have already been reported for EOAE [46] , but it is notable that with our larger twin sample heritability estimates were exceptionally high ( e . g . , EOAE strength h2 = 0 . 80–0 . 93 compared to the 0 . 65–0 . 85 reported by McFadden [46] ) ( Figure 2 ) . The activity of the baroreflex in resting participants has also been shown to be heritable in a twin study [43] , and we could confirm this here in an independent cohort ( Figure 3 ) . Heat pain thresholds have been reported to be heritable in a twin study [14]; however , in our cohort we could not detect significant heritability of this parameter ( Figure 4 ) . In contrast to the studies of Norbury and colleagues , which exclusively recruited female twins [14] , we recruited both males and females , which may have made it difficult for us to detect a heritability that is perhaps more robust in females . The starting hypothesis of this study was that common genetic factors may influence hearing and touch . One strong hint that this may be so is that intermodal mechanosensory correlations were almost as strong as the expected intramodal correlations between traits . Thus statistically significant correlations were found between hearing acuity and tactile acuity , EOAE reproducibility and tactile acuity , and EOAE strength and baroreflex sequence frequency ( Figure 5 ) . These findings suggested , but do not prove , that gene variants may have an influence on more than one mechanosensory modality . We followed up on these results by asking if individuals with congenital deafness have altered cutaneous sensation . Strikingly , we found that hearing impaired individuals perform on average very poorly compared to controls in both touch tasks used here , and this was highly significant ( Figure 6 ) . It is likely that this effect is due to a subpopulation of individuals in this cohort who have very poor touch performance . Thus when one takes all individuals together who perform poorly ( defined as > control mean + 2 SD ) in both the acuity task and the vibration detection test , then up to 20% of the cohort could be considered to have a touch impairment . The cohort chosen was a random sample of hearing impaired individuals and thus probably represents a wide range of affected genetic loci that cause deafness . It follows that the surprisingly high proportion of individuals with poor touch performance is unlikely to be due to the influence of just one deafness gene . An alternative explanation is that the lack of auditory nerve activity in these individuals negatively affects the development of the somatosensory system . Interplay between touch processing and auditory processing has been reported; for example , the auditory cortex can be activated by tactile stimuli [47] , [48] . It seems unlikely that lack of auditory input would adversely affect processing of touch-related sensory information , since it has been shown that the auditory cortex is even more active in response to tactile stimuli in deaf test persons compared to normal hearing individuals [24] . Our finding that most individuals with severe hearing impairment do not have altered touch sensitivity shows that auditory impairment per se does not necessarily negatively impact touch . How could hearing genes influence touch sensitivity ? Common features of congenital hearing loss range from disorganization of the stereocilia , where mechanotransduction takes place , to complete degeneration and loss of sensory hair cells [1]–[3] . There is , however , no indication that somatic sensory neurons require cilia for their function and so loss of hearing gene function could act at many other levels . In the case of late onset deafness caused by dominant negative mutations in the gene encoding the potassium ion channel protein KCNQ4 ( DFNA2-type monogenic hearing loss ) , hair cells degenerate apparently due to sustained depolarization [49] . We have recently shown that the KCNQ4 protein has a specialized role in the transformation of receptor potentials into action potentials in specific types of mechanoreceptors [50] . Furthermore , loss of KCNQ4 function is associated with better vibration detection threshold performance , but only at low vibration frequencies [50] . Some hearing genes have also been shown to affect synapses made between the hair cell and the sensory afferents that convey the auditory signals to the brain [51] , [52] . It is thus quite conceivable that deafness genes that influence synaptic properties may also have consequences for the functional properties of sensory neuron synapses in the somatosensory system . The effect of sensory loss on touch sensitivity has been studied before in the case of blindness . Despite the common notion that blind people are superior in tactile tasks , the picture arising from previous studies is not as clear . A number of different tests have been employed to address this question , with some producing better results in blind cohorts and some not [37] , [53]–[55] . This was also the case in our blind cohort; thus we found vibration detection thresholds unchanged in a blind cohort , but the tactile acuity clearly enhanced ( Figure 7 ) . Tactile acuity has previously been tested in blind participants with conflicting results [36] , [37] , [53]; however , blindness has never been associated with reduced touch sensitivity . We wished to identify single genes that influence hearing and touch and thus decided to study individuals with Usher syndrome . Usher syndrome is very well characterized at the genetic level , with many alleles known to affect at least 10 genes including MYO7A ( MIM:276903 ) , USH1C ( MIM:605242 ) , CDH23 ( MIM:605516 ) , PCDH15 ( MIM: 605514 ) , SANS ( MIM: 607696 ) , USH2A , VLGR1 ( MIM: 602851 ) , WRHN ( MIM: 607928 ) , USH3A ( MIM: 606397 ) , and PDZD7 ( MIM: 612971 ) [2] , [56] , [57] . Interestingly , all the Usher genes characterized in detail so far are expressed in sensory hair cells , and in several cases the protein product has even localized to sites of transduction at the tips of the stereocilia [2] . Indeed , there is solid evidence showing that the tip-link , which is necessary for transferring force to open mechanotransduction channels , is made up of two Usher gene protein products , cadherin-23 and protocadherin-15 [58] , [59] . In DRG neurons we have recently obtained evidence that a very large extracellular protein tether ( ∼100 nm in length ) is required for the gating of mechanosensitive currents found in touch receptors [60] , [61]; the identity of this tether protein is not known , but its biochemical properties do not match that of tip link proteins cadherin-23 and protocadherin-15 [60] . Here we show that elevated tactile acuity and vibration detection thresholds were observed in patients suffering from Usher syndrome type II caused by mutations in USH2A ( Figure 7A ) . USH2A is a transmembrane protein with a very large extracellular domain , in principle long enough to extend 100 nm into the extracellular space [62] . In hair cells the USH2A protein is localized at the base of the stereocilia and is thought to be part of the ankle links that connect adjacent stereocilia [63] , [64] . USH2A protein could be detected only in the developing cochlear hair cells , but was also detected at later stages in vestibular hair cells . USH2A has been shown to bind to other Usher proteins [63] , [65] as well as to collagen IV [66] and could be a link between the inner network of Usher proteins and the extracellular matrix . Stereocilia bundles are disorganized in mice with a targeted deletion of the USH2A gene , and these mice are also deaf [67] . The biochemical properties of the USH2A protein make it a conceivable candidate for the tether visualized in sensory neurons . Human USH2A is an extraordinarily large gene consisting of 72 exons , which can encode a protein with a length of 5 , 222 amino acids [62] . Several different transcripts have been identified for the USH2A gene and so it is conceivable that mutations in this gene may have differential effects on protein products expressed in different tissues ( e . g . , hair cells versus sensory neurons ) . Most patients in which one pathogenic USH2A mutation has been identified are probably also carriers of a second mutation in the same gene [68] . Consistent with this prediction we observed that a mixed cohort of patients homozygous or heterozygous for pathogenic USH2A mutations also exhibited impaired touch sensitivity , although this difference was not statistically significant . Interestingly , many individuals with Usher syndrome type II in our study did not exhibit impaired touch acuity , although previous analyses of large populations of these patients have provided estimates that the majority ( up to 70% ) may carry pathogenic mutations in the USH2A gene [33] , [69] , [70] . Interestingly , Usher type 2 patients for which no mutation in the USH2A was known performed on average better in the touch acuity test than controls ( Figure 7D ) . These patients also suffered from visual impairment , and it is possible that they , like blind people , had learned to improve their tactile acuity . Mutation in two other genes , WHRN and VLGR1 , can lead to Usher syndrome type II [71] , [72] , but we could not confirm any such cases in our cohort . The newly demonstrated effect of USH2A gene mutations on touch acuity shown here clearly warrants a detailed study of this protein in the somatosensory system . In summary , our study demonstrates that human touch sensitivity is indeed accessible at a genetic level and we provide evidence for shared genetic factors influencing different mechanosensory systems , especially touch and hearing . It is in fact quite likely that the identification of single gene mutations that affect touch may provide a wealth of new insight into genes that determine the development , connectivity , as well as the nature of mechanosensory transduction in the touch system .
With few exceptions the testing procedure was as follows: Twins were tested starting in the morning in a quiet room . The setting was usually a hospital examination room that was centrally heated with regular air exchange via a centralized ventilation system . Twins took turns being tested for baroreflex sensitivity and audiometry assessments . The twins were subsequently tested for tactile acuity , vibration detection threshold , and temperature sensitivity ( in the order: cold detection , warmth detection , heat pain , and cold pain thresholds ) . Finally blood samples were taken . Audiometry testing was carried out by trained personnel of the Clinic for Audiology and Phoniatrics of the Charité–Berlin . All other testing was carried out by the same investigator . The entire testing procedure for one twin pair typically lasted about 4 h . Vibration detection thresholds were determined using the CASE IV system ( WR Medical Electronics ) [73] . In the vibration detection test a transformed-rule up and down method was applied [74] in connection with a two-interval forced choice test . A vibration stimulator was applied below the nail of the little finger . To prevent possible auditory detection of the vibration stimulator , ( normal hearing ) test persons wore headphones , which produced a low , continuous tone during the test . A sinusoidal 125 Hz vibration stimulus with duration of 1 . 68 s was applied during one of two periods indicated to the test persons . The participants then chose the period ( indicated by a 1 or 2 ) during which they thought the vibration had been applied . A step towards the next smallest amplitude was made when the test persons responded correctly to six times in a maximum of eight trials; otherwise a step to the next largest amplitude was made . Eight such reversal points were determined . The calculated vibration detection threshold corresponded to the vibration amplitude at which approximately 75% of the answers are correct [67] . The amplitude magnitude steps were just noticeable differences ( JNDs ) that have been previously determined and roughly resemble a logarithmic representation of the amplitude in µm ( Table S5 ) . Tactile acuity was determined with a two interval forced choice grating orientation determination test using the Tactile Acuity Cube . In the tactile acuity test a transformed-rule up and down method was applied [74] . Test persons placed their hand , with the palmar surface facing upward , on a table and ( sighted ) test persons were blindfolded . The Tactile Acuity Cube was applied for 1 s to the fingerpad , in a way that the cube exerts its whole weight on the finger ( 233 g ) . Test persons had to determine if the orientation of the gratings on the cube was parallel or perpendicular to the fingers , starting with the widest grating width . Each grating width was tested two times and if two answers were correct , the next , smaller width was tested; this was continued until the test person answered incorrectly . The grating width was then increased stepwise again until the two orientations of a width were determined correctly again . Thirteen of these reversal points were determined and the mean of the last 10 taken as the threshold . The threshold corresponds to the grating width where the probability of a correct answer is 0 . 707 . Thresholds were determined for the little finger and the index finger and the mean of this threshold taken as the tactile acuity [74] . In each case the participants were asked which their preferred hand was and this hand was used for the tactile acuity measurement . Audiometry was carried out in the Klinik für Audiologie und Phoniatrie , Charité–Universitätsmedizin Berlin employing the standard procedures for clinical use . For hearing acuity the pure tone thresholds in decibels ( dB , Sound pressure level , SPL ) at 0 . 5 , 1 , 2 , and 4 kHz were determined using a ST36 Audiometer ( Maico ) and the mean calculated . The otoacoustic emissions were measured using an OAE ( Otodynamics ) . Otoacoustic emissions were evoked by 1 ms clicks spanning a frequency range from 0–6 kHz . The measured parameters were the reproducibility of the frequency distribution of consecutively evoked emissions in percentages and the overall intensity of the emissions in dB ( SPL ) . Studies were conducted with the participant in a supine body position . Five-minute recordings were obtained after 10 min of rest . Blood pressure ( BP ) was measured in the left arm by automated oscillometric device ( Dinamap ) as well as continuously by Finapres ( Ohmeda ) BP monitor attached to the middle finger of the right hand . The participant's right hand was kept at heart level . ECG was recorded continuously . Data were analog-digital converted ( both channels at 1 kHz ) , peak detection ( R peak , systolic BP , and diastolic BP ) , and subsequent analyses were done using the PV-wave software ( VisualNumerics ) . Sequences of at least three coupled minimum steps of 0 . 5 mmHg BP changes and 5 ms RR–interval changes with minimum correlation coefficients of 0 . 85 were detected and their slopes taken as the baroreflex sensitivity in ms/mmHg . Blood pressure levels were allowed for by regression for both baroreflex sequence slope and frequency before analysis of heritability or phenotypic correlations . Temperature sensitivity was determined using the TSA-II System ( Medoc advanced medical systems ) according to the manufacturer's instructions . The thresholds were determined using the ascending method of limits . A peltier thermode ( 3 cm×3 cm ) was placed in the middle of the volar forearm . The baseline temperature for all four tests was 32°C and the temperature change rate was 0 . 5°C/s . In the temperature change detection tests , the test persons indicated when they felt a change in skin temperature . For warming and cooling ramps , the mean of four thresholds was calculated . In the temperature pain threshold tests , the test persons indicated when a rising or falling temperature became painful . Here the mean of three thresholds was calculated . Heritability ( h2 ) estimates were calculated by structural equation modeling using the Mx software [27] . At first , heritabilities were estimated in an ACE model , in which the variation of a trait is composed of the variations of additive genetic effects ( A ) , shared environment effects ( C ) , and unique environment effects ( E ) ; accordingly the co-variation of a trait between monozygotic twins is equivalent to the variation of A and E , whereas it is 0 . 5 times the variation of A and the variation of E in dizygotic twins . Subsequently , AE and CE sub-models were tested and the best fitting model selected according to the AIC ( Akaikes information criterion ) . For the selected model the heritability ( proportion of A effects ) was estimated . An age correction was performed before the genetic analysis in analogy to the age correction outlined in Table S1 using the data of the twins only . Transformation of datasets was conducted , if necessary , so that a normality test was passed ( Kolgomorov-Smirnov test ) . Zygosity was tested using 9 , 080 informative autosomal SNPs from a custom designed single nucleotide polymorphism ( SNP ) Immunochip array [25] , [26] . Informative SNPs for zygosity testing were selected on the basis of the following criteria; there was a minimal distance of 100 , 000 base pairs between SNPs , the SNPs had minimal minor allele frequencies ( >0 . 1 ) , redundant SNPs were excluded ( i . e . , those in linkage disequilibrium ) , and SNPs on the X-chromosome were excluded . The data were analyzed and genotype calls made using the Illumina Genome studio software [75] . Standard statistical analysis was done using GraphPad Prism software , calculation of false discovery rates was done using the QVALUE software written by David Siegmund and John Storey [28] . | In humans many genes have been identified that cause deafness when mutated , but no equivalent genes have been identified that are required for touch . Here , we asked whether genes that influence hearing can also influence touch . Using identical and non-identical human twins it was possible to show that touch performance is substantially influenced by genes . Furthermore , people who have excellent hearing are more likely to have a fine sense of touch and vice versa . Interestingly , people who suffer from congenital deafness have a higher chance of having poor touch performance . In a genetically defined form of human deafness , Usher syndrome type II , a single mutated gene was identified that also impairs touch . Touch and hearing are thus intricately intertwined and there may be other touch/hearing genes waiting to be discovered . | [
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"... | 2012 | A Genetic Basis for Mechanosensory Traits in Humans |
Historically the western sahelian dry regions of Mali are known to be highly endemic for cutaneous leishmaniasis ( CL ) caused by Leishmania major , while cases are rarely reported from the Southern savanna forest of the country . Here , we report baseline prevalence of CL infection in 3 ecologically distinct districts of Mali ( dry sahelian , north savanna and southern savanna forest areas ) . We screened 195 to 250 subjects from 50 to 60 randomly selected households in each of the 6 villages ( four from the western sahelian district of Diema in Kayes region , one from the central district of Kolokani and one from the southern savanna district of Kolodieba , region of Sikasso ) . The screening consisted of: 1] A Leishmanin Skin Test ( LST ) for detection of exposure to Leishmania parasites; 2] clinical examination of suspected lesions , followed by validation with PCR and 3] finger prick blood sample to determine antibody levels to sand fly saliva . LST positivity was higher in the western district of Diema ( 49 . 9% ) than in Kolokani ( 24 . 9% ) and was much lower in Kolondieba ( 2 . 6% ) . LST positivity increased with age rising from 13 . 8% to 88% in Diema for age groups 2–5 years and 41–65 years , respectively . All eight PCR-confirmed L . major CL cases were diagnosed in subjects below 18 years of age and all were residents of the district of Diema . Exposure to sand fly bites , measured by anti-saliva antibody titers , was comparable in individuals living in all three districts . However , antibody titers were significantly higher in LST positive individuals ( P<0 . 0001 ) . In conclusion , CL transmission remains active in the western region of Mali where lesions were mainly prevalent among children under 18 years old . LST positivity correlated to higher levels of antibodies to sand fly salivary proteins , suggesting their potential as a risk marker for CL acquisition in Mali .
Leishmaniasis is a disease caused by Leishmania , a protozoan transmitted to humans by the bite of the sand fly [1] . There are different forms and clinical manifestations of the disease that depend primarily on the Leishmania species incriminated . The major clinical manifestations of leishmaniasis are visceral , muco-cutaneous or cutaneous . CL is currently endemic in 87 countries worldwide [2] including 20 countries of the New World ( South and Central America ) and in 67 countries in the Old World ( Europe , Africa , Middle East , central Asia and the Indian subcontinent ) . An estimated 500 , 000–1 , 000 , 000 new cases occur annually but only a small fraction of cases ( 19%–37% ) are actually reported to health authorities [3] . In the Old World , cutaneous leishmaniasis ( CL ) is the most common form of the disease . Cutaneous leishmaniasis caused by L . major frequently appears as severely inflamed and ulcerated skin , which usually heals spontaneously within 2–8 months . It usually produces ulcers on the exposed parts of the body , such as the face , arms and legs . There may be multiple lesions , especially in non-immune patients , which can cause serious disability . When the ulcers heal , they invariably leave permanent scars , which are often the cause of serious social prejudice . [4] [5] . In Mali , L . major was identified as the predominant causative Leishmania species responsible for CL disease [6] . Moreover , Phebotomus duboscqi was incriminated as the main vector of L . major [7] . Although the reservoirs of CL in Mali have not been established , rodent species reported from the country are well known reservoirs for L . major throughout its distribution range in West Africa [8 , 9] . Compared to other parts of the country , the region of Kayes in the western part of Mali is known for its higher endemicity for Leishmania infection [10 , 11] . The last study using a leishmanin skin test ( LST ) to determine the prevalence of CL in Kayes region dates back to late 1960’s [12] . No data is available on the epidemiology of the disease in southern regions , while previous studies carried out in the central district of Baroueli , reported a LST positivity ranging from 20% to 45% [10] . The objectives of this study were to determine the baseline prevalence of LST positivity , the prevalence of CL lesions , and the level of anti-P . duboscqi salivary antibodies in populations living in western , central and southern Mali , three ecologically distinct study sites . The finding of this study provides an update on the prevalence of CL in these regions .
The study protocol ( Protocol # 12–0075 ) was approved by the Institutional Review Boards ( IRB ) of the Faculty of Medicine , Pharmacy and Odontostomatology ( FMPOS ) , Bamako , Mali , and of the National Institutes of Allergy and Infectious Diseases ( USA ) . A collective village-wide oral consent was obtained from the villages’ elders , and all adult participants signed individual written informed consent and a parent or guardian of any child participant provided informed consent on their behalf . The study was carried out in June 2014 in three ecologically distinct districts ( Fig 1 ) : 1] The district of Diema in the Region of Kayes , located at 345 km from Bamako in the western part of the country . The study was carried out in 4 villages: Nafadji ( 9 . 233399W , 14 . 557710N ) , Guemou ( 9 . 312490W , 14 . 546290N ) , Debo Massassi ( 9 . 368320W , 14 . 579850N ) and Tinkare ( 9 . 179729W , 14 . 490520N ) . In Diema , the undulating topography is dominated by sandy plains and a plateau with a few rocky outcrops , and is an extension of Mont Manding . The climate is typical Sahelian with sandy clayey soil , characterized by the alternation of two seasons with temperatures varying between 15°C and 45°C depending on the season . The rainy season is short lasting from July to October , with rainfall ranging from 400 mm to 800 mm . The dry season lasts from November to June . The site is influenced by the harmattan , a dry wind that blows from the northeast to the southwest , and the monsoon bringing rain . The vegetation is characterized by shrub and tree vegetation . The population is composed mainly of Sarakolé , Bambara , Peulh , Moor , and Kagoro ethnicities . 2] The district of Kolokani , in the region of Koulikoro , is located at 105 km northwest of Bamako in central Mali . The study was carried out in the village of Tieneguebougou ( 8 . 077450W , 13 . 57639N ) . The north of the district is dry , Sahel land , primarily used for livestock . The study site is at the interface between the sahelian and the wetter Sudan to the south . The population is composed mainly of Bambara , Peulh , Mossi , and Dioula ethnicities 3] The district of Kolondieba , region of Sikasso , located at 250 km from Bamako in the southern part of Mali . The study was carried out in the village of Boundioba ( 6 . 982890W , 11 . 040190N ) . The climate is typically south-savannah type with clear forests and an average of 1250 mm of rain spread over 60 days per year . The average temperatures vary from 20°C to 31°C in the same year . The population is composed of Bambara , Peulh , Senoufos and Sarakolés ethnicities . Based on historical data from the National referral dermatologic hospital of Bamako , CL cases are regularly recorded from Diema , but no population-based prevalence of CL is available . The districts of Kolokani and Kolondieba are known to be endemic for lymphatic filarial ( LF ) but not for CL . In each village , 40 to 50 households were randomly selected ( with an average of 5–7 persons per household , and 195 to 250 subjects per village ) and screened for CL . Households were randomly selected from a list obtained from a census data collected by the study team . Subjects living in the randomly selected households were included in the study: 2–65 years old in the district of Diema ( Nafadji , Guemou , Debo Massassi and Tinkare ) , and 18–65 years old in the districts of Kolokani ( Tieneguebougou ) and Kolondieba ( Boundioba ) . We targeted adults for recruitment in the districts of Kolokani and Kolondieba due to their low endemicity for CL . After informed consent , all members of the selected households were invited to participate the screening . The screening consisted of clinical examination by a dermatologist , a LST and a finger prick to collect blood samples for immunological studies . The data were recorded on a case form ( CRF ) , entered in iDataFax management ( Version 2014 . 1 . 0 ) , and analyzed using the Statistical Package for the Social Sciences ( SPSS , Chicago , IL , USA ) . Descriptive analyses were used to assess the association between LST and demographic variables . Fisher's exact test was used to assess the association between infection and demographic variables . Age means between villages were compared using an independent samples t-test . One-way ANOVA with Bonferroni's multiple comparison test was applied to evaluate the difference in the mean size of the LST reaction between the two study sites .
Significant differences in LST positivity ( LST+ ) per district were observed among individuals in the age groups 19–40 and 41–65 years of age ( Fig 2A ) . The highest LST+ was observed in Diema ( 85 . 1% ) followed by Kolokani ( 24 . 6% ) and was lowest at 2 . 7% in Kolondieba . Compared to Kolokani and Kolondieba , a significantly higher prevalence of LST positivity was observed for each of the study villages of the district of Diema ( Fig 2B ) . Moreover , the high prevalence rates of LST+ were comparable among 3 of the 4 villages ( Nafadji , Guemou , Debo Massassi ) of the district of Diema ( Fig 2B ) . In the district of Diema , the percentage of participants with a positive LST increased steadily with age at 13 . 8% [9 . 2–19 . 5] , 41 . 3% [36 . 3–46 . 0] and 83 . 9% [78 . 4–94 . 4] for age groups 2–5 years , 6–18 years , 19–40 years and 41–65 years , respectively ( Fig 3A , Table 2 ) . The high LST positivity in the district of Diema was stable among its four tested villages , and was greater than the positivity observed in Tienekebougou and Boundioba villages ( Fig 3B ) . Moreover , it was significantly different among age groups 19–40 years ( P = 0 . 0329 ) and 41–65 years ( P = 0 . 0251 ) between the site of Boundioba compared to the four sites of Nafadji , Guemou , Debo massassi and Tinkare ( Fig 3B ) . In the district of Kolondieba , the prevalence of LST positivity was low and stable ( around 2–3% ) across age groups ( Fig 3B ) . Levels of P . duboscqi saliva specific IgG antibodies were similar in all the study sites and were significantly higher ( P<0 . 0001 ) compared to non-endemic controls ( US healthy volunteers with no previous history of exposure to Leishmania ) ( Fig 4A ) . Moreover , the median value of anti-salivary IgG levels were not significant in LST+ compared to LST- study subjects ( Fig 4B ) . During active case detection , 11 suspected lesions were found , of which 8 were confirmed by PCR amplification ( Fig 5 ) and microscopy . The positive cases were from district of Diema , six cases in Nafadji , one in Guemou and one in Tinkare . The lesions were clinically characterized as simple or multiple ulcero-crusted lesions , and were mostly observed on the limbs and forehead ( Fig 6 , Table 3 ) . All eight patients positive for CL were treated with Meglumine Antimoniate ( GLUCANTIME ) without complications . The mean age of confirmed CL cases were 6 . 5±5 . 35 years . The minimum age was 2 years and the maximum was 17 years ( Table 3 ) .
LST has been used for years as an aid for diagnosis , and in epidemiological studies for assessing exposure to Leishmania infection [5 , 9 , 10] . LST remains an important tool in measurement of delayed-type hypersensitivity reactions ( DTH ) and consequently in assessment of cell-mediated immunity . It plays a major role in defining the immunity status of volunteers to leishmaniasis in vaccine trials [19] , the assessment of vaccine efficacy , and the effectiveness of vaccination [20] [21] . In this cross-sectional study we present results of LST positivity in three different climatic areas of Mali in the districts of Diema , Kolokani and Kolondieba in western , central and southeast Mali , respectively . Our data showed a higher prevalence of LST+ in all the villages located in the Sahelian district of Diema compared to those in the district of Kolokani in central Mali and Kolondieba in southern Mali . Indeed , the overall prevalence of LST in ages 19 to 65 years was 84 . 2% ( N = 101 ) in Nafadji , 85 . 0% ( N = 80 ) in Guemou , 87 . 9% ( N = 91 ) in Debo Massassi and 82 . 1% ( N = 56 ) in Tinkare . In Tiénekebougou located in the district of Kolokani , central Mali , LST prevalence was 24 . 6% ( N = 175 ) , while in Boundioba in the district of Kolondieba , southern Mali , it was only 2 . 7% ( N = 224 ) . This north to south decrease in the prevalence of LST+ can be partly explained by the same pattern of decrease observed in the densities of P . duboscqi , the Phlebotomus species incriminated in the transmission of CL in Mali [13] . In a study carried out ten years ago in Baroueli , a neighboring district of Kolokani , Oliveira et al [10] observed a LST positivity ranging from 45 . 4% to 19 . 9% suggesting that transmission is stable in central Mali . Our data also showed that the prevalence of LST positivity increased with age in Diema and Kolokani districts , consistent with other studies [10 , 22 , 23] . This age pattern was not observed in Boundioba probably because of the low exposure of the population to infected sand fly bites compared to Diema , and at a lesser extent to Kolokani . Interestingly , the overall mean anti-saliva specific IgG antibody level was similar in all study districts but was significantly higher ( P<0 . 0001 ) in LST positive compared to LST negative subjects . This result is in accordance with previous studies using salivary proteins from Lutzomyia longipalpis [15] and P . sergenti [24] where anti-saliva antibody levels positively correlated with previous exposure to Leishmania parasites . Similar to previous reports from Mali [6 , 7 , 25 , 26] , all active cases of CL identified during this study were caused by L . major . Though L . major is a self-healing disease , the diagnosed lesions were mostly large , disfiguring and ulcerative in nature reflecting the need for surveillance , early treatment and control . All the confirmed cases were from the western part of Mali , in the district of Diema corroborating data from the current LST survey as well as previous studies [6 , 11 , 12 , 27] of its high endemicity for CL . Six of the eight confirmed CL cases in this locality were from Nafadji , and the rest were from Guemou and Tinkare . The active cases were mostly in children , the oldest patient being 17 years of age . This suggests that transmission is peridomestic where children are exposed to bites of infected sand flies . Additionally , children may be more exposed to sand fly bites due to the way they dress as well as the nature of their activities such as shepherding animals in pastures . Similar to previous reports from other parts of the country [10 , 28] , no active CL cases were detected from the districts of Kolokani and the district of Kolondieba despite a evidence of exposure to Leishmania by LST of 24 . 6% and 2 . 7% , respectively . The presence of P . duboscqi anti-saliva specific IgG antibodies in all study districts is consistent with the prevalence of this species ( the main vector of CL in Mali ) in all 3 districts [29] . Additionally , a similar level of antibodies in LST+ and LST- individuals may be a reflection of the low number of infected flies in sand fly populations , in Mali [7] . The difference the prevalence of LST positivity and the distribution of active cases of CL among the 3 districts may be due to a difference in the concentration of rodent reservoirs . Further studies are needed to fully understand the ecology and infection dynamics in both sand flies and reservoirs to elucidate the observed differences in the epidemiology of CL among the 3 districts . In summary , the proportion of positive skin tests increased with age suggesting that the children are high risk to developing CL in Mali . Moreover , comparing 3 ecologically distinct areas in western , central and southeast Mali , we determined that the prevalence of LST positivity and active disease remains highest in the western part of Mali . Future steps will focus on the characterization of the Leishmania strains circulating in Diema area and correlating the specific human immune response to sand fly salivary proteins with CL outcome . | It is generally assumed that neglected tropical diseases ( NTDS ) such as leishmaniasis are concentrated in poor populations . It affects as many as 12 million people , with 1 . 5 to 2 million new cases every year around the world . Depending on the species of Leishmania , the host can develop cutaneous leishmaniasis ( CL ) or visceral leishmaniasis . In Mali , CL caused by Leishmania major is transmitted through the bite of infected sand flies belonging to the species Phlebotomus duboscqi . The objectives of this study were to determine the baseline prevalence of LST positivity , a test of previous exposure to Leishmania parasites , the prevalence of CL lesions , and the level of anti-P . duboscqi salivary antibodies , indicative of exposure to vector bites , in populations living in western , central and southern Mali , three ecologically distinct study sites . LST positivity was higher in the western district of Diema ( 85 . 1% ) than in Kolokani ( 24 . 6% ) and was much lower in Kolondieba ( 2 . 7% ) . All eight PCR-confirmed L . major CL cases were diagnosed in subjects below 18 years of age and all were residents of the district of Diema . Exposure to sand fly bites , measured by anti-saliva antibody titers , was established in individuals living in these three districts and antibody titers were higher in LST positive individuals ( P<0 . 0001 ) . The finding of this study provides an update on the prevalence of CL in these regions . | [
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"pr... | 2016 | Prevalence of Cutaneous Leishmaniasis in Districts of High and Low Endemicity in Mali |
Modelling disease dynamics is most useful when data are limited . We present a spatial transmission model for the spread of canine rabies in the currently rabies-free wild dog population of Australia . The introduction of a sub-clinically infected dog from Indonesia is a distinct possibility , as is the spillover infection of wild dogs . Ranges for parameters were estimated from the literature and expert opinion , or set to span an order of magnitude . Rabies was judged to have spread spatially if a new infectious case appeared 120 km from the index case . We found 21% of initial value settings resulted in canine rabies spreading 120km , and on doing so at a median speed of 67 km/year . Parameters governing dog movements and behaviour , around which there is a paucity of knowledge , explained most of the variance in model outcomes . Dog density , especially when interactions with other parameters were included , explained some of the variance in whether rabies spread 120km , but dog demography ( mean lifespan and mean replacement period ) had minimal impact . These results provide a clear research direction if Australia is to improve its preparedness for rabies .
Rabies is a zoonotic disease caused by a virus of the genus Lyssavirus . Annually , it causes an estimated 61 , 000 human deaths in over 150 countries and territories where it is endemic [1–3] . The canine strain of the virus is the most widely distributed globally [4] , with dogs accounting for the vast majority ( up to 99% ) of human rabies infections and deaths [3 , 5] . A key feature of rabies that assists in its spread and persistence is the potentially long incubation period [6 , 7] . For canine rabies the incubation period can vary from 10 days to 6 months , but for most cases lasts between 2 weeks and 3 months [2] . The extended incubation period often allows rabies to remain undetected and enter new areas [8 , 9] , and is one reason why the eventual incursion of canine rabies into Australia is likely [2 , 10] . Australia is historically free of canine rabies , with only the bat strain ( Australian bat lyssavirus ) endemic on the continent [1 , 2 , 10 , 11] . In South-East Asia , though , canine rabies is endemic and currently spreading eastward along the Indonesian archipelago such that it is now less than 300 km from the northern Australian border [2 , 8–12] . Two locations have been identified as probable entry points , namely Arnhem Land in the Northern Territory and Cape York Peninsula in Queensland [2] . Canine rabies introduction is anticipated to occur via a sub-clinically infected dog illegally brought into the country by means of a fishing vessel , pleasure craft , or boat continuing cross-cultural traditions established centuries ago [13 , 14] . Incursion alone , however , is insufficient for disease establishment . Instead , the index case will also have to contact , bite , and successfully transmit the virus to at least one other resident dog . Several factors make this first transmission event possible: ( 1 ) although much of northern Australia is largely uninhabited , many of the remote communities are on or close to the coast , ( 2 ) free-roaming dogs are common in these communities [15 , 16] , and ( 3 ) wild dogs , comprising mostly dingoes and their hybrids with free-roaming domestic dogs , are ubiquitous across northern Australia [17 , 18] . Sparkes et al . [19] have noted Australia stands much to lose should canine rabies become established in its wild dog population . The first , and most significant , loss could be that of human life , especially in peri-urban areas where wild and domestic dogs frequently coincide and the likelihood of human exposure is greatest . There will also be economic losses due to reduction in ecotourism , rabies-related livestock losses , and the large-scale costs associated with mass vaccination of domestic dogs and wildlife , and the administration of post-exposure prophylaxis to humans . Perhaps harder to quantify , but nevertheless important , is the impact rabies would have on the Australian nation’s affinity for , interaction with , and conservation of wildlife . Australia is renowned for , and prides itself on , its unique fauna with which people frequently interact . This forms a key component to the national image as a whole . The presence of rabies in wild dogs , however , would certainly lead to greater restraint on wildlife interactions due to the fear of potential rabies exposure . This will be especially true should other native wildlife besides wild dogs also be at risk of canine rabies infection . Consequent to each of these foreseeable losses , risk assessment for the sustained transmission of canine rabies within the Australian wild dog population has been identified as a national research priority [10] . Mathematical models of infectious disease transmission are playing an increasing role in informing and directing public health policy , as well as providing opportunities for authorities to explore control options [20] . An example is canine rabies in Australia , for which there is an absence of epidemiological data . Spatial rabies transmission in small ( ≤2 . 2 km2 ) , remote communities of northern Australia with high densities of domestic dogs ( ≥137 dogs/km2 ) has been modelled [21] but there are no existing models for wild dogs which are territorial by nature and occupy an expansive Australian landscape at much lower densities . We therefore present a stochastic transmission network ( percolation ) model for the spatial spread of canine rabies through the Australian wild dog population that allows us to estimate the probability a canine rabies epidemic will occur , given its introduction , and the geographic rate at which rabies will spread . We also conduct a global sensitivity analysis to identify where knowledge is most critical for predicting model outcomes .
We simulated the spread of canine rabies through the Australian wild dog population by implementing the model of Davis [22] . Each simulation began by distributing nodes ( representing the centroids of potential wild dog home ranges ) uniformly at random across a landscape of width 250 km and height 125 km ( Fig 1 ) . The density of nodes distributed in each simulation was calculated using the formula derived in S1 Appendix . Because the incubation period of rabies is on the order of weeks to months , and because wild dogs inhabit an expansive Australian landscape , we expected the spread of rabies through the wild dog population to take on the order of months to years . Consequently , we included wild dog demographic processes in the model whereby home ranges become vacant through natural mortality and are subsequently reoccupied through migration or recruitment . Each node was therefore either occupied by a wild dog or unoccupied at any given point in time during a simulation . When a node was occupied , its location represented the average position of the dog inhabiting the corresponding home range . Consequently , one should not think of the dogs as being fixed at these locations . Instead , each dog makes contact with other dogs over time by way of their normal everyday movement across the landscape for activities such as foraging , finding water , taking shelter , communicating , finding a mate , and raising young [2 , 23 , 24] . We propose the rate at which two dogs i and j contact one another , kij , is a function of the Euclidian distance , sij , between the two dogs’ mean positions ( nodes ) , as well as their individual inclination towards making contact with other dogs ( sociability ) , xi and xj respectively . Specifically , we defined the contact rate as kij=λ2e−λsijxixj , ( 1 ) where λ ( units: km-1 ) is a spatial parameter that captures the scale over which distance between two dogs affects their rate of contact . For large values of λ the maximum contact rate ( sij = 0 ) is high and the contact rate declines sharply with distance ( Fig 2 ) such that dogs effectively only come into contact with their nearest neighbours . Conversely , for small values of λ the maximum contact rate is low but dogs come into contact with other dogs that are far away almost as often as their nearest neighbours . Eq 1 has the property that the average number of contacts a dog has with other dogs over some period of time is independent of λ [22] . Another appealing feature , revealed by dimensional analysis ( S1 Appendix ) , is that a dog’s sociability , xi , is related to the area of land it traverses per day . To simulate the spread of rabies we employed long-range percolation [22 , 25 , 26] on the dynamic wild dog contact networks described above . Rabies was introduced into the wild dog population by selecting the dog closest to the centre of the upper boundary of the landscape as the index case and infecting it . Thereafter a directed edge from an infectious dog i to a susceptible dog j , representing a successful transmission event , was generated with probability pij=1−e−τkijδ , ( 2 ) where τ is the transmission probability given contact and δ = 1 day is the model time step . Dogs exposed to infection were assigned a gamma distributed incubation period after which they were rabid ( infectious ) for an exponentially distributed period of time [27–29] . When a dog died ( either due to natural mortality or disease ) its corresponding node was rendered ‘unoccupied’ and remained so until a new susceptible dog ( with its own unique sociability ) arrived . We refer to the average length of time until an unoccupied home range was reoccupied as the ‘replacement period . ’ In each simulation rabies was transmitted from dog to dog and eventually either died out or progressed 120 km from the index case ( lightest grey semi-circle , Fig 1 ) . Because , in spatial disease systems , the percolation of disease beyond various milestone distances can be construed as alternative definitions of an epidemic [22 , 25] , we considered four arbitrarily chosen milestone distances , namely 30 , 60 , 90 , and 120 km ( dark to light grey semicircles , Fig 1 ) . For each transmission network realization ( simulation ) we recorded 11 outcome variables of interest: If the disease percolated beyond the fourth milestone distance ( 120 km ) a further 3 conditional outcomes were recorded at the time of percolation: To quantify the sensitivity of outcomes 1–5 to each model input variable we performed a global sensitivity analysis . In particular , we calculated Sobol’s ( sensitivity ) indices because they describe the proportional contribution of each input variable’s uncertainty to the variance of each respective outcome [30 , 31] . In addition , Sobol’s indices capture any interaction effects between the input variables and do not require or assume any particular model structure ( e . g . linear or monotonic ) [30] . To calculate Sobol’s indices we implemented the Monte Carlo procedure proposed by Saltelli [31] for which the technical details are described in S1 Appendix . Briefly , though , we defined a distribution for each input variable by specifying a range of values ( see Parameterization section below ) that captures either its natural variation ( e . g . geographic , seasonal , etc . ) or any other uncertainty in its value ( e . g . no published estimates ) and assuming a uniform distribution across this range . Next , each input variable distribution was sampled 50 , 000 times ( assuming independent input variables ) by employing ( quasi-Monte Carlo ) Sobol sequences , which are deterministic , uniformly distributed sequences that ensure more uniform sampling over the input parameter space than is achieved by traditional pseudo-random sampling . Doing so facilitates faster convergence of the sensitivity indices such that fewer samples are required [32] . The sample values were then stored in two matrices M and M′ ( half in each matrix ) such that each row of the matrices denoted a sample vector from the model input parameter space and each column contained the sample values of a single input variable . For each input variable , e . g . yj , a further two matrices Nj and N¬j were defined as a combination of the columns of M and M′ ( see S1 Appendix ) . Thus for our model with 6 input variables ( 5 biological , 1 random number generator seed–see below ) a total of 14 matrices of size ( 25 , 000 × 6 ) were generated . Next , a transmission network realization was generated for each row ( sample vector ) of each matrix . This resulted in 14 different , but closely related , distributions of values for each model outcome from which the sensitivity indices were subsequently calculated using the formulas described in S1 Appendix ( Equations S17—S20 ) . Two sensitivity indices were calculated for each input and outcome variable pair , namely the first-order and total effects . The first-order effect , Sj , is the average proportional reduction in the variance of an outcome variable , z , when an input variable , yj , has no uncertainty ( i . e . it is assigned a fixed value ) . The greater the value of Sj the more influential is input variable yj in determining the value of z . Conversely , the total effect , SjT , is defined as the average proportion of variance that remains when all input variables are assigned fixed values ( i . e . are known ) except for input variable yj . The total effect may be interpreted as the sum of an input variable’s first-order effect and any additional effects resulting from its interaction with other input variables . We note here that because the canine rabies model is a stochastic simulation model , even if all biological input variables were assigned fixed values , model outcomes would still vary from one simulation to the next . That is to say , model outcome variance is not completely explained by biological input variable uncertainty alone . This is important because this additional variance will affect the accuracy of global sensitivity indices estimated using the procedure above . To address this , the random number generator seed ( which explains all model variance over and above that accounted for by the biological input variables ) must be treated as an additional input variable , sampled from its own unique distribution , and assigned a value at the start of each simulation ( as is done for the biological input variables ) . For the rabies model , we sampled the seed from a discrete uniform distribution with range [0 , 232−1] . This range was selected as it includes all permissible integer values for the random number generator seed ( MATLAB R2012b ) , thereby ensuring no bias was inadvertently introduced into model outcome distributions by falsely limiting the range of values the seed could assume ( this is especially important when there are strong interactions between a model’s input variables ) . An important indication that sampling the random number generator seed has been done correctly is that the seed accounts for a non-negligible proportion of model outcome variance ( at the very least this should be true for the total effect ) and that when model outcomes are plotted against the seed value a horizontal trend line is observed ( i . e . model outcomes should be independent of the seed ) . Lastly , to estimate 95% confidence intervals for Sobol’s indices we employed the bootstrapping procedure prescribed in [33] . Because many wild dogs are hybrids of dingoes and modern domestic dog breeds [34] we assumed canine rabies pathogenesis parameters for wild dogs will be similar in value to those reported in the literature for domestic dogs . More information is available for canine rabies pathogenesis parameters than those relating to wild dog ecology in northern Australia [2 , 10 , 27 , 28 , 35 , 36] . Consequently , we assigned disease related parameters point estimates obtained from the literature whereas for ecological parameters we considered a range of feasible values guided by a combination of expert opinion and the literature ( Table 1 ) . If absolutely no information was available for a particular ecological parameter , a range of values spanning an order of magnitude was selected based on mechanistic reasoning . At present , there are three published , field-data derived estimates for the mean incubation period of canine rabies . Hampson et al . [28] and Hampson et al . [27] report values of 25 . 5 days and 22 . 3 ( 95%CI: 20 . 0–25 . 0 ) days respectively , derived directly from Tanzanian rabid dog natural infection and contact tracing data . Coleman et al . [37] , on the other hand , obtain their estimate of 4 . 18 ( SE: 0 . 27 ) weeks from a thesis [35] which calculates the mean incubation period from observed canine rabies cases in Zimbabwe . We therefore assumed the incubation period was gamma distributed ( as observed by [27] ) with a mean of 28 days ( fixed shape parameter = 7 , fixed scale parameter = 4 ) . Under these assumptions , 93 . 5% of simulated incubation periods in the transmission network model lasted between 2 weeks and 3 months , with the remaining 6 . 5% shorter than 2 weeks . The infectious period of canine rabies is typically much shorter than the incubation period . Tepsumethanon et al . [36] reported that naturally infected rabid dogs in Thailand survived a median of 4 ( 95%CI: 3 . 7–4 . 3 ) days after first displaying symptoms ( abnormal behaviour or bite event ) . Two field-data derived mean infectious periods of 3 . 1 ( 95%CI: 2 . 9–3 . 4 ) days and 0 . 81 ( range: 0 . 29–1 . 71 ) weeks have also been published by Hampson et al . [27] and Coleman et al . [37] respectively . There is some evidence supporting a gamma distributed infectious period [27] . Nevertheless , the infectious period is often modelled using the closely related exponential distribution [28 , 29] . We adopted this same approach here assuming a fixed mean of 3 days such that 96 . 4% of simulated infectious periods were shorter than 10 days . In Eq 2 , τ is defined as the transmission probability given contact . Rabies transmission , however , requires that an infectious dog inoculate a susceptible dog by biting it rather than by merely being in its vicinity . Thus , assuming independence , τ is equivalent to the probability a rabid dog bites another dog given contact ( for which we assumed a fixed value of 0 . 5 ) multiplied by the probability of successful transmission given a rabid dog bites a susceptible dog ( estimated by Hampson et al . [27] to be 0 . 49 ( 95%CI: 0 . 45–0 . 52 ) and which we took as 0 . 5 ) . No estimates for the population density of wild dogs ( occupied nodes ) in tropical northern Australia , where a rabid dog incursion is most likely , have been published to date . Therefore , to select a range of feasible values we considered wild dog population density estimates obtained for other regions around Australia ( which have different terrain types , climates , and levels of anthropogenic impact ) . In general , wild dog density appears to depend largely on landscape carrying capacity , with lower densities observed in arid regions ( 0 . 08 dogs/km2 ) and higher densities in higher-rainfall areas ( 0 . 14–0 . 3 dogs/km2 ) [2] . It is also likely that dog densities have responded positively to agriculture since anthropogenic resources subsidize both food ( sheep , cattle , goats , kangaroos , and rabbits ) and water ( nowhere in the cattle zone is further than 10 km from water ) [38 , 39] . Conversely , wild dog densities may be reduced by human-related control measures such as baiting . To account for the variation in wild dog densities resulting from each of these factors we considered densities within the range 0 . 05–0 . 38 dogs/km2 . The literature provides no estimates for the area of land a wild dog traverses per day , reporting only home ranges , which are not rates and therefore not equivalent [2 , 10] . Nevertheless , because wild dog home ranges vary between 10 and 100 km2 [10] , and because a wild dog probably only covers a small portion of its home range each day ( although potentially frequenting some areas more than once each day ) , we considered mean values for the area of land traversed per day from a plausible range spanning an order of magnitude ( 0 . 5–5 km2/day ) . Assuming a wild dog’s sociability was equal to the square root of the area it traverses per day ( S1 Appendix ) we then calculated a range for the mean sociability of 0 . 5–5 km/day0 . 5 . To assign each wild dog a unique sociability , and thereby incorporate individual contact heterogeneity into the model , we assumed wild dog sociabilities were gamma distributed . Specifically , we assumed the sociability shape parameter takes a fixed value ( arbitrarily chosen to be 30 ) whilst the sociability scale parameter was sampled from the range [1300 . 5 , 1305] to ensure the desired range of values for the mean area of land traversed per day . There are no recorded estimates in the literature for the introduced spatial scale parameter λ . Nor are there any appropriate data ( e . g . proximity log or GIS tracking data ) relating the contact rate of wild dogs to the distance between their mean positions that can be used to estimate λ . Wild dog contact distance distributions , however , are likely to be highly left-skewed [38] . From a mechanistic perspective , it seems reasonable to expect wild dog contact rates to decline substantially over distances between 1 km and 10 km . We therefore sampled λ from a range of values spanning an order of magnitude ( 0 . 1–1 . 0 km-1 ) , where for λ = 0 . 1 km-1 the wild dog contact rate declines by 50% every 7 km and for λ = 1 . 0 km-1 it declines by 50% every 0 . 7 km . The natural mortality of wild dogs is a complex parameter that is most likely age- ( highest in juveniles <18 months ) and density-dependent; the precise relationship , however , has not yet been quantified . It has been noted , though , that wild dogs live up to 10 years , with most dying by 5–7 years [10 , 23 , 24] . Therefore , to maintain model simplicity we assumed wild dog lifespan was exponentially distributed and sampled the mean value from a range of 2–4 years such that the probability a wild dog lived ≥10 years varied from 0 . 7–8 . 3% . To maintain model simplicity we assumed the replacement period was exponentially distributed and sampled the mean value from a range of 1–100 days based on expert opinion [38] .
Of the 50 , 000 transmission network realizations obtained from evaluating the model on the rows of matrices M and M′ , canine rabies percolated 30 , 60 , 90 , and 120 km a total of 11 , 306 ( 23% ) , 10 , 782 ( 22% ) , 10 , 695 ( 21% ) , and 10 , 672 ( 21% ) times respectively . We therefore estimate there is a 21% probability the introduction of a single sub-clinically infected dog into the wild dog population of Australia and subsequent transmission of rabies virus will result in a canine rabies epidemic . Furthermore , because there is little difference between the probabilities rabies percolates 30 km and 120 km , once an epidemic starts it is unlikely to stop naturally . We also note that for 99 . 4% of the simulations in which rabies percolated 120 km , rabies was still present within milestone 1 ( 30 km ) at the time of percolation beyond milestone 4 . This suggests the percolation of rabies 120 km or further is a reasonable indicator that canine rabies has become established in the wild dog population ( at least in the region close to where the initial transmission event occurred ) . The basic reproduction number ( R0 ) was estimated as the mean number ( 1 . 09 , 95%CI: 1 . 07–1 . 10 ) of wild dogs infected by the index case over all 50 , 000 transmission network realizations . Given the current absence of epidemiological and ecological data for canine rabies in Australian wild dogs we used the time taken for rabies to percolate beyond the fourth milestone distance ( 120km ) , given that it percolated 120km , to plot a baseline distribution of speeds the disease might spread across the Australian landscape ( Fig 3 ) . The distribution generated indicates a mean ( median ) rate of spread of 90 ( 67 ) km/year . The distributions of model outcomes for the 50 , 000 transmission network realizations were used to investigate the relationships between the outcomes and six input variables ( Figs 4 and 5 , S1 Fig–S6 Fig ) . To do this , each simulation was binned by input variable value and thereafter the median ( or mean ) value of each outcome in each bin calculated . Here we report the results for the percolation probability and time to percolation . We refer the reader to S1 Fig–S6 Fig for the results of the remaining model outcomes . In Fig 4 the probability canine rabies percolates beyond each milestone distance is plotted as a function of each input variable . Once again we observe from the closely overlapping curves that there is little difference between the probability rabies will spread 30 km ( milestone 1 ) or 120 km ( milestone 4 ) . Also noticeable is that as values for wild dog density or the sociability scale parameter increase the probability of percolation increases rapidly . Specifically , for wild dog densities below 0 . 15 dogs/km2 and a sociability scale parameter less than 36 . 0 the probability rabies will percolate beyond milestone 4 is less than 12% and 5% respectively . Once wild dog densities rise above 0 . 35 dogs/km2 and the sociability scale parameter increases to 67 . 0 the probability of percolation exceeds 40% . Lastly , the more territorial wild dogs are in their behaviour ( i . e . as values for the spatial scale parameter , λ , get larger ) the lower the probability of percolation . In Fig 5 the median time for rabies to percolate beyond each milestone distance , given rabies percolated beyond each respective milestone distance , is shown as a function of each input variable . Three features are immediately apparent , the first of which is that the curves are equidistant . This indicates that the wave front speed was constant over time and with distance from the index case . The second feature is that the median time for percolation grows almost linearly as the spatial scale parameter , λ , increases in value . Thirdly , we note that at low values for wild dog density and the sociability scale parameter the median time to percolation is highly variable . This is explained by the fact that in this region of parameter space canine rabies very seldom percolated beyond the milestone distances ( see Fig 4 ) . Consequently , only a small number of simulations contribute to the calculation of the median time to percolation in each bin resulting in greater variance between bins . Once wild dog density and the sociability scale parameter are larger than 0 . 15 dogs/km2 and 40 . 0 respectively , such that the probability of percolation is greater than 0 . 10 , the estimates for the median time to percolation become less variable . The results of the global sensitivity analysis are reported in Tables 2 and 3 , and plotted in Fig 6 . Before describing them in detail , it is instructive to point out that some of the sensitivity indices are negative even though by definition they should fall within the range [0 , 1] . This is because the reported global sensitivity indices are only estimates of the true sensitivities , having been calculated from a finite number of samples . Monte Carlo variability generates estimates that are marginally different from their true values , such that when the true values are close to 0 or 1 the sensitivity estimates may fall just below 0 or above 1 [33] . Although increasing sample size typically resolves this small source of error ( causing the difference between sensitivity estimates and their true values to converge to zero ) , it is possible to obtain this error for any sample size if a true sensitivity index is precisely 0 or 1 . In practice then , the key to selecting an appropriate sample size involves both ensuring the convergence of the sensitivity estimates and deciding what an acceptable ( negligible ) difference ( error bound ) is . In the results presented here we considered estimates outside the range [0 , 1] by less than 0 . 01 acceptable ( assuming the true sensitivity values were 0 or 1 as appropriate ) . For the binary outcomes indicating whether or not rabies percolated beyond each respective milestone distance , as well as the number of dogs infected by the index case , the wild dog sociability scale parameter and wild dog density were the first and second most influential input variables respectively ( Table 2 , Fig 6 Panels A and B ) . Specifically , the sociability scale parameter accounted for 15% of the variance in whether rabies percolated or not whilst wild dog density explained 11% of its variance . For the number of dogs infected by the index case these values declined slightly to 10% and 7% respectively . The importance of density , though , was more pronounced when input variable interactions were taken into account explaining over 83% of the variance in all outcomes ( Table 3 , Fig 6 Panels A and B ) .
Canine rabies typically persists in the context of an urban transmission cycle in which stray dogs and unvaccinated , free-roaming owned dogs account for a substantial proportion of the population [2 , 8 , 12 , 27 , 28 , 40–42] . We have investigated the potentially unique scenario of canine rabies spreading in a sylvatic transmission cycle with the Australian wild dog as reservoir host . We estimate there is a 21% probability the incursion of a single sub-clinically infected dog and subsequent infection of one or more wild dogs will result in the sustained spatial transmission of rabies within the wild dog population , and on so doing spread at a mean ( median ) speed of 90 ( 67 ) km/year . Given current levels of uncertainty for wild dog ecology parameters , global sensitivity analysis indicates wild dog sociability and wild dog density are the first and second most influential parameters in determining whether a rabies epidemic will occur . Rate of spread , on the other hand , is governed by the spatial scale over which distance begins to affect wild dog contact rates . In recent years , wildlife contact networks have attracted increasing levels of attention [43] . This is because individual contact behaviour ( e . g . heterogeneity and territoriality ) influences whether an epidemic can occur , how quickly a disease will spread , and the epidemic final size [44 , 45] . Observation of real contact networks also facilitates the construction of contact network models with relevant properties , such that disease spread through populations can be simulated . These models can be used to identify factors driving transmission , predict patterns of disease spread , and design effective surveillance and intervention strategies [43] . Given the current absence of Australian wild dog contact network data , we employed a long-range percolation model from [22] to simulate the spread of rabies through Australia’s wild dog population . Importantly , the model naturally accommodates both heterogeneity ( by assigning each dog its own sociability ) and territoriality ( through the spatial scale parameter λ ) . This model may be applied in a similar manner to other wildlife populations for which there is either no contact network information or the observation thereof is difficult . This is true even when the host population is not territorial ( and the corresponding contact network is small-world in nature ) since a sufficiently small value for λ would render each host capable of contacting every other host in the population . Global sensitivity analysis is an increasingly popular approach to performing sensitivity analysis of epidemiological models [30] . This is because it complements a model’s ability to answer questions such as “What is the probability an epidemic will occur ? ” by identifying those factors most influential in determining its answer . One can therefore regard global sensitivity analysis as a tool which tells researchers ‘where to look’ and improves our understanding of the processes driving the spread of disease . That is to say , it highlights the parameters worth exploring further and measuring more accurately in the field such that improved model outcome predictions can be obtained . This is particularly useful when several model parameters have wide ranges reflecting large uncertainty or natural variation over space and time ( as was the case for nearly all the wild dog ecology parameters in the current study ) since a wide range of values for a particular parameter does not imply model outcomes will be sensitive to it ( e . g . the mean replacement period ) . Given the parameter identified as most influential in determining whether a canine rabies epidemic will occur in Australian wild dogs ( i . e . the sociability scale parameter ) had a range defined as an order of magnitude ( due to lack of published information ) field data on wild dog movements and contact behaviour is glaringly missing . In this sense the main finding of the paper is consistent with [2] . We therefore recommend future research should focus on the observation of wild dog contact networks as this will go a long way to addressing this knowledge gap and could at the same time serve to confirm , or generate new hypotheses for , the assumed wild dog contact rate functional form ( Eq 1 ) . The assumption that a wild dog’s sociability is equal to the square root of the area of land it traverses per day is a consequence of the wild dog contact rate proposed in Eq 1 ( see S1 Appendix ) . Interestingly , the area of land traversed per day has the same units as those of the diffusion co-efficient D in a system of partial differential equations describing the spatial propagation of travelling infection waves . Furthermore , the velocity at which these travelling waves propagate is proportional to the square root of the diffusion co-efficient , D [46] . Thus our finding that wild dog sociability is a key parameter in the percolation of rabies through the wild dog population beyond milestone distances is consistent with established theory describing the spatial propagation of infection through a host population ( e . g . fox rabies in Europe ) . This work has some important caveats to bear in mind . The first is that rabies infection could modify wild dog behaviour in a manner significant for the sociability and spatial scale parameter , λ , values . Predicting exactly how these parameters , and possibly even the contact rate ( Eq 1 ) , might differ for rabid dogs , though , is not easy , especially given rabies presents in two forms , namely furious and dumb ( paralytic ) [2] . Future work should investigate the sensitivity of model outcomes to these factors by comparing results generated for alternative contact rate functions , as well as when the values for sociability and λ are conditional on wild dog disease status ( i . e . furious or dumb ) . A second caveat is that we assumed home ranges ( nodes ) remained unoccupied for an exponentially distributed period of time . In reality , the mean replacement period is likely to be a complex function of wild dog migration between home ranges ( movements over and above normal everyday activity captured by the contact rate ) , seasonal birth rates , and wild dog density . Future research should include investigating whether incorporating more realistic wild dog demography ( for which there is currently a paucity of knowledge , particularly for tropical ecosystems in northern Australia ) will significantly alter the model outcomes and global sensitivity analysis results presented here . This will be especially important when comparing the risk of rabies spread in multiple locations , e . g . Arnhem Land and Cape York . Thirdly , when simulating the spread of rabies we chose to fix the values of the canine rabies pathogenesis parameters as they are relatively well defined in the literature . Importantly , though , just because they were assigned fixed values does not mean model outcomes are insensitive to them . The pathogenesis parameters were assigned fixed values with the specific aim of identifying which ecological parameters ( for which there is little known at present ) are worth exploring further . The global sensitivity analysis results therefore need to be interpreted in this light . A fourth caveat is that when parameterizing the model , we fixed the sociability shape parameter to an arbitrarily chosen value of 30 . Choosing a larger value would have reduced the upper limit of the range of values from which the sociability scale parameter was sampled . This , in turn , would have reduced both the probability of percolation ( Fig 4 Panel D ) and the proportion of model outcome variance explained by the sociability scale parameter . If instead we had chosen a smaller value for the sociability shape parameter the opposite would be true . Consequently , accurately quantifying the parameters describing the distribution of wild dog sociabilities ( from the area of land they traverse per day ) will be important for future predictions of canine rabies spread in Australian wild dogs . In order for a disease to become established each infectious dog must on average produce at least one subsequent infectious dog . To do this an infectious dog must first come into contact with enough other dogs such that it has sufficient opportunity to successfully infect at least one of them . When a dog in our model is sociable ( i . e . when it covers a large area of land per unit time ) and when wild dog density is high this requirement is satisfied , and therefore it is reasonable that wild dog sociability and density explain whether a rabies epidemic will occur . This interpretation agrees well with the finding that the number of dogs infected by the index case was also most sensitive to these two input variables . To understand the nearly linear relationship between the rate of spread ( time to milestone ) and spatial scale parameter , λ , one has to first consider the implications of the territorial nature of wild dogs ( represented in our model by the spatial scale parameter λ ) . When an infectious disease spreads in a territorial population ( where the traditional random mixing assumption does not apply ) a localized depletion of susceptible hosts may occur [22 , 25] . Thus , in order for the disease to continue spreading , long-range transmission ( host dispersal ) is required such that it can ‘escape’ the localized outbreak . The further transmission ( dispersal ) occurs , however , the faster the disease will spread and the shorter the time taken until percolation . In the model presented here this occurs precisely when wild dogs traverse large distances ( i . e . when λ is relatively small ) . This finding supports the hypothesis in [2] that rabies may spread faster in resource-poor areas , e . g . semi-arid or desert regions , where wild dogs traverse greater distances and have larger home ranges [47] . Rate of spread is an important outcome to consider since it determines the level of intervention required to contain an epidemic and bring about disease elimination once detected . Our estimates for the epidemic rate of spread are similar to , if not slightly above , the 20–80 km/year published in the literature for other sylvatic rabies virus strains and host populations [2 , 48] . This may be because wild dogs are anatomically larger , with allometrically-scaled larger home ranges [49] , and therefore genuinely more mobile ( higher sociabilities , smaller λ ) than raccoons , foxes , and badgers , but alternatively could just be a result of the wide parameter ranges we considered . In either case , the preparedness of Australia could be greatly improved by targeted field studies that aim to better understand wild dog movement and behaviour . | Canine rabies typically persists in developing countries where stray and unvaccinated , free-roaming domestic dogs account for a substantial proportion of the population . In this paper we investigate whether sustained canine rabies transmission can occur within the wild dog population of Australia , which comprises dingoes and their hybrids with domestic dogs . To do this we simulated the transmission of rabies between wild dogs across a sizeable landscape area . Whenever the disease spread 120 km we considered an epidemic as having occurred and recorded the length of time it took to do so . This allowed us to estimate the probability the sustained transmission of canine rabies will occur ( 21% ) upon its introduction along with the speed at which it will spread ( 67 km/year ) . We also determined that wild dog movements and behaviour ( for which there is the least amount of information available at present ) together with wild dog density are the most important factors influencing these two outcomes , whereas wild dog demography ( including average lifespan and average replacement period ) had minimal impact . Future research should therefore focus on studying wild dog movements and behaviour to improve Australia’s readiness to control and prevent canine rabies transmission and establishment . | [
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"vertebrate... | 2017 | Predicted Spatial Spread of Canine Rabies in Australia |
The endogenous mechanism that determines vertebrate body length is unknown but must involve loss of chordo-neural-hinge ( CNH ) /axial stem cells and mesoderm progenitors in the tailbud . In early embryos , Fibroblast growth factor ( FGF ) maintains a cell pool that progressively generates the body and differentiation onset is driven by retinoid repression of FGF signalling . This raises the possibility that FGF maintains key tailbud cell populations and that rising retinoid activity underlies cessation of body axis elongation . Here we show that sudden loss of the mesodermal gene ( Brachyury ) from CNH and the mesoderm progenitor domain correlates with FGF signalling decline in the late chick tailbud . This is accompanied by expansion of neural gene expression and a similar change in cell fate markers is apparent in the human tailbud . Fate mapping of chick tailbud further revealed that spread of neural gene expression results from continued ingression of CNH-derived cells into the position of the mesoderm progenitor domain . Using gain and loss of function approaches in vitro and in vivo , we then show that attenuation of FGF/Erk signalling mediates this loss of Brachyury upstream of Wnt signalling , while high-level FGF maintains Brachyury and can induce ectopic CNH-like cell foci . We further demonstrate a rise in endogenous retinoid signalling in the tailbud and show that here FGF no longer opposes retinoid synthesis and activity . Furthermore , reduction of retinoid signalling at late stages elevated FGF activity and ectopically maintained mesodermal gene expression , implicating endogenous retinoid signalling in loss of mesoderm identity . Finally , axis termination is concluded by local cell death , which is reduced by blocking retinoid signalling , but involves an FGFR-independent mechanism . We propose that cessation of body elongation involves loss of FGF-dependent mesoderm identity in late stage tailbud and provide evidence that rising endogenous retinoid activity mediates this step and ultimately promotes cell death in chick tailbud .
Cells located in the tailbud of the vertebrate embryo generate the body progressively . These cell populations include axial stem cells in the chordoneural hinge ( CNH , classically defined as caudal-most ventral neural tissue and distal notochord ) that contribute to notochord , somites , and ventral neural tube in a self-renewing manner [1]–[3] and more caudally located somitic mesoderm progenitors , which have a limited self-renewing ability ( Figure 1A ) [4] , [5] . Extrinsic signals , including Wnt and FGF , are required for continued body axis elongation in the early embryo ( reviewed in [2] ) , and this process relies on the regulated differentiation of newly generated cells as they exit the tail end . At a specific point , however , body axis elongation ceases and this must involve the regulated differentiation and/or loss of axial stem and mesoderm progenitor cells . Changes in a number of signalling pathways can induce axial truncations in the early embryo , although how they act on specific cell populations in the later forming tailbud has not been explored . Exposure to exogenous retinoic acid ( RA ) can arrest body elongation [6] , [7] by inhibiting expression of Wnt3a [7]–[9] . A critical level of bone morphogenetic protein signalling is also required for normal axis elongation; loss of the bone morphogenetic protein antagonist Noggin or Noggin overexpression both generate a truncation phenotype , reviewed in [2] . Loss of FGF signalling also leads to body axis truncation at early , pre-tailbud stages , reviewed in [2] . Importantly , Wnt3a is required for maintenance of Fgf8 in the caudal regions [10] , and this regulatory relationship provides a link from Wnt signalling to mechanisms that protect tail-end cells from endogenous RA , all of which in higher vertebrates are , so far , downstream consequences of FGF signalling . FGF signals maintain expression of Cyp26A ( which catabolises RA ) in the tail end , repress expression of the retinoic acid receptor RARß in the neuroepithelium , and critically inhibit expression of the retinoid synthesising enzyme retinaldehyde dehydrogenase 2 ( Raldh2 ) in the forming somites [11]–[13] . At early stages while FGF signalling antagonises the RA pathway , RA in turn drives differentiation in neural and mesodermal tissues by inhibiting expression of Fgf8 [11] , [14] . However , while cells generated at the tail end continue to experience sequentially FGF and then retinoid signalling as they leave this region , it is not known whether this mutual antagonism between these signalling pathways is maintained throughout development . Indeed , it is clear that as the body axis elongates in chick and mouse , the presomitic mesoderm initially gets longer , separating FGF and RA signalling centres , but then shortens following tailbud formation . The tailbud forms after about half the body axis has been generated , from the remnants of the primitive streak and adjacent epiblast or stem zone , and in the chick generates body axis tissues from the level of somites 27–26 to ∼52 and in mouse from somite 35 to ∼65 ( [15] , [16]; reviewed in [2] ) . So although established in early embryo , it has yet to be determined whether FGF still represses Raldh2 at tailbud stages or if RA can still inhibit expression of Fgf ligands and , as somite formation nears the tailbud , finally overwhelm FGF signalling to arrest axis elongation . A further indication that this may be the case is the recent observation that Raldh2 appears in a new domain within the tailbud itself [17] . Mutation of several transcription factors also truncates the body axis . These include Cdx and Hox genes that are part of a positive regulatory loop with FGF and Wnt signalling pathways [18]–[22] . The T-box factor , Brachyury ( Bra ) or T also controls body axis elongation as indicated by the T-mouse mutant phenotype [23] , [24] . In zebrafish , retinoic acid represses Bra/Ntl , which normally protects mesoderm progenitors in the tailbud from retinoid signalling by inducing expression of Cyp26a [25] . However , it is not clear that these regulatory relationships are conserved in higher vertebrates , as caudal Cyp26a expression is dependent on FGF signalling in chick and mouse embryos [13] , [26] , while in zebrafish neither Cyp26a nor Bra/Ntl require FGF [25] . It is also worth noting that different enhancers control the expression of Bra in distinct domains in the notochord and paraxial mesoderm [27] , [28] and that little is known about how Bra expression is regulated in distinct cell populations within the mature tailbud nor how these might be influenced by retinoid signalling . Exogenous RA and loss of Wnt3a can induce programmed cell death in the tailbud , however such signalling events do not always elicit apoptosis when the body axis is truncated [18] , [29] . It is not known whether endogenous RA signalling acts in the tailbud by promoting programmed cell death nor if this takes place in specific cell populations at key times within the maturing tailbud . Intriguingly , an imbalance of retinoid , Wnt3a , or FGF signalling can also alter the neural versus mesodermal cell fate decision in caudal regions of the early embryo [8] , [9] , [29]–[34] . Rising endogenous RA activity in the late tailbud might therefore act by altering signals that maintain the axial stem cell niche and/or mesoderm progenitors , resulting in loss of multipotency and diversion towards neural fate . Although many gene regulatory relationships are conserved between vertebrate embryos , the development of caudal-most structures seems to differ; in humans , this appears to be closest to that observed in the chick embryo: both undergo secondary neurulation and caudal regression and lack the extended tail characteristic of the mouse . Indeed , recent work has shown that the chick but not the mouse tailbud is an endogenous source of RA [17] , [35] , raising the possibility that distinct mechanisms may operate in different species . Here we address the mechanism of body axis termination in the chick by tracking FGF and retinoid signalling dynamics in specific tailbud cell populations and correlate this with cell movements and cell specification changes as elongation ceases . Using in vivo and in vitro approaches we demonstrate that continued generation of mesoderm along the body axis , as indicated by Brachyury expression , depends on FGF signalling and show that it is attenuated by rising retinoid signalling , which also eventually promotes cell death .
The expression patterns of key FGF pathway genes were examined in detail in distinct tailbud cell populations ( see Figure 1A ) at stages of tailbud formation and maturation in the chick ( Hamburger and Hamilton [HH] stage16/17 to HH27 ) [36] . The FGF ligands Fgf8 and Fgf4 were strongly expressed in the tailbud but declined in the last 2 days of axis elongation from HH22 ( day 4 ) to HH26/7 ( day 6 ) ( Figure 1B–E′ and Figure 1F–1I′ ) ( with the segmentation of the paraxial mesoderm ceasing at HH24 [15] , [16] ) . Initially , Fgf8 is expressed in the caudal-most neural tube and distal notochord , which together form the chordoneural hinge ( CNH ) and in the surrounding mesoderm progenitors ( Figure 1C″ ) and is downregulated by HH24/25 in mesoderm progenitors , neural CNH , and most notochord CNH ( Figure 1D″ , 1D′″ ) . Fgf4 is restricted to mesoderm progenitors at all stages in the tailbud ( Figure 1G–1H″ ) and is similarly lost as this tissue matures ( Figure 1F–1H″ ) . The expression pattern of the FGF/Erk feedback antagonist Sprouty2 ( Spry2 ) serves as a reporter for FGF activity [37] and is detected in caudal neural tube , mesoderm progenitors , and CNH at all stages to HH22 ( Figure 1J–K″ ) but is lost from the neural CNH and reduced in the mesoderm progenitors by HH24 ( e . g . , compare Figure 1K–1K″ to 1L–1L″ ) . Transcripts for Fgf pathway ligands and Spry2 are then lost completely from the tailbud by HH26/27 ( Figure 1E , 1E′ , 1I , 1I′ and 1M , 1M′ ) . These expression patterns indicate a rapid decline in FGF signalling in neural CNH and mesoderm progenitors in the mature HH24 tailbud ( summarised by Spry2 expression in Figure 1N ) . In the early embryo , FGF activity is required to promote mesodermal over neural cell fate , and so declining FGF signalling might cause body axis elongation to cease if normal cell fate specification is lost in the tailbud . To examine whether cell fates change in specific cell populations in the maturing tailbud , we analysed expression of two key marker genes in detail . Strikingly , the neural progenitor marker Sox2 was found to expand into the positionally defined mesodermal progenitor domain between HH22–24 , separating the distal swelling of the notochord from the mesoderm progenitors ( Figure 2A–2C′ ) ( Sox2 is also detected in the remnant of the tail gut at HH20 ( Figure S1 ) , but is lost as this cell population degenerates by HH22 [38] ) . Concomitant with expansion of the Sox2 domain , transcripts of the early mesoderm marker Bra were lost from the position of the medial mesoderm progenitors , which surround the notochord tip ( arrows in Figure 2B′ and 2F′ ) . In addition , Bra was downregulated in the CNH ( both the distal notochord swelling and caudal-most ventral neural tube ) ( Figure 2F–2H′ ) . This dramatic local change in gene expression correlates with the loss of the morphologically defined contiguous notochord/CNH ( asterisks in Figure 2E′ , 2F′ ) . These observations suggest that the axial stem cell population of the CNH and the “mesodermal progenitors” in the tailbud lose their mesoderm potential coincident with cessation of segmentation at HH24 . At later stages , the broadened Sox2-expressing domain then differentiated into a multi-lumen neural tube ( Figure 2I–2I″ ) . This critical change in gene expression was further assessed at the protein level by analysis of double immunocytochemistry for Bra and Sox2 at HH22 , HH24 , and HH26 ( Figure 2J–L″ ) . This revealed a sharp loss of Bra protein in the distal notochord tip and surrounding cells in the position of mesoderm progenitors ( asterisks in Figure 2J′ , 2K′ ) and a reduction in Bra in the neural CNH ( defined by Sox2/Bra co-expressing cells continuous with the caudal-most neural tube ) , which progressively diminishes from HH24 ( Figure 2J″–L″ ) . In addition , in the positionally defined mesoderm progenitor domain , Sox2 positive cells ( white arrows in Figure K , K″ ) were also apparent as well as some Sox2/Bra co-expressing cells ( which here may reflect a transitional cell state as Bra is lost in this cell population ) . Importantly , analysis of these proteins in serial sections of the human tailbud at Carnegie Stage ( CS ) 12 ( 26–30 days , 21–29 somites , equivalent to early tailbud HH18 in terms of somite number ) ( n = 2 ) and at CS16 ( 37–42 days , 38–39 somites; corresponds to the end of axis elongation [39] equivalent to HH26/27 ) ( n = 1 ) revealed a similar loss of CNH ( Sox2/Bra cell population ) and of Bra expression as this tailbud matures ( Figure 2M–N′ ) . At CS12 Sox2 was detected at high levels in neural tube , but also in an adjacent discrete cell group that co-expresses Bra , which may be equivalent to the CNH ( Figure 2M , 2M′ ) . Bra was also detected at high levels in notochord at CS12 ( Figure 4M′ ) . Analysis of serial sections through the later CS16 human tailbud revealed that by the end of somitogenesis this was capped at its terminal end by Sox2 expressing neuroepithelium and Bra was now confined to the notochord ( Figure 2N , 2N′ ) . These observations indicate that arrest of body axis elongation in chick and human is characterised by loss of Bra expression in the tailbud , leaving terminal cells with a neural character . To understand the contribution that cell movement might make to these changing patterns of gene expression , a series of fate mapping studies were carried out in the tailbud in ovo ( Figure 3A and see Materials and Methods ) . DiI was used to label focal groups of ∼20–40 cells in the caudal-most neural tube , the “neural CNH , ” or the mesoderm progenitor domain at HH20 ( local labelling was confirmed by observation in the whole embryo , and to ensure accurate labelling , a subset of embryos were fixed immediately and sectioned to define position of DiI labelled cells; Figure 3B–3D , Table 1 ) . After 30 h incubation ( to HH24/25 ) , neural tube cells were found to contribute only to neural tissue ( Figure 3B′–3B′″ , Table 1 ) , while cells in the CNH contribute to neural tissue and the positionally defined mesoderm progenitor domain , presomitic mesoderm , and somites ( Figure 3C–3C′″ , Table 1 ) . This suggests that at least some of the Sox2 positive cells that appear in this “mesoderm progenitor” territory at HH24 are derived from the CNH ( Figure 3C′″ ) . We also wished to ascertain whether cells present in the Bra positive mesoderm progenitor domain at HH20 ( prior to the appearance of Sox2 here at HH24 ) later come to express Sox2 . Mesoderm progenitors labelled at HH20 were therefore assessed for Sox2 expression at HH24 . However , the majority of DiI-labelled cells left the mesoderm progenitor domain and contributed to pre-somitic , somitic , and/or more lateral mesoderm , and few remaining cells were in the new Sox2 expressing domain ( Figure 3D′″ , Table 1 ) . These data ( summarized in Figure 3E ) therefore suggest that Sox2 expansion into the tailbud core is due to maintenance of this gene and loss of Bra in cells that have recently ingressed from the CNH . The loss of Bra and expansion of the Sox2 expression domain into the tailbud at HH24 correlate well with the decrease in FGF signalling in the mesoderm progenitor domain and neural CNH ( summarized in Figure 1N and compare Figure 1K″ and 1L″ with Figure 2E′ and 2F′ ) . To test whether decline in FGF signalling underlies this striking change , FGF signalling was blocked in HH19–22 tailbuds using the Fgf receptor inhibitor PD173074 or the Mitogen-activated protein kinase kinase ( MEK ) antagonist PD184352 , which blocks downstream Erk1/2 activity . Control tailbuds exposed to DMSO alone expressed Spry2 in the mesoderm progenitor domain ( Figure 4A ) and Bra in the mesoderm progenitors and notochord ( while Bra expression in intervening CNH/medial mesoderm progenitors was downregulated as normal ) ( arrow in Figure 4B ) , Sox2 expression extended to the tailbud tip ( Figure 4C ) , and Tbx6L was expressed in mesoderm progenitors and extended into caudal presomitic mesoderm ( Figure 4D ) ; all gene expression patterns typical of the normal HH23–24 tailbud . Exposure to PD173074 or PD184352 inhibited expression of Spry2 ( DMSO n = 1/16 , PD173074 n = 14/16; DMSO n = 1/4 , PD184352 n = 3/4 ) ( Figure 4A–4A′″ ) and repressed all Bra expression except in the proximal notochord ( Bra , DMSO n = 0/10 , PD173074 n = 10/10; DMSO n = 0/4 , PD184352 n = 6/7; Figure 4B–B″ ) , while Sox2 transcripts were more widely detected caudally in comparison with controls ( Sox2 , DMSO n = 1/6 , PD173074 n = 4/7; DMSO n = 0/3 , PD184352 n = 3/5; Figure 4C–4C″ ) . A recent study has shown that the further T-box gene Tbx6 is required to downregulate Sox2 expression in the early mouse embryo [40] . The homologous gene Tbx6L is downregulated as the chick tailbud matures [17] , and so its loss provides a potential mechanism to explain the upregulation of Sox2 in the mesoderm progenitor cell population . Importantly , blocking FGFR- or MEK-mediated signalling also inhibited Tbx6L ( Tbx6L , DMSO n = 0/7 , PD173074 n = 7/7; DMSO n = 0/10 , PD184352 n = 11/11; Figure 4D–4D″ ) , demonstrating that maintenance of this further mesoderm identity gene is dependent on FGF/Erk activity . As Bra is a known direct target of Wnt signalling , we also assessed the effects of FGFR and MEK inhibition on expression of the key ligand Wnt3a . This gene is expressed in tailbud mesoderm and dorsal neural tube , and its expression was repressed by both FGFR or MEK antagonists specifically in the tailbud mesoderm ( DMSO n = 0/3; PD173074 n = 8/8; DMSO n = 0/6 , PD184352 n = 10/10; Figure 4E–4E″ ) . Finally , grafting an FGF4 delivering bead into the HH20–22 tailbud induced ectopic expression of Bra ( BSA bead control n = 0/7; FGF4 beads n = 4/6 embryos; Figure 4F–4F″″ ) . FGF4 beads were also found to elicit an ectopic patch ( es ) of Sox2 expression in cells close to the bead ( BSA n = 0/6; FGF4 n = 5/7; Figure 4G–4G″ ) . This initially surprising result may reflect the regulation of Sox2 by distinct enhancer elements active around the organiser region ( N1 ) and in the developing spinal cord ( N4 ) [41]; expansion of Sox2 expression on FGF inhibition ( results above ) may be indicative of spinal cord differentiation via N4 activity , while induction of Sox2 by high-level FGF may reflect the N1 element , which is FGF/Wnt responsive [42] and might be indicative of formation of an ectopic CNH . To address if a CNH-like state is induced , we determined whether FGF-induced ectopic Sox2 positive cells co-express Bra , as observed in the endogenous CNH . In most cases , ectopic co-expression was detected , suggesting that these cells have CNH character ( BSA n = 0/4; Figure 4H–4H′; FGF4 n = 4/6; Figure 4I–4I′ ) . Some of the ectopic Sox2/Bra cells formed small groups , but we also detected strong Sox2 expression in Bra positive cells in mesoderm progenitor domain/presomitic mesoderm in tails with FGF beads ( Figure 4I–4I′″ ) . In 3/6 embryos , ectopic Sox2/Bra foci exhibited a polarised configuration , being flanked at either end by Sox2 or Bra only expressing cells , reminiscent of tissue organisation around the endogenous CNH ( Figure 4I′–4I′″ ) . Together these findings ( summarised in Figure 4J ) therefore suggest that maintenance of mesoderm progenitors and the CNH depends on FGF/Erk signalling and that reduction of such signalling leads to neural differentiation in the tailbud , while high-level FGF activity can promote a CNH-like cell state . As FGF signalling is attenuated by RA provided by somites in the early embryo , we next tested whether the RA synthesising enzyme Raldh2 continues to be expressed in somites throughout body axis elongation and if expression of RA signalling pathway genes alter in the tailbud . Expression of Raldh2 was detected in the most recently formed somites throughout body axis elongation ( Figure 5A–5D′ ) . However , we additionally observed the appearance of domains of Raldh2 in the tailbud itself prior to elongation arrest by HH24 ( also see [17] for report of whole embryo expression pattern ) . Tissue localisation of Raldh2 in sections revealed expression in caudal mesoderm progenitors in the tailbud by HH24 ( Figure 5C–5C″ ) . These observations suggest a means by which endogenous retinoid signalling can be locally increased in the tailbud prior to body elongation arrest . Initially transcripts for the retinoic-acid-catabolising enzyme Cyp26a were restricted to superficial ventral ectodermal cells , distal notochord , and the CNH at HH16 ( Figure 5E–5F′ ) . In contrast , by HH24 , Cyp26a was lost from the distal notochord/CNH ( Figure 5G–5H′ ) , laying this cell population open to an increase in retinoid signalling . Cyp26a expression may thus protect the axial stem cells in the CNH from retinoid signalling in the early tailbud . To assess the pattern of retinoid activity , we first analysed the expression of RARβ ( a canonical Direct Repeat ( DR ) 5 RARE ( Retinoic Acid Response Element ) –driven gene [43] ) . However , while RARβ was detected in neural tube flanked by somites at all stages , it was only weakly detected in the tailbud at HH24/25 ( Figure S2 ) . As Raldh2 is expressed in the tailbud at this time , these observations support the possibility that the RARβ ( DR5 ) RARE does not reveal all sites of retinoid activity [44] . We therefore analysed expression of a gene with different RARE elements , as these might be recognised by distinct combinations of RAR/RXRs [45] . Cellular retinoic acid binding protein ( Crabp2 ) is an established RA target and its transcription is trans-activated by RARE1 and RARE2 DR motifs that are separated by one ( DR1 ) and two ( DR2 ) base pairs , respectively [43] , [45] , [46] . The Crabp2 gene is expressed in early chick and mouse embryos [47]–[50] , and here we examined its transcription in the maturing chick tailbud ( Figure 5I–5L ) . From HH15/16 , Crabp2 transcripts are detected at low levels in the tailbud ( Figure S3A–S3A′ ) , and detailed analysis from HH19/20 to HH26 revealed that Crabp2 then steadily encroaches on mesoderm progenitors and the CNH ( Figure 5I–5L″″ ) : at HH19–22 Crabp2 is absent from the CNH but detected in mesoderm progenitors ( Figure 5I′–5J″″ ) ; by HH24 , Crabp2 is detected in neural/CNH ( ventral caudal-most neural tube ) and surrounding medial-most mesoderm progenitors , but not the distal-notochord portion of the CNH ( Figure 5K′–5K″″ ) ; by HH26 , a small group of Crabp2-expressing cells are then detected in the now abrupt distal end of the notochord ( Figure 5L–5L″ ) , while transverse sections through the tail tip at this stage confirmed the multi-lumen nature of the terminating neural tube ( Figure 5L′″ , 5L″″ ) . These expression patterns correlate well with onset of Raldh2 and downregulation of Cyp26a at HH22–24 and indicate an increase in retinoid signalling in key cell populations of the maturing tailbud ( summarized in Figure 5M ) . Although Crabp2 is an established RA target in cultured cells , this has not been demonstrated in the embryo . To test this , RA-delivering beads were implanted between the caudal lateral epiblast/stem zone and presomitic mesoderm in HH9–10 chick embryos ( Figure 5N ) . RA upregulated Crabp2 expression at the tail-end ( n = 4/5 ) compared to control DMSO beads ( n = 0/5 ) , assessed after 16 h ( Figure 5N′–5N″ ) . In addition , tailbuds at HH20/22 treated with 100 nM RA for 24 h in vitro also demonstrated increased Crabp2 expression in comparison with DMSO-treated controls ( Figure S3B–S3B′ ) . To determine whether Crabp2 transcription depends on RA signalling , Crabp2 expression was next assessed in Vitamin-A-deficient quail embryos . Crabp2 transcripts were much reduced in VAD quails ( n = 3/3 ) compared to normal quails fixed and processed in parallel ( n = 7 ) ( Figure 5O–5O′ ) . To specifically test the requirement for RA for Crabp2 expression in the tailbud , retinoid signalling was blocked using RAR/RXR antagonists; this leads to Crabp2 repression in 9/12 explant pairs ( Figure 5P–5P″ ) . In addition , tailbud explants were treated with the RA synthesis inhibitor disulfiram , and RAR/RXR antagonist delivering beads were transplanted into the HH20/21 tailbud in vivo , and in both conditions Crabp2 expression was reduced compared with DMSO-treated controls ( Figure S3C , S3C′ , S3D and S3D′ ) . These data demonstrate that Crabp2 provides an alternative , D1/D2–RARE-mediated reporter for retinoid signalling in vivo and that it reports increasing RA activity in the tailbud . From the early tailbud ( ∼HH22 ) Crabp2 overlaps with Fgf-responding domains , raising the question of whether FGF and RA signalling maintain their mutually inhibitory interactions in the tailbud . To test the regulatory relationships between these two pathways , we first exposed the prospective tailbud region at HH9–10 to exogenous RA delivered locally on a bead in vivo . This led to loss of Fgf8 and truncation of the body axis ( RA beads n = 8/9 , DMSO beads n = 0/5 embryos ) ( Figure 6A , 6B ) . To address the effects of RA on tailbuds at later stages we explanted and cultured tails from HH16 chick embryos for 24 h in DMSO with or without RA . In all cases , while control ( DMSO exposed ) tissue maintained Fgf8 expression ( n = 6 ) , RA-treated tailbuds all lost Fgf8 transcripts ( n = 6/6 ) ( Figure 6C–D′ ) . To determine whether RA retains the ability to repress FGF signalling , RA was administered to HH19–22 tailbuds . After 24 h , Fgf8 and Fgf4 expression were still detected in control explants ( n = 9 and n = 8 , respectively ) , but in the presence of high ( 10 µM ) or low ( 100 nM ) RA , both these genes were repressed ( Fgf8 10 µM , n = 9/10; Fgf8 100 nM , n = 3/4; Fgf4 10 µM , n = 8/8; Fgf4 100 nM , n = 3/4 ) ( Figure 6E–G′ ) . These data show that retinoid signalling can repress FGF signalling during axis elongation and that it retains this ability in the late stage tailbud . To test if somitic Raldh2 can be repressed by FGF signalling at early tailbud stages , pairs of ( HH16 ) trunk explants were dissected from the same embryo and either exposed to BSA vehicle control only or FGF8 ( Figure 6H ) . After 24 h , Raldh2 is still expressed in somites and adjacent lateral plate in control explants , but in FGF8-treated explants , Raldh2 is downregulated in both domains ( n = 8/9 pairs ) ( Figure 6I–I′ ) . To test this regulatory relationship at later stages , pairs of HH19–22 trunk explants were cultured for 24 h . Raldh2 was still markedly reduced in all tissues in the presence of FGF8 ( n = 19/22 pairs ) ( Figure 6J , 6K , 6K′ ) . Together these experiments indicate that throughout axis elongation caudal FGF signalling continues to repress onset of RA synthesis in forming somites and that Fgf4 and Fgf8 transcription in the tailbud remains susceptible to inhibition by RA . FGF signalling interferes with the RA pathway in the early embryo [2] , [12]; however , in the early tailbud , there is an overlap between RA ( Crabp2 ) and FGF ( Sprouty2 ) activity in mesoderm progenitors . We therefore tested whether FGF retains the ability to block RA activity in the tailbud and found that exposure to Fgf8 protein did not downregulate expression of Crabp2 , in tailbud pairs ( 12/12 ) ( Figure 6J , 6L , 6L′ ) . To test whether tailbud Raldh2 is regulated by FGF , HH19–22 tailbuds were cultured for 24 h with or without FGF8 . Raldh2 was detected in control ( n = 8/8 pairs ) and also FGF8b-treated tailbud explants ( n = 7/7 pairs ) ( while control neural tube treated with FGF8b exhibited a reduction in neuron numbers , n = 4/4 , indicative of active FGF signalling [51]; unpublished data ) . To confirm that trunk and tail Raldh2 domains are regulated differently , trunk and tailbud were explanted from the same embryos ( Figure 6J , 6K , 6K′ , 6M , 6M′ ) . While somitic Raldh2 was inhibited ( n = 8/10 pairs ) , tailbud Raldh2 was unchanged in all cases ( n = 10 tailbud pairs ) . This failure of FGF to repress tailbud Raldh2 and Crabp2 transcription was further confirmed by local delivery of FGF4 into the late stage tailbud in vivo ( Raldh2 , 5/5 control BSA beads , 6/7 FGF4 beads; Crabp2 , BSA beads 5/5 , FGF4 beads , 5/5 ) ( Figure 6N , 6N′ , 6O , 6O′ ) . In the distinct signalling context of the tailbud , therefore , FGF no longer antagonizes retinoid synthesis or RA activity . We have shown that Fgf4 and Fgf8 are inhibited in the tailbud by exposure to RA . Consistent with this , we find that treatment of HH19–22 tailbuds with RA ( 100 nM ) for 24 h abolishes expression of Spry2 and additionally leads to loss of Bra , caudal expansion of Sox2 , and also attenuation of Tbx6L in the mesoderm progenitor domain of the tailbud ( Spry2 , DMSO n = 0/4 , RA n = 4/4 [Figure 7A–7A″]; Bra , DMSO n = 2/11; RA n = 8/14 [Figure 7B–7B′]; Sox2 , DMSO n = 2/16 , RA n = 12/17 [Figure 7C–7C′]; Tbx6L , DMSO n = 0/7 , RA n = 7/9 [Figure 7D–7D′] ) . To test the requirement for endogenous retinoid signalling specifically in the tailbud , we further treated tailbuds with the RA synthesis inhibitor disulfiram , which reduced Crabp2 expression ( Figure S3B , S3B′ ) . This led to expansion of Spry2 and Bra expression in the region of the mesoderm progenitor domain ( Spry2 , DMSO n = 0/4 , disulfiram n = 4/4 [Figure 7A′ , 7A″]; Bra , DMSO n = 0/6 , disulfiram n = 9/11 [Figure 7B , 7B″] ) , while Sox2 expression was reduced caudally ( Sox2 , DMSO n = 0/11 , disulfiram n = 10/13 [Figure 7C , 7C″] ) , and Tbx6L remained strongly expressed , but did not consistently expand its domain in this timeframe ( Tbx6L , DMSO n = 4/4 , disulfiram n = 4/4 [Figure 7D , 7D″] ) . These findings suggest that levels of endogenous RA determine FGF activity and subsequent neural versus mesoderm cell fate choice in the mesoderm progenitor domain of the tailbud . To test whether endogenous RA activity mediates this step in an in vivo context , Vitamin A–deficient ( VAD ) quail embryos were examined at early tailbud stages HH14–15; retinoid deficiency is embryonic lethal due to heart defects [52] , [53] , and this was the latest stage at which such embryos were readily available . These embryos exhibit ectopic dorsal domains of Bra and Fgf8 within the neural tube in the caudal-most region ( n = 0/4 normal quails , n = 5/6 VAD quails , Bra [Figure 7E–7F′]; and n = 0/5 normal quails , n = 4/4 VAD quails , Fgf8 , [Figure 7G–7H′] ) . Sox2 expression was detected throughout the neural tube in normal quails ( n = 4 ) , while asymmetric reduction was observed in the dorsal neural tube of some VAD embryos ( n = 2/4 ) ( Figure 7I–7J′ ) . These findings indicate that endogenous RA signalling is required for the correct assignment of neural and mesodermal cell fates and for the normal downregulation of Fgf8 in the early tailbud . To test the continued involvement of retinoid signalling at the latest tailbud stages in vivo , we then grafted beads soaked in RAR/RXR antagonists into the tailbud at HH20–21 and cultured them for a further 24 h ( Figure 7K ) . We first confirmed that delivery of these antagonists in vivo reduced expression of Crabp2 ( Figure S3D , S3D′ ) ( and in more rostral regions RARb , DMSO n = 0/7 , RAR/RXR antagonists n = 5/6; unpublished data ) . We then ascertained that blocking RA signalling in this context lead to increased FGF signalling as indicated by levels of Spry2 ( Spry2 , DMSO n = 1/5; RAR/RXR , n = 6/6 [Figure 7L , 7L′] ) and to ectopic expression of Bra in the tailbud ( Bra , DMSO n = 0/8; RAR/RXR n = 3/6 [Figure 7M–7M′] ) . In addition , Bra was also ectopically maintained by exposure to RAR antagonist alone ( Figure S4A–S4B′ ) controlling for potential effects on heterodimers formed by RXRs with receptors unrelated to retinoid signalling . These antagonists did not , however , elicit a consistent change in Sox2 expression ( Sox2 , DMSO n = 6; RAR/RXR n = 5 cases [Figure 7N–7N′] ) , which may reflect maintenance rather than excess levels of FGF signalling in this condition . In conclusion , in all three assays , VAD embryos , tailbuds explanted and treated with disulfiram or exposed to RAR/RXR antagonists in vivo , retinoid deficiency lead to ectopic maintenance of FGF signalling and Brachyury expression , supporting a role for endogenous retinoid activity in the loss of FGF signalling and mesodermal cell fate assignment at the end of axis elongation . Previous reports show that endogenous cell death increases in the caudal region of the chick embryo during development [15] , [54] , [55] , however this has not been examined with respect to specific tailbud cell populations . The precise timing of apoptosis in individual cells varies within a given tissue , and so TUNEL labelled cells were examined in at least five embryos at each stage to establish the prevailing cell death pattern . Apoptotic cells were detected in the chick-primitive streak from HH10 ( unpublished data; and Figure 8A ) , however only from HH24 in the chick were dying cells present in the CNH and mesoderm progenitors ( Figure 8B–8C′″ ) . Dying cells were then detected intensely throughout the tailbud tip by HH26 ( Figure 8D–8E′″ ) . This indicates that extensive apoptosis is only active in axial stem and mesoderm progenitor cell populations following increased RA activity after HH24 ( compare Figures 5K″ and 8E–8E″ ) . Previous studies have shown that exposure to excess RA can promote cell death in the tailbud , and this was confirmed by RA treatment of tailbud explants ( Figure S5A , S5A′ ) . To investigate whether endogenous retinoid signalling normally regulates cell death , chick tailbuds explanted at HH20–22 were exposed to RAR/RXR inhibitors for 24 h . This revealed a reduction in cell death around and within the CNH ( 0/8 DMSO; 6/8 RAR/RXR antagonist treated ) ( Figure 8F–8H″ ) . Although exposure to exogenous FGF8 reduced cell death in explanted tailbud pairs ( Figure S6A , S6A′ ) , inhibition of FGFR signalling with PD173074 did not increase cell death in comparison with DMSO controls ( Figure S6B , S6B′ ) . These findings show that during normal development apoptosis is only widespread after loss of Bra in neural CNH and mesoderm progenitors and Sox2 expansion in this tissue and suggest that rising endogenous retinoid signalling can promote cell death in the chick tailbud via an FGF-independent mechanism .
This study provides a detailed account of the cellular and molecular changes in the chordo-neural-hinge and mesoderm progenitors in the chick tailbud that underlie the normal process of body axis termination . It reveals a critical loss of mesodermal cell identity in these cell populations in the late tailbud and shows that this is due to a decline in FGFR/Erk signalling , high levels of which can promote a CNH-like cell state . We provide evidence that rising endogenous Retinoid signalling mediates FGF signalling loss and regulates the survival of CNH/axial stem cells and their tailbud derivatives . Fate maps of the tailbud made with chick/quail chimeras or GFP expressing cells at stage HH15 ( 25 somites ) have shown that medially located cells defined as CNH can still give rise to both neural and mesodermal derivatives [1] , [5] . In agreement , we observe that later at HH22 cells located in the equivalent region express both Bra and Sox2 . Moreover , our data reveal a striking loss of the mesodermal gene Brachyury specifically in the neural and notochord portions of the CNH and in cells located in the mesoderm progenitor domain . This is accompanied by an expansion of the neural progenitor marker Sox2 into this “mesoderm progenitor” territory in the late ( HH24 ) chick tailbud . Importantly , we show that the neural and notochord CNH persist and remain contiguous with the mesoderm progenitor domain at HH22 ( ∼44 somite stage ) and that cells continue to ingress from the neural CNH into the tailbud at stages leading up to HH24 . These findings therefore indicate that the conditions for cessation of body axis elongation arise discretely in the late tailbud and coincide with the end of segmentation at HH24 . This cell state change is coincident with FGF signalling decline in these cell populations , and we demonstrate that blocking FGF/Erk signalling precisely mimics this step , specifically repressing Bra in the CNH and mesoderm progenitors and leading to caudal expansion of the Sox2 expression domain . Consistent with this role for FGF signalling , a recent report analysing the spectrum of phenotypes for human syndromes generated by heterozygous constitutive activation of FGFR2 ( Pfeiffer , Crouzon , and Beare-Stevenson syndromes ) included an ectopic “caudal appendage” [56] , suggesting conservation in humans of a mechanism that attenuates FGFR signalling for normal cessation of body axis elongation . Our finding that the maturing human tailbud also exhibits loss of Bra and terminates in a Sox2-expressing neural structure further supports conservation of the role of FGF in determining cell fates at the end of body axis elongation . Interestingly , human tails with activated FGFR2 have a variable form , with or without vertebrae [56] , indicating that excess FGF signalling can generate ectopic and disorganised tail end structures . This phenotype may depend on the level of FGF activity and relate to our finding of ectopic CNH-like cells co-expressing Bra and Sox2 in response to high-level FGF delivered by beads . Our gain- and loss-of-FGF-function experiments indicate that while loss of FGF promotes neural differentiation , FGF maintains mesoderm progenitors and even higher levels may then promote the axial stem cell state of the CNH . The effects of elevated FGF and Wnt signalling may also explain the large club-end truncation phenotype of Cdx2 mis-expressing mice [57] as Cdx genes are upstream of these pathways [18] , [19] , which need to be downregulated for segmentation and differentiation in the body axis . Similarly , long-term loss of RA ( leading to maintenance of FGF ) in the tailbud is not predicted to lead to an ever-lengthening body axis , because RA is also required for subsequent differentiation . Indeed , it is likely that driving FGF only in the CNH would be required for continued axis elongation . Our findings are consistent with the direct regulation of Bra by FGF signalling in lower vertebrates [58] , the positive regulatory loop of FGF and Wnt signalling in the early mouse embryo [22] and of Bra and Wnt3a in caudal regions in mouse and zebrafish early embryos [27] , [59] , [60] . Furthermore , mice mutant for Bra exhibit body axis truncation [23] , [24] , and it may be important that the sudden loss of Bra/Wnt/FGF that we observe coincides with the cessation of segmentation of the presomitic mesoderm in the chick at HH24 [15]–[17] . Indeed , Bra/T box proteins exert direct positive regulation on the Notch pathway ligand Delta1 and oscillations in this pathway underpin the segmentation process ( [61]; reviewed in [62] ) . The regulation of Bra by FGF/Erk signalling thus appears to be a pivotal mechanism that determines vertebrate body length by maintaining mesoderm potential and also by control of a key Notch pathway gene integral to the segmentation of the body axis . Importantly , using in vivo and in vitro assays , we show that specifically in the tailbud FGF no longer inhibits Raldh2 expression or RA activity . The downregulation of the p450 enzyme Cyp26a in the early tailbud , a known FGF-dependent gene in caudal regions in chick and mouse [13] , [26] , further suggests that RA is no longer degraded in the chick tailbud . However , we show that RA retains the ability to repress Fgf4 and Fgf8 in the tailbud; this could be indirect via loss of Wnt3a , which maintains Fgf8 ( although we also show here that FGF in turn maintains Wnt3a , as in the early mouse embryo ) [10] , [63] , [64] and/or directly through binding of an RARE in the Fgf8 promoter [65] , [66] . This breakdown of the oppositional signalling between FGF and RA pathways and the loss of homeostatic regulation of RA turnover provide a mechanism for the steady increase in endogenous RA levels . We present multiple lines of evidence that rising endogenous retinoid signalling then contributes to loss of FGF/Bra in the tailbud . Using Crabp2 we show that as FGF signalling declines , endogenous RA activity increases in mesoderm progenitors and the neural CNH and , eventually , in the CNH/notochord distal tip . Crabp2 transcription is well-established as an RA target in primary cells and cell lines ( e . g . , human skin fibroblasts [67] , F9 teratocarcinoma cells [68] , P19 embryonic carcinoma cells [46] , and ES cells [69] ) . It is validated here as a reporter of RA activity in the embryo by its increase in response to exogenous RA delivered by beads or directly on explanted tailbud and its reduction in Vitamin A–deficient quails and inhibition by RA synthesis inhibitor disulfiram and by RAR/RXR antagonists in in vitro and in vivo late stage tailbuds . In these multiple retinoid deficiency assays , we then consistently find ectopic maintenance of FGF signalling and Bra , strongly suggesting a role for endogenous retinoid signalling upstream of the loss of mesodermal potential in this tissue . In addition , recent work has confirmed that the chick tailbud is a source of RA , as explanted late-stage tailbuds stimulate expression of an RARb-RARE-LacZ reporter cell line [17] , [70] . In the same assay , the mouse tailbud appears not to provide RA , despite also up-regulating Raldh2 [17] , [35] . Most recent data suggest that retinoic acid is not synthesised in the mouse tailbud [35] and so implicate a different mechanism for attenuation of FGF/Wnt signalling as elongation ceases in this animal [4] . This species difference may relate to the retention of the mouse tail , while rising retinoid signalling in the chick additionally regulates cell death and possibly the later normal process of caudal regression in chick and human [71] . Increasing levels of apoptosis were detected in the CNH and medial mesoderm progenitors after HH24 . This may serve to remove remaining axial stem and mesoderm progenitor cells but appears too late to be responsible for the localised loss of Bra expression . Fate maps of the HH15 tailbud do not indicate that most terminal tissue contributes to neural tube when assessed at HH30 [1] , further suggesting that terminal neural tissue identified here may also subsequently undergo apoptosis . A secondary role for cell death in the regulation of axis elongation is consistent with the phenotypes of mice in which RA levels are raised; in Cyp26a null embryos , ectopic neural tubes form at the expense of mesoderm , but cell death is not increased in the truncated body axis [29] , nor are cell death patterns altered in truncations induced by Cdx mutations [18] . This suggests that endogenous cell death is induced by exposure to higher or longer duration RA and , as inhibition of FGFR signalling did not increase cell death in our assays , may act via a distinct mechanism to promote this further step . In conclusion , these data define a series of regulatory events that control the cessation of body axis elongation in the late stage chick tailbud ( Figure 9 ) . It will be important now to identify the signals that promote Raldh2 expression in the tailbud , what regulatory mechanism blocks retinoid synthesis in mouse tailbud , and the extent to which the mechanisms identified here in the chick are conserved across other vertebrate species . It will further be interesting to assess if the same signalling dynamics are deployed to arrest elongation of other axial outgrowths , such as the limb and beak that are also truncated by excess retinoic acid [72] , [73] .
Fertile hen's eggs ( Henry Stewart ) were incubated to required stages after [74] . Early embryos were set up in EC culture [75] , and older embryos were accessed in ovo for bead grafts ( detailed protocol available on request ) . AGX-21 beads were soaked in 0 . 5 mg/ml 9-cis RA or DMSO for controls , and heparin-coated beads were used to present FGF4 ( 100 µg/ml ) or control ( bovine serum albumen ) BSA . Vitamin A–deficient quails were a kind gift of Malcolm Maden ( Kings College London ) . Explants of chick tissue were isolated as indicated in the text and cultured as described previously [51] with mouse FGF8b ( 250 ng/ml ) ( R&D Systems ) with heparin ( 0 . 1 ng/ml ) and BSA ( 0 . 0001% ) ; and FGFR inhibitor PD173074 ( 250 nM ) [76] or MEK antagonist PD184352 ( 3 µM ) [77] or DMSO control; 9-cis RA ( 10 µM or 1 µM , Sigma ) or all-trans RA ( 10 µM , 1 µM , or 100 nM , Sigma ) had similar effects and were used as indicated in figure legends or control DMSO , RA synthesis inhibitor Disulfiram ( 1 or 10 µM ) , or RAR and RXR antagonists LG100815 and LG101208 ( 5 µM ) ( Ligand Pharmaceuticals ) , or DMSO control , as described previously [11] . HH stage 20/21 embryos were accessed in ovo for bead grafts . AGX-21 beads were soaked in RAR and RXR antagonists LG100815 and LG101208 ( 1 mM ) ( Ligand Pharmaceuticals ) or DMSO for controls , and heparin-coated beads were used to present FGF4 ( 100 µM ) or control BSA . After opening the egg shell , the vitelline membrane , blood vessels , and the amnion around the tail bud region were gently displaced/peeled away with fine forceps . The tail bud was lifted and stabilised on its side by placing a thin slice of agarose gel ( 2% agarose , 1×PBS , 0 . 01% Fast green ) beneath it ( see Figure 4F ) . A small opening was made in the caudal tail bud using a tungsten needle into which a bead was implanted . The agar was then slid away and the tailbud eased back into its normal vertically hanging position , the egg sealed , and re-incubated for 24 h . DiI labelling of cell groups was carried out as in [78] . DiI ( 1 , 1′-dioctadecyl-3 , 3 , 3′ , 3′ tetram-ethyl-indocarbocyanineperchlorate , Cell Tracker CM-DiI , Invitrogen ) 1 µg/µl in DMF ( N , N-dimethylformamide ) was used to label small cell groups in the caudal most neural tube , the chordo-neural-hinge , and mesoderm progenitors in the stage 20/21 embryos . DiI was injected by glass capillary after temporary stabilization of the exposed tail bud on the agarose gel as described above . Following labelling , cell position was confirmed , agar removed , and tailbud eased back into position before sealing the egg and incubation for a further ∼30 h ( a subset of embryos were fixed immediately , imaged , and sectioned to determine labelling accuracy; see Figure 3 legend ) . Embryos were then fixed in 4% PFA for 2 h and washed in PBS . Whole tails were imaged before sectioning and analysis ( see Table 1 ) . Standard methods for whole mount in situ hybridisation were used . Explanted tissues were processed using an InsituPro machine ( Intavis ) . Normal expression patterns were observed in at least four embryos per stage , per gene . Tailbud cell populations were analysed in sections prepared following standard cryosectioning . Immunocytochemistry in human embryos at CS12-17 for Sox2 and Bra was carried out using a standard protocol for wax sections , using citrate buffer antigen retrieval . Primary antibodies were as follows: Goat anti-Sox2 Immune Systems Ltd ( GT15098; batch 401196 ) ( 1∶100 ) ; Rabbit anti-Brachyury ( Santa Cruz ) ( SC-20109 ) ( 1∶50 ) ; and for Chick sections , Rabbit anti-Sox2 ( Millipore Ab 5603 ) ( 1∶100 ) and Goat anti-Brachyury R&D Systems . Secondaries were all from Molecular Probes and used at 1∶200; for Human tissue , Donkey anti-Goat A594 and Donkey anti-rabbit-A488; and for Chick tissue , Donkey anti-Goat A488 and Donkey anti-Rabbit A594 . Labelled sections were imaged using a Delta-Vision widefield microscope . The ApopTag In Situ Apoptosis Detection Kit ( Chemicon ) or NucViewTM 488 Caspase-3 substrate , a novel , fixable cell membrane-permeable fluorogenic caspase substrate that detects Caspase-3 activity within live cells ( Biotium , 30029 ) , was used according to the manufacturers' instructions . A chick Crabp2 probe was generated from ChEST74f2 ( BBSRC Chicken EST database ) . ChEST74f2 was sequenced and coding region ( 414 bp ) and short flanking sequences identified . Blast searches indicated a single match to crabp2-like gene in meleagris gallopavo ( accession number XM 003213787 . 1 ) . | The mechanism that determines body length is unknown but likely operates at the elongating tail end of vertebrate embryos . In the early embryo , fibroblast growth factor ( FGF ) signalling maintains a proliferative pool of cells in the tailbud that progressively generates the body . It also protects these cells from the differentiating influence of retinoic acid , which is produced by the maturing mesoderm tissues of the extending body . We show here , in the chick embryo , that the “endgame”—that is , the termination of body axis elongation—comes when the mesodermal gene brachyury is suddenly lost from axial stem cell population and presumptive mesoderm cells in the tailbud late in development . Using gain- and loss-of-function approaches , we demonstrate that this step is mediated by loss of FGF signalling . We present evidence that this is due to rising retinoid signalling in the tailbud and that FGF signalling in the tailbud no longer opposes retinoid synthesis and activity . Finally , we reveal that these events are followed by local cell death in the tailbud , which can be reduced by the attenuation of retinoid signalling but involves a mechanism that is independent of FGF signalling via its usual receptor . We propose that cessation of body elongation involves loss of FGF-dependent mesoderm identity in the late tailbud and that this is mediated by rising endogenous retinoid activity , which ultimately promotes cell death in the chick tailbud . | [
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] | 2012 | Loss of FGF-Dependent Mesoderm Identity and Rise of Endogenous Retinoid Signalling Determine Cessation of Body Axis Elongation |
Mounting evidence suggests that Q-fever is more prevalent in Iran than originally believed . However , in most parts of the country , clinicians do not pay enough attention to Q fever in their differential diagnosis . The aim of this study was to investigate the prevalence of Coxiella burnetii in suspected cases of acute Q fever in north-western Iran using molecular techniques . Febrile patients were enrolled in the study and investigated for C . burnetii infection . Sera samples were tested using real-time PCR for detection of IS1111 gene , and positive samples were confirmed with nested PCR . Nine patients ( 4 . 2% ) out of 216 suspected cases were positive for C . burnetii . Weakness and fatigue , headache , and lethargy were the most prevalent clinical symptoms in acute Q fever patients . According to the results of this study and other reports of human cases in Iran , the diagnosis system of Q fever in Iran should be urgently revamped .
Q fever is a zoonotic infectious disease caused by Coxiella burnetii , an obligate intracellular bacterium . Outbreaks have been reported in both developing and developed countries . Domestic ruminants ( cattle , sheep , and goats ) are the most common source of human infection [1] . In animals , C . burnetii infection is mostly asymptomatic , but can lead to abortion , stillbirth , infertility , endometritis and metritis . Infected animals shed C . burnetii in urine , feces , milk , and especially in birth or abortion products . Main route of transmission to humans is inhalation of infected aerosols and dust with C . burnetii . Ingestion of contaminated raw milk and dairy products , skin or mucosal contact , tick bites , blood transfusion , sexual transmission , and embryo transfer are less common routes of infection transmission to humans [1 , 2] . In humans , the incubation period for primary infection has been estimated to be between 7 and 32 days after exposure [3] . Acute Q fever is the primary form of infection by C . burnetii , and more than half of the patients are asymptomatic . Acute Q fever usually presents itself as a non-specific febrile self-limiting influenza-like illness , but may also manifest as atypical pneumonia or hepatitis [4] . Cardiac involvement , neurological signs , acute lymphadenitis , cholecystitis , autoimmunity , bone marrow involvement , and dermatological signs have also been reported in some acute cases of Q fever [1] . The main manifestation of chronic Q fever is life-threatening endocarditis and vascular infection . Other less common forms of chronic Q fever includes abortion , lymphadenitis , osteomyelitis , prosthetic joint arthritis and osteoarticular infection [1 , 5] . Due to the wide range of clinical symptoms of Q fever in humans , clinical diagnosis of the disease can be challenging based on symptoms alone . Therefore , laboratory confirmation is a major and crucial part in the diagnosis of clinical cases [4] . Laboratory diagnosis of Q fever in humans is mainly based on serological tests , ELISA and IFA as the gold standard test . C . burnetii isolation from clinical samples is not performed in most diagnostic laboratories because it requires eukaryotic cell cultures and access to BSL3 facilities . In recent years , PCR-based molecular assays were developed to detect C . burnetii in clinical specimens . PCR-based techniques are more adapted than serology for early diagnosis of acute Q fever because of delay in the antibody response , which is detectable only after 2–3 weeks following infection [1 , 6] . In Iran , Q fever is an endemic disease with high seroprevalence among humans and domestic animals [7] . In recent years , many acute and chronic Q fever cases have been reported in Iran [8–11] . Furthermore , several investigations have been published on the prevalence of Q fever among domestic livestock in Iran [7] . However , human cases of Q fever remain undiagnosed in most regions of Iran , especially because most clinicians do not consider this disease in their differential diagnosis . The incidence of acute Q fever is underestimated in most parts of the world . The clinical presentations in acute Q fever patients is very pleomorphic , nonspecific and confusing . Less than 4% of patients with acute fever require hospitalization [1 , 12] . This disease is often disregarded by physicians and healthcare system and diagnosis relies upon the physicians’ awareness of the clinical symptoms of acute Q fever and access to reliable diagnostic laboratory facilities including serology and PCR [4] . Diagnosed acute cases with C . burnetii must be treated promptly to avoid to chronic Q fever [13] . The rapid and timely diagnosis of acute fever can help cure patients and avoid the spreading of the disease . Conducting molecular studies , such as the current study , can help to rapidly diagnosis of patients with acute febrile illness , as well as can raise awareness and sensitivity of clinicians and the health care system about Q fever in Iran . The aim of this study was to investigate the prevalence of C . burnetii in suspected cases of acute Q fever by molecular methods .
The samples of this study were collected from two surveys carried out in Tabriz County in the East Azerbaijan Province ( North West of Iran ) in 2013 and Ghaemshahr County in the Mazandaran province ( Northern Iran ) in 2015–2016 . Patients which met the following criteria , were enrolled to the study as suspected acute Q fever cases: Suspected patients were examined by clinical practitioners , and all the symptoms were diagnosed by them . The clinical symptoms and epidemiological evidences were recorded by practitioners in the questionnaires . Eligible individuals were selected by the practitioners and enrolled to the study based on the inclusion criteria . Demographic characteristics , clinical signs and risk factors were recorded for each participant by a standardized questionnaire developed for this study ( S1 Questionnaire ) . Blood sample was taken from each patient . Sera were subsequently extracted and used for molecular investigation . This study was approved by the Ethics Committee for Biomedical Research of Tarbiat Modarres University ( Ethic Code: IR . TMU . REC . 1395 . 510 ) . The Ethics Committee for Biomedical Research of Tarbiat Modarres University approved the consent procedure , the proposal and protocol of this study , covering all the samples taken ( blood ) , questionnaire and verbal or written informed consent . All participants signed an informed consent: Written informed consent was obtained from adult’s patients and parents of patients below the age of 18 . Also , for participants who were illiterate , the consent form was read aloud to them and the interviewer signed the consent form with the permission of these individuals on their behalf . A 200 μL aliquot of each serum was used for DNA extraction . Genomic DNA was isolated using the Roche High Pure PCR Template Preparation Kit ( Roche , Germany ) , according to the manufacturer's instruction . All samples were tested by real-time PCR for detection of IS1111 gene of C . burnetii , and positive samples were confirmed with nested PCR ( Table 1 ) . Real-time PCR was performed using specific primers and probe sequences targeting IS1111 gene ( Table 1 ) . Real-time PCR reactions were performed using the following reaction mixture: 10 μL of 2x RealQ Plus Master Mix for Probe ( Ampliqon , Denmark ) , 900 nM forward primer , 900 nM reverse primer , 200 nM probe and 4 μL of DNA template . Real-time PCR was performed on the Corbett 6000 Rotor-Gene system ( Corbett , Victoria , Australia ) , with a final volume of 20 μL for each reaction . The PCR amplification program were 10 mins at 95°C , followed by 45 cycles of 15 s at 94°C and 60 s at 60°C [14] . Nested PCR method was performed via two runs of PCR using two sets of primers including Trans1 and Trans2 for first amplification followed by 261F and 463R for second amplification reaction . The products of first PCR were separately used as DNA template in a second round of PCR . Each PCR reaction contained 5μL of DNA , 12 . 5μL Taq DNA Polymerase Master Mix RED ( Ampliqon , Denmark ) , and 10 pmol/μL from each primer in a final volume of 25μL . PCR was performed in a thermal cycler ( Bioneer , South Korea ) . The first amplification of PCR was done at 95°C for 2 min , followed by five cycles at 94°C for 30 s , 66 to 61°C ( touchdown assay ) for 1 min and 72°C for 1 min . These cycles were followed by 35 cycles consisting of 94°C for 30 s , 61°C for 30 s , and 72°C for 1 min , then a final extension step of 10 min at 72°C . In the second amplification , the cycling conditions included an initial denaturation of DNA at 94°C for 3 min , followed by 35 cycles at 94°C for 30 s , 50°C for 45 s , 72°C for 1 min , then a final extension step of 10 min at 72°C . The amplicons were electrophoresed on 1 . 5% agarose gel and visualized under UV light [10] .
In total , 8500 patients were invited to participate in the study and were screened by the clinical practitioners; among them 235 patients had the clinical and epidemiological sings to be suspected for Acute Q fever as the discussed inclusion criteria . Participants who matched the inclusion criteria were selected randomly . Finally , 216 out of 235 suspected febrile patients were enrolled ( 138 patients from Tabriz County in East Azerbaijan Province and 78 patients from Ghaemshahr County in Mazandaran Province ) ( S1 Fig ) . The age of participants ranged between 2–82 years with a mean age of 41 . 5 . In total , 61 . 1% of individuals were male and 38 . 9% were female . Residency in rural and urban regions among participants was 60 . 1% and 39 . 9% , respectively . Also , 61 . 6% of participants had a history of keeping domestic animals ( Table 2 ) . Nine patients’ samples ( 4 . 2% ) were positive for C . burnetii . All nine sera samples were positive by nested PCR and real-time PCR ( Table 3 ) . Prevalence of acute Q fever in Tabriz County and Ghaemshahr County were 3 . 6% and 5 . 1% , respectively . Weakness and fatigue ( 100% ) , headache ( 88 . 9% ) , and lethargy ( 66 . 7% ) were the most prevalent clinical symptoms in positive cases ( Table 4 ) . Seven ( 77 . 8% ) of nine identified patients had a history of keeping livestock . Also , Seven ( 77 . 78% ) of the nine detected acute Q fever cases were female and five ( 55 . 5% ) were residents in rural areas . The demographic and epidemiological findings and were not statistically significant risk factors for Q fever infection .
This study is the first molecular investigation of human Q fever cases in the north and north-west of Iran . Among 216 investigated febrile patients in this study , 4 . 2% were confirmed to be infected with C . burnetii . Based on recent evidence , Q fever shows high prevalence in livestock and milk and also a high seroprevalence in many different human populations in Iran . The seroprevalence of IgG phase I and II antibodies of Q fever in human has been reported to be 19 . 80% and 32 . 86% , respectively . Also , the prevalence of C . burnetii antibodies in goat , sheep and cattle were reported to be 31 . 97% , 24 . 66% and 13 . 30% , respectively [7] . Despite all evidence , the disease is underestimated by clinicians and the health system in Iran . In fact , most of the clinically diagnosed cases of Q fever have been the outcome of research projects . The results of this study and other reports about human cases in Iran , suggest that the physicians and health care system should pay more attention to diagnosis of Q fever cases in Iran . Special training should be provided for diagnosis of Q fever for clinicians and infectious diseases specialists . In addition to these measures , laboratory diagnostic facilities for the diagnosis of C . burnetii infection should be expanded throughout the country . It is essential that the healthcare system provides the necessary training for people to understand the disease and to prevent it . Patients with suspected clinical symptoms of acute Q fever must be advised to follow up on specific tests as well as on the completion of appropriate treatment . This way , a higher number of suspected Q fever patients will be diagnosed and treated and thus prevent possible progression of the disease toward chronic Q fever . It is noteworthy that all patients in our study diagnosed with acute Q fever were treated with appropriate antibiotics ( Doxycycline and Hydroxychloroquine ) and all effectively recovered . In this study , 4 . 2% of the 216 suspected febrile patients were positive for IS1111 gene of C . burnetii as confirmed by nested PCR and real-time PCR . Prevalence of acute Q fever in Tabriz county ( East Azarbaijan province ) and Ghaemshahr county ( Mazandaran province ) were 3 . 6% and 5 . 3% , respectively . In a similar study that was conducted in northeastern Iran , 7 . 4% of 92 patients were positive for C . burnetii , as confirmed by nested PCR [10] . In similar studies conducted in other countries; molecular prevalence of C . burnetii in acute febrile patients were 0 . 4% in Senegal [15] , 4 . 5% in India [16] and 14 . 1% in Poland [17] . The low molecular prevalence of acute Q fever in febrile cases in our study compared to other studies may be due to a number of factors , such as differences in geographical location and climate . More comprehensive studies in this region and other regions of Iran can be helpful for accurate estimation of the C . burnetii infection in acute illness . Acute Q fever generally presents as a flu-like illness with wide range of nonspecific clinical manifestations [4] . Patients with cute Q fever may develop respiratory illness or hepatitis . Pneumonia is an important clinical manifestation of acute Q fever , and C . burnetii might be an underrecognized cause of community-acquired pneumonia [13 , 15] . Based on available information and review of the literature , most clinical data of acute Q fever were obtained from patients with Q fever pneumonia [1 , 4 , 14 , 18–20] . Due to the above reasons , we enrolled acute febrile patients with pneumonia ( acute lower respiratory tract infections ) . For future studies , it is recommended that a wider range of clinical symptoms along with pneumonia and undifferentiated fever be considered in order to cast a wider net for the diagnosis of the clinical cases of acute Q fever . Serologic tests are known as reference methods for diagnosis of clinical cases of Q fever . The reason for the use of serology as detection method is partially the limitation of culture methods in isolation of C . burnetii and also the strong immune response to the infection ( the antibody produced against the bacterium ) in the human body , which is easily detectable by serological tests [13] . Unfortunately , serology has limitations in diagnosis of acute febrile illnesses , because it requires two serum specimens ( from the acute phase and the convalescent period ) and looks for a fourfold increase in antibody content in paired serum samples . Access to the second serum sample takes time ( approximately 4 weeks ) [4] . Molecular tests are an attractive alternative; they allow for rapid , one-step , diagnosis of patients with acute Q fever and can be performed at an early stage of the C . burnetii infection [1 , 14] . In our study , we developed a diagnostics assay based on real-time PCR for diagnosis of suspected patients and we used nested PCR for confirmation of positive results by Real time-PCR . All nine positive cases were confirmed with nested PCR . Employing this laboratory diagnostic protocol ( real-time PCR ) can improve and accelerate primary molecular detection , after which the initial positive results can be confirmed by the nested PCR . It is worth noting that the initial and confirmation tests identify and amplify different regions of the IS1111 gene of C . burnetii , increasing the fidelity of the detection technique . Based on our results , we recommend that molecular tests be combined with the accepted serological tests to diagnose patients with suspected Q fever in shorter time and at earlier stages of the disease . One of the limitations of our study was the small number of positive cases , which made us unable to do a proper statistical analysis of risk factors and epidemiologic factors . In addition , more precision in the entry of eligible individuals and those who were more closely related to the criteria for diagnosis of acute Q fever , could provide a more precise prevalence of acute Q fever . The combination of molecular tests with serologic tests ( as the gold standard diagnostics method ) allows for proper identification of all suspected patients . Another limitation of our study was lack of attention to whether antibiotics against C . burnetii were administered during the sampling time . Therefore , it is suggested that the mentioned limitations should be considered in subsequent studies . | Q fever is a zoonotic infectious disease caused by Coxiella burnetii . Domestic ruminants are the most common source of human infection . Main route of transmission to humans is inhalation of infected aerosols and dust with C . burnetii . Acute Q fever is usually presented as a non-specific febrile and self-limiting influenza-like illness , but in severe acute cases , may manifest as atypical pneumonia or hepatitis . In Iran , Q fever is an endemic disease with high seroprevalence among humans and domestic animals . However , human Q fever cases remain undiagnosed in most regions of Iran , especially because most clinicians fail to spot this disease in their differential diagnosis . The aim of this study was to investigate the prevalence of acute Q fever in suspected cases ( 216 suspected cases ) using molecular techniques . Nine acute Q fever patients were diagnosed by Real-time PCR and Nested PCR . Weakness and fatigue , headache , and lethargy were the most prevalent clinical symptoms in positive cases . Human Q fever cases described in this , and previous studies , indicate the need to implement diagnostic techniques for this disease across the country . | [
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"bacterial",... | 2019 | Genetic evidence of Coxiella burnetii infection in acute febrile illnesses in Iran |
Buruli ulcer disease ( BU ) , due to the bacteria Mycobacterium ulcerans , represents an important and emerging public health problem , especially in many African countries . Few elements are known nowadays about the routes of transmission of this environmental bacterium to the human population . In this study , we have investigated the relationships between the incidence of BU in Côte d'Ivoire , western Africa , and a group of environmental variables . These environmental variables concern vegetation , crops ( rice and banana ) , dams , and lakes . Using a geographical information system and multivariate analyses , we show a link between cases of BU and different environmental factors for the first time on a country-wide scale . As a result , irrigated rice field cultures areas , and , to a lesser extent , banana fields as well as areas in the vicinity of dams used for irrigation and aquaculture purposes , represent high-risk zones for the human population to contract BU in Côte d'Ivoire . This is much more relevant in the central part of the country . As already suspected by several case-control studies in different African countries , we strengthen in this work the identification of high-risk areas of BU on a national spatial scale . This first study should now be followed by many others in other countries and at a multi-year temporal scale . This goal implies a strong improvement in data collection and sharing in order to achieve to a global picture of the environmental conditions that drive BU emergence and persistence in human populations .
Buruli ulcer is a severe human skin disease caused by Mycobacterium ulcerans . It represents now the third mycobacterial infection in the world behind tuberculosis due to M . tuberculosis and leprosy , caused by M . leprae . The first clinical description of the disease agent was done in Australia in 1958 [1] , even though disease cases have been recorded since the end of the XIXth century in Uganda , in the Buruli area [2] . During the last decades , a dramatic extension of the spatial distribution of Buruli ulcer disease as well as increase of the number of infected people has been reported in many parts of the world . Highest incidences are now observed in Western Africa with 20 , 000 , 6 , 000 and 4 , 000 cases observed in 2005 in Côte d'Ivoire , Ghana and Benin , respectively [1] , [3] , [4] . Mycobacterium ulcerans is an environmental bacterium and its mode of transmission to humans is still unclear , this is why the disease is often referred to as the “mysterious disease” or the “new leprosy” . Recent findings on the life cycle of the Buruli ulcer's agent have enhanced current evidence on several points . First , it has been shown that M . ulcerans can develop as biofilms on the surface of aquatic plants [5] . More specifically , some freshwater aquatic plants might be involved in the mycobacterium life-cycle as potential intermediate hosts or trophic chain concentrators [5]–[7] . Secondly , contrasted animal species were found infected by the bacterium in natural conditions , e . g . fishes , frogs [8]–[9] or koalas ( see [10] for review ) . Recently , an impressive field study also generated additional environmental data regarding M . ulcerans in nature [11] . Third , aquatic insects are also suspected to act as vector and to transmit the disease by biting . It has been demonstrated that M . ulcerans is present in salivary glands of African water bugs of the family Naucoridae , and that infected water bugs could transfer the pathogen to mice [6] , [12] , [13] . Infected insects were also found in endemic areas . Finally , it is generally admitted by medical and scientific communities that specific environmental niches , which still need to be precisely defined , favour the occurrence of the disease [11] , [14]–[18] . Based on several case-control studies performed in different African countries , freshwater ecosystems like rivers , man-made ponds and lakes , or marshy zones and irrigated perimeters represent risk factors to BU [19]–[21] . At nation-wide or regional scale , other studies also showed relations between BU infections and different environmental factors [22]–[24] , as for instance landscape cover attributes [25] or arsenic in water [26] in Ghana . Despite these specific studies , Buruli ulcer is still a mysterious disease and all new findings will contribute to help national and international public health authorities to fight this highly deleterious pathogen and neglected disease . For this reason , all information on the relations between the environment and the disease occurrence are highly relevant for a better understanding of the disease as a whole . Here we propose to perform a first nation-wide scale study in Côte d'Ivoire of the link between environmental but also socio-economic factors and Buruli ulcer cases , based on spatial mapping and multivariate statistical analyses . First detection of BU in Côte d'Ivoire occurred in 1981 but the number of cases clearly increased in 1987 and became then a national public health problem [20] .
The Buruli ulcer notifications database was generated by the Institute Raoul Follereau in Côte d'Ivoire . All notified cases were diagnosed by medical doctors from the institution based mainly on clinical signs and sometimes confirmed also by tissue biopsy or ulcer swab examination . Data correspond to annual cases reports from primary healthcare centers and central hospitals distributed all over the national territory in 1997 . The central hospitals serve towns and cities with more than 5 , 000 inhabitants . In Côte d'Ivoire , up to 54 percent of the human population lives in a town with a primary health care center or hospital; and 91 percent lives less than 15 km from such a town [27] . Permission to use these data for the present study has been granted by the National Program for Buruli ulcer Control from the Ministry of Health and Public Hygiene of Côte d'Ivoire ( Pr H . Asse ) . Environmental data correspond to vegetation types and surfaces ( forest or cultivated areas ) and dams ( location and superficies ) . Data concerning vegetation were extracted from two sources . To provide an overview of the global vegetation zones in the country , we first generated a map ( Figure 1A ) based on vegetation map by Guillaumet [28] which presents the principal zones of vegetation based on 1979 aerial photographs ( 50 centimeter resolution ) . Secondly , in order to obtain the most realistic information about forest patches corresponding to the BU reported period , we used the map of forest reservation in Côte d'Ivoire from the National Bureau of Technical and Development Studies [29] representing the recent state of the forest cover , based on 1993 and updated with 2000 SPOT images ( 20 meters resolution ) to build a second map representing the current state of forest distribution in the country ( Figure 1B ) . This recent map also enabled us to compute detailed geometrical characteristics ( distances and areas ) used in statistical analyses . Côte d'Ivoire is characterized by some well-known contrasting ecosystems ( Figure 1A ) : areas of wet dense forest and bush extending roughly South of the 8°N latitude line , and dry forest and Sudanian savanna ecosystems in the Northern region of the country . The origin of this division is primarily climatic , since the transition from wet dense forest to savanna-like ecosystems is associated with the change from four climatic equatorial seasons in the South to two tropical seasons in the North [28] . Data concerning dams were retrieved at the Côte d'Ivoire National Centre for mapping and remote-sensing , at the University of Abidjan-Cocody , by one of the authors ( TB ) . They correspond to the dams' geographical position and total surface area for the year 1997 . There are over 500 dams in Côte d'Ivoire and the majority of them were built between 1970 and 1990 in order to allow the cultivation of water-demanding crops , especially in the Central region and more specifically in the Northern region , where rainfall is not sufficient to sustain this kind of production . These water bodies have facilitated the establishment of irrigated rice cultures , and they are also a source of freshwater for cattle and fish farms . Regarding socio-economic parameters , agricultural data were provided by the Côte d'Ivoire Ministry of Agriculture . They concerned i ) the type of cultivated areas , and ii ) the crop production ( in tons ) for the year 1997 for each of the 54 prefectures . For the present study , we only considered rice and banana fields , which require a constant high humidity and intensive water supply . Thus we did not consider crops which do not require irrigation , such as cassava ground nut and corn . Epidemiological data correspond to cases detected at hospital or primary healthcare centers situated in each prefecture ( Figure 2 ) . Raw disease case data ( Figure 3A ) were transformed to numbers of cases per 100 , 000 inhabitants , considering the whole population of the prefecture of each 202 hospitals or health care centers . Moreover , it is known that each inhabitant lives in an average 10 km distance from a hospital in Cote d'Ivoire [27] . For this reason , we interpolated our epidemiological data on a regular 10 km×10 km grid-square by the inverse distance weighting method , using the Surfer software [30] , [31] ( Figure 3B ) . In order to illustrate environmental data , we generated maps representing both epidemiological and environment data by interpolating environment point data ( i . e . dams and rice crops data ) on the same regular grid ( Figures 1 and 4 ) . However , we did not use these interpolated data but the raw data for the following statistical analysis . Surface data concerning forest area and vegetation zones were not transformed . The surface areas for the different types of ecosystems studied , e . g . , evergreen forest patches and rice-field cultures were transformed according to the percentage of surface coverage on the total prefecture surface area size in km-square . We used Geographical Information System ( GIS ) to explore the relationships between environmental and epidemiological data [32] . In this analysis , we used the original data instead of the interpolated data . We first arbitrarily defined 4 influence-by-distance classes ( i . e . , <5 km , 5–10 km , 10–15 km , 15–20 km ) for Buruli ulcer disease occurrence , which corresponded to different distances between a given case and the nearest hydro-agricultural dam or the nearest forest patch respectively . Based on this distribution , we analysed the variation of the rate of disease incidence according to the different distances for the two environmental parameters under scrutiny , i . e . , dams or forest patches . We thus considered here the location of dams and forest patches but not their surface . Concerning dams , we also made a regional study distinguishing three major hydrological regions: the Kossou region in the Centre of country , the Buyo region in the West , and the Southern region . Linear trends were tested by using simple linear model . Finally , we used logistic regression [33] to analyse the statistical relationships between hydro-agricultural environmental conditions and incidence of Buruli ulcer disease by administrative division . The explicative variables considered were: forest surface area ( in hectare ) , irrigated rice production ( in thousands of tons ) , banana production ( in thousands of tons ) , and dam surface area ( in hectare ) . To make the analysis more complete , i . e . , to better capture the multivariate dimension of the environmental conditions on disease emergence , we integrated the two-factors interaction terms .
Figure 3 illustrates Buruli ulcer cases and the aggregated distribution of incidences on a 10 km grid-square , in Côte d'Ivoire for the year 1997 . Southern Côte d'Ivoire , rich in evergreen forest and forest/savanna-like landscapes ( Figure 1A ) , had a very high Buruli ulcer incidence ( more than 5 , 000 infected people for 100 , 000 inhabitants ) , whereas we observed a very low incidence in the North ( less than 10 infected people for 100 , 000 inhabitants ) . In particular , the highest incidence ( more than 10 , 000 cases for 100 , 000 inhabitants ) was near the contact zone between forest and savanna , notably in proximity of the “V-Baoulé” region in the centre of Côte d'Ivoire . Figure 1B also illustrates the spatial relationship between Buruli ulcer cases and evergreen forest patches . Figure 4A shows that the rice field areas exhibited the highest rate of disease notifications , notably near the large lakes in the Centre of the country , an area which provides nearly 40 per cent of the national rice production [34] . Together with the regions of high rice production , highest incidence spots of Buruli ulcer disease notification were mainly located in areas with a high density of dams ( Figure 4B ) . It was particularly true for the area of the great lakes , in the Centre of the country , which hosts more than a half of the country's hydro-agricultural equipments , with an average reservoir surface area of 2 sq-km . When considering the geographical distance between Buruli ulcer disease cases and distance to dams or forest patches , we found significant negative relationships ( Figure 5 ) . In the Kossou region , located in the central part of Côte d'Ivoire , Buruli ulcer disease incidence was up to 100 notifications per 100 , 000 inhabitants within a 5 km-distance radius from the closest reservoir , but it dropped to 40 cases for a 5–10 km-distance , and to less than 20 cases for a 20 km-distance ( r2 = 0 . 86 , P<0 . 01; Figure 5A ) . Concerning the Buyo region in the West , and the whole Southern region , the relationship between Buruli ulcer incidence and distance to dams was also significant ( r2 = 0 . 84 , P<0 . 01; and r2 = 0 . 71 , P<0 . 01 , respectively; Figure 5 ) . We observed the same negative relationship between disease incidence and increasing distance from a primary forest patch ( r2 = 0 . 69 , P<0 . 01; Figure 5B ) . Table 1 illustrates the main results from the logistic regression analysis performed to explain Buruli ulcer disease incidence . This analysis showed that both the irrigated rice crops ( P<0 . 001 ) and , to a lesser extent , the forest patch surface ( P = 0 . 001 ) could increase the BU risk within communities by 77 . 5% and 3% , respectively . In contrast , the dam surface is clearly associated with a lower risk of BU infection ( OR equals 0 . 74 ) even if we previously showed a significant positive correlation between BU and distance to dams ( Figure 5 ) . This apparent discrepancy can easily be explained by the different effects of the presence and the use by populations ( contact with population ) of dams , as discussed below . However , since interactions were included in the model , these results are to be interpreted with care . Main effects are to be interpreted as odds-ratios for one covariate when all other covariates are fixed at their base value . For instance , the value 0 . 75 for dam area means that surface of dams is associated with a reduced Buruli ulcer incidence , only in places where there is no rice , no banana , etc . When rice is present , the odds-ratio for an increase of one unit in dam area is equal to 0 . 75×1 . 25 = 0 . 94 ( with 1 . 25 corresponding to the OR value for the interaction between both parameters ) , not significant . The odds-ratio for a 1 , 000 ton increase in rice production and an increase of dam area with respect to no-rice is 1 . 77×0 . 75×1 . 25 = 1 . 66 . In reality , the different habitats are superimposed in space ( see Figures 1 and 4 ) , an observation which was in accordance with the statistical results of the influence of interactions , and , thus , could not be considered as specific variables . Two-way interaction terms between the forest patch coverage-banana crops and the dam surface-banana crops variables also appeared marginally significant to explain the increase of BU in Côte d'Ivoire , with odds-ratio 1 . 0067 ( P<0 . 001; near 0 . 6% ) and 1 . 0588 ( P<0 . 001; near an order of 6% ) , respectively . Finally , the irrigated rice crops versus the banana crops interaction term was a significant protective factor ( OR = 0 . 8938 , P<10−6 ) in the present study .
Infectious agents indirectly transmitted to or between humans because of human-modified environments account for many emerging and re-emerging non-zoonotic or zoonotic diseases today [35] . The list of emerging diseases associated with human behaviours and environmental perturbations is still increasing [36] , [37] . Concerning Buruli ulcer disease , although humans are dead-end hosts for the causative agent , Mycobacterium ulcerans , it is well known that the risk of infection is greatly increased by marked exposure to aquatic environments [38] . Even though the disease agent life-cycle is poorly understood , there is a consensus within the public health and scientific community that human behavior , i . e . frequentation of freshwaters for professional or recreational activities , and environmental aquatic alterations are major disease risk factors [19] . However , an extensive quantitative analysis of disease risk is dramatically lacking for Buruli ulcer [39] , and risk factor analyses , like the one described in this paper , in parallel to the understanding of the evolutionary properties of the pathogenic microorganism , are definitely necessary to circumvent and control the spread of the disease . Based on a country-wide statistical investigation on Buruli ulcer cases in Côte d'Ivoire , we have observed the highest incidence of Buruli ulcer disease in the Centre of Côte d'Ivoire , and more particularly in the sector of “V Baoulé” , where there is also the highest concentration of hydro-agricultural dams ( the majority of the villages are located less than 5 km from a dam ) , used for the development of agriculture , especially semi-intensive rice-field and banana production . Interestingly , high occurrence of Buruli ulcer disease was also notified in the South-Western region of the country , where there is a very low density of man-made dams , but where environmental conditions , i . e . pristine ombrophilous dense forest , favour the existence of natural swamps and marshy aquatic ecosystems that could act as reservoirs for the microorganism . Regular agricultural activities , e . g . , rice-field or banana production , in the vicinity of the forest remnants cause each year the transformation of the wet dense forest into an open-vegetation landscape , constituted by a mosaic of degraded forest patches , cultivated fields , fallows and degraded landscapes [40] , [41] . Community encroachments and settlements in the vicinity of these patchy , altered environments may , thus , constitute foci for Buruli ulcer disease emergence and spread within human communities . On the contrary , in the South/South-East region of Côte d'Ivoire , where a vast coastal lagoon network , e . g . Ebrié , Aby , and two large dams , i . e . Ayamé 1 and 2 , are associated with high human density settlements , Buruli ulcer disease is rare because coastal lagoons are brackish water ecosystems , thus potentially hostile to the development of M . ulcerans and/or its hosts/vectors , and man-made lakes are not associated with extensive agriculture on their shorelines . Finally , in the North of the country , small dams and rice fields are not associated to a high incidence of Buruli ulcer disease , although this area has known the development of the most important network of hydro-agricultural equipments in the last 20 years . The low rate of incidence recorded in the Northern area of Côte d'Ivoire can be best explained by the existence of a latitudinal limit possibly related to the bioclimatic North-South gradient . Indeed , contrary to the Southern region , due to the longer dry season period ( more than 4 months ) few dams and rivers are permanent throughout the year . This creates environmental conditions which are not favorable to the disease agent or its hosts/vectors persistence , thus , decreasing the contact opportunities for disease transmission to human populations . Indeed , the country-wide scale ecosystem signature of Buruli ulcer occurrence in Côte d'Ivoire , i . e . the highest incidence in the Centre of the country , where wet conditions are present all year around , in comparison to the North and the South , might be best explained by the existence of concomitant environmental factors , like the occurrence of specific biological species diversity that could be involved in the disease transmission . The statistical regression analysis indicates that the most important risk factors for BU in human communities of Côte d'Ivoire are the irrigated rice crops ( increase of risk by 77 . 5% ) . Other environmental factors , introduced into the analysis , are more marginal for the explanation of the disease risk: the interaction between dam surface and banana crops in the vicinity ( increase of risk by 5 . 9% ) , and forest patch area and banana crops ( increase of risk by 0 . 7% ) . We also showed that dam surfaces gave significantly lower odds for disease incidence whereas the increasing distance to dams was related to an increase in BU in Figure 5A . It seems that the distance to small dams is important for increased risk of BU infection , whereas the size of the dams is negatively related to the disease . This apparent discrepancy can be possibly explained by 2 main ways . First , this could be interpreted as a local-scale factor where small dams alter the environment in a way that is different from very large dams ( similar to large lakes that usually do not have cases nearby ) . Small dammed areas can be much different in terms of the environment compared to large dams ( lakes , reservoirs ) , and this is probably important to disease transmission and can imply different risk levels for BU . Here , we speculate that the risk of contamination is likely higher near small dams because of population behaviors . They used small dams for the irrigation of rice-field and banana crops . These small dams and irrigated crops are part of the everyday life of the neighbouring communities , and the dams constitute in most of cases , particularly in the Centre and North of Côte d'Ivoire , the only source of water supply for agro-pastoralism . Moreover , the potential interactions between primary forest patches and the development of agriculture may also constitute favorable ecotone zones that provide new environmental niches for the persistence and spread of M . ulcerans , and should be addressed . To sum-up , it seems that the most important risk factor of BU regarding dams is not only its presence but mainly the contact between populations and these areas . To go further in this way and to be able to decide between ( or define the proportion of ) the two hypotheses discussed above ( i . e . different environmental conditions and/or population behavior ) , field work is now required to determine the presence of the bacterium in environment and its potential different distribution in different areas ( rice fields , small dams , large dams ) . Thus , ecosystem dynamics and its evolution , e . g . land-use changes , biodiversity alteration , as well as socio-economic factors should be systematically taken into account in BUI research , putting this kind of study on a very promising way in order to better understand the transmission route of the bacterium from the environment to human populations , and then a better control of the disease . Further studies at different places and also at a multi-annual scale are now required to acquire a “bigger picture” of this disease . All these findings are also consistent with two case-control studies in Benin and Ghana [19] , [42] as well as a recent nation-wide study in Ghana [25] , but they remain innovative since they define new risk factors ( e . g . type of crops ) for a new country , i . e . Côte d'Ivoire , and add socio-economic factors in the analyses . By extending the analysis of BU risk to a country-wide scale and to socio-economic factors , we highlight here , the importance of a multifaceted approach for disease surveillance . The ultimate goal of our research is to develop a quantitative , spatially realistic model for the BU system that will constitute the framework for the development of a sensible control plan . The present work demonstrates the importance of applying an environmental approach to the study of Buruli ulcer epidemiological problems and more generally highlights the strong necessity for an inter-disciplinary approach . | Buruli ulcer ( BU ) is one of the most neglected but treatable tropical diseases . The causative organism , Mycobacterium ulcerans , is from the family of bacteria that causes tuberculosis and leprosy . This severe skin disease leads to long-term functional disability if not treated . BU has been reported in over 30 countries mainly with tropical and subtropical climates , but Côte d'Ivoire is one of the most affected countries . M . ulcerans is an environmental bacterium and its mode of transmission to humans is still unclear , such that the disease is often referred to as the “mysterious disease” or the “new leprosy” . Here , we explored the relationship between environmental and socioeconomic factors and BU cases on a nationwide scale . We found that irrigated rice field cultures areas , and , to a lesser extent , banana fields as well as areas in the vicinity of dams used for irrigation and aquaculture purposes , represent high risk zones for the human population to contract BU in Côte d'Ivoire . This work identifies high-risk areas for BU in Côte d'Ivoire and deserves to be extended to different countries . We need now to obtain a global vision and understanding of the route of transmission of M . ulcerans to humans in order to better implement control strategies . | [
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The precise control of synaptic connectivity is essential for the development and function of neuronal circuits . While there have been significant advances in our understanding how cell adhesion molecules mediate axon guidance and synapse formation , the mechanisms controlling synapse maintenance or plasticity in vivo remain largely uncharacterized . In an unbiased RNAi screen we identified the Drosophila L1-type CAM Neuroglian ( Nrg ) as a central coordinator of synapse growth , function , and stability . We demonstrate that the extracellular Ig-domains and the intracellular Ankyrin-interaction motif are essential for synapse development and stability . Nrg binds to Ankyrin2 in vivo and mutations reducing the binding affinities to Ankyrin2 cause an increase in Nrg mobility in motoneurons . We then demonstrate that the Nrg–Ank2 interaction controls the balance of synapse growth and stability at the neuromuscular junction . In contrast , at a central synapse , transsynaptic interactions of pre- and postsynaptic Nrg require a dynamic , temporal and spatial , regulation of the intracellular Ankyrin-binding motif to coordinate pre- and postsynaptic development . Our study at two complementary model synapses identifies the regulation of the interaction between the L1-type CAM and Ankyrin as an important novel module enabling local control of synaptic connectivity and function while maintaining general neuronal circuit architecture .
Transsynaptic interactions mediated by cell adhesion molecules ( CAMs ) control the formation , function , and stability of synaptic connections within neuronal circuits . While a large number of synaptogenic CAMs controlling the initial steps of synapse formation have been identified [1] , [2] , we have only limited knowledge regarding the identity or regulation of CAMs selectively controlling synapse maintenance or plasticity . Information processing within neuronal circuits is adjusted by the selective addition or elimination of individual synapses both during development and in response to activity [3] , [4] . These changes in connectivity can occur in very close proximity to stable synapses [5] , [6] indicating the existence of mechanisms capable of local alterations of transsynaptic adhesion . Potential mechanisms to alter binding affinities of CAMs include direct alterations of extracellular domains through binding of ligands like metal ions ( e . g . , Ca2+ ) or indirect mechanisms through the selective association of CAMs with the intracellular cytoskeleton via adaptor proteins [7] . Modulation of intracellular interactions via posttranslational modifications can alter mobility , clustering , and adhesive force of CAMs [8] . For example , it has been demonstrated for the Cadherin–Catenin complex that changes in biophysical properties can induce changes in synapse morphology , strength , or stability and modulate transsynaptic signaling [9] , [10] . To identify cell adhesion molecules potentially controlling synapse maintenance and plasticity we performed an unbiased in vivo RNA interference ( RNAi ) screen at the larval neuromuscular junction ( NMJ ) of 287 transmembrane proteins that are predicted to function as synaptic CAMs based on their domain structure [11] . These included Ig-domain containing proteins , Leucine-rich repeat proteins , Cadherins , Integrins , Semaphorins , and others ( Table S1 ) . In this high-resolution screen we identified the Drosophila L1-type CAM Neuroglian ( Nrg ) as a key regulator for synapse stability . Nrg encodes the Drosophila ortholog of the L1-type protein family [12] that is composed of four closely related members in vertebrates: L1 , CHL1 ( close homolog of L1 ) , NrCAM ( neuronal CAM ) , and Neurofascin [13] , [14] . L1-type IgCAMs usually consist of 6 Ig-domains , 3–5 fibronectin type III domains , a single transmembrane domain , and an intracellular tail . The extracellular domain of L1 family proteins can mediate cell–cell adhesion via homophilic interactions and can also engage in a variety of heterophilic interactions with other Ig-domain proteins ( e . g . , NCAM , TAG-1 , Contactin , and others ) , extracellular matrix proteins , or integrins [13]–[15] . The intracellular tail contains distinct protein–protein interaction domains potentially controlling the localization and function of L1 proteins [16]–[18] . Most prominent is a central Ankyrin interacting motif that is highly conserved among all vertebrate L1 family proteins and Drosophila Neuroglian [16] , [19] . Phosphorylation of the tyrosine within this FIGQY motif abolishes binding to Ankyrins [20]–[23] . The Ankyrin-binding domain is essential for mediating neuronal function in vivo in C . elegans , however it is dispensable for L1-mediated homophilic adhesion in transfected cells in culture [24] , [25] . Importantly , we previously identified Drosophila ankyrin2 ( ank2 ) as an essential gene for synapse stability at the larval neuromuscular junction [26] , [27] . Ank2 together with the presynaptic spectrin cytoskeleton and the actin capping protein Hts/Adducin controls NMJ formation and maintenance and provides a scaffold to link the actin and microtubule cytoskeleton to synaptic cell adhesion molecules [28] , [29] . Based on this potential biochemical interaction Nrg might encode the CAM upstream of this Ank2/Spectrin scaffold to control synapse development . Human mutations in L1CAM cause a broad spectrum of neurological disorders ( L1 or CRASH syndrome ) including MASA syndrome ( mental retardation , aphasia , shuffling gait , adducted thumbs ) , agenesis of the corpus callosum , and spastic paraplegia . In addition , hypomorphic mutations in L1CAM and NrCAM have been linked to psychiatric diseases [14] , [15] , [30] . In correspondence with the human disease , animal models implicated L1-type proteins in nervous system development [13] , [14] . At the cellular level L1-type proteins are involved in the control of neurite outgrowth , axon pathfinding , and fasciculation and synapse development [14] , [16] , [31] . The subcellular localization of L1-type proteins contributes to the establishment and maintenance of specialized neuronal membrane compartments including the axon initial segment ( AIS ) and nodes of Ranvier [32]–[35] . While these studies highlight essential functions of L1-type proteins , potential redundant or antagonistic functions between different L1-type proteins may mask the full extent of their importance for nervous system development . Indeed , evidence for redundant functions between L1-type proteins was provided by a double mutant analysis of L1CAM and NrCAM [36] . Together with the requirement of L1-type proteins for early nervous system development , this confounds our current understanding of the contribution of L1-type CAM to synapse development and plasticity . In addition , mechanistic insights into the in vivo control of L1-type CAM function at synapses are lacking to date . Nrg encodes the sole homolog of L1-type CAMs in Drosophila with equal homology to all four vertebrate proteins . This provides a unique opportunity to unravel the contributions and mechanisms of regulation of L1-type CAMs in synapse development and maintenance . Here , we generate a series of Pacman-based mutations that allowed us to identify the specific contributions of extra- and intracellular domains of Nrg for synapse stability . We then provide evidence that binding of Nrg to Ank2 is critical for the control of mobility of Nrg in vivo . We demonstrate that modulation of the Nrg–Ank2 interaction allows precise control over the balance between synapse formation and stability . Finally , we demonstrate that dynamic regulation of the Ankyrin-binding domain of Nrg is essential for the coordination of pre- and postsynaptic development via transsynaptic signaling mechanisms at central synapses .
To identify cell adhesion molecules necessary for the maintenance of synaptic connections we performed a transgenic RNAi-based screen [37] of 287 transmembrane proteins encoding potential cell adhesion molecules based on their domain structure and previously described functions in axon guidance or synapse development ( Table S1 ) . We knocked down candidate genes simultaneously in presynaptic neurons and postsynaptic muscles and analyzed third instar NMJs for defects in synapse stability using selective pre- and postsynaptic markers ( Figure 1A–F ) . In wild-type animals the presynaptic active zone marker Bruchpilot ( Brp ) is found in close opposition to postsynaptic glutamate receptor cluster at all individual synapses within the presynaptic nerve terminal demarcated by the membrane marker Hrp . In contrast , NMJs displaying postsynaptic glutamate receptor clusters without opposing presynaptic active zone markers and a fragmentation of the presynaptic membrane indicate synapse retractions [27] , [28] . We identified Drosophila Nrg as the major hit in our screen resulting in synaptic retractions at more than 50% of all NMJs on muscle 4 . We first tested whether specific knockdown of Nrg either in the motoneuron or the muscle also impairs synapse stability . Presynaptic knockdown of Nrg was sufficient to cause synaptic retractions equivalent to the simultaneous pre- and postsynaptic knockdown ( Figure 1B , G ) . In contrast , muscle-specific nrg RNAi did not lead to a significant increase in synaptic retractions ( Figure 1C , G ) . We obtained similar results when we expressed an independent nrg RNAi line ( Figure 1E–G ) and were able to enhance the phenotype by combining different motoneuron Gal4 drivers or by co-expressing UAS-dcr2 to enhance RNAi efficacy ( Figure 1G ) . In addition , we observed similar rates and severities of synapse retractions when using independent pre- and postsynaptic markers and when analyzing different subsets of muscles ( Figure 1D–F; Figure S1; Table S2 ) . To monitor the efficiency of our RNAi mediated knockdown , we directly analyzed Nrg protein levels . Drosophila nrg encodes two specific isoforms , the ubiquitous isoform Nrg167 and the neuronal specific isoform Nrg180 ( Figure 2A ) [38] . Nrg180 was present throughout motoneuron axons and within the presynaptic nerve terminal ( Figure S2A ) , and in addition , Nrg167 was present in muscles and glial cells ( Figure S2E ) . Western blots of larval brain extracts and analysis at the larval NMJ demonstrated that all combinations of presynaptic nrg RNAi efficiently knocked down Nrg180 ( Figure 1H; Figure S2B ) . Similarly , muscle-specific knockdown of Nrg caused a loss of Nrg3c1 staining in the muscle that can be attributed to a loss of Nrg167 ( Figure S2F ) . The loss of postsynaptic Nrg resulted in a significant change in presynaptic Nrg levels and distribution at the NMJ ( Figure S2C , F; reduction to 62 . 9±3 . 7% of wild-type protein within the presynaptic terminal , p<0 . 001 ) , indicating a requirement of postsynaptic Nrg for presynaptic Nrg localization . However , this reduction in presynaptic Nrg180 levels was not sufficient to impair synapse stability ( Figure 1C , F , G ) , implicating the potential existence of alternative postsynaptic Nrg interaction partners essential for NMJ maintenance [39] . Likewise , presynaptic knockdown of Nrg reduced Nrg staining in the axon and at the synaptic terminal but did not significantly alter Nrg distribution within the postsynaptic subsynaptic retriculum ( SSR ) ( Figure S2G , H ) . To validate the specificity of our RNAi-mediated knockdown we aimed to rescue the synaptic phenotypes by co-expressing wild-type Nrg180 . Simultaneous expression of UAS–nrg180 significantly rescued synapse stability by restoring Nrg180 protein levels both in larval brains and at the NMJ ( Figure 1G , H and Figure S2D ) . In contrast , co-expressing UAS–mCD8–GFP or UAS–fasciclinII ( NCAM homolog ) failed to rescue the synaptic retraction phenotype ( Figure 1G ) . Thus , the specific loss of pre- but not postsynaptic Nrg caused an impairment of synapse stability . To gain insights into the molecular processes inducing synaptic retractions in animals lacking presynaptic Nrg , we analyzed the distribution of two presynaptic components , Ank2 and Futsch , at early stages of synaptic retractions in comparison to active zone and vesicle markers . The presynaptic adaptor protein Ank2 is an essential molecule for synapse stability and Ankyrins can directly bind to Nrg [26] , [27] , [40] . The microtubule-associated protein Futsch serves as a marker for presynaptic microtubules as loss of microtubules represents an early step in synaptic retractions at the Drosophila NMJ [27] , [29] , [41] . At wild-type NMJs , Ank2 and Futsch were present in all terminal boutons together with Brp and DvGlut . In contrast , after presynaptic knockdown of Nrg , we observed NMJs lacking Ank2 or Futsch in terminal boutons that still contained presynaptic Brp or DvGlut ( Figure 1I–K ) . Thus loss of Ank2 and of the associated microtubule cytoskeleton may represent early steps during synapse retractions caused by the loss of Nrg . Two domains of Nrg that may be essential for synapse maintenance are the extracellular Ig domains mediating association with postsynaptic CAMs and the intracellular Ankyrin binding domain that provides a link to the presynaptic cytoskeleton . To directly test for a potential role of these domains we generated genomic rescue constructs that allow expression of wild-type and mutated nrg at endogenous levels using a site-directed Pacman-based approach [42] , [43] . We first generated a transgenic construct encompassing the entire nrg locus including 25 kb upstream and 10 kb downstream regulatory sequences ( P[nrg_wt]; Figure 2A ) . This construct fully rescued the embryonic lethal nrg null mutations nrg14 and nrg17 . We then used galK-mediated recombineering [44] to generate a deletion of the extracellular Ig domains 3 and 4 ( P[nrgΔIg3–4] ) , thereby completely disrupting hetero- and homophilic binding capacities of Nrg [45] . In addition , we generated specific deletions of the Ankyrin-binding domains of Nrg167 and Nrg180 that are encoded by unique exons ( P[nrg167ΔFIGQY] and P[nrg180ΔFIGQY]; Figure 2A ) . All constructs were inserted into the genomic insertion site attP40 to ensure identical expression levels . While P[nrg167ΔFIGQY] and P[nrg180ΔFIGQY] rescued the embryonic lethality of nrg14 mutants similar to the wild-type construct , P[nrgΔIg3–4] failed to rescue lethality . In order to analyze the larval NMJ of nrg14; P[nrgΔIg3–4] mutant animals , we combined the Pacman rescue approach with the MARCM technique [46] . This allows the generation of mCD8–GFP-marked motoneurons expressing only the mutated form of Nrg . First we analyzed motoneurons completely lacking nrg using the nrg14 null mutation and observed two striking phenotypes . We found synapse retractions indicated by NMJs displaying remnants of the presynaptic MARCM membrane marker opposite postsynaptic glutamate receptors but lacking the presynaptic marker Brp ( Figure 2C ) . While synapse retractions were only observed at low frequency , about 50% of all MARCM motoneuron axons ended in “bulb-like” structures within nerve bundles and were not connected to a postsynaptic muscle ( Figure 2F and Figure S3E ) . The wild-type nrg Pacman construct fully rescued the axonal and NMJ phenotypes ( Figure 2B , F and Figure S3A , D ) , however , P[nrgΔIg3–4] failed to rescue these defects and the presence of P[nrg180ΔFIGQY] resulted only in a partial rescue . In both genotypes we observed synaptic retractions as well as axons ending in bulbs distant from a potential target muscle ( Figure 2D–F; Figure S3C , G; Table S1 ) . In contrast , no defects were observed in the presence of P[nrg167ΔFIGQY] indicating that only the Ankyrin binding motif of Nrg180 is essential within motoneurons ( Figure 2F; Figure S3B , F; Table S2 ) . Prior studies showed a delay of axonal outgrowth in nrg mutant embryos [47]–[49] . Our axonal phenotypes would be equally consistent with a stalling of axons or with a retraction of axons after initial innervation of target muscles . Importantly , we observed mutant MARCM motoneurons where we could link synapse eliminations to axons ending in bulb-like structures . Figure 2E shows an example of a complete elimination of an NMJ indicated by the loss of presynaptic vesicles , while fragments of the mCD8–GFP-marked motoneuron membrane were still present opposite postsynaptic Dlg . We traced fragmented membrane remnants over a distance of more than 150 µm to the retraction bulb-like structure ( Figure 2E; see Figure S3C for another example ) . Additionally , we observed large axonal swellings in the same axon further proximal toward the cell body of the motoneuron ( Figure 2E ) . Rates of retraction bulbs and axonal swellings were identical in MARCM clones of nrg14 and nrg14; P[nrg180ΔIg3–4] animals ( Figure 2F ) . Finally , analysis of the innervation pattern of motoneuron axons forming stable NMJs at this larval stage demonstrated that loss of Nrg did not result in obvious axon guidance defects . We observed similar rates of innervations for all four major classes of motoneurons in all genotypes ( Figure 2G and Figure S2H ) . In summary , while we cannot exclude a role for Nrg in axonal outgrowth , we provide clear evidence that Nrg is required for the maintenance of the NMJ and that this function requires both the extracellular domain and the intracellular Ankyrin-binding domain of Nrg180 but not of Nrg167 . Based on these results we aimed to unravel the molecular mechanisms controlling the synaptic function of Nrg through the interaction with the Ankyrin-associated cytoskeleton . Prior studies in vertebrates demonstrated that phosphorylation of the conserved tyrosine residue within the FIGQY motif of L1-type proteins has the potential to abolish the interaction with Ankyrins [20]–[23] . Similarly , Yeast-2-Hybrid assays showed that Nrg can bind to Drosophila Ankyrin1 and Ankyrin2 and that replacing the tyrosine with a phenylalanine ( Y-F ) reduces binding capacities [50] . Therefore , we first tested whether the neuronal isoform Nrg180 can directly bind to the large isoform of Ank2 ( Ank2-L ) that is present within the presynaptic nerve terminal ( Figure 1I , J ) . Using the Nrg180-specific antibody Nrg180BP104 we were able to co-immunoprecipitate Ank2-L from larval brain extracts demonstrating that Nrg180 and Ank2-L interact in vivo ( Figure 3A ) . Next we used IP-assays to further characterize the interaction between Nrg180 and Ank2 . We generated tagged Nrg180 and Ank2 UAS constructs ( Ank2-S , short isoform of Ank2 containing all potential Nrg interacting domains ) and co-expressed the constructs in Drosophila S2 cells . We were able to efficiently pull-down Nrg180 using Ank2-S and vice versa ( Figure 3B and unpublished data ) . To alter the binding properties and to potentially mimic a gradual increase in phosphorylation levels of Nrg180 in vivo , we generated a series of mutations replacing the tyrosine with a phenylalanine ( Y-F ) , aspartate ( Y-D ) , or alanine ( Y-A ) or by deleting the entire FIGQY motif ( ΔFIGQY ) . Compared to wild-type we observed a 30% decrease in binding capacities for the Y-F , a 70% decrease for the Y-D , and a 90% reduction for the Y-A mutation . The deletion of the FIGQY motif essentially abolished the Nrg–Ank2 interaction ( Figure 3B , C ) . Thus , we have identified a series of mutations that allows us to characterize the function and regulation of Nrg in vivo . These mutations potentially allow the differentiation between processes depending on Nrg bound to Ankyrins and processes depending on a differential regulation of the FIGQY motif . Studies in Drosophila and vertebrates demonstrated that impairing the interaction of CAM with the cytoskeleton results in an increase in lateral mobility and a simultaneous reduction of adhesive properties [20] , [51] , [52] . To test whether Nrg is regulated in a similar manner we analyzed the impact of the FIGQY-mutations on the biophysical behavior of Nrg in vivo . Therefore , we generated GFP-tagged UAS-constructs of all nrg180-FIGQY mutations and used site-specific integration to generate transgenic flies that will express equal protein levels after Gal4 activation . Analysis of larval brain extracts after expression of the constructs in motoneurons ( ok371–Gal4 ) in a wild-type background demonstrated equal protein levels comparable to endogenous nontagged Nrg180 ( Figure 3E upper and middle panels , analysis with an anti-GFP antibody and with a newly generated anti-Nrg180cyto antibody that recognizes the cytoplasmic tail of Nrg180 C-terminal to the FIGQY motif ) . The Nrg180-specific antibody Nrg180BP104 specifically recognizes the Nrg180 FIGQY motif as the antibody detects wild-type GFP-tagged Nrg180 but not any mutant proteins ( Figure 3E , lower panel ) . We used fluorescence recovery after photobleaching ( FRAP ) to test whether the FIGQY mutations affect the mobility of Nrg180-GFP within motoneurons in vivo . For wild-type Nrg180 we observed a mobile fraction of about 40% of total protein ( Figure 3D , F , G ) . The deletion of the Ank2 binding domain increased the mobile fraction of Nrg180 by a factor of two to about 80% . For the tyrosine-specific point mutations , we observed a significant increase in the mobile fraction compared to wild type but to a lesser extent than for Nrg180ΔFIGQY ( Figure 3D , F , G ) . Thus , selectively impairing the interaction between Nrg180 and Ank2 significantly changes the mobility of Nrg180 in motoneurons in vivo . To address the relevance of this Nrg180–Ank2 interaction in vivo , we introduced all FIGQY-specific mutations into the wild-type nrg Pacman construct . In addition , we generated a deletion of the Nrg180 specific C-terminus including the FIGQY motif , a complete deletion of the FIGQY motif of Nrg167 as well as a specific deletion of the last three amino acids of Nrg180 as this potential PDZ-protein interacting domain has been implicated in axon outgrowth of mushroom body neurons ( Figure 2A and Table S2 ) [53] . All constructs were inserted into the attP40 genomic landing site and crossed into the background of the nrg14 null mutation to create flies that express only mutant Neuroglian protein under endogenous control , thereby mimicking the effect of knock-in mutations . All modifications of the intracellular cytoplasmic domains of Nrg167 and Nrg180 rescued the embryonic lethality associated with nrg null mutations ( nrg14 and nrg17 ) , allowing an analysis at the third instar larval stage . To confirm the specificity of our mutations and the expression levels and localization of the mutant proteins , we analyzed the animals with specific antibodies recognizing either both isoforms of Nrg ( Nrg3c1 ) or only Nrg180 ( Nrg180BP104 ) [38] . As observed for the UAS-constructs , Nrg180BP104 recognizes only wild-type Nrg180 but none of our FIGQY mutations ( Figure S4A , B; Figure 3E; unpublished data ) , thereby demonstrating that our Pacman-rescued flies indeed only express the mutant version of the protein and no wild-type Nrg180 protein . We verified this using the Nrg180cyto antibody as well as a second new antibody , NrgFIGQY , that recognizes the wild-type FIGQY motif of both Nrg167 and Nrg180 but not any mutant versions ( Figure S4A , B ) . We were able to unambiguously identify all mutated proteins to demonstrate that all constructs are expressed at wild-type levels within larval brains ( Figure S4B , D ) . In addition , all constructs enabled Nrg180 localization to the presynaptic nerve terminal and Nrg167 expression within glial cells and muscles ( Figure S4A , C ) . Our data demonstrate that the FIGQY domain is not essential for presynaptic localization of Nrg180 . Next , we systematically determined the requirement of the different domains for synapse development in third instar larvae . Our analysis of synapse stability revealed a significant increase in the frequency and severity of synaptic retractions in mutants with severely disrupted Nrg180–Ank2 interactions ( nrg180Y-D , nrg180Y-A , nrg180ΔFIGQY ) but not in animals expressing Nrg180 with only slightly impaired Ank2 binding capacities ( nrg180Y-F ) ( Figure 4A–E and Table S2 ) . The FIGQY motif of Nrg167 and the PDZ protein-binding domain of Nrg180 are not essential for normal NMJ development ( Figure 4D , E; Figure S5B; Table S1 ) . These phenotypes are consistent with our results for the nrg14; P[nrg180ΔFIGQY] and nrg14; P[nrg167ΔFIGQY] MARCM clones ( Figure 2F and Table S2 ) . The observation that impairing the Nrg–Ank2 interaction only in motoneurons weakened synapse stability provided an alternative way to test for a potential contribution of postsynaptic ( muscle ) Nrg for synapse maintenance . Therefore , we selectively knocked down Nrg in muscles of nrg14; P[nrg_wt] or nrg14; P[nrg180ΔFIGQY] animals . Indeed , we observed a significant increase in the frequency and severity of synaptic retraction when knocking down postsynaptic Nrg in the sensitized animals lacking the Ank2 interaction domain but not in animals expressing the wild-type Pacman construct ( Figure S6A–D ) . We next asked whether the Nrg180 FIGQY motif is required for the synaptic localization of Ank2-L to mediate NMJ stability . Interestingly , we did not observe obvious alterations of presynaptic Ank2-L localization or protein levels in P[nrg_wt , 180Y-F , or 180ΔFIGQY] mutant animals at stable synapses when compared to control animals ( Figure S7A; p>0 . 05 for comparison of protein levels; unpublished data ) . Thus , Nrg and Ank2 do not depend on each other for initial synaptic localization but display a high sensitivity toward normal levels of their interaction partner as they are among the first proteins to be lost at ank2 or nrg mutant semi-stable NMJs ( Figure 1I , J and Figure S7B–D ) . In addition to the synapse stability defects , we observed a second striking defect in these animals . With an increasing reduction in Ank2 binding capacities of Nrg180 we observed an increase in growth of the NMJ as reflected by an increase in the span of the presynaptic nerve terminal and an increase in the number of synaptic boutons ( Figure 5A–E ) . At the same time we observed a corresponding decrease in synaptic bouton area ( Figure 5F ) . Interestingly , only subtle alterations were observed for the Nrg180 Y-F mutation that still binds Ank2 efficiently ( Figure 5B , E , F ) . The identical phenotypes of the FIGQY deletion and of the C-terminal deletion indicate that control of NMJ growth critically depends on the Nrg180-FIGQY motif ( Figure 5E , F ) . Similar to the analysis of synapse stability we did not observe any phenotypes in nrg14; P[nrg167ΔFIGQY] or nrg14; P[nrg180ΔPDZ] mutant animals ( Figure 5E , F and Figure S5D ) . Finally , we tested whether we could mimic these growth defects by ectopically expressing mutant Nrg180 ( UAS-nrg180ΔFIGQY-GFP ) in wild-type animals . High levels of expression of Nrg180ΔFIGQY-GFP but not of Nrg180-GFP resulted in an almost 2-fold increase in bouton number and in the span of the presynaptic nerve terminal ( Figure S8A–D ) . Together , our data demonstrate that the loss of Ank2 binding capacities of Nrg180 correlates with both a loss of NMJ growth control and an impairment of synapse stability , suggesting that these two parameters are tightly coupled . To address synaptic functions of the Nrg180–Ank2 interaction in the central nervous system ( CNS ) , we extended our analysis to the adult Giant Fiber ( GF ) circuitry . We used the GF to TTMn ( Tergo-trochanteral motoneuron ) connection as a model neuro-neuronal synapse as it provides precise genetic control of pre- and postsynaptic neurons [54] . Previous analysis of nrg mutations affecting either homophilic cell adhesion properties ( nrg849 ) or protein levels ( nrg305 ) identified both axon guidance and synaptic defects at the GF terminal [55] , [56] . Our Pacman-based mutants enabled us to directly determine potential requirements of the intracellular regulation of the Nrg–Ankyrin interaction for GF circuit formation and function . We analyzed the function of the GF to TTM ( Tergo-Trochanteral Muscle ) pathway in all viable nrg14; P[nrg-mutant] animals by intracellular recordings from the TTM using either brain or thoracic stimulation to differentiate between potential GF-TTMn synapse or TTMn NMJ defects . Importantly , presence of the wild-type nrg construct in nrg null mutants ( nrg14; P[nrg_wt] ) established normal function of the GF-TTMn circuit . We observed no significant differences in average response latencies or following frequencies after a train of stimulations at 100 Hz when compared to wild-type control animals ( Figure 6 ) . In contrast , all mutations affecting the Nrg180 FIGQY motif caused equally severe impairments of GF circuit function . The average response latency , a measure for synaptic strength , was significantly increased ( Figure 6 ) , and mutant animals were not able to follow trains of high-frequency stimulations; in some animals we observed a complete absence of responses ( Figure 6B , D ) . In contrast , when we bypassed the GF and stimulated the motoneurons directly using thoracic stimulation , both response latency and ability to follow high-frequency stimulation were normal in all tested animals ( unpublished data ) . This indicates that the observed defects were specific to the GF-TTMn synaptic connection . Similarly , we observed a disruption of synapse function when expressing UAS-Nrg180ΔFIGQY-GFP simultaneously pre- and postsynaptically at the GF synapse in wild-type animals ( average latency increased to 1 . 01±0 . 057 ms; following frequency reduced to 52 . 34±7 . 17% ) , further demonstrating the importance of the Nrg180 FIGQY motif for normal GF synapse development . In contrast , neither the Nrg167 FIGQY nor the C-terminal Nrg180 PDZ protein-binding domains were essential for GF circuit function ( Figure 6C , D ) . In order to identify potential morphological phenotypes and to distinguish between axon guidance and synaptic defects , we co-injected large ( Rhodamin-dextran ) and small ( Biotin ) fluorescent dyes into the GF . In wild-type animals the large dye is confined to the GF and reveals the morphology of the synaptic terminal . In wild-type animals the GF-TTMn synapse grows to a large presynaptic terminal with mixed electrical and chemical synapses [54] . Biotin can pass through gap-junctions and thereby dye-couple pre- and postsynaptic neurons in animals with a synaptic connection . While we observed no obvious morphological alterations of GF terminals in nrg14; P[nrg_wt] , nrg14; P[nrg167ΔFIGQY] , or nrg14; P[nrg180ΔPDZ] mutant flies , all mutations affecting the Nrg180 FIGQY motif resulted in severely disrupted GF terminals ( Figure 7A , B ) . The GFs were present within the synaptic target area , however large areas of the synaptic terminals were either thinner or swollen and often contained large vacuole-like structures ( Figure 7A , insets ) . Similar to the electrophysiological phenotypes , we observed no obvious qualitative or quantitative differences between different Nrg180 FIGQY mutations . Next , we directly tested for the presence of a synaptic connection between the GF and the postsynaptic TTMn using the dye-coupling assay . We found a residual synaptic connection in more than 90% of animals carrying mutations in the Nrg180 FIGQY motif ( Figure 7A , C ) . However , dye-coupling was often weaker or required longer injection times in mutant animals when compared to animals rescued with the wild-type construct . These results indicate that at most GF terminals of Nrg180FIGQY mutants , synaptic connections with at least a small number of gap junctions were established . When we correlated the ability to dye-couple with the electrophysiological properties of these synapses , we observed that approximately 40% of Nrg180FIGQY mutant animals that were positive in the dye-coupling assay did not show any functional response ( Figure 7C ) . This suggests that the synaptic strength was below the threshold to trigger an action potential in the postsynaptic TTMn . In contrast , neither the deletion of the FIGQY motif of Nrg167 nor of the PDZ protein-binding domain of Nrg180 affected GF morphology or function ( Figure 7 ) . Thus , we conclude that a wild-type Ankyrin binding motif of Nrg180 but not of Nrg167 is essential for normal GF-TTMn synapse maturation and function , but it is not required for GF axon guidance or synapse targeting . The similar phenotypes of nrg180 mutations affecting Ank2 binding either weakly ( Y-F ) or strongly ( ΔFIGQY ) indicate that normal GF synapse development requires a dynamic regulation of this interaction by phosphorylation , a feature disrupted by all mutations . Our Pacman-based mutants enabled us to determine temporal and spatial requirements of a wild-type , modifiable , Nrg180-FIGQY motif as we can express wild-type Nrg180 in the background of our mutants using the Gal4/UAS system . We used previously characterized Gal4-driver lines that allow expression of Nrg180 either simultaneously in pre- and postsynaptic neurons of the GF-TTMn synapse , only in one of the two partner neurons , or only during late stages of synaptic development in the GF ( Figure 8A ) [57] . Simultaneous expression of wild-type Nrg180 in pre- and postsynaptic neurons throughout GF circuit development was able to rescue all electrophysiological and morphological defects associated with the Nrg180-FIGQY mutations ( using Y-F , Y-A , and Nrg180ΔFIGQY as representative examples ) ( Figure 8B–D and unpublished data ) . Thus , this assay is suitable to determine specific pre- or postsynaptic requirements of the Nrg–Ank2 interaction . To our surprise , we were able to rescue the anatomical and physiological phenotypes to a similar extent by expressing wild-type nrg180 either in the pre- or the postsynaptic neuron in the background of the Pacman-based mutants ( Figure 8 ) . We did not observe any nonresponding animals , the average response latency was significantly restored , and only subtle and rare defects in the ability to follow multiple stimuli at 100 Hz were evident ( Figure 8B–D ) . Furthermore , pre- or postsynaptic expression was also sufficient to rescue the morphological phenotypes of the GF synapse terminal of nrg14; P[nrg180ΔFIGQY] mutant animals ( Figure 8E ) . Finally , we tested whether there might be different temporal requirements of Nrg180 wild-type expression during GF synapse development . For this , we used a Gal4 line that drives presynaptic expression only after the initial connection between the GF and the TTMn has been established ( Figure 8A ) [54] , [57] . Strikingly , this late expression of wild-type Nrg180 was not sufficient to rescue nrg14; P[nrg180ΔFIGQY] or nrg14; P[nrg180Y-A] mutant animals but efficiently restored electrophysiological properties in nrg14; P[nrg180Y-F] mutants ( Figure 8B–D ) .
A large number of cell adhesion molecules have been implicated as important mediators of synapse development , but the regulatory mechanisms controlling structural synapse plasticity and maintenance remain largely unknown . In an unbiased RNAi screen , we identified the Drosophila L1-type CAM Neuroglian as essential for synapse stability at the neuromuscular junction . We demonstrate that knockdown of presynaptic Nrg induces synapse disassembly that shares all cellular hallmarks of synapse retractions observed in ank2 , spec , or hts mutant animals [26]–[29] . By analyzing individual motoneurons lacking any Nrg expression , we verified this presynaptic requirement of Nrg for synapse maintenance . In addition , this allowed us to unravel the cellular events occurring in response to loss of cell adhesion at the presynaptic nerve terminal . In nrg mutant motoneurons , we observed both synaptic retractions and motoneuron axons ending in “retraction bulb”–like structures . Excitingly , we directly observed eliminated NMJs that were still connected via traces of clonally marked presynaptic membrane remnants to retraction bulb–like structures . This demonstrates that loss of synapse stability can induce a cellular program resulting in the retraction of the motoneuron axon accompanied by shedding of presynaptic membrane . This phenotype shares striking similarities with developmental synapse elimination at the vertebrate NMJ [58] and points to similar cellular programs underlying synapse loss in development and disease . It will be of particular interest to analyze the contribution of glial cells in this process as they are part of a pro-degenerative signaling system at the NMJ and actively clear membrane remnants of degenerating or pruning axons in both Drosophila and vertebrates [58]–[61] . It is important to note that some of the axonal phenotypes would also be consistent with a stalling of the axonal growth cone before reaching the appropriate target . Indeed , prior studies in both Drosophila and vertebrates demonstrated a function of Nrg and L1CAM during neurite outgrowth [14] , [16] , [47]–[49] , indicating that both defects in axon growth and loss of synapse stability may have contributed to the observed phenotypes . Although Nrg is critical for NMJ maintenance , our observation that 50% of larval NMJs were still stable in nrg null mutants indicates that redundant mechanisms control synapse stability at the level of synaptic cell adhesion molecules . A candidate to provide such redundancy would be the Drosophila NCAM homolog FasII , which has been previously implicated in NMJ maintenance [62] and can substitute for Nrg during axonal outgrowth of ocellar neurons [48] . However , we demonstrate that FasII cannot compensate for the loss of presynaptic Nrg at the larval NMJ ( Figure 1G and Figure S1D ) . The identification of the entire combinatorial code of CAMs contributing to synapse stability will be of high interest in the future . The dynamic nature of many neuronal circuits requires controlled changes in synapse assembly and disassembly without a disruption of neuronal circuit function . While interactions of synaptic cell adhesion molecules are essential to maintain synaptic connectivity , mechanistic insights regarding the regulation of these interactions to alter transsynaptic adhesion are limited to date . The process is probably best understood for Cadherins where adhesive properties are modulated either via binding of extracellular calcium or by altering their association with intracellular Catenins via posttranslational phosphorylation [7] , [8] . These changes alter localization , clustering , and transsynaptic signaling of Cadherins leading to modulations of synaptic connectivity and function [9] , [10] . Here we identify the interaction between the L1-type CAM Nrg and the adaptor protein Ank2 as a similar control module . First , we demonstrate that Nrg180 directly interacts with Ank2 in vivo . Second , a series of specific mutations in the Ankyrin binding motif allowed us to differentially modulate the Ankyrin-binding capacity of Nrg180 . We demonstrate that decreasing Ank2-binding capacities correlate with an up to 2-fold increase in lateral mobility of Nrg180 in motoneurons . This is consistent with studies in vertebrates demonstrating that phosphorylation of the conserved tyrosine of the FIGQY motif reduces or abolishes binding to Ankyrins and increases mobility of L1-type CAMs [20]–[23] , [52] . Finally , Pacman-based nrg mutants with altered Ankyrin-binding capacity caused two striking phenotypes . There was a significant increase in synapse retractions in mutants with severely impaired Ank2 binding but not in mutants with partial binding ( Nrg180Y-F ) . In addition , we observed increased NMJ growth that correlated in a similar manner with the decrease in Ank2 binding capacities . The reduction in Ank2 binding potentially decreases the static population and adhesive force mediated by Nrg and thereby impairs synapse stability . At the same time this reduction in transsynaptic adhesion might allow for increased NMJ growth . We previously identified similar switch-like alterations of synapse growth and stability in animals lacking the spectrin-binding and actin-capping protein Hts/Adducin [28] . Importantly , studies of adducin2 mutant mice demonstrated that Adducin2 provides a similar function in vertebrates and is essential to mediate changes in synaptic connectivity relevant for learning and memory [63] , [64] . Interestingly , we did not observe significant alterations in presynaptic Nrg180 or Ank2 levels in these animals similar to previous observations for the axonal localization of these proteins [50] , [65] . However , we found a clear dependence on the respective partner protein at semi-stable nrg and ank2 mutant synapses , indicating that the Nrg–Ank2 interaction is required to maintain their synaptic localization . A similar late loss of AnkyrinG has been observed in neurofascin mutant Purkinje cells , demonstrating a function of the L1-CAM paralog for maintenance but not for initial localization of AnkG to the AIS [33] , [35] and likewise AnkG is required for the maintenance of Neurofascin [32] . Together these data indicate that modulation of the Nrg–Ank2 interaction balances synapse growth and stability . Changing the interaction via posttranslational phosphorylation could thus locally decrease synapse stability , thereby allowing the formation of new synapses without impairing general neuronal circuit architecture . Despite our detailed knowledge regarding the expression of synaptic cell adhesion molecules , mechanistic insights into the transsynaptic control of synapse maturation or function are only recently emerging [1] , [2] , [9] , [10] . Here we provide evidence that transsynaptic coordination of synapse development can be controlled via a dynamic regulation of the L1-type CAM Nrg . In contrast to the larval NMJ , the lack of significant differences in phenotypic strength between the mutations in Nrg180-FIGQY motif demonstrates that normal GF synapse development requires a dynamic regulation of the Nrg–Ank2 interaction via phosphorylation . To address the importance of this regulation for transsynaptic development , we selectively reintroduced wild-type Nrg180 either pre- or postsynaptically in the background of the different FIGQY motif mutations . Surprisingly , pre- or postsynaptic expression of wild-type Nrg in the presence of mutant Nrg on both sides of the synapse was sufficient to restore synaptic function in all mutants , but late presynaptic expression could only rescue the Nrg180Y-F mutation . This highlights two important novel aspects of Nrg function at central synapses . First , while Nrg180 is required on both sides of the synapse , regulation of the FIGQY motif is sufficient on either side of the synapse , demonstrating that Nrg can control synapse development in a transsynaptic manner . Second , constitutive binding to Ank2 is sufficient for early stages of synapse development , but GF synapse maturation requires dynamic regulation of the Nrg–Ank2 interaction . A potential function of the phosphorylation of Nrg could be an increase of lateral mobility of Nrg to allow precise spatial alignment with postsynaptic CAMs . Alternatively , phosphorylation may enable an interaction with proteins that bind only phosphorylated Nrg . One candidate would be the microtubule binding protein Doublecortin that binds only phosphorylated Neurofascin [66] , but physiological relevance for this interaction in nervous system development is lacking to date . While we observe distinct functions and modes of regulation of Nrg at peripheral versus central synapses in both cases , the Nrg180–Ank2 interaction did not influence axon outgrowth or guidance . In addition , we did not observe any requirements for the Nrg167 FIGQY motif or for the PDZ-binding motif of Nrg180 , which has recently been implicated in controlling axonal outgrowth in Drosophila mushroom bodies [53] . A surprising observation from studies of vertebrate L1 family proteins was that mutations within the intracellular domain that are linked to human L1/CRASH syndrome and neuropathological diseases [30] resulted in significantly weaker phenotypes in mice compared to the complete L1 knockout [14] , [31] , [67] , [68] . While extracellular interactions are essential for early nervous system development including neurite outgrowth and axon targeting [16] , here we provide evidence that reversible phosphorylation of the intracellular Ankyrin binding motif might provide a regulatory module to fine tune synaptic connectivity without impairing overall circuit stability . The expansion of the L1-type CAM family to four independent proteins in vertebrates may provide the means to cope with the diversity and complexity of synaptic connectivity in the vertebrate CNS . Indeed , while mutations in the L1CAM Ankyrin motif did not affect the general organization of the nervous system , they resulted in specific impairments of particular neuronal circuits and at subsets of synapses [31] , [67] , [68] . The functions of the different L1-type proteins may be distinct , partly opposing or redundant as evident by an analysis of cerebellar granule cell development in L1CAM and NrCAM double mutants [36] . Our data suggest that the coordinated phosphorylation of a subpopulation of synaptic L1 family proteins may allow differential modulation of biophysical properties of L1 complexes to precisely control distinct aspects of synapse development . Elucidating the synaptic L1-family protein code at specific synapses and identifying their phosphorylation status during synapse development and in response to activity might uncover new mechanisms controlling synaptic plasticity in development and during learning and memory .
Flies were maintained at 25°C on standard food . Crosses and most experiments were performed at 25°C , while RNAi assays were performed at 27°C . The following fly strains have been used in this study: w1118 ( wild-type ) , nrg14 ( nrg1 ) , nrg17 ( nrg2 ) , UAS–mCD8–GFP , UAS–fasII , elavC155–Gal4 , ok371–Gal4 , sca–Gal4 , mef2–Gal4 , BG57–Gal4 , UAS–dcr2 , ok307–Gal4 ( A307–Gal4 ) , P ( hsFLP ) 86E , P ( hsFLP ) 1 , P ( neoFRT ) 19A ( all Bloomington stock center ) , c17-Gal4 , c42 . 2–Gal4 , and shakB–Gal4 [57] . RNAi lines were obtained from the Vienna Drosophila RNAi Center [37]: Nrg RNAi line1 ( stock ID6688 ) and Nrg RNAi line2 ( stock ID107991 ) . The full-length Nrg180 ORF was amplified from the plasmid pMT–Neuroglian and the Nrg167 ORF from cDNA GH03573 ( both obtained from the Drosophila Genomic Research Center , Indiana , USA ) . Full-length ORFs were cloned into pENTR vector via TOPO cloning ( Invitrogen ) . To obtain pUASTattB-10xUAS destination vectors suited for gateway cloning , a gateway cassette with a C-terminal 3xHA or EGFP tag was introduced into the pWALIUM10-moe plasmid ( TRiP collection , Harvard Medical School ) . Final expression constructs were generated via gateway cloning using standard procedures ( Invitrogen ) . Deletions and point mutations were introduced into pENTR clones using the QuickChange II site-directed mutagenesis kit following the manufacturer's instructions ( Agilent Technologies ) . All constructs were verified by sequencing ( FMI sequencing facility ) . The P[acman] clone CH321-4H20 was obtained from BACPAC Resources Center ( BPRC , Oakland , California ) and modified using galK-mediated recombineering [44] according to [42] ( NCI Frederick National Laboratory ) . Site-specific integration via the phi-C31 system [43] was used to generate insertions at the attP40-landing site for both pUAST and Pacman constructs . Primers used in this study are listed in Table S3 . Wandering third instar larvae were dissected in standard dissecting saline and fixed with Bouin's fixative for 2–3 min ( Sigma-Aldrich ) . Primary antibodies were incubated at 4°C overnight . Primary antibodies were used at the following dilutions: anti-Nrg180 ( BP104 ) 1∶250 [38] , anti-Bruchpilot ( nc82 ) 1∶250 , anti-Futsch ( 22c10 ) 1∶500 , anti-Synapsin ( 3c11 ) 1∶100 ( all obtained from Developmental Studies Hybridoma Bank , IA ) , rabbit anti-Dlg 1∶30 , 000 , rabbit anti-DGluRIII [28] 1∶2 , 500 , rabbit anti-DvGlut , rat anti-CD8 ( Caltag Laboratories ) 1∶1 , 000 , anti-Nrg ( 3c1 , gift from M . Hortsch , Ann Arbor , MI , USA [38] ) 1∶500 , rabbit anti-NrgFIGQY ( raised against the peptide: TEDGSFIGQYVPGKLQP ) 1∶100 , and rabbit anti-Nrg180cyto ( raised against the peptide: NNSAAAHQAAPTAGGGSGAA ) 1∶500 . Monoclonal rat anti-Ank2-L 1∶40 was generated against a protein fragment containing aa 3134–3728 ( according to the 4 , 083 aa isoform of Ank2-L ) . Rabbit anti-Ank1-4 antibody was generated against the Ankyrin domains 1–4 . This antibody recognizes both Ankyrin1 and Ankyrin2 . Antibodies were generated at David's Biotechnology ( Regensburg , Germany ) . Alexa conjugated secondary antibodies ( Invitrogen ) were used at 1∶1 , 000 for 2 h at RT . Directly conjugated anti-Hrp ( Alexa or Cy-dyes ) were used at 1∶100–1 , 000 ( Jackson Immunoresearch Laboratories ) . Larval preparations were mounted in Prolong Gold ( Invitrogen ) . Images were captured at room temperature using a Leica SPE confocal microscope . To process , analyze images , and quantify phenotypes Adobe Photoshop , Imaris ( Bitplane ) , Image Access ( Imagic ) , and the open source tool FIJI/ImageJ were used . Synaptic retractions were quantified using presynaptic Brp and postsynaptic DGluRIII staining and counting the number of unopposed postsynaptic footprints . Complete loss of the presynaptic marker Brp was considered an elimination of the presynaptic nerve terminal . Synapse retraction frequencies are presented as values per animal . NMJs on the indicated muscles in segments A2–A5 ( 10 NMJs/animal ) were scored . n indicates the number of independent animals per quantification . Bouton area , number , and NMJ length were quantified using Synapsin , Dlg , and Hrp staining . Bouton area and NMJ length were quantified using the Image access software ( Imagic ) . Hrp staining was used to visualize the bouton area and 10 A3 muscle 4 NMJs were quantified per genotype . To measure NMJ length 20 muscle 4 NMJs ( segments A3 and A4 , 10 each ) were analyzed . Bouton number was quantified on muscle 4 in segments A2–A6 using Synapsin/Dlg staining . Larval brains were dissected and transferred into 2× sample buffer ( Invitrogen ) . Five brains per lane were analyzed on NuPage gels ( Invitrogen ) according to standard procedures . Primary antibodies were incubated overnight at 4°C . Secondary Hrp-conjugated goat anti-mouse and goat anti-rabbit antibodies were used at 1∶10 , 000 ( Jackson Immunoresearch ) for 2 h at RT . PVDF-membranes were incubated with ECL substrate ( SuperSignal West Pico Kit , Thermo Scientific ) and developed on film ( Fujifilm ) . For immunoprecipitations ( IPs ) 100 larval brains were collected , grinded in NP40-based lysis buffer , and incubated on ice for 30 min . The supernatant was split equally between control IPs using empty protein-G beads ( Dynabeads , Life Science ) and protein-G beads pre-incubated with Nrg180BP104 antibody . IPs were analyzed with anti-Nrg180BP104 ( 1∶200 ) and rat anti-Ank2-L ( 1∶20 ) . For IPs of mutated Nrg180 proteins , S2 cells were co-transfected with act5C–Gal4 , UAS–Ank2-S–EGFP [27] , and UAS–Nrg–3xHA plasmids using Fugene ( Roche ) following the manufacturer's instructions . IPs were analyzed using mouse anti-HA ( 12CA5 ) 1∶200 and rabbit anti-GFP ( Molecular Probes ) 1∶500 antibodies . Rabbit anti-Ank2 ( anti–Ank1–4 ) 1∶1 , 000 was used for visualization of the input . Quantification of Ank2 binding between Nrg180 mutants was performed using four independent IP experiments and Odyssey2 . 1 software ( LI-COR ) . Wandering third instar larvae expressing nrg–GFP using ok371–Gal4 were dissected in HL3 saline and prepared for live imaging using a magnetic pinholder device . 1-Naphthylacetyl-spermin-trihydrochloride ( NSH ) ( 100 mM; Sigma , St Louis , MO ) was added to the HL3 saline to block postsynaptic glutamate receptor activation and muscle contractions . Six to nine motoneuron axons from three to four independent animals were analyzed . Motoneuron axons were photo-bleached using a Zeiss LSM700 by scanning the targeted region for 30 iterations at 100% laser-power using the 488 nm line . Ten images were acquired before the bleach and 40 after the bleach with a time interval of 5 s at low laser power . Images of the FRAP series were corrected for animal movement using the FIJI registration plugin ( StackReg option ) . Images were corrected by substracting background fluorescence from regions outside the axons and corrected for bleaching using a control area within the same axon . The recovery curves were fit to a double exponential curve as follows: The maximum was calculated from the fitting curve ( maxfitting ) . To calculate the real max value , the following formula has been used: The mobile fraction was calculated using the following formula: The nrg null mutation nrg14 was recombined with the P ( neoFRT ) 19A chromosome . The indicated Pacman constructs were crossed into this background to create stable stocks . These lines were crossed to P ( hsFLP ) 1 , P ( neoFRT ) 19A , tubGal80; ok371-Gal4 , UAS–CD8–GFP; MKRS , P ( hsFLP ) 86E flies . Embryos were collected for 2 h , aged for 3 h , and heat shocked for 1 h at 37°C . All statistical analyses were performed using Microsoft Office Excel and an online source for unpaired Student's t test ( http://www . physics . csbsju . edu/stats/t-test . html ) . p≤0 . 05 was accepted as statistically significant ( *p≤0 . 05 , **p≤0 . 01 , ***p≤0 . 001 ) . Adult Drosophila nervous system was dissected , dye filled , and fixed as previously described [55] . Young 2- to 5-d-old flies were used for all the experiments . To visualize the morphology of GF–TTMn connection either a 10 mM Alexa Fluor 568 Hydrazide ( Molecular Probes ) in 200 mM KCl or a dye solution of 10% w/v Neurobiotin ( Vector labs ) and tetramethyl rhodamine-labeled dextran ( Invitrogen ) in 2 M potassium acetate was injected into the GF axons by passing hyperpolarizing or depolarizing current , respectively . Preparation of GF samples for confocal microscopy has been described previously [55] . Samples were analyzed using a Nikon C1si Fast Spectral Confocal system . Images were processed using Nikon Elements Advance Research 4 . 0 software . Electrophysiological recordings from the giant fiber circuit were obtained as described in detail in [69] . The flies were given 10 single pulses at 30–60 mV for 0 . 03 ms with a 5-s interval between the stimuli and the shortest response latency of each fly was averaged . To determine the reliability of the circuit , the ability to follow frequencies at 100 Hz was determined . For this 10 trains of 10 stimuli were given at 100 Hz with an interval of 2 s between the trains and percent of the total responses was calculated . All traces were recorded , stored , and analyzed using pClamp 10 ( Molecular Devices ) software . Mann–Whitney Rank sum test was used to determine significant differences between different genotypes in average response latencies and following frequencies ( Sigma Plot 11 software ) . | The function of neuronal circuits relies on precise connectivity , and processes like learning and memory involve refining this connectivity through the selective formation and elimination of synapses . Cell adhesion molecules ( CAMs ) that directly mediate cell–cell interactions at synaptic contacts are thought to mediate this structural synaptic plasticity . In this study , we used an unbiased genetic screen to identify the Drosophila L1-type CAM Neuroglian as a central regulator of synapse formation and maintenance . We show that the intracellular Ankyrin interaction motif , which links Neuroglian to the cytoskeleton , is an essential regulatory site for Neuroglian mobility , adhesion , and synaptic function . In motoneurons , the strength of Ankyrin binding directly controls the balance between synapse formation and maintenance . At a central synapse , however , a dynamic regulation of the Neuroglian–Ankyrin interaction is required to coordinate transsynaptic development . Our study identifies the interaction of the L1-type CAM with Ankyrin as a novel regulatory module enabling local and precise control of synaptic connectivity without altering general neuronal circuit architecture . This interaction is relevant for normal nervous system development and disease as mutations in L1-type CAMs cause mental retardation and psychiatric diseases in humans . | [
"Abstract",
"Introduction",
"Results",
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... | 2013 | Transsynaptic Coordination of Synaptic Growth, Function, and Stability by the L1-Type CAM Neuroglian |
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